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Strategic and Tactical Allocation Insights

January 2026 | Human + AI + Data
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Executive Summary Short Term

January 2026

Reconciling insights from professional strategists with AI-powered analysis and forward-looking quant models to provide 6-to-12-month investment outlooks.

Risk On/Off Outlook
Consensus Conviction
High (precise)
Medium
Low (uncertain)
Moderately Risk-On
Max Risk Off Neutral Max Risk On
Gauge generated from aggregation of Human / AI views and the latest higher frequency macro/market data
Global Macro Outlook
Growth
Moderately Strong
Below Trend At Trend Above Trend
Inflation
Moderately Inflationary
Max Weaker Neutral Max Stronger
Monetary Policy
Moderately Loose
Max Tighter Neutral Max Easier
Core Asset Class Views
High (precise)
Medium
Low (uncertain)
Core Sector Views
High (precise)
Medium
Low (uncertain)

Top Non Core Ideas

*Ideas are ranked based on combined consensus from human strategists and AI models, incorporating Non-Core Idea Score and Conviction Level.

Top Macro Risks

*Risks are ranked based on combined consensus from human strategists and AI models.

Last updated: November 30, 2025 • All data as of month-end

Risk Sentiment Index Short Term

Aggregating risk appetite signals from professional strategists, AI models, and quantitative market/macro indicators to assess the current risk-on/risk-off environment.

Neutral
Macro
Market
Max Risk Off Neutral Max Risk On
Consensus view aggregated from Macro and Market composite scores
Conviction Level (Circle Size)
High (precise)
Medium
Low (uncertain)

High Frequency Data

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Data Type Series Status

Historical Trend

Macro
Market
Composite
Hypothetical/Illustrative
Actual Data
Score ranges from -1 (Max Risk Off) to +1 (Max Risk On). Composite = average of Macro and Market.
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Most Risk Off
Source: Ruffer LLP
View: Risk Off
Conviction: High
Rationale: Extreme equity underweight citing valuation compression
Most Risk On
Source: Invesco Ltd
View: Risk On
Conviction: High
Rationale: Aggressive cyclical exposure on global easing
Max Risk Off Neutral Max Risk On
*View derived from aggregated strategist asset class positioning
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Loading human strategist consensus...

Historical Trend

Hypothetical/Illustrative
Actual
Score ranges from -2 (Max Risk Off Sentiment) to +2 (Max Risk On Sentiment)
Risk-On
Claude
Perplexity
ChatGPT
Max Risk Off Neutral Max Risk On
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Loading AI model consensus...

Historical Trend

Claude
Perplexity
ChatGPT
Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Max Risk Off Sentiment) to +2 (Max Risk On Sentiment)
Moderately Risk-On
Data
AI
Human
Max Risk Off Neutral Max Risk On
Gauge generated from aggregation of Human / AI views and the latest higher frequency macro/market data
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Loading hybrid consensus...

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Max Risk Off Sentiment) to +2 (Max Risk On Sentiment)

Growth Cycle Index Short Term

Tracking economic expansion and contraction signals across leading indicators, employment data, and sentiment surveys to gauge the current growth trajectory.

Strong
Most Bearish
Source: Hussman Strategic Advisors
View: Below Trend
Score: -1.0
Conviction: High
Rationale: Recession risk from valuation compression
Most Bullish
Source: J.P. Morgan Private Bank
View: Above Trend
Score: +1.5
Conviction: High
Rationale: AI capex and fiscal support drive expansion
Below Trend At Trend Above Trend
*View relative to trend growth levels, aggregated from extracted insights of 50+ professional strategists. Hover over consensus circle for more information, including source coverage and strategist view breakdown.
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

AI-related capex boosts U.S. growth, with OBBBA fiscal expansion and Fed late-2025 rate cuts beneficial for the outlook. Low private sector leverage remains central to cycle resilience, as the economy survived rate hikes and Liberation Day shock with consumption remaining resilient despite soft labor market conditions. The U.S. rate-cutting cycle should support rebound in global growth through reduced economic policy uncertainty and benefits from lower short-term rates. Forces are balancing toward growth re-acceleration with support from fiscal and monetary policies, robust consumer spending, easing financial conditions, targeted fiscal support, and reduced uncertainty.

Counterarguments

Some strategists worry about recession risk with unemployment increases anticipated, as institutional teams brace for macro shifts. Combined slowing in demographic labor force growth and productivity growth presents headwinds. The labor market has suffered a sharp slowdown in demand and hiring, while supply is declining due to tighter immigration. The unemployment rate is only creeping higher due to supply decline, with consumer spending remaining resilient despite these pressures.

Historical Trend

Hypothetical/Illustrative
Actual
Score ranges from -2 (Below Trend) to +2 (Above Trend), relative to trend growth levels
Expansion
Max Weak Neutral Max Strong
Current cycle phase based on OECD G7 CLI level and 3-month trend. Expansion: CLI > 0, rising. Slowdown: CLI > 0, falling. Contraction: CLI < 0, falling. Recovery: CLI < 0, rising.

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OECD Composite Lead Indicator

Historical CLI
Current (Dec 2025)
G7 Composite Leading Indicator normalized to -2 to +2 scale (weakest to strongest historical reading).

Global Snapshot

Expansion
Slowdown
Contraction
Recovery
Regional positioning by CLI level (x-axis) and 3-month change (y-axis). Quadrants represent cycle phases. Source: OECD
Region CLI Level 3M Change Cycle Phase
*Status phase calculated from combination of z score level and momentum
Weak
Claude
Perplexity
ChatGPT
Below Trend At Trend Above Trend
View relative to trend growth levels. Consensus derived from Claude, Perplexity, and ChatGPT models.
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Historical Trend

Claude
Perplexity
ChatGPT
Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Below Trend) to +2 (Above Trend), relative to trend growth levels
Moderately Strong
AI
Human
Data
Below Trend At Trend Above Trend
View relative to trend growth levels. Gauge generated from aggregation of Human, Data, and AI views.
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Loading hybrid narrative...

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Below Trend) to +2 (Above Trend), relative to trend growth levels

Core Inflation Index Short Term

Tracking price pressure indicators across CPI components, wage growth, and inflation expectations to assess the inflationary/deflationary environment.

Moderately Inflationary
Most Dovish
Source: Standard Chartered Bank
View: Below Trend
Score: -1.5
Conviction: Medium
Rationale: US inflation expectations for next 1-2 years continue to soften; shelter disinflation continuing; impact of tariffs on goods inflation remains limited
Most Hawkish
Source: Principal Asset Management (Principal Fixed Income)
View: Above Trend
Score: +1.5
Conviction: High
Rationale: Progress in core PCE has stalled; Tariff burden expected to pass to consumers over time; COVID-era stimulus reshaped leverage making inflation stickier; Goods sector inflation from tariffs
Max Weaker Inflation Neutral Max Stronger Inflation
*View aggregated from extracted insights of 50+ professional strategists. Hover over consensus circle for more information, including source coverage and strategist view breakdown.
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Strategist consensus is Slightly Above Trend inflation with Medium conviction. The majority of sources express hawkish or neutral views on inflation trajectory, citing persistent price pressures from tariffs, structural factors, and sticky services inflation. A smaller minority expects lower inflation, emphasizing softening expectations and ongoing disinflation in shelter and goods. While US inflation remains above the Fed's target, the debate centers on whether disinflation momentum will continue or whether tariff pass-through and labor market tightness will keep inflation elevated above trend.

Counterarguments

Dovish sources emphasize that inflation expectations continue to soften, shelter disinflation is continuing, and the impact of tariffs on goods inflation remains limited so far. They point to benign price environments in Asia, well-contained energy prices, and housing price disinflation offsetting upward pressures. The disinflation trend supports continued rate cuts, and scant evidence exists that tariffs are reigniting inflation given normal money supply growth.

Major Divergences

The most hawkish sources (Fidelity, Principal Fixed Income, Nomura) cite core PCE stalling, tariff pass-through, and inflation expectations above 3% becoming entrenched. The most dovish sources (Standard Chartered, DBS, Merrill Lynch) emphasize softening expectations, contained inflation, and subsiding concerns. Neutral sources see balanced risks with regional divergence—US facing upward pressures while Europe and Asia see disinflationary forces.

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Max Weaker Inflation Sentiment) to +2 (Max Stronger Inflation Sentiment)
Stable Inflation
Max Deflation Neutral Max Inflation
Current inflation phase based on level and momentum of inflation z score

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US CPI

Historical Data
Current (Dec 2025)
US Inflation z score based on historical range. Source: BLS

Global Snapshot

Reflation
Stable Inflation
Disinflation
Deflation
Regions positioned by composite inflation score. Source: BLS, Eurostat, National Sources
Region Composite Score Phase
*Composite score calculated from combination of z score level and momentum
Moderately Inflationary
Claude
ChatGPT
Perplexity
Max Weaker Inflation Neutral Max Stronger Inflation
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Historical Trend

Claude
Perplexity
ChatGPT
Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Max Weaker Inflation Sentiment) to +2 (Max Stronger Inflation Sentiment)
Moderately Inflationary
Human
Data
AI
Max Weaker Inflation Neutral Max Stronger Inflation
Gauge generated from aggregation of Human, Data, and AI views
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Consensus across human strategists, AI models, and inflation data indicators reflects Medium conviction around a Slightly Above Trend outlook with headline inflation remaining modestly elevated through 2026, anchored in persistent services inflation and tariff pass-through while goods deflation provides partial offset. Human strategists see Slightly Above Trend inflation, emphasizing hawkish concerns around core PCE stalling, tariff burden passing to consumers, and structural factors keeping inflation sticky. AI models see Mildly Above Trend inflation, projecting persistent services inflation from labor costs stabilizing at higher levels, rent dynamics with extended lags, and structural factors including deglobalization and energy transition elevating neutral inflation above pre-pandemic baseline. Data indicators confirm Stable Inflation phase, calculated using a weighted z-score formula that blends current CPI state with recent momentum, with regional positioning showing most major economies in near-trend phases rather than extreme levels. The hybrid consensus reflects Slightly Above Trend, synthesizing Human's hawkish assessment, Data's stable readings, and AI's mildly-above-trend view into a modestly elevated perspective on inflation trajectory.

Counterarguments

Integration of data signals reveals alignment between Human strategists' hawkish positioning and AI models' cautious assessment, with Data View's Stable Inflation reading providing a slightly more benign anchor. A minority of dovish sources (Standard Chartered, DBS, Merrill Lynch) emphasize that inflation expectations continue to soften, shelter disinflation is ongoing, and tariff impacts remain limited so far. Tariff pass-through risks remain contentious as hawkish sources quantify significant upward pressure from tariff escalation, while dovish sources point to muted pass-through from margin compression and trade diversion. The moderate spread between component positions represents the key interpretive challenge—whether hawkish concerns about persistent inflation materialize or dovish expectations of continued disinflation prove accurate.

Where Strategists, LLMs and Data Diverge

Most fundamental divergence centers on inflation phase interpretation where Human strategists see Slightly Above Trend, AI models see Mildly Above Trend, and Data View lands in Stable Inflation phase. Human and AI frameworks are broadly aligned on hawkish concerns, while Data provides more neutral current readings. Within the Human camp, the most hawkish sources (Fidelity, Principal Fixed Income, Nomura) cite core PCE stalling and inflation expectations becoming entrenched, while the most dovish (Standard Chartered, DBS, Merrill Lynch) emphasize softening expectations and contained prices. Regional inflation dynamics reveal additional complexity as Data confirms most advanced economies in near-trend phases, but simultaneously shows China in low inflation territory validating concerns about deflationary export pressures transmitting globally while US faces upward tariff pressures. The Hybrid consensus (Slightly Above Trend) appropriately synthesizes these perspectives, reflecting that hawkish concerns dominate the near-term outlook while acknowledging dovish counterarguments and stable current readings.

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Max Weaker Inflation Sentiment) to +2 (Max Stronger Inflation Sentiment)

Monetary Policy Index Short Term

Assessing central bank stance across major economies, tracking policy rate expectations and liquidity conditions to gauge the monetary policy trajectory.

Moderately Loose
Most Hawkish
Source: Fidelity International
View: Neutral
Score: 0.0
Conviction: Medium
Rationale: Fed under pressure to cut further than warranted; Trump admin pushing for lower rates; sticky inflation may hinder easing path; 100+ CB rate cuts globally in 2025
Most Dovish
Source: Eastspring Investments (Singapore) Limited
View: Very Loose
Score: +2.0
Conviction: High
Rationale: Further easing in monetary policy is key factor supporting Asian economies. Fed cuts facilitate further policy rate cuts by other central banks. Real policy rates above historic norms in most Asian countries.
Very Tight Neutral Very Loose
*View aggregated from extracted insights of 50+ professional strategists. Hover over consensus circle for more information, including source coverage and strategist view breakdown.
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Fed began easing cycle with September 25bps cut, with labor market weakening supporting further cuts. The Fed has resumed its cutting cycle while the ECB signalled a prolonged pause and the BoE is delivering gradual easing with downside risks to employment potentially accelerating cuts. Fed resumed easing in September 2025 after a 9-month pause and is expected to continue into the first half of 2026, with ECB already cutting and EM central banks also easing. The Fed may cut further than market expects due to shifting reaction function toward labor market concerns, with ECB at 2.0% and further cuts possible if inflation undershoots.

Counterarguments

Fed independence concerns remain a risk, with inflation potentially limiting the pace of cuts. The Fed may be under pressure to cut further than warranted, with sticky inflation hindering the easing path. Over 100 central bank rate cuts occurred globally in 2025. The Fed is prioritizing downside growth risks over inflation concerns, while the ECB has paused to assess transmission effects. The BoJ remains reluctant to normalize but market forces may compel action.

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Very Tight Policy) to +2 (Very Loose Policy)
Easing
Max Tight Neutral Max Loose
Hover over the gauge circle for detailed methodology. Phase based on US real rate z-score level and 3-month change. Conviction based on cross-regional policy agreement.

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US Real Monetary Policy

Historical Data
Current (Dec 2025)
US real rates z-score based on level and recent trend. Positive values indicate tighter policy (restrictive), negative values indicate easier policy (accommodative). Source: Federal Reserve Economic Data (FRED)

Global Snapshot

Tight
Tightening
Easing
Loose
Regional positioning by policy rate level (x-axis) and 3-month change (y-axis). Quadrants represent policy phases: Tight (elevated rates, rising), Tightening (moderate rates, rising), Easing (elevated rates, falling), Loose (low rates, falling).
Region Real Policy Rates Z Score 3M Change Policy Phase

US Liquidity Cycle

Historical Data
Current (Q3 2025)
Overall US economic liquidity, multiple sources
Moderately Loose
Perplexity
Claude
ChatGPT
Very Tight Neutral Very Loose
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Historical Trend

Claude
Perplexity
ChatGPT
Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Very Tight Policy) to +2 (Very Loose Policy)
Moderately Loose
Human
Data
AI
Very Tight Neutral Very Loose
Gauge generated from aggregation of Human, Data, and AI views
Consensus Conviction Level
High (precise)
Medium
Low (uncertain)

Consensus across human strategists, AI models, and monetary policy data indicators reflects high-conviction easing trajectory with major central banks pivoting from restrictive stance toward policy normalization through 2026. Human strategists convey major central banks have reached end of tightening cycles with pause through year-end before measured cuts beginning 2026 as policy shifts from restrictive toward neutral underpinned by easing inflation and softening growth, with dovish strategists projecting substantially lower Fed Funds by late 2026 on weakening labor and tight credit while hawkish strategists expect rates to remain elevated through mid-2026 on persistent services inflation. AI models project synchronized global easing with the Fed delivering cuts as disinflation progresses toward targets and labor markets normalize, with balance-sheet runoff persisting but credit growth remaining subdued in interest-sensitive sectors constraining stimulative impact. Data indicators provide concrete validation through US real monetary policy positioning in Easing phase based on z-score of real rate level and recent trend, confirming strategist and AI assessment that tightening cycle conclusively behind and accommodation timeline commenced, with regional policy positioning showing most major advanced economies in Easing or Neutral zones rather than Tight territories. Credit tightening standards show net easing indicating banks relaxing lending standards after period of restriction, supporting view that financial conditions transitioning toward accommodation alongside policy rate trajectory. Disinflation progress creates shared foundation with strategists emphasizing inflation moderating through 2026, AI noting goods deflation durable and services moderating on labor slack, and Data view's real rates positioning validating that policy sufficiently restrictive to contain inflation but actively easing rather than tightening further. Labor market rebalancing supports policy pivot across frameworks with strategists highlighting unemployment rising gradually without crisis dynamics, AI emphasizing slack developing as vacancies normalize, and Data confirming monetary conditions in Easing rather than Tight phase suggesting employment softening gradual rather than precipitous allowing measured policy response. This tri-partite alignment indicates baseline easing through 2026 represents high-confidence central case with Data confirming policy already transitioned from Tight to Easing phase, strategists converging on normalization timeline, and AI models validating mechanics of disinflation enabling accommodation without requiring renewed tightening absent major exogenous shocks.

Counterarguments

Executive Summary
Data suggests measured easing pace vs. strategist/AI expectations of aggressive cuts. Three reversal risks: tariff-inflation spiral, financial dislocation, or fiscal crisis. Regional divergence creates EM stress risk if Fed easing drives dollar strength.
Data view's Easing phase positioning suggests accommodation more measured than human or AI projections. AI models split between aggressive easing (Fed cuts on labor softening) and cautious scenarios (tariff-inflation spiral, financial dislocation, or fiscal crisis could force reversal). Regional divergence shows most advanced economies in Easing/Neutral zones, but Russia and some EMs remain restrictive, creating potential stress if Fed easing drives dollar strength.
Integration of data signals introduces timing tensions as Data view's positioning in Easing phase (based on US real rates z-score showing rates declining from restrictive levels) suggests policy accommodation more measured than either human strategist emphasis on aggressive future cuts or AI projection of synchronized easing, creating interpretation challenge whether Data validates gradual normalization path or represents lagged indicator before full tightening transmission materializes forcing policy recalibration. AI models project two divergent scenarios where aggressive camp sees synchronized easing with substantial Fed cuts as labor softening and below-trend growth trigger employment mandate concerns despite elevated inflation reflecting central banks' preference to support growth over inflation when facing stagflation, with Fed's September pivot while core PCE remained elevated signaling irreversible dovish shift and QT cessation driving real rates lower by Q3 2026, yet Data showing real rates positioning moderately above neutral without triggering inflation reacceleration suggests either strategist confidence in calibrated normalization justified or AI concerns around easing prematurity yet to manifest requiring months more data before resolution. Conversely, AI cautious camp presents case for less aggressive easing noting advanced economies moving cautiously from restrictive holds, with three reversal scenarios including tariff-inflation spiral forcing Fed pause then hikes despite rising unemployment, financial dislocation from disorderly USD decline or equity crash forcing hawkish hold with crisis QE, or fiscal crisis triggering sovereign risk repricing constraining easing versus market-priced expectations, yet Data view's current Easing phase combined with credit standards showing net easing offers zero forward visibility on exogenous shocks that could drive rapid phase transition from Easing back to Tight within single quarter on tariff implementation or financial instability. Regional divergence creates allocation complexity as Data confirms most advanced economies in Easing/Neutral zones supporting human and AI baseline projections, but regional policy scatter chart reveals Russia in extreme Tight territory and some emerging markets maintaining restrictive stances validating concerns about asynchronous global policy cycles and potential emerging market stress if Fed easing drives dollar strength creating balance of payments pressures for economies unable to ease due to inflation or currency constraints. Growth-inflation tradeoff interpretation separates frameworks as strategists emphasize disinflation sufficient to permit normalization supporting soft landing, AI warns stagflation dynamics with modest growth and elevated inflation force central banks into uncomfortable easing despite above-target prices prioritizing employment mandate, yet Data showing US real rates in Easing phase alongside credit standards easing suggests macro configuration currently benign enough to validate strategist confidence though vulnerable to deterioration if real rates positioning or credit conditions transition to stressed quadrant creating pressure for policy recalibration. The hybrid gauge positioning near neutral-to-mild-easing range suggests balanced assessment recognizing easing trajectory underway while acknowledging measurement uncertainty and tail risk scenarios could materially shift outlook over tactical 12-month horizon.

Where Strategists, LLMs and Data Diverge

Gauge Position Clustering and Magnitude Interpretation: The three frameworks demonstrate tight quantitative clustering with gauges spanning a narrow range—yet this narrow spread masks fundamental disagreements about accommodation timing and terminal rate destination. Human strategists emphasize future-oriented measured cuts beginning 2026 with policy "shifting from restrictive toward neutral," treating current positioning as the inflection point before normalization commences and terminal Fed Funds settling at moderately elevated levels by late 2026. Data view reflects real rates z-score already in Easing phase with rates declining from restrictive levels, measuring current realized state of policy loosening underway rather than forecasting future path, providing empirical validation that accommodation commenced but remaining agnostic on pace and ultimate destination of easing cycle. AI consensus projects more aggressive synchronized global easing with wider uncertainty bands spanning dovish scenario (Fed to lower levels) versus hawkish scenario (maintaining elevated levels), treating current positioning as meaningfully restrictive requiring substantial normalization but acknowledging material tail risks around inflation persistence or growth deterioration. This gauge range represents terminal rate uncertainty—strategists clustering at moderately elevated levels, Data indicating trajectory consistent with gradual easing but providing no endpoint forecast, AI models spanning wider range depending on macro evolution—creating material duration positioning implications despite apparent directional consensus on easing trajectory.

Policy Phase Timing and Accommodation Progress: Most fundamental divergence centers on interpreting how much accommodation has occurred versus remains ahead, with Data view showing US real rates moderately above neutral in Easing phase suggesting policy modestly restrictive but actively normalizing, contrasting with human strategist framing emphasizing future cuts as primary accommodation mechanism and AI positioning indicating more substantial easing required. Human strategists demonstrate High Conviction emphasizing measured normalization with dovish strategists projecting substantially lower Fed Funds by late 2026 while hawkish strategists expect rates to remain elevated through mid-2026, whereas Data's positioning based on realized z-score of US real rates suggests accommodation already modestly underway validating neither extreme dovish nor hawkish strategist positioning but rather measured middle path. AI models split dramatically on easing magnitude with aggressive camp projecting substantial cuts prioritizing employment mandate versus cautious camp warning three reversal scenarios (tariff-inflation spiral forcing hikes, financial dislocation compelling hawkish hold, fiscal crisis constraining easing) could prevent anticipated accommodation, yet Data showing real rates in Easing phase alongside credit standards net easing validates that financial conditions transitioning accommodative supporting baseline easing continuation absent major exogenous shock. The tight clustering suggests frameworks largely aligned on current positioning near neutral-to-mild-easing with policy actively normalizing, though strategists emphasize future trajectory while Data measures realized progress and AI highlights wider outcome distribution around baseline path.

Regional Policy Synchronization and Global Coordination: Regional policy assessment diverges as human strategists project coordinated Fed-ECB-BoE easing creating synchronized global liquidity expansion with major central banks moving together from restrictive toward neutral, AI emphasizes idiosyncratic paths with ECB potentially forced toward aggressive cuts driven by Eurozone recession probability while BoJ maintains ultra-accommodation and some emerging markets remain restrictive, and Data view's regional policy positioning chart confirms most advanced economies clustered in Easing/Neutral zones supporting strategist synchronization thesis yet simultaneously showing Russia in extreme Tight territory and select emerging markets maintaining restrictive stances validating AI concerns about asynchronous global cycles creating dollar strength and EM stress vulnerabilities. Human gauge implicitly weights major developed market coordination heavily given strategist focus on Fed-ECB-BoJ policy paths, while AI incorporates broader geographic dispersion including emerging market heterogeneity driving slightly more dovish positioning, and Data focuses primarily on US real rates as anchor indicator with regional scatter showing dispersion around this US-centric baseline. Terminal rate beliefs separate frameworks with strategists anchoring neutral rate at moderately low levels implying Fed Funds declining toward levels maintaining slightly restrictive stance, AI projecting neutral lower suggesting Fed Funds gravitating toward levels representing neutral-to-accommodative endpoint, and Data's positioning (where neutral represents the midpoint) implying current real rates only modestly above neutral consistent with strategist neutral rate assessment over AI's lower estimate.

Growth-Inflation Tradeoff and Policy Error Risk: Frameworks diverge on which policy error risk dominates—premature easing reigniting inflation versus delayed easing triggering recession—with implications for conviction level and tail scenario weighting. Human strategists with High Conviction emphasize disinflation sufficiently entrenched (inflation moderating substantially through 2026) that measured easing supports soft landing without inflation resurging, assigning low probability to either premature easing or delayed easing outcomes given balanced approach. AI with High Conviction warns stagflation dynamics with modest growth and elevated inflation force uncomfortable easing despite above-target prices, creating material probability of policy error where either premature easing causes inflation reacceleration or delayed easing triggers sharper labor deterioration, reflected in wider outcome distribution and reduced conviction versus strategist certainty. Data showing real rates in Easing phase with credit standards net easing provides no forward guidance on whether this configuration sustainable, measuring current benign alignment (from cross-referencing Growth and Inflation Data views where indicators show Expansion and Stable Inflation) without probability weighting on transition risk to stressed quadrant if growth weakens or inflation reaccelerates. The Hybrid gauge representing weighted average reflects this uncertainty, positioning near neutral-to-mild-easing range acknowledging easing trajectory underway (validated by Data's Easing phase) while incorporating Human high-conviction measured normalization view balanced against AI high-conviction wider uncertainty, producing assessment that baseline easing through 2026 represents high-probability outcome but with meaningful tail risk scenarios in both directions that prevent maximum conviction positioning.

Historical Trend

Hypothetical/Illustrative
Actual Data
Score ranges from -2 (Very Tight Policy) to +2 (Very Loose Policy)

Top Macro Risks Short Term

Identifying and ranking the most significant macro risks across geopolitical, economic, and market dimensions based on strategist consensus and AI analysis.

*Click the 📊 icon to view spider chart analysis. Click "View Details" to see full risk commentary. Risks are ranked by frequency of mention across strategist sources.
*Click "View Details" to see full risk commentary. Risks are ranked by cross-model consensus across Claude, ChatGPT, and Perplexity.

Core Asset Views Short Term

Cross-asset allocation signals aggregating views on equities, fixed income, commodities, and alternatives from professional strategists and AI models.

*View aggregated from reconciling Human, Data and AI core asset class views. Click on any Asset Class for additional details. Hover over any gauge consensus band or gauge label for view information.
Consensus Conviction Level:
High (precise)
Medium
Low (uncertain)
Max Bearish
Neutral
Max Bullish
High (precise)
Medium
Low (uncertain)

Core Sector Views Short Term

Equity sector positioning using a zero-sum relative value framework. Overweights exactly offset underweights to isolate pure sector rotation signals.

*View aggregated from extracted insights of 50+ professional strategists. This is a relative value framework: all sector scores are zero-centred to remove overall equity beta. Composite scores sum to zero (overweights exactly offset underweights). Click on any Sector for additional details. Hover over any gauge consensus band or gauge label for further information.
Consensus Conviction Level:
High (precise)
Medium
Low (uncertain)
Max Bearish
Neutral
Max Bullish

Non-Core Investment Ideas Short Term

Tactical investment opportunities beyond core asset allocation, including thematic trades, relative value ideas, and opportunistic positions from strategist research.

*Ideas are ranked by strategist frequency and conviction. Click "View Details" for full analysis including rationales and counterarguments.
Methodology:
Ranking is based on frequency of mention across 50+ professional strategist sources weighted by conviction level
*Ideas are ranked by cross-model consensus from Claude, ChatGPT, and Perplexity. Click "View Details" for full analysis including thesis and counterarguments.
Methodology:
Ranking is based on cross-model agreement weighted by conviction level across Claude, ChatGPT, and Perplexity
*Ideas are ranked by combined consensus from human strategists (60% weight) and AI models (40% weight). Click "View Details" for full analysis.
Methodology:
Ranking combines human strategist consensus (60% weight) and AI model consensus (40% weight), factoring in source count and conviction levels

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🎯 Dashboard Signals Explained
What are Dashboard Signals?
Consensus views aggregated from the dashboard's tactical analysis across all asset classes.
Signal sources:
  • Growth/Inflation/Monetary Policy outlook
  • Sector and regional views
  • Risk-on/Risk-off sentiment
  • Top macro risks assessment
Signal scoring (-2 to +2):
+2: Strongly bullish • +1: Mildly bullish
0: Neutral • -1: Mildly bearish • -2: Strongly bearish
OVERALL ALIGNMENT SCORE
0%
Significant divergence from signals
📊 Alignment Score Methodology
How Signal Weights are calculated:
1. Start with base allocation (balanced portfolio benchmark)
2. Apply dashboard signal adjustments (±5% max per asset)
How Delta is calculated:
Delta = Your Weight − Signal Weight
  • Positive = OVERWEIGHT vs consensus
  • Negative = UNDERWEIGHT vs consensus
Alignment Score:
Score = 100 − (Σ|Deltas|) × 1.5
Interpretation:
  • 80-100%: Strong alignment
  • 50-79%: Moderate divergence
  • 0-49%: Significant divergence
Your % ← Underweight | Overweight →
Signal %
📊 Signal % Calculation
How target allocations are calculated:
Signal % = Neutral Weight + (Signal Adj × 0.5)
Step 1: Neutral Weights (60/40 baseline)
  • Equities: 60% ÷ 6 = 10% each
  • Fixed Income: 30% ÷ 5 = 6% each
  • Alternatives: 10% ÷ 3 = 3.3% each
Step 2: Signal Adjustments
Dashboard hybrid view signals (-12 to +12 scale) are sourced from the Growth/Inflation/Monetary tabs, scaled by 0.5× to moderate impact.
Example: DM Large Cap
Neutral: 10% + (Signal +12 × 0.5) = 16%
Complete Step 1 to see delta analysis
📈

Portfolio Analytics (Based on Long-Term CMAs)

EXPECTED RETURN
--%
10-year annualised
EXPECTED VOLATILITY
--%
Annualised std dev
SHARPE RATIO
--
Risk-adjusted return
INCOME YIELD
--%
Estimated annual
EQUITY BETA
--
vs Global Equities

📋 Score Interpretation Guide

80-100%: Strong Alignment
Portfolio closely matches consensus signals
⚠️
50-79%: Moderate Divergence
Some intentional tilts from consensus
🔴
0-49%: Significant Divergence
Portfolio differs significantly from signals

⚡ Suggested Actions

Based on largest divergences from signals

💡 Actionable suggestions will appear here

🔧 Optimize Allocation

Fine-tune your portfolio using signal tilts and optimization tools.

🎚️ Tilt Sensitivity

How aggressively should dashboard signals adjust your base weights?

Conservative Aggressive
Moderate (±3% per signal)

⚖️ Portfolio Weights

Total: 100%
Asset Signal Base Tilt New
Weight

Quick Actions

📊 Live Metrics

Metric
Before
After
Δ
Expected Return
Volatility
Sharpe Ratio
Max Drawdown
Equity Beta
Tactical Alignment

🍩 Allocation Breakdown

Equity
60%
US Equity 32%
Intl Dev Equity 12%
EM Equity 17%
Bonds 35%
Cash & Other 4%

📈 Risk/Return Positioning

💡 Your portfolio is near the efficient frontier — good risk/return tradeoff.

⚖️ vs Benchmark

Metric You Benchmark Delta
Return 6.3% 6.5% -0.2% ▼
Volatility 9.9% 10.2% -0.3% ▼
Sharpe 0.33 0.34 -0.01 ▼
Max DD -18.1% -14.5% -3.6% ▼
Beta 0.61 0.60 +0.01
Allocation Difference
US Equity
-3%
Intl Equity
-3%
EM Equity
-3%
US Bonds
+3%
Intl Bonds
-3%
Cash
-3%
⚠️ Summary: You're taking -0.3% less risk for -0.2% less return. Risk-adjusted, this is worse than benchmark.

📋 Review & Export

Final portfolio with ETF recommendations. Review and export for implementation.

📄 Final Portfolio

Asset Class Weight ETF Value*
TOTAL 100% $500,000
*Based on portfolio size:
Expected Return
+6.3%
Volatility
9.9%
Sharpe
0.33
Max DD
-18.1%
Equity Beta
0.61

🔄 Before & After Comparison

📊 Current Portfolio
Return
Vol
Sharpe
Alignment
✨ Proposed Portfolio
Return
Vol
Sharpe
Alignment

📝 Portfolio Rationale

Built: January 11, 2026
Base Portfolio: 60/40 Balanced
View Source: Hybrid (60% Human + 40% AI)
Key Tilts:
Divergences Resolved: None

📤 Export Options

📊
Excel
Trade list & weights
📄
PDF
Summary report
📑
Add to IC Report
Include in deck
💾
Save Portfolio
For comparison
⚠️

Important: This tool is for informational purposes only. Delta analysis compares your stated allocation to aggregated strategist views and should not be construed as investment advice. Signals reflect consensus opinions which may be wrong. Past performance does not guarantee future results. Consider consulting a qualified financial advisor before making investment decisions.

Long-Term Capital Market Assumptions Long Term

10-Year Expected Nominal Returns

Consensus 10-year return forecasts aggregated from major asset managers and research houses. Toggle between GBP/USD views and conviction-weighted vs risk-adjusted displays.

💷 British Pounds (GBP)
Display all return expectations, volatilities, and risk metrics in GBP terms for UK-based investors.
✓ See returns in your home currency with appropriate hedging assumptions
How to use
Click to switch. GBP view includes UK Gilts as the domestic bond allocation.
💵 US Dollars (USD)
Display all return expectations, volatilities, and risk metrics in USD terms for US-based or dollar-denominated investors.
✓ Global benchmark currency view with US Treasury as domestic bonds
How to use
Click to switch. USD view shows US Treasuries as the domestic bond allocation.
🎯 Conviction View
Displays return forecasts with visual opacity indicating conviction level. High conviction = solid colors, low conviction = faded.
✓ Quickly identify which forecasts have strongest analytical support
How to use
Default view. Hover over bars to see conviction rating (High/Medium/Low) and rationale.
📊 Risk Adjusted View
Penalises low-conviction forecasts by inflating their displayed volatility. This better reflects the true uncertainty in less-confident estimates.
✓ More conservative risk estimates for portfolio optimisation inputs
How to use
Click to switch. Use the scaling slider to adjust how severely low conviction inflates volatility.
🎯 Theme Adjustments
Reveals how long-term macro themes (AI, deglobalisation, demographics, etc.) impact each asset class's return expectations.
✓ Understand the structural drivers behind CMA adjustments
How to use
Toggle on to see theme impact bars. Customise theme weights in Long Term Macro Themes tab.

➕ Add Custom Asset

Add a custom asset to the CMA universe with your own assumptions

Required. Max 40 characters.
10-year nominal return assumption
Annualized standard deviation

ℹ️ Correlations will be inherited from the "REITs" template. This determines how your custom asset correlates with other assets in the portfolio.

⚙️ Customize CMAs
Manually override individual asset class return, volatility, and correlation assumptions to reflect your own views or stress test scenarios.
✓ Tailor assumptions to your investment thesis or house views
How to use
Click to expand panel. Adjust sliders for each asset. Changes flow through to SAA optimisation.
⚡ Quick Presets
One-click macro scenario adjustments. Apply pre-configured CMA shifts for scenarios like "Stagflation", "Risk-Off", "Goldilocks", etc.
✓ Instantly see how different macro regimes affect expected returns
How to use
Click to expand. Select a preset to apply. Use "Reset" to return to baseline assumptions.
📤 Export CMA Deck
Export your CMA assumptions as a professional PowerPoint presentation for IC meetings.
✓ Scatter charts, rankings, and methodology included
How to use
Click to open export options. Select sections and generate your deck.
📋 Export CSV
Download raw CMA data as CSV for use in Excel, Python, or other tools.
✓ Includes returns, volatility, correlations, Sharpe ratios
How to use
Click to download. Opens in Excel or any spreadsheet app.
💾 Scenario Manager
Save, load, and compare multiple CMA scenarios. Store your customised assumptions for different market regimes or investment theses.
✓ Compare outcomes across scenarios side-by-side
How to use
Save current settings as a scenario. Load saved scenarios. Use "Compare" to see differences.
➕ Add Custom Asset
Add your own custom assets to the CMA universe. Define name, expected return, volatility, and inherit correlations from a template asset.
✓ Analyse private assets, alternatives, or custom strategies alongside platform CMAs
How to use
Click to open modal. Enter asset details. Custom assets appear with square markers in charts.
🏆 Highest Return
Loading...
--%
Click to highlight
⚖️ Best Risk-Adjusted
Loading...
--
Sharpe Ratio
🛡️ Best Diversifier
Loading...
--%
Lowest Covariance
💎 Highest Conviction
Loading...
--%
Source Agreement
💼

Portfolio Impact Summary

How current CMA assumptions affect a model portfolio

Expected Return
--%
vs baseline
Expected Volatility
--%
annualized
Sharpe Ratio
--
risk-adjusted
Max Drawdown Est.
--%
2σ stress scenario
Income Yield
--%
dividend + coupon
Real Return
--%
after 2.5% inflation
Visualise and customise asset correlations to global equities

Expected Return vs Volatility

Equities
Fixed Income
Alternatives
High Conviction
Medium
Low

Sharpe Ratio vs Correlation to Global Equities

Equities
Fixed Income
Alternatives
High Conviction
Medium
Low

Expected Return vs Covariance (Portfolio Attractiveness)

Equities
Fixed Income
Alternatives
High Conviction
Medium
Low

Covariance = Correlation × Asset Volatility × Global Equity Volatility (15.5%)

Asset Class Data

Compare Assets: Click table rows to select up to 2 assets
Asset Class Category Expected Return Expected Volatility Correlation Sharpe Ratio Covariance Conviction

Methodology

Expected returns and volatilities are calculated using time-weighted averages of forecasts from BlackRock (Sep '25), Research Affiliates (Nov '25), and J.P. Morgan (Sep '25). More recent forecasts receive higher weightings using an exponential decay function with a 12-month half-life — appropriate for 10-year capital market assumptions where forecasts don't become stale as quickly as short-term views.

All figures represent 10-year geometric nominal return expectations in the selected currency. Sharpe ratios use the expected Cash return as the risk-free rate (GBP: 2.93%, USD: 3.48%). Correlations are to a global equity benchmark.

Conviction scores combine two factors: (1) time-weighted source coverage — more recent sources contribute more to the coverage score, and (2) forecast dispersion — tighter agreement between sources yields higher conviction. Data older than 12 months is flagged as potentially stale.

🎯

Theme-Informed CMA Guidance

Qualitative adjustments based on 15 structural themes analyzed in Long Term Macro Themes tab

Framework: The following guidance translates structural macro themes into directional CMA adjustments. Consider these as overlays to baseline forecasts, reflecting medium-to-long-term forces that may not be fully captured in traditional CMA methodologies. Adjustments are derived from theme consensus levels (3/3, 2/3, 1/3), LLM conviction ratings (High/Medium/Low), and asset-specific impact scores from the heat maps.

📈

Upward Return Bias

📉

Downward Return Bias

Elevated Volatility Considerations

💡 Implementation Note: These are qualitative directional signals, not precise quantitative adjustments. Use this guidance to inform discussions around baseline CMA departures. For Phase 2 implementation, these theme impacts can be systematically quantified into explicit basis point adjustments using the heat map scoring methodology. View the Long Term Macro Themes tab for detailed theme analysis including cycle impacts, asset class implications, and LLM consensus/divergence.

Strategic Asset Allocation Long Term

Balanced (10% Vol Target)

Mean-variance optimised portfolios maximising expected long-term returns at different risk profile levels using CMA inputs.

Risk Profile
Expected Return
7.18%
nominal
Expected Volatility
9.86%
target ≤10%
Sharpe Ratio
0.42
risk-adjusted
Equity Beta
0.59
market sensitivity

vs 60/40 Benchmark

60% Global Equity / 40% Aggregate Bonds
Expected Return
7.18%
Optimized
vs
4.80%
60/40
+2.38% ↑
Volatility
9.86%
Optimized
vs
12.20%
60/40
-2.34% ↓
Sharpe Ratio
0.42
Optimized
vs
0.14
60/40
+0.28 ↑
Max Drawdown
-35.3%
Optimized
vs
-18.3%
60/40
-17.0% worse
Verdict
Higher Return
Similar Risk

📈 Risk Profile Curve

Risk-adjusted returns across volatility targets
Plotting A Risk Profile Portfolio Curve of Risk-Adjusted Returns
Monitoring the shape of the risk-reward curve
⚠️ Insight: The risk-reward curve appears inverted - taking more risk currently delivers less expected return after accounting for drawdown risk.
Risk Profile Levels
Best Fit Line
Currently Selected

📊 12-Month Scenario Analysis

Testing portfolio sensitivity to different market environments over the next 12 months
📊 Base Case
Current CMA consensus assumptions with normal market conditions
Historical analog: Long-term average conditions
Assumptions:
Equity mkt: 0%
Volatility: ×1.00
Correlation: +0.00
Inflation: 2.50%
Effective β: 0.59
12M Expected Return
7.18%
Base Case
12M Volatility
9.86%
Base Case
12M Real Return
0.86%
Base Case

Full Scenario Comparison

Metric 🐻 Bear 📊 Base 🐂 Bull

Drawdown Probability Distribution

Return Outcome Distribution

Portfolio Holdings

Ticker Asset Type Allocation Eq Beta TER
Equities
Fixed Income
Click any row for detailed CMA information

Allocation by Asset Class

Equities68.91%
Fixed Income31.09%

Performance Metrics

📈 Return Metrics

Nominal Return 7.18%
Expected Inflation 2.50%
Real Return 4.68%
Expected 12m Drawdown -3.82%
Real Return (After Losses) 0.86%

⚠️ Risk Metrics

Volatility 9.86%
Equity Beta 0.59
Max Drawdown (60% eq fall) -35.3%
Bad Drawdown (35% eq fall) -20.6%
Normal Drawdown (10% eq fall) -5.9%

⚖️ Efficiency Ratios

Cash Rate 3.00%
Sharpe Ratio 0.42
Sharpe (Loss-Adjusted) -0.05
Information Ratio 0.73
Info Ratio (Loss-Adjusted) 0.34

Drawdown Risk Analysis

🔴
MAX STRESS
60% Equity Fall
Portfolio Drawdown
-35.3%
Probability
2%/yr
Exp Loss
0.71%
🟠
BAD MARKET
35% Equity Fall
Portfolio Drawdown
-20.6%
Probability
8%/yr
Exp Loss
1.65%
🟡
CORRECTION
10% Equity Fall
Portfolio Drawdown
-5.9%
Probability
40%/yr
Exp Loss
2.35%
Total Expected Annual Loss 3.82%
Max 0.71%
Bad 1.65%
Normal 2.35%

Return Waterfall: From Nominal to Real After Losses

7.18%
Nominal Return
-3.82%
Expected Losses
-2.50%
Inflation
0.86%
Real Return (After Losses)

Portfolio Builder Calculator

£
ETF Target % Amount to Invest Est. Annual Cost
Total 100.00% £100,000 £190
* Amounts rounded to nearest £1. Annual cost based on Total Expense Ratios (TERs).

Methodology

This portfolio is optimized for maximum expected return subject to a 10% volatility constraint. Expected losses are calculated using three drawdown scenarios (Max 60%, Bad 35%, Normal 10% equity falls) with assumed annual probabilities (2%, 8%, 40% respectively). Portfolio drawdown = Equity Beta × Equity Market Drawdown. Returns and volatilities are sourced from the Long-Term Capital Market Assumptions consensus (BlackRock, Research Affiliates, J.P. Morgan).

Risk Premia Analysis Long Term

Relative & Historical Attractiveness

Evaluating the relative and historical attractiveness of portfolio risk premia to guide risk-taking decisions and portfolio positioning.

📋
Executive Summary
Risk Premia Analysis • January 2026

⚠️ Risk premia at 50-year lows — the market is not paying investors to take risk. Favor capital preservation over aggressive positioning until conditions normalize.

The composite gauge reading of 21% ranks in the bottom quintile of historical observations. The risk-reward curve is inverted — defensive portfolios currently offer better risk-adjusted returns than growth portfolios.

Composite Reading
21%
▼ Below Average
Recommended
Very
Defensive
Target Vol: 5-6%
Risk-Reward Curve
Inverted
⚠️ More risk ≠ more return
Thesis Confidence
83%
High conviction
Last Updated: January 4, 2026
Current Composite Portfolio Risk Premia Attractiveness —
Average of All Risk Profiles
Source: Author's dataset
21%
Max Unattractive
Unattractive
Neutral
Attractive
Max Attractive
ℹ️ How is this gauge constructed? (click to expand)

What it represents: The Composite Portfolio Risk Premia Attractiveness Gauge condenses six valuation and return series into a single 0–100 score. It translates 50 years of portfolio data into one signal — showing when investors should lean in or stand aside. The analysis draws on 250 reconstructed "point-in-time" strategic portfolios across five risk profile levels.

How it works: Each input series is normalized to its historical z-score relative to its long-term mean, then weighted by its importance in explaining forward 10-year realized returns. The gauge expresses where today's risk premia rank in historical percentile terms.

Component Series & Weights:
Component Series Weight
Expected 10Y Nominal Portfolio Return (avg all risk profiles) 10%
Expected 10Y Real Portfolio Return (avg all risk profiles) 15%
Expected 10Y Real Return adjusted for 12m drawdown risk 20%
Expected 10Y Portfolio Sharpe Ratio (avg all risk profiles) 15%
Expected 10Y Sharpe Ratio adjusted for 12m drawdown risk 20%
Real Return per unit of Portfolio Equity Beta (adjusted for expected losses) 20%

Key insight: Risk premia cycle predictably — fat after crises, thin after rallies. Readings below 30% have historically preceded periods of poor risk-adjusted returns, while readings above 70% have signaled attractive entry points.

Composite attractiveness (%)
Market Peak
Market Trough
Long-run average
Current Composite Reading
21%
Below Average
Long-run Average: 50%
Historical Range: 9% - 97%
⚠️ Risk Premia Currently Unattractive

The composite risk premia attractiveness reading of 21% is well below the long-run average of 50%, suggesting that expected returns relative to risk are historically low. This is consistent with elevated equity valuations and compressed credit spreads.

Implication: Consider defensive positioning. Historically, readings this low have preceded periods of below-average risk-adjusted returns.

Recommended Risk Profile Portfolio
Consolidated metrics — which risk profile offers the best trade-off?
Source: Author's dataset
19%
Very Defensive
Defensive
Balanced
Growth
High Growth
0% 20% 40% 60% 80% 100%
Current Recommendation
Very Defensive
Target Vol: 5-6%
Composite Score
19%
Percentile Rank
🔍 Why 19% here vs 21% above?

The 21% Composite Gauge measures overall attractiveness of risk premia using 6 return/risk metrics.

The 19% Risk Profile Gauge uses those same 6 metrics plus 4 additional "curve slope" components that measure whether moving up the risk spectrum is being rewarded or penalized.

Why 19% is lower: The curve slope metrics are currently negative (inverted) — meaning aggressive portfolios are less efficient than defensive ones. This drags the 19% score below the 21%, signaling that not only are risk premia thin, but the market is actively penalizing higher-risk strategies.

📊 Why Very Defensive? The Data Speaks
Return per Unit Vol
Very Defensive
0.087
▲ Best efficiency
Return per Unit Vol
Balanced
0.036
59% less efficient
Return per Unit Vol
High Growth
0.016
82% less efficient
Key Insight #1

The risk-reward curve is inverted. Moving from Very Defensive to High Growth delivers only +0.3% more adjusted return, but requires taking on +8% more volatility. That's paying 27x more risk for marginal extra return.

Key Insight #2

Drawdown risk isn't compensated. High Growth portfolios face ~4.2% expected annual drawdown vs ~2.2% for Very Defensive, but their raw real return advantage (4.6% vs 2.8%) is almost entirely offset by this higher loss expectation.

Key Insight #3

Behavioral risk amplifies in thin-premia environments. When expected returns barely exceed expected losses, any drawdown feels devastating. Investors panic-sell at bottoms, turning temporary losses into permanent ones. Lower-volatility portfolios reduce this behavioral trap.

Bottom Line

Risk tolerance ≠ optimal risk. What you can tolerate and what you should take are different questions. Right now, the market isn't paying investors to take risk. Until risk premia normalize, capital preservation beats aggressive positioning — patience is the most undervalued alpha.

🎯 Action: Tilt strategic allocation toward the Defensive end of the spectrum. This isn't market timing — it's aligning portfolio construction with the prevailing price of risk. When conditions improve (composite score rises above 50%), re-risk with greater confidence and higher prospective reward.

ℹ️ How is this recommendation calculated? (click to expand)

What it represents: The Recommended Risk Profile Portfolio gauge consolidates multiple portfolio risk-premia indicators into a single 0–100 score that guides the appropriate risk stance. The score is expressed as a percentile: 50% = typical conditions, 0% = max defensive signal, 100% = max growth signal.

Key difference from the Composite Gauge: While the Composite gauge measures overall attractiveness of risk premia, this gauge additionally incorporates relative value across risk profiles — specifically, whether the risk-reward curve is normally sloped (more risk = more reward) or inverted (more risk = less efficiency).

Component Series & Weights:
Component Series Weight
Expected 10Y Nominal Portfolio Return 4%
Expected 10Y Real Portfolio Return 8%
Expected 10Y Real Return (drawdown-adjusted) 10%
Expected 10Y Portfolio Sharpe Ratio 8%
Expected Sharpe Ratio (drawdown-adjusted) 10%
Real Return per unit of Equity Beta (loss-adjusted) 10%
Nominal Return Risk Profile Curve (Higher vs Lower) 8%
Real Return Risk Profile Curve (Higher vs Lower) 12%
Real Return Curve adjusted for Drawdowns 15%
Sharpe Ratio Curve adjusted for Drawdowns 15%

Core principle: Risk tolerance defines what you can take; risk premia define what you should take. When the curve slope components are negative (inverted), defensive portfolios offer superior risk-adjusted returns, regardless of individual risk tolerance.

0% = Max Defensive signal (worst risk premia) • 50% = Neutral • 100% = Max Growth signal (best risk premia)
⚖️
How We Could Be Wrong
Risks to the defensive thesis — intellectual honesty matters

No forecast is certain. Three factors could invalidate the defensive stance: a structural productivity surge (AI/energy), historically atypical drawdown patterns, or a real-rate regime shift. Click "Expand" to explore each risk in detail.

📊 Component Breakdown

Component Importance Weight Current Z-Score Percentile Signal
Expected Nominal Return (10Y) Low 10% -1.15 16% ⬇ Bearish
Expected Real Return (10Y) Medium 15% -1.37 12% ⬇ Bearish
Real Return (Adj. for Drawdown) High 20% -1.39 8% ⬇ Bearish
Portfolio Sharpe Ratio (10Y) Medium 15% -0.70 24% ⬇ Bearish
Sharpe Ratio (Adj. for Drawdown) High 20% -0.78 20% ⬇ Bearish
Return per Unit Beta High 20% -1.33 12% ⬇ Bearish
Methodology: Each component is converted to a z-score (standardized), weighted by importance, then the composite is scaled to 0-100 using min-max normalization. Higher values indicate more attractive risk premia. The long-run average is 50% by construction.
Risk Profile Curve: Expected Return vs Volatility
Real drawdown-adjusted returns by risk profile level — is more risk being rewarded?
⚠️
CURVE INVERTED
More risk = less expected return
Risk Profile Levels
Fitted Curve
Normal Curve (Illustrative)
Risk Profile Target Vol Exp. Real Return Exp. Drawdown Adj. Return Return/Vol
Very Defensive 6% 2.8% -2.2% 0.52% 0.087
Defensive ✓ 8% 3.5% -3.1% 0.51% 0.064
Balanced 10% 4.2% -3.8% 0.36% 0.036
Growth 12% 4.5% -4.2% 0.26% 0.022
High Growth 14% 4.6% -4.2% 0.22% 0.016
📉 What This Means

The risk profile curve is inverted — moving from Defensive to High Growth portfolios reduces expected risk-adjusted returns. Historically, this curve slopes upward (more risk = more reward). Today's inversion signals that investors are penalized, not rewarded, for taking additional risk. This supports a strategic tilt toward the Defensive end of the spectrum until risk premia normalize.

Adj. Return = Expected Real Return minus scenario-weighted 12-month expected drawdown. Return/Vol = Adj. Return ÷ Target Volatility.
Expected Portfolio Losses by Scenario
Scenario-weighted 12-month drawdown outlook across risk profiles
Source: Author's dataset based on long-term historical frequencies
🔴
Severe Crisis
60% equity fall • 2% annual prob.
🟠
Bad Market
35% equity fall • 8% annual prob.
🟡
Normal Correction
10% equity fall • 40% annual prob.
Scenario / Risk Profile Very Defensive Defensive Balanced Growth High Growth
🔴 Severe Crisis (-60% equity)
Portfolio Drawdown
-20.1% -28.6% -35.3% -38.6% -38.6%
Expected Loss (2% prob.) -0.40% -0.57% -0.71% -0.77% -0.77%
🟠 Bad Market (-35% equity)
Portfolio Drawdown
-11.7% -16.7% -20.6% -22.5% -22.5%
Expected Loss (8% prob.) -0.94% -1.34% -1.65% -1.80% -1.80%
🟡 Normal Correction (-10% equity)
Portfolio Drawdown
-3.3% -4.8% -5.9% -6.4% -6.4%
Expected Loss (40% prob.) -1.32% -1.92% -2.36% -2.56% -2.56%
📊 Total Expected 12m Loss
Probability-weighted sum
-2.2% -3.1% -3.8% -4.2% -4.2%
💡 The Math of Risk

Very Defensive loses 2.0% less annually in expected drawdowns vs High Growth. Over 10 years, that compounds to significant capital preservation — without sacrificing much adjusted return.

⚠️ Why This Matters Now

Normal corrections (10%+ falls) occur in 4 out of 10 years on average. With drawdown frequency at historic lows, mean reversion suggests elevated correction risk ahead.

Portfolio drawdowns derived from equity beta × scenario equity fall. Probabilities based on 150-year historical frequencies.
Historical Drawdown Frequency
Rolling 10-year frequency of major equity market drawdowns (1880-2025)
Source: S&P 500 / Dow Jones blend, Author's calculations
📉
DRAWDOWN FREQUENCY AT HISTORIC LOWS
Mean reversion suggests elevated correction risk ahead
10%+ Drawdown Frequency (10Y Rolling)
20%+ Drawdown Frequency (10Y Rolling)
Long-term Average
Current 10%+ Frequency
32%
of months in drawdown
Long-term Avg (10%+)
59%
of months in drawdown
Current 20%+ Frequency
18%
of months in drawdown
Long-term Avg (20%+)
36%
of months in drawdown
🔮 What History Tells Us

Drawdown frequency is mean-reverting. Current readings are ~45% below the 145-year average for 10%+ drawdowns. Every prior period of unusually low drawdown frequency was followed by a return to — or overshoot of — the long-term average. This doesn't predict when corrections will occur, but it does suggest investors should build resilience now rather than assume recent calm will persist. History doesn't repeat exactly, but it does rhyme.

Drawdown = peak-to-trough decline from prior all-time high. Frequency = % of months in the 10-year window experiencing drawdown ≥ threshold.
Historical Major Market Drawdowns
12 significant equity market declines since 1976 — context for drawdown risk
Worst Drawdown
-55%
GFC (2007-09)
Average Drawdown
-29%
across 12 events
Avg Recovery Time
4.2 yrs
to prior peak
Avg Frequency
1 in 4
years (20%+ falls)
📚 What These Events Teach Us

Causes vary, drawdowns are constant. From geopolitical shocks to bubbles to pandemics — the specific triggers differ but significant drawdowns occur roughly once every 4 years.

Recovery time is unpredictable. COVID recovered in 6 months; the dot-com crash took 7 years. Assuming quick recovery is a behavioral trap.

Defensive portfolios protect more than returns. They preserve the emotional capital needed to stay invested through drawdowns rather than panic-selling at bottoms.

Current environment resembles late 1990s. Extended bull run, elevated valuations, technology-led gains, low volatility. That ended with a 47% drawdown.

Based on S&P 500 Total Return Index. Recovery = time from trough to prior peak. Data from Author's analysis.
US Equity Nominal Expected Return Decomposition (% p.a.)
Custom model nominal returns forecast and factor contributions vs realized returns
Source: Robert Shiller dataset, Author's calculations
Nominal Real
Dividends
Growth
Inflation
Valuation
Buybacks
Total
Dividends
1.2%
Growth
2.5%
Inflation
3.1%
Valuation
-3.3%
Buybacks
1.5%
Total
4.9%
Methodology: Expected 10-year nominal returns decomposed into: Dividend Yield (current), Real GDP Growth (trend), CPI Inflation (expected), Valuation Change (CAPE mean-reversion), and Buyback Yield. The sum of components equals Total Expected Return. Negative valuation indicates expected P/E compression from current elevated levels.

Mega Trends Long Term

AI-Enhanced Thematic Investment Framework

Screening and evaluating long-term thematic investment opportunities using AI-enhanced analysis across structural growth themes.

1
Screen
148 → 19
2
AI Check
6 Criteria
3
Reconcile
Human + AI
4
Portfolio
5 ETFs
148
Universe
19
Screened
5
Portfolio
1
HIGH
4
MEDIUM
14
EXCLUDED
0.65%
Wtd TER
~17%
Target Vol
📚
What is Thematic Investing 2.0?
AI + Human hybrid approach to capture megatrends via ETF portfolio

Investment Process Steps

Click any step to expand details

1
Fundamental Theme Screen
Barclays Thematic Roadmap • 148 → 19 themes
🔵 Human
2
AI Sanity Check
6 criteria analysis • 1 High, 6 Medium, 12 Low
🟢 AI
3
Final Rating Reconciliation
Human + AI → Final • 1 High, 4 Medium included
🟡 Hybrid
4
Portfolio Construction
5 ETFs • 17% vol target • Equal weight
🟣 AI-Opt

💾 Save Theme Configuration

Save your current theme weights and impact overrides for later use

Required. Max 50 characters.
Optional. Max 200 characters.

📋 What will be saved:

📂 Theme Configuration Manager

Load, manage, or delete your saved theme configurations

Long Term Macro Themes Long Term

Multi-year structural forces with 3-10 year horizons. Themes synthesised from Claude, Perplexity, and ChatGPT analysis with consensus scoring.

📊
Synthesis Methodology

15 themes identified from cross-referencing outputs from Claude, Perplexity, and ChatGPT (10 themes each). 6 themes have universal consensus (3/3), 3 themes have strong consensus (2/3), and 6 themes are unique perspectives (1/3). Each theme shows LLM conviction levels and asset class implications.

🎚️
Theme Weighting: ℹ️
🎚️ Theme Weighting Explained
Controls how much influence each structural theme has on the asset class views, heatmaps, and portfolio recommendations below.
Preset Options
Consensus (Default)
Tier 1 themes (3/3 LLMs) = 100%, Tier 2 (2/3) = 67%, Tier 3 (1/3) = 33%. High conviction themes weighted more.
Equal Weight
All 15 themes contribute equally (100% each). Ignores tier and conviction levels.
Tier 1 Focus Only
Only includes 6 themes with universal consensus (all 3 LLMs agreed). Others excluded.
High Conviction Only
Only includes themes where majority of LLMs expressed "High" conviction. Low conviction excluded.
Custom...
Opens sliders to manually set each theme's weight (0-200%). Express your own views.
↓ FLOWS THROUGH TO
Asset Class Heatmap • Cycle Impact Scores • Theme Rankings • Portfolio Recommendations • All weighted calculations
⚙️ Customize Theme Weights
Adjust how much each structural theme influences the overall portfolio view. Increase weights for themes you believe are most important.
✓ Tailor the analysis to your investment thesis
How to use
  1. Click to reveal weight sliders for each theme
  2. Drag sliders to adjust weights (0-100%)
  3. Higher weights = more influence on views
  4. Changes update all displays in real-time
🔄 Reset All Settings
Restore all theme weights to their default values and clear any impact overrides or disabled themes.
✓ Start fresh with a clean slate
What gets reset
  1. All theme weights → Default (100%)
  2. All impact overrides → Removed
  3. All disabled themes → Re-enabled
  4. Preset selection → Equal Weight
📤 Export Theme Settings
Save your custom theme weights, impact overrides, and disabled themes to a JSON file on your computer.
✓ Never lose your customisations when updating the dashboard
How to use
  1. Click Export to download a JSON file
  2. Save it alongside your dashboard file
  3. Share with colleagues if needed
📥 Import Theme Settings
Restore your previously exported theme settings from a JSON file. Instantly applies all saved customisations.
✓ Restore your exact setup in seconds after dashboard updates
How to use
  1. Click Import to open file picker
  2. Select your saved JSON file
  3. All settings are instantly restored!
💾 Save Configuration
Save your current theme weights, preset, and impact overrides as a named configuration that you can load later.
✓ Create multiple setups for different scenarios (e.g., "Inflation Focus", "Risk-Off")
How to use
  1. Set up your desired theme weights
  2. Click "Save As..." to name your configuration
  3. Load it anytime from the Config Manager
  4. Apply in CMAs & SAA tabs instantly
📂 Configuration Manager
View, load, or delete your saved theme configurations. Quickly switch between different scenario setups.
✓ Instantly apply your saved setups to see different portfolio impacts
Features
  1. View all saved configurations
  2. Load any configuration with one click
  3. Delete configurations you no longer need
  4. Up to 20 configurations stored
❓ Save & Restore Guide
Learn how to preserve your theme customisations across browser sessions and dashboard updates.
✓ Click to expand full step-by-step instructions below
Why this matters
  1. Browser memory can clear settings
  2. Dashboard updates reset customisations
  3. Export/Import keeps your work safe

15 Long Term Structural Themes

🌊

Theme → Impact Flow

Visualize how macro themes flow to impacts • Line thickness = impact magnitude × weight

🔥 Cycle Impact Heat Map

How each theme impacts the macro cycle. Hover over any cell for detailed analysis. Use the view toggles above to switch between LLM perspectives.

IMPACT:
Very Negative
Negative
Mixed
Positive
Very Positive
📊 Hybrid View

💼 Core Asset Class Impact Heat Map

How each theme impacts core asset classes. Hover over any cell for detailed analysis. Use arrow buttons to scroll through all 16 asset classes.

IMPACT:
Very Negative
Negative
Mixed
Positive
Very Positive
📊 Hybrid View
◀ Scroll to see all 16 asset classes ▶

Speed Read: Long Term Macro Themes

×

Strategic Glossary Long Term

Key Concepts & Definitions

Key concepts for strategic investment planning, CMAs, risk premia analysis, and the interactive dashboard tools.

This glossary explains key concepts and methodologies used in the Long-Term Capital Market Assumptions (CMAs), Strategic Asset Allocation, Risk Premia Analysis, Mega Trends, and Long Term Macro Themes tabs. It also documents the interactive dashboard tools and features. Click any term to expand its definition.

Core Concepts

Capital Market Assumptions (CMAs) +
Expected Return (Nominal, Geometric) +
Volatility (Standard Deviation) +
Sharpe Ratio +

Correlation & Diversification

Correlation (to Global Equities) +
Covariance +
Diversification Benefit +

Methodology

Time-Weighted Averaging +
Conviction Score +
Nominal Returns +
Conviction View +
Risk Adjusted View & Volatility Scaling +

Portfolio Optimization & Risk

Portfolio Optimization +
Volatility Target / Constraint +
Equity Beta +
Drawdown +
Expected Losses +
Loss-Adjusted Returns & Ratios +
Information Ratio +
FX Exposure +

Asset Classes

Equities +
Fixed Income +
Alternatives +

Data Sources

Source Providers +
Risk-Free Rate +
Global Equity Volatility (15.5%) +

Risk Premia Analysis

Risk Premium / Risk Premia +
Z-Score +
Percentile +
Risk Profile Curve +
Composite Risk Premia Attractiveness (21%) +
Recommended Risk Profile (19%) +
Risk Profiles (Very Defensive → High Growth) +

Return Decomposition

Expected Return Decomposition +
Dividend Yield +
Earnings Growth +
Valuation Change (Multiple Expansion/Contraction) +
Buybacks (Share Repurchases) +
Realized Returns (vs Forecast) +

Forecast Accuracy Statistics

Correlation (Forecast vs Realized) +
R-Squared (R²) +
Mean Error (Forecast Bias) +
Mean Absolute Error (MAE) +
Root Mean Square Error (RMSE) +
Confidence Intervals +
Bias Adjustment +

Thematic Investing Concepts

Thematic Investing +
TAM (Total Addressable Market) +
Secular Tailwinds +
Execution Risk +
Asymmetric Reconciliation +
TER (Total Expense Ratio) +

Long-Term Macro Theme Concepts

Structural / Macro Theme +
Consensus Tier (3/3, 2/3, 1/3) +
Fiscal Dominance +
Term Premium +
Neutral Rate (r*) +
Deglobalisation / Friend-shoring / Nearshoring +
Greenflation +
Stranded Assets +
Critical Minerals +
Financial Repression +
Demographic Dividend +
Supply-Side Inflation +

Dashboard Tools & Interactive Features

Quick Scenario Presets +
Portfolio Impact Summary +
Theme-to-Asset Sankey Diagram +
Asset Correlation Heatmap +
Scenario Manager & Comparison +
CMA Customisation Panel +
Theme Weighting System +
Cycle Impact Heat Map +
Data Export Options +
Source Colour-Coding System +
Getting Started Panel & Guided Paths +
My Portfolio & Delta Analysis +
Why Triangulate? (Human + AI + Data) +

Glossary Short Term

Key Concepts & Definitions

Key concepts and methodologies used throughout the dashboard. Click any term to expand its definition and see worked examples.

This glossary explains key concepts and methodologies used throughout the dashboard. Click any term to expand its definition and see worked examples.

Conviction Level

+

Growth View

+

Inflation View

+

Monetary Policy View

+

Top Macro Risks

+

Non-Core Investment Ideas

+

Key Ideas (Quick Access)

+

Core Asset Class Views

+

Core Sector Views

+

Risk On/Off View

+

Executive Summary Tab

+

More definitions coming soon...

Portfolio Glossary Portfolios

Portfolio Terms & Definitions

Key terms and concepts used in portfolio construction, analysis, and optimization.

📊 Portfolio Construction

Asset Allocation

The process of dividing an investment portfolio among different asset categories such as stocks, bonds, and cash. The allocation is based on an investor's goals, risk tolerance, and investment horizon.

Example: A 60/40 portfolio allocates 60% to equities and 40% to fixed income.

Strategic Asset Allocation (SAA)

A long-term portfolio strategy that sets target allocations for various asset classes and rebalances periodically. SAA is based on expected returns, volatilities, and correlations derived from Capital Market Assumptions (CMAs).

Key Point: SAA forms the baseline allocation that tactical tilts are applied to.

Tactical Asset Allocation (TAA)

Short-term adjustments to the strategic allocation based on current market conditions, valuations, or views. TAA "tilts" the portfolio to overweight or underweight certain assets relative to the strategic benchmark.

In This Dashboard: Signal strengths (+100 to -100) translate to tactical tilts on your base allocation.

Tilt Sensitivity

A parameter that controls how aggressively signal strengths translate into portfolio weight changes. Higher sensitivity means larger tilts for the same signal strength.

Range: 1 (±1% per signal) to 5 (±5% per signal) in this dashboard.

📉 Risk Metrics

Volatility (Standard Deviation)

A measure of the dispersion of returns. Higher volatility indicates greater uncertainty and risk. Typically expressed as an annualized percentage.

Typical Ranges: Cash ~1%, Bonds 4-8%, Equities 15-20%, EM Equities 20-25%

Maximum Drawdown (Max DD)

The largest peak-to-trough decline in portfolio value before a new peak is achieved. Measures the worst-case scenario an investor would have experienced.

Example: A Max DD of -18% means the portfolio fell 18% from its peak before recovering.

Beta (Equity Beta)

A measure of the portfolio's sensitivity to overall market movements. A beta of 1.0 means the portfolio moves in line with the market; below 1.0 is less volatile, above 1.0 is more volatile.

Interpretation: Beta 0.6 = portfolio moves ~60% as much as the market.

Sharpe Ratio

A measure of risk-adjusted return. Calculated as (Portfolio Return - Risk-Free Rate) / Portfolio Volatility. Higher is better—it means more return per unit of risk.

Benchmarks: Sharpe < 0.3 = poor, 0.3-0.5 = average, 0.5-0.7 = good, > 0.7 = excellent

Efficient Frontier

The set of optimal portfolios that offer the highest expected return for a given level of risk, or the lowest risk for a given level of expected return. Portfolios on the frontier are considered "efficient."

Goal: Position your portfolio on or near the efficient frontier for optimal risk/return.

🎯 Signals & Alignment

Signal Strength

A score from -100 to +100 indicating the directional view on an asset. Positive signals suggest overweight, negative suggest underweight, and zero is neutral.

Sources: Human (strategist consensus), AI (model synthesis), Data (quant indicators), Hybrid (weighted blend).

Alignment Score

A percentage (0-100%) measuring how well your portfolio allocation matches the current signal recommendations. Higher alignment means your portfolio reflects the consensus views more closely.

Note: High alignment isn't always the goal—you may intentionally diverge from signals based on your own views.

Divergence Alert

A warning that appears when Human and AI signals differ significantly (typically >25 points). Divergences may represent opportunities or risks that warrant closer examination.

Action: Review the divergence and decide whether to follow Human, AI, or maintain the Hybrid blend.

💼 Asset Classes

DM Equities (Developed Markets)

Stocks from developed economies including US, Europe, Japan, UK, Canada, and Australia. Sub-categories include Large Cap, Small Cap, Growth, and Value styles.

ETF Examples: VTI (US Total), VEA (Intl Developed), VUG (Growth), VTV (Value)

EM Equities (Emerging Markets)

Stocks from developing economies including China, India, Brazil, Taiwan, Korea, and others. Higher growth potential but also higher volatility and political risk.

ETF Example: VWO (Vanguard FTSE Emerging Markets)

Fixed Income (Bonds)

Debt securities including government bonds, investment grade corporate credit, high yield bonds, and inflation-linked bonds (TIPS). Provides income and typically lower volatility than equities.

ETF Examples: BND (Total Bond), LQD (IG Credit), HYG (High Yield), TIP (TIPS)

Alternatives

Non-traditional asset classes including Real Estate (REITs), Commodities, Gold, and Cash. Often used for diversification as they may have lower correlation to stocks and bonds.

ETF Examples: VNQ (REITs), GLD (Gold), DJP (Commodities), SGOV (Cash/T-Bills)

📊 My Portfolio (OLD) LEGACY

⚠️
Legacy View - For Comparison Only
This is the old holdings-only view. The new "My Portfolio (NEW)" integrates this with analysis, optimization, and comparison in a 4-step workflow.

📊 Current Holdings

Total: 0%

📈 Equities 0%

📉 Fixed Income 0%

💎 Alternatives 0%

📋 Quick Templates

🔍 Divergence Analyzer Short Term

Where Human & AI Disagree

Identifying disagreements between Human and AI views to highlight where assumptions differ and where alpha opportunities may exist.

💡
Why Divergence Matters
When Human and AI agree, conviction is high. When they diverge, that's the signal — it highlights where assumptions differ and where opportunities or risks may be mispriced. The alpha lives in understanding why they disagree.
--
Total Divergences
--
High (>30pt gap)
--
Medium (15-30pt)
--
Aligned (<15pt)

🗺️ Divergence Heatmap

Click any cell to see detailed comparison

AI VIEW →
HUMAN VIEW →
UW
Neutral
OW
UW
✓ Agree
0
⚠ Mild
0
🔥 MAX
0
Neutral
⚠ Mild
0
✓ Agree
0
⚠ Mild
0
OW
🔥 MAX
0
⚠ Mild
0
✓ Agree
0
Agreement
Mild Divergence
Max Divergence

🎯 Actionable Insights

🔥 High Conviction Divergences
Loading...
👀 Watch List — Emerging Divergences
Loading...
Strong Consensus — High Conviction
Loading...

📊 All Divergences — Deep Dive

Asset / Sector 👤 Human 🤖 AI Gap Severity Action
📄

Report Builder

Build custom presentations from platform insights

📊 Overview
Select All
⋮⋮ 📋
Executive Summary
High-level overview of current positioning
1 slide
⋮⋮ 🎯
Risk Sentiment Dashboard
Risk-on/off gauge and key signals
1 slide
⋮⋮ 💡
Key Investment Themes
Top themes and market narratives
1 slide
🌍 Macro Views
Select All
⋮⋮ 📈
Growth Outlook
Economic growth expectations and regime
1 slide
⋮⋮ 🔥
Inflation Outlook
Inflation trajectory and expectations
1 slide
⋮⋮ 🏦
Monetary Policy
Central bank policy stance and outlook
1 slide
💰 Asset Classes
Select All
⋮⋮ 📊
Asset Class Summary
Overview heatmap of all asset views
1 slide
⋮⋮ 📈
Equities Deep Dive
Regional equity views with rationale
2 slides
⋮⋮ 📉
Fixed Income Deep Dive
Bond and credit views with rationale
2 slides
⋮⋮ 🏠
Alternatives & Commodities
Real assets, gold, crypto views
1 slide
🏭 Sectors
Select All
⋮⋮ 🗺️
Sector Heatmap
All 11 sectors at a glance
1 slide
⋮⋮ 📋
Sector Detail Pages
Individual sector views and drivers
3 slides
💼 Portfolio
Select All
⋮⋮ 🎯
Strategic Allocation (SAA)
Long-term target allocation
1 slide
⋮⋮ 📐
Tactical Tilts (TAA)
Current tactical over/underweights
1 slide
⋮⋮ ⚖️
Rebalance & Trades
Implementation trade list
1 slide
💡 Ideas & Risk
Select All
⋮⋮ 🔥
Highest Conviction Ideas
Top-ranked investment opportunities
1 slide
⋮⋮ ⚠️
Top Macro Risks
Key risk factors and mitigations
1 slide
⋮⋮ 📊
Risk Premia Analysis
Compensation for risk taken
1 slide
Report Summary
Sections Selected 9
Estimated Slides ~10
Format PPTX
💡 Drag sections to reorder
Check/uncheck to include
🏢 Firm Identity
📊 No logo
🎨 Color Scheme
Headers, accents
Backgrounds
Highlights, positive
📝 Footer & Disclaimer
Output Format
Orientation
Data Source
Slide Theme
Content Density
Additional Options
👁️ Live Preview
📊

Click "Refresh" to generate preview

Preview updates based on your content and branding selections

Slide 1 of 1
Select sections in Step 1 to see thumbnails
💾 Saved Report Templates
📁

No saved templates yet

Configure your report and click "Save as Template" to create one

Quick Start Templates
📊
IC Monthly Deck
Full presentation for Investment Committee (10 slides)
📋
Executive Brief
Quick summary for leadership (4 slides)
👥
Client Update
Market overview for client meetings (6 slides)
⚠️
Risk Focus
Risk-centric report for risk committee (5 slides)
🏭
Sector Deep Dive
Detailed sector analysis (5 slides)
💼
Portfolio Review
SAA/TAA and rebalancing (5 slides)

Idea Details

×

Risk Details

×

Speed Read: Critical Insights Hybrid View

×

Speed Read: Risk On/Off View

×

Speed Read: Growth View

×

Speed Read: Inflation View

×

Speed Read: Monetary Policy View

×
Conclusion
Consensus view is mild easing as major central banks transition from restrictive policy toward neutral, driven by moderating inflation pressures and softening growth dynamics that provide flexibility to begin normalizing rates through measured cuts while maintaining credibility on price stability, with synchronized Fed-ECB-BoE easing expected to improve liquidity conditions modestly without dramatic accommodation surge.
Key Counterarguments
Dovish perspectives emphasize weakening labor markets and tighter credit conditions may force faster and more aggressive easing to prevent economic contraction, while hawkish outliers warn that persistent services inflation and continued fiscal stimulus could require extended restrictive policy to anchor expectations—additional tail risks include tariff-driven inflation spiral forcing policy reversal, financial dislocation from disorderly currency moves compelling hawkish holds despite growth weakness, or fiscal crises constraining easing capacity despite economic deterioration.
Major Divergences
Strategists express stronger conviction viewing measured normalization supporting soft landing with terminal rates settling moderately above neutral, while AI models maintain broader uncertainty bands spanning aggressive easing scenarios prioritizing employment mandates versus cautious approaches given stagflation concerns—data indicators show real rates already in easing phase validating that accommodation is underway, yet this confirms neither the dovish nor hawkish extremes but rather a measured middle path, with critical uncertainty around whether current benign configuration of easing policy alongside stable inflation and growth proves sustainable or transitions to stressed conditions requiring policy recalibration.

Speed Read: Top Macro Risks

×
Primary macro risks center on fiscal sustainability concerns as debt trajectories exceed sustainable levels amid political gridlock constraining consolidation efforts, persistent services inflation driven by tight labor markets and wage-price spirals resisting disinflation despite central bank tightening, elevated equity valuations concentrated in narrow market leadership creating vulnerability to corrections, policy error risks spanning premature easing that reignites inflation versus excessive tightening breaking growth, geopolitical escalation threatening energy and supply chain disruptions, China economic deceleration potentially exporting deflationary pressures globally, and trade war reescalation fragmenting supply chains while raising production costs.
Counterarguments emphasize that historical precedents show advanced economies sustaining elevated debt loads through growth rather than crisis, goods disinflation momentum remains powerful as supply chains normalize, central bank credibility keeps inflation expectations anchored despite transitory overshoots, strong earnings growth and AI productivity gains could justify current valuations, policymakers possess substantial tools to manage stress episodes, geopolitical tensions historically remain rhetorical rather than escalating to economic disruption, and Chinese authorities retain policy space to stabilize growth through fiscal and monetary support preventing hard landing scenarios.
Strategists emphasize political economy constraints and near-term market discipline mechanisms while focusing on valuation extremes and policy uncertainty as primary concerns, whereas AI models weight structural factors more heavily including interest rate sensitivity analysis, demographic pressures on fiscal trajectories, and quantified transmission channels through specific vulnerability points—critically, AI models identify higher-for-longer real rates as standalone top-tier risk that strategists embed within other categories rather than treating separately, reflecting different frameworks where AI emphasizes regime shift requiring portfolio repositioning while strategists view rate dynamics as cyclical conditions within standard policy normalization.

Speed Read: Core Asset Views

×

Speed Read: Non-Core Investment Ideas

×

Speed Read: Core Sector Views

×

Speed Read: Mega Trends

×
📊 Multi-Dimensional Factor Analysis
Loading chart...

🎯 CMA Wizard

Configure your Capital Market Assumptions step by step

1 Themes
2 Customize
3 Outputs

📊 Step 1: Theme Weighting

Long-term macro themes (AI Revolution, Deglobalisation, etc.) may influence Capital Market Assumptions. Choose whether and how to apply them:

Yes, Use Themes RECOMMENDED
Adjust CMAs based on structural macro themes
✨ Theme-Adjusted CMAs
Incorporate long-term macro themes (AI, deglobalisation, demographics, etc.) that systematically adjust asset class return expectations based on structural economic trends.
✓ More forward-looking, thesis-driven assumptions
📈
No, Standard CMAs
Use consensus assumptions only, no theme adjustments
📈 Standard Consensus CMAs
Use pure consensus capital market assumptions from leading strategists without any macro theme adjustments. Returns reflect aggregated multi-firm forecasts only.
✓ Simple, transparent baseline assumptions
Choose Weighting Method:
⚖️
Consensus (Default) RECOMMENDED
Tier 1 = 100%, Tier 2 = 67%, Tier 3 = 33%
⚖️ Consensus Tiered Weighting
Weights themes by their consensus tier. Tier 1 themes (identified by all 3 AI models) get full weight, Tier 2 (2 models) get 67%, and Tier 3 (1 model) get 33%.
✓ Balanced approach favouring highest-conviction themes
🔢
Equal Weight
All themes weighted equally at 100%
🔢 Equal Weight All Themes
Applies identical 100% weight to all macro themes regardless of consensus tier. No theme is prioritised over another.
✓ Diversified exposure across all structural trends
🎯
Tier 1 Focus Only
Only highest-consensus themes
🎯 Tier 1 High-Conviction Focus
Uses only the themes that all three AI models (Claude, ChatGPT, Perplexity) independently identified. Lower-consensus themes are disabled (0% weight).
✓ Highest-conviction themes with strongest consensus
⚙️
Custom Weights
Manually adjust each theme (0-200%)
⚙️ Custom Theme Weights
Full control over individual theme weights from 0% (disabled) to 200% (double impact). Adjust each theme based on your own macro views.
✓ Maximum flexibility for bespoke views
Consensus weighting active: Tier 1 themes at full impact, Tier 2 at 67%, Tier 3 at 33%.
📊 How Themes Adjust Your CMAs

Each theme has predefined impacts on specific asset classes. When you enable a theme, its adjustments are applied to the baseline consensus CMAs:

Example: "AI Revolution" Theme (Tier 1)
US Equity: +0.8% expected return
DM Equity: +0.5% expected return
EM Equity: +0.3% expected return
Commodities: +0.2% (energy demand)

With recommended settings: Using consensus weighting, a typical portfolio might see US Equity expectations increase by ~1-2% and EM Equity by ~0.5-1% versus standard CMAs, reflecting the aggregate impact of all enabled themes weighted by conviction level.

💡
Quick summary: Themes like "AI Revolution" boost Tech-heavy assets, "Deglobalisation" tempers EM expectations, and "Green Transition" favours renewable-linked sectors. The consensus weighting ensures higher-conviction themes have proportionally greater influence.
Want more control? After setup, visit the Long Term Macro Themes tab to override individual asset class impacts for each theme.

⚙️ Step 2: Customize CMAs

Fine-tune your Capital Market Assumptions. All steps are optional – skip any that don't apply.

A Select Base Currency
£ GBP

Choose whether to view CMAs in British Pounds or US Dollars.

B CMA Source Weightings
Optional

Adjust how different research sources are weighted in the consensus calculation.

C Risk-Free Rate

Used to calculate Sharpe ratios. Defaults to weighted consensus Cash return from your selected sources.

Drag to override manually
D Volatility Treatment
Uncertainty Adjusted

Choose whether to penalise forecasts where sources disagree:

📊
Standard
Raw Estimates

Weighted average of source estimates. No adjustments for forecast uncertainty.

Output: Vol = Weighted average from BlackRock, RA, JPM based on your source weights.
⚠️
Uncertainty Adjusted
RECOMMENDED

Inflates volatility when sources disagree. High dispersion = higher vol estimate.

Output: Vol = Weighted avg × Scaling Factor (based on source agreement).
Max Volatility Scaling 1.5×

Assets with high source dispersion have vol multiplied by up to this factor. 1× = no penalty, 3× = severe penalty.

E Review & Adjust CMAs
Optional

Review your calculated CMAs based on your selections. Click any value to adjust it directly.

Return Volatility Correlation Uncertainty Adj Manual Override
Asset Class Return % Vol % Corr Sharpe
15 assets configured 0 modified from consensus
F Add Custom Assets
Optional

Add your own asset classes (e.g., Private Equity, Crypto, specific funds) with custom CMAs.

ℹ️ Inheritance: Correlations and theme sensitivities will be inherited from "REITs".

📤 Step 3: Generate Outputs

Select which outputs you'd like to generate from your configured CMAs.

📊
Export to CSV/Excel
Download raw data including returns, volatility, correlations, and Sharpe ratios
📽️
Presentation Deck
Professional slide deck with scatter charts, rankings, and methodology for IC meetings
📄 Title Slide (click to collapse)
No file selected
📑 Sections to Include
⇅ Drag to reorder slides
⋮⋮ 📋 Summary Page
⋮⋮ Return/Vol Matrix
⋮⋮ Sharpe vs Correlation
⋮⋮ 📊 Return vs Covariance
⋮⋮ 🎯 Theme Impact
⋮⋮ 📈 Full Data Table
⋮⋮ 📚 Appendix: Methodology
📄 How to Create PDF / PowerPoint
Step 1: Click "Generate HTML" below to download the slide deck
Step 2: Open the downloaded HTML file in Chrome or Edge
Step 3: Press Ctrl+P (or Cmd+P on Mac)
Step 4: Set these options:
  • Destination: Save as PDF
  • Layout: Landscape
⚠️ CRITICAL: Click "More settings" and enable "Background graphics" - without this, colours won't print!
To create PowerPoint: Open the PDF in PowerPoint (File → Open → select PDF) - it converts automatically to editable slides.
🎨 Branding & Style
📊 Data Options
⚠️ Uncertainty Adjusted (from Step 2D)
🎉
Almost done! Click "Generate Outputs" to create your selected deliverables. You can always generate more outputs later from the CMAs tab.

📊 SAA Wizard

Build your Strategic Asset Allocation step by step

1 Currency
2 Risk
3 Assets
4 Constraints
5 Objective
6 CMAs
7 Optimize
8 Stress
9 Adjust
10 Implement
11 Report

💱 Step 1: Select Base Currency

Choose your reporting currency. All returns, volatility, and risk metrics will be calculated in this currency.

🇬🇧
£ GBP
British Pound Sterling. UK investor perspective with long-term Cash return from CMAs.
Risk-Free Rate ? --%
🇺🇸
$ USD
US Dollar. Global investor perspective with long-term Cash return from CMAs.
Risk-Free Rate ? --%
💡 Currency Impact
Currency choice affects: expected returns (hedged vs unhedged positions), the risk-free rate baseline for Sharpe ratio calculations, and correlation assumptions for international assets. Most UK investors should use GBP.

📊 Step 2: Select Risk Profile

Choose your risk tolerance level. This sets the volatility target for portfolio optimization.

⚠️ Risk Profile Guidance
Very Defensive/Defensive: Suitable for investors nearing retirement or with low risk tolerance. Balanced: Appropriate for most long-term investors with 10+ year horizons. Growth/High Growth: For investors with high risk tolerance and very long time horizons who can withstand significant drawdowns.

🌍 Step 3: Select Investment Universe

Choose which asset classes to include in your portfolio. You can also include custom assets defined in the CMA Wizard.

📈 Equities 0/0
📊 Fixed Income 0/0
💎 Alternatives 0/0
0 assets selected

⚙️ Step 4: Set Portfolio Constraints

Define minimum and maximum allocation limits. The optimizer will respect these bounds when finding the optimal portfolio.

Presets:
📊 Asset Class Group Constraints ?
📈 Total Equities Sum of all equity allocations ?
%
%
📊 Total Fixed Income Sum of all bond allocations ?
%
%
💎 Total Alternatives Sum of all alternative allocations ?
%
%
🎯 Single Asset Maximum No single asset can exceed this ?
%
📋 Asset-Level Constraints ? (Optional - click to expand)
✓ Constraint Validation
All constraints are valid. The optimizer can find a feasible solution.

🎯 Step 5: Set Optimisation Objectives

Choose how the optimizer should construct your portfolio. Different objectives lead to different risk-return trade-offs.

📊 Maximum Sharpe Ratio Recommended ?
Optimize risk-adjusted returns by maximizing return per unit of volatility. Best for most investors seeking efficient portfolios.
Formula: (Expected Return − Risk-Free Rate) ÷ Volatility
📈 Maximum Return (Volatility Constrained) ?
Maximize expected return while staying within your risk profile's volatility target (10.0%).
Maximize: Expected Return | Subject to: Volatility ≤ Target
🛡️ Minimum Volatility ?
Find the lowest-risk portfolio regardless of return. Conservative approach for capital preservation.
Minimize: Portfolio Volatility
⚖️ Risk Parity ?
Equal risk contribution from each asset class. Balances diversification across all risk sources rather than optimizing for return.
Target: Each asset contributes equally to total portfolio risk
⚙️ Custom Multi-Objective ?
Blend multiple objectives with your own weightings. Advanced users only.
📋 Optimization Summary
Objective: Maximum Sharpe Ratio
Risk Profile: Balanced (10.0% Vol Target)
Assets: 0 selected
CMA Source: Consensus Average

📈 Step 6: Select Capital Market Assumptions

Choose which CMA source to use for expected returns and volatility estimates.

📊
Consensus Average RECOMMENDED
Blends BlackRock, JPM & RA expectations. Most balanced approach.
🏦
BlackRock
BlackRock Investment Institute CMAs
🏛️
J.P. Morgan
JPM Long-Term Capital Market Assumptions
📈
Research Affiliates
RA Asset Allocation Interactive
🎯 Apply Theme Adjustments ?
Adjust CMAs based on macro themes from the CMA Wizard
📈 Upward Bias
Loading...
📉 Downward Bias
Loading...
📋 CMA Preview (Top 10 by Sharpe Ratio) ?
Asset Class Return Volatility Sharpe Eq Corr

Step 7: Optimize Portfolio

Run the mean-variance optimizer to find your optimal asset allocation based on your settings.

📋 Configuration Review All settings configured
Show Details ▼
Currency GBP
Profile Balanced
Assets 27
Objective Max Sharpe
CMA Hybrid
📏 Compare Against: ?
60/40 Global: Classic balanced portfolio with 60% global equities and 40% aggregate bonds. The most common institutional benchmark.
📐
Optimization Methodology
Mean-Variance Optimization using a single-factor correlation model with category-aware shrinkage. Pairwise correlations are derived from each asset's correlation to global equities, then adjusted using empirical priors for within-category relationships.

🔬 Step 8: Run Stress Tests

Test your optimized portfolio under different historical and hypothetical market scenarios.

📊 Select Scenarios to Test ?
📜 Historical Fixed scenarios based on actual market events (2008 GFC, COVID, etc.)
⚙️ Customizable Hypothetical scenarios you can adjust — click the ⚙️ icon to modify asset shocks & correlations
📉
2008 GFC
Global Financial Crisis peak drawdown
Equities -50%, Credit -20%
🦠
2020 COVID Crash
March 2020 market panic
Equities -34%, Bonds +5%
📈
2022 Rate Shock
Aggressive Fed tightening cycle
Bonds -15%, Equities -20%
💻
2000 Dot-Com Bust
Tech bubble collapse
Tech -78%, Value +10%
🔥
Stagflation Customizable
High inflation + low growth
Equities -25%, Bonds -10%
Asset Shocks
Equities -25%
Bonds -10%
Commodities +15%
Gold +10%
Correlation Regime
Reset to Defaults
⚙️
❄️
Deflation Customizable
Japan-style deflation scenario
Equities -15%, Bonds +15%
Asset Shocks
Equities -15%
Bonds +15%
Commodities -20%
Gold +5%
Correlation Regime
Reset to Defaults
⚙️
🚀
Strong Recovery Customizable
Post-crisis bull market
Equities +30%, Credit +10%
Asset Shocks
Equities +30%
Credit +10%
Bonds -5%
EM Equity +25%
Correlation Regime
Reset to Defaults
⚙️
Vol Spike Customizable
VIX doubles, correlations spike
All risky assets -15%
Asset Shocks
Equities -15%
Credit -10%
EM Equity -20%
Bonds +5%
Gold +8%
Correlation Regime
Reset to Defaults
⚙️

✏️ Step 9: Final Adjustments

Fine-tune your portfolio allocations before finalizing. Lock positions you want to keep fixed.

Total Allocation 100.0%
Expected Return -
Expected Vol -
Sharpe Ratio -
Max Drawdown ? -
Equity Beta ? -
✏️ Adjust Allocations
0 positions
Lock ? Asset Weight ? Value ? Contrib ?
Asset Class Breakdown
Equities 0%
Fixed Income 0%
Alternatives 0%

🏦 Step 10: Implement with ETFs

Set your portfolio value, select ETFs, and see exact position sizes for implementation.

£
Total Positions
-
Total Invested
-
Portfolio TER ?
-
Est. Annual Cost ?
-
📋 Implementation Schedule 0 instruments
Asset Class Weight Amount ETF Selection Ticker TER Cost/yr
💡 Custom ETF Entry Select "Custom..." from the dropdown to enter your own ETF. You can specify the ticker, name, and TER for instruments not in our database. TER ranges: Low (<0.20%) | Medium (0.20-0.50%) | High (>0.50%)

📄 Step 11: Generate Reports

Create professional portfolio reports and export your optimized allocation in multiple formats.

📊 Portfolio Summary
Expected Return
-
Expected Volatility
-
Sharpe Ratio
-
Positions
-
Equities -
Fixed Income -
Alternatives -
👤 Portfolio Details
📊 Benchmark Comparison
60% Equity / 40% Bonds
Benchmark Return
-
Benchmark Vol
-
Benchmark Sharpe
-
📄 Title Slide
📑 Slide Deck Sections
Drag to reorder • Click to toggle
⋮⋮
Objective → Allocation → Result flow
⋮⋮
Volatility, VaR, stress tests, risk decomposition
⋮⋮
Regional breakdown with treemap visualization
⋮⋮
CMA reconciliation & optimization logic
⋮⋮
ETF tickers, TER, return/risk waterfall
⋮⋮
11-step SAA methodology overview
⋮⋮
Technical details & configuration summary
🎨 Branding & Style
No file selected
📄 How to Create PDF / PowerPoint
Step 1: Click "Generate Report" — opens slide deck in a new tab
Step 2: Press Ctrl+P (or Cmd+P on Mac)
Step 3: Set print options:
  • Destination: Save as PDF
  • Layout: Landscape
⚠️ CRITICAL: Click "More settings" and enable "Background graphics" — without this, colours won't print!
To create editable PowerPoint: Open the saved PDF in PowerPoint (File → Open → select PDF) — it converts automatically to editable slides.
📄
Portfolio Factsheet
Single-page summary (Portrait PDF) — Morningstar X-Ray style
Opens in new tab → Print as Portrait PDF

🎯 Risk Profile Assessment

Discover your appropriate investment risk level based on your financial situation, goals, and preferences

1Time Horizon
2Capacity
3Loss Tolerance
4Preference
5Results
6Report

🕐 Step 1: Investment Time Horizon ?
Why we weight this 30%
Research identifies time horizon as the strongest single predictor of appropriate risk level because it determines your ability to wait for market recovery. — CFA Institute Research Foundation (2017)

Time horizon is the single most important determinant of appropriate risk level. Longer horizons allow recovery from market downturns.

Question 1 of 7
When do you anticipate needing to access the majority of these funds?
🕐
< 3 years
Short-term needs
Capital preservation critical
📅
3-5 years
Medium-term
Limited recovery time
📆
5-10 years
Long-term
Weather moderate cycles
🗓️
10-20 years
Extended horizon
Full cycle recovery
🚀
20+ years
Very long-term
Maximum flexibility
💡 Why Time Horizon Matters
A 30% market decline typically recovers within 2-4 years historically. Investors with shorter horizons may not have time to wait for recovery, making capital preservation more important than growth potential.
📊 Historical Market Recovery Times

2008 Financial Crisis (−57%): Recovered by March 2013 — 4.5 years
2020 COVID Crash (−34%): Recovered by August 2020 — 5 months
2000 Dot-Com Bust (−49%): Recovered by May 2007 — 7 years

Since 1950, there has never been a 15-year period with negative returns for diversified portfolios. The longer your horizon, the more volatility you can safely accept.

Data: S&P 500 Total Return Index, 1950-2024

💼 Step 2: Financial Capacity ?
Capacity vs. Tolerance
The CFA Institute distinguishes between capacity (objective ability to bear losses) and tolerance (subjective willingness). Our algorithm uses the lower of the two as a protective constraint. — CFA Institute Research Foundation (2017)

Your financial cushion determines your objective ability to withstand investment losses without affecting your lifestyle.

📝 This step has 2 questions - please answer both to continue
Question 2 of 7
Which best describes your current financial situation? ?
Why we ask this
Emergency reserves and income stability determine whether you can stay invested during downturns, or might be forced to sell at the worst possible time.
🔴 Limited savings, income uncertain
Less than 3 months emergency fund • Variable or commission-based income
🟡 Adequate reserves, stable income
3-6 months emergency fund • Secure employment with steady salary
🟢 Strong reserves, secure income
12+ months expenses in liquid savings • Highly stable income stream
💎 Substantial reserves, diversified income
Multiple income sources, minimal debt • Investments surplus to lifestyle needs
Question 3 of 7
What portion of your total net worth does this investment represent? ?
Why this matters
If this portfolio represents most of your wealth, losses have a larger impact on your financial security. Research found portfolio significance strongly predicts actual risk-taking behavior. — Barsky et al. (1997), Quarterly Journal of Economics
📊
> 75%
Most of my investable assets
📈
50-75%
A significant portion
📉
25-50%
A moderate portion
💰
< 25%
A small portion
⚠️ Risk Capacity Note
Your financial capacity score sets an upper limit on recommended risk regardless of your stated preference. Even if you're comfortable with volatility, your financial situation may not support high-risk investments.
🔒 The Protective Constraint Principle

You might feel comfortable with aggressive investments, but if you don't have the financial cushion to avoid selling during a downturn, that willingness becomes dangerous.

"Sequence of returns risk" — being forced to sell when markets are down can permanently impair your wealth accumulation. This is why we use min(capacity, tolerance) in our algorithm.

Example: An investor with high tolerance but limited savings might panic-sell during a -30% drawdown because they need funds for an emergency — locking in losses and missing the recovery.

— CFA Institute: "Investment Risk Profiling: A Guide for Financial Advisors" (2017)

🧠 Step 3: How Do You React to Losses? ?
Research backing
Guillemette et al. (2012) found that loss aversion questions like these predict actual portfolio behavior 21% better than traditional "how much risk can you handle?" questions. — Journal of Financial Planning, 25(5), 36-44

Research shows your emotional response to losses is the best predictor of how you'll actually behave during market stress.

📝 This step has 2 questions - please answer both to continue
Question 4 of 7
Imagine your portfolio dropped 20% in value over 3 months. What would you most likely do? ?
Why -20%?
This represents a significant but common drawdown (occurs roughly every 4-5 years). Your honest reaction here predicts whether you'd actually stay invested during real market stress.
📉 Scenario: Your £1,000,000 portfolio is now worth £800,000
-£200,000 (-20%)
🚨
Sell everything
Move to cash to prevent further losses
📉
Sell some
Reduce exposure to limit downside
⏸️
Do nothing
Wait for the market to recover
📈
Buy more
Take advantage of lower prices
Question 5 of 7
For a £100,000 investment, which option would you choose? ?
Why this gamble question?
Barsky et al. (1997) showed that hypothetical gamble questions reveal true risk preferences better than self-assessments. Your choice indicates your actual trade-off between certainty and potential gain. — Quarterly Journal of Economics, 112(2)
🔒
Guaranteed £5,000 return (5%)
Outcome: £105,000 with 100% certainty
🎲
50% chance of +£15,000 / 50% chance of -£5,000
Expected value: £105,000 • Risk: moderate
🎯
50% chance of +£30,000 / 50% chance of -£15,000
Expected value: £107,500 • Risk: higher
🚀
50% chance of +£50,000 / 50% chance of -£25,000
Expected value: £112,500 • Risk: aggressive
🧠 The Science of Loss Aversion
Nobel laureate Daniel Kahneman's research found that losses feel approximately 2.25× more painful than equivalent gains feel good. This asymmetry explains why a -20% loss often triggers panic selling, even when staying invested would be better. We use this insight to calibrate your true tolerance, not just your stated one.
📊 Historical Context
A 20% market drop has occurred approximately 15 times since 1950. Average recovery time to previous highs: 14 months. Investors who sold during declines typically locked in losses and missed the recovery.
📉 Why Investors Underperform Their Own Investments

Dalbar's annual "Quantitative Analysis of Investor Behavior" consistently finds that the average investor earns 1-2% less per year than the funds they invest in. Why?

The behavior gap: Investors buy after markets rise (greed) and sell after markets fall (fear). This "buy high, sell low" pattern destroys returns over time.

Research shows that investors who sold during the 2008 crisis missed an average of 50% of the subsequent recovery gains because they re-entered too late.

Your answers to these scenario questions help us recommend a risk level you can actually stick with during turbulent markets.

— Dalbar Inc., "QAIB" Annual Studies; Guillemette et al. (2012)

🎯 Step 4: Your Investment Approach ?
Cross-validation
Self-assessments are valuable but often inflated during calm markets. We cross-validate your answers here against your behavioral responses in Step 3 to flag any inconsistencies. — Pan & Statman (2012), Journal of Investment Consulting

Your investment experience and philosophy help us understand your overall approach to risk.

📝 This step has 2 questions - please answer both to continue
Question 6 of 7
How would you describe your investment experience? ?
Why experience matters
Experienced investors have typically lived through market cycles and have more calibrated expectations. However, experience alone doesn't mean you should take more risk — capacity and tolerance still govern.
🌱
Beginner
Limited experience, mainly savings accounts
📚
Intermediate
Some experience with funds/ETFs
📊
Experienced
Regular trading, understand cycles
🎯
Expert
Professional background, deep knowledge
Question 7 of 7
Select the investment philosophy that resonates most with you: ?
Overconfidence check
If your self-assessed philosophy here is significantly more aggressive than your behavioral responses in Step 3, we'll flag this discrepancy in your results. Be honest with yourself! — Pan & Statman (2012)
🛡️
Capital Preservation
"I prioritize protecting what I have, even if it means lower returns. Sleep well at night is key."
Typical: 20-30% equities • Volatility: 5-8%
⚖️
Balanced Approach
"I want growth but with meaningful downside protection. I can accept some volatility."
Typical: 50-65% equities • Volatility: 9-11%
📈
Growth Oriented
"I focus on growing my wealth and understand this means accepting larger market swings."
Typical: 70-85% equities • Volatility: 11-14%
🚀
Maximum Growth
"I pursue the highest possible returns and can stomach significant volatility and drawdowns."
Typical: 85-100% equities • Volatility: 14-18%
🎭 The Overconfidence Problem

Pan & Statman (2012) found that investors consistently overestimate their risk tolerance when markets are calm. During the 2008 crisis, many "aggressive" investors panicked and sold at the bottom.

Why 20% weight? Self-assessment tells us about your intentions, but behavioral questions (Step 3) tell us about your likely actions. We weight behavior more heavily because it's a better predictor of real-world decisions.

If you selected "Maximum Growth" here but chose conservative options in Step 3, your results will reflect this inconsistency — protecting you from taking on more risk than you can emotionally handle.

— Pan & Statman (2012), "Questionnaires of Risk Tolerance, Regret, Overconfidence, and Other Investor Propensities"

Your Risk Profile Results

Based on your responses, we've determined your appropriate risk profile for investment portfolio construction.

⚖️
Balanced
Risk Score: 52 / 100
Risk Score Position
52
Very DefensiveDefensiveBalancedGrowthHigh Growth
Score Breakdown ?
How we calculate your score
Weights are based on academic research:
Time Horizon (30%) — strongest predictor (CFA Institute)
Capacity (25%) — objective ability to bear loss
Tolerance (25%) — behavioral prediction
Preference (20%) — self-assessment (cross-validated)
Time Horizon (30%)
60%
Financial Capacity (25%)
48%
Loss Tolerance (25%)
56%
Risk Preference (20%)
41%
📊 Profile Characteristics ?
Historical performance data
These figures are based on historical analysis of portfolios with similar risk profiles (1970-2024). Past performance does not guarantee future results.
Volatility Target10.0% annually
Typical Equity Allocation55-65%
Max Drawdown (estimated)-25% to -35%
Typical Recovery Time2-4 years
?
What is this?
This optional feature adjusts your profile based on current risk premia conditions from the dashboard's Risk Premia Analysis section.

When risk premia are unattractive (cheap expected returns relative to risk), the algorithm suggests shifting to a more defensive profile.

When risk premia are attractive, it suggests you can safely take more risk because the market is compensating you well for it. — Based on 50 years of risk premia data
?
Override option
You can select a different profile if you have specific circumstances our questionnaire didn't capture. Note: Overriding to a more aggressive profile increases drawdown risk.
📖 Research Foundation

This questionnaire is based on peer-reviewed academic research and regulatory best practices:

1. Guillemette, Finke & Gilliam (2012) — Loss aversion questions predict behavior 21% better than traditional risk tolerance scales. Journal of Financial Planning, 25(5).

2. Kahneman & Tversky (1979) — Prospect Theory established the 2.25× loss aversion coefficient, explaining why investors panic sell. Econometrica, 47(2).

3. CFA Institute (2017) — Separating capacity from tolerance and using the lower value as a constraint. Investment Risk Profiling: A Guide.

4. Pan & Statman (2012) — Self-assessments often overestimate tolerance; cross-validation needed. Journal of Investment Consulting, 13(1).

5. Barsky et al. (1997) — Gamble questions reveal true preference parameters. Quarterly Journal of Economics, 112(2).

This methodology also aligns with MiFID II suitability requirements (investment objectives, financial situation, knowledge/experience, risk tolerance, and capacity for loss).

✅ Assessment Complete
Your risk profile has been determined. Click "Next Step" to generate a personalized Risk Profile Factsheet that summarizes your assessment and recommendation.

📄 Risk Profile Report

Generate a personalized factsheet summarizing your risk assessment, investor profile, and recommended allocation.

📄
Risk Profile Factsheet
Single-page summary (Portrait PDF) — Personalized investor assessment
📋 What's Included:
✓ Personalized investor profile summary
✓ Risk score breakdown by category
✓ Profile characteristics & targets
✓ Historical stress test scenarios
✓ Profile comparison spectrum
✓ Valuation adjustment rationale
Opens in new tab → Print as Portrait PDF
🎨 Branding & Style
No file selected
📄 How to Save as PDF
1. Click "Generate Factsheet" — opens in a new tab
2. Press Ctrl+P (or Cmd+P on Mac)
3. Set destination to "Save as PDF" and enable "Background graphics"
✅ Ready for SAA Wizard
Your risk profile will be saved and automatically applied in the SAA Wizard. Click "Complete & Continue" to proceed to SAA Portfolio Build — the next step in the workflow.

🏗️ TAA Portfolio Build Wizard

Build tactical portfolios from your SAA base and TAA views

1 SAA Base
2 TAA Views
3 Risk Budget
4 Core Tilts
5 Sectors
6 Satellite
7 Analysis
8 Implement
9 Report

📁 Step 1: Import SAA Base Portfolio

Load your strategic asset allocation as the foundation for tactical tilts.

💾
SAA Wizard Config
Load from saved SAA portfolios
📊
Manual Entry
Enter portfolio weights directly
📋
Standard Benchmark
Use predefined portfolio template
📁
No SAA Configuration Selected
Select a saved SAA configuration above to load your strategic portfolio
💡 Why Start with SAA?
Your Strategic Asset Allocation provides the long-term benchmark. Tactical tilts are expressed as deviations from this base portfolio. This ensures your TAA decisions stay anchored to your strategic investment policy and risk budget remains controlled.

📈 Step 2: Import TAA Views

Load your tactical views to drive portfolio tilts.

📈
No TAA Views Configuration Selected
Select a saved TAA Views configuration above to load your tactical signals
💡 How Asset Mapping Works
TAA views are automatically mapped to your SAA portfolio assets based on asset class names. Mapped views will drive tilts in Step 4. Unmapped views (like FX or duration overlays) become satellite opportunities in Step 6. Sector views are applied within your equity allocation in Step 5.

⚖️ Step 3: Risk Budget & Constraints

Define how aggressive your tactical tilts can be relative to the SAA benchmark.

📊 Tracking Error Budget
0.5% Conservative 2% Moderate 5% Aggressive
Tracking Error measures how much your TAA portfolio deviates from SAA. A 2% TE means ~68% of the time, TAA returns will be within ±2% of SAA.
🎚️ Tilt Sensitivity
0.25x Muted 1.0x Normal 2.0x Amplified

Controls how strongly views translate to weight tilts. At 1.0x, a +50 view might produce a +2.5% tilt.

📏 Maximum Tilts by Asset Group
±10%
±8%
±5%
±5%
Max deviation from SAA weight for any asset
🔒 Position Constraints
📋 Constraint Summary
These limits will be enforced in Step 4
Tracking Error
2.0%
Max Equity Tilt
±10%
Max FI Tilt
±8%
Max Single Tilt
±5%
Sensitivity
1.0x
💡 How Constraints Work
These constraints define the maximum deviation from your SAA benchmark. In Step 4, your TAA views will be translated into tilts that respect these limits. Zero-sum ensures overweights are funded by underweights (no cash drag). Tracking error is the expected standard deviation of active returns.

🔄 Step 4: Views to Tilts Translation

Convert your tactical signals into portfolio weight changes within your risk budget.

📋 Note: Only TAA views that match your SAA portfolio assets appear here as tilts. Views for assets not in your SAA (e.g., China equity, India equity) appear in Step 6 Satellite instead.
⚙️ Translation Method
Proportional: Stronger views → larger tilts. A +75 view creates a bigger tilt than a +40 view.
📊 Tilt Summary
Overweights
Sum of all positive tilts (overweight positions)
+0%
Underweights
Sum of all negative tilts (underweight positions)
-0%
Net Tilt
Net tilt = Overweights + Underweights. Zero means fully funded (zero-sum).
0%
Exp. Alpha
Expected alpha in basis points. Calculated as sum of (vs CMA × Tilt) for each position.
0 bps
Tracking Error
Expected volatility of active returns (TAA vs SAA). Calculated using asset covariance matrix.
0.00%
vs 2.0% budget
Info Ratio
Information Ratio = Expected Alpha ÷ Tracking Error. Higher is better. Above 0.5 is excellent.
0.00
Rel. Eq. Beta
TAA Portfolio Beta ÷ SAA Beta. Target is derived from your Risk Stance: Risk On → higher target (>1.0), Risk Off → lower target (<1.0). Green = on target.
1.00x
Target: 1.00x
📋 Proposed Asset Tilts
Edit TAA Tilt inline • 12M Forecast set in Step 2
Asset Class SAA % TAA View TAA Tilt TAA % CMA 12M Fcst Alpha Risk %
Total 100% 0% 100% 0 bps
📈 Portfolio Analytics
Phase 3 Optimization Metrics
Active Share
0.0%
Active Positions
0
Largest Tilt
0.0%
Constrained
0
Risk Contribution by Asset
Select an optimized method to see risk decomposition
Constraint Utilization
TE Budget:
0%
Max Tilt:
0%
Equity Tilt:
0%
FI Tilt:
0%
See how TAA assets correlate with each other
💡 How Tilts Are Calculated
Each asset's tilt = View × Sensitivity ÷ Scale Factor, capped by your constraints. A +50 view at 1.0x sensitivity produces roughly a +2.5% tilt. Edit TAA Tilt inline to override the calculation with a manual value.

📊 Step 5: Equity Sector Allocation

Carve out sector positions from your SAA equity allocation. Only sectors with bullish views receive allocations – you can't underweight sectors not in your SAA.

⚙️ Sector Tilts Configuration
0%
3%
⚙️ Sector Allocation Method
Proportional: Allocation = View × Scale. Stronger views (+75) get larger allocations than moderate views (+40).
📋 Sector Allocations (Funded from SAA Equity)
Sectors carved out from your broad equity allocation
Sector View SAA % Alloc. Final % α bps Port. Wt
Total Equity 0% 0 0%
📊 Sector Allocation Summary
Sectors Allocated
Sectors with positive allocations from SAA equity
0
Total Sector Wt
Sum of all sector allocations
0%
Remaining SAA Equity
Broad equity after sector carve-outs
0%
Exp. Alpha
Alpha from sector allocations
0 bps
Sector IR
Alpha ÷ Tracking Error
0.00
Historical correlations used for sector risk calculations
💡 Proportional Method

Sector tilts are applied within your equity allocation from Step 4.

Each sector's tilt = (View ÷ 100) × Max Sector Tilt. A +50 view with ±3% max tilt produces a +1.5% overweight.

For example, if you have 60% equity and overweight Technology by +3%, Technology becomes ~12% of your equity sleeve (vs 9.1% benchmark), which is ~7.2% of total portfolio.

The benchmark uses equal-weight sectors (9.1% each for 11 sectors).

💡 Step 6: Satellite Opportunities

Add out-of-benchmark positions for idiosyncratic views and unmapped assets.

💰 Satellite Budget
5%

Satellite positions sit outside your SAA benchmark. Set to 0% to skip satellites entirely.

Core Allocation
95%
Satellite Used
0%
⚙️ Satellite Allocation Method
Proportional: Stronger views get larger allocations. A +75 view gets more than a +50 view.
Satellite Opportunities
Thematic positions outside standard asset classes
🎯 Idiosyncratic Ideas
Thematic or opportunistic positions
📋 Selected Satellites
Position View Alloc.
Total Satellite Allocation 0%
💡 Proportional Method

Satellite positions are out-of-benchmark allocations for views that don't fit your SAA structure.

Allocation = (|View| ÷ 100) × Budget. Stronger views get larger allocations within your satellite budget.

Satellite allocations are funded by reducing core positions proportionally.

Keep satellites small (typically 5-10%) to maintain portfolio discipline.

📊 Step 7: Portfolio Analysis

Review your tactical portfolio against the strategic benchmark.

📈 Portfolio Comparison
📋 Strategic (SAA)
L/T Expected Return
0.0%
L/T Volatility
0.0%
L/T Sharpe Ratio
0.00
Equity Weight
0%
🎯 Tactical (TAA)
12m Expected Return
0.0%
12m Volatility
0.0%
12m Sharpe Ratio
0.00
Equity Weight
0%
Estimated Tracking Error
Deviation from SAA benchmark
0.0%
Budget: 2.0%
💰 Expected Alpha
From views × conviction × vol
+0 bps
📊 Information Ratio
Alpha / Tracking Error
0.00
📋 Position Breakdown
SAA vs TAA weights by asset
Asset SAA % TAA % Δ Tilt View
🎯 Active Positions Summary
Overweights
0
Underweights
0
Satellites
0
Active Share
0%
📈 Top Overweights
📉 Top Underweights
⚠️ Risk Observations
💡 About This Analysis
Expected returns and volatility are calculated using asset-class level assumptions. Tracking error estimates how much your TAA portfolio may deviate from SAA. Active share measures the percentage of the portfolio that differs from benchmark. These are simplified estimates - actual results will depend on market conditions and implementation.

🏦 Step 8: Implement TAA Trades

Select instruments, calculate position sizes, and generate your trade list for implementation.

£
Buy Orders
0
Sell Orders
0
Total Turnover
£0
Portfolio TER
0.00%
Est. Annual Cost
£0
Est. Trading Cost
~£0
📋 Implementation Schedule
0 trades
Asset Class SAA % TAA % Tilt Action Instrument Ticker Trade Value TER
Complete previous steps to see implementation schedule
📈 Buy Orders
No buy orders
Total Purchases: £0
📉 Sell Orders
No sell orders
Total Sales: £0
Net Investment Required
£0
Buys and sells are balanced
📥
Export Trade List
Download implementation schedule as CSV
📊 Cost & Break-Even Analysis
Annual Holding Cost
0.15%
Portfolio TER drag
One-Time Trading Cost
0.00%
~10bps × turnover
Total Year-1 Cost
0.15%
Alpha hurdle
Break-Even Alpha Required 15 bps
Expected Alpha: -- bps
0 bps 100 bps
🔄 Rebalancing Recommendations
📅 Recommended Review Frequency
Monthly
Based on TE budget of 2.0%
📐 Drift Tolerance Threshold
±1.0%
Rebalance if any position drifts beyond this
📋 Rebalancing Rules
Calendar: Review positions at recommended frequency
Drift: Rebalance immediately if any position exceeds threshold
Cost-Aware: Only trade if benefit exceeds ~10bps transaction cost
💷 Tax Efficiency Considerations
ISA/SIPP: Execute trades in tax-advantaged accounts first to avoid CGT
CGT Allowance: Consider crystallising gains within annual allowance (£3,000)
Bed & ISA: Sell in taxable account and rebuy in ISA to shelter future gains
Loss Harvesting: Realise losses to offset gains; mind 30-day wash sale rule
💡 Implementation Guidance
Timing: Execute trades during market hours for best liquidity. Consider splitting large trades across multiple days.

Costs: Portfolio TER is the weighted average expense ratio. Trading costs assume ~10bps round-trip spread.

Rebalancing: Based on your tracking error budget, consider reviewing positions monthly if drift exceeds tolerance thresholds.

📄 Step 9: Generate Report & Trade List

Create professional TAA reports and export your tactical allocation.

📊 Full Presentation
Multi-slide deck with tilts, sectors, trades, risks, methodology. Best for committees.
💼 Portfolio Factsheet
1-page implementation summary with SAA→TAA tilts, trades, costs.
🎯 TAA Portfolio Summary
Expected Return
-
Volatility
-
Tracking Error
-
Active Share
-
Overweights -
Underweights -
Satellites -
📄 Title Slide
📑 Slide Deck Sections
Click to toggle
SAA vs TAA comparison at a glance
Core position tilts with view scores
Equity sector tilts within sleeve
Out-of-benchmark opportunities
Actions, instruments, costs, rebalancing
Key risks and observations
Investment process and data sources
Source weights and wizard settings
🎨 Branding & Style
No file selected
📄 How to Create PDF / PowerPoint
Step 1: Click "Generate Report" — opens slide deck in a new tab
Step 2: Press Ctrl+P (or Cmd+P on Mac)
Step 3: Set print options:
  • Destination: Save as PDF
  • Layout: Landscape
⚠️ CRITICAL: Click "More settings" and enable "Background graphics" — without this, colours won't print!
📋
Trade List Export
Download implementation trades as CSV
💡 About TAA Reports
The report includes your tactical tilts vs SAA benchmark, sector allocations, satellite positions, and an actionable trade list. Use the slide deck for presentations or convert to PDF/PPTX for sharing.

⚖️ Rebalancing Wizard

Rebalance your portfolio to SAA or TAA Portfolio targets

1 Portfolio
2 Target
3 Mapping
4 Drift
5 Strategy
6 Trades
7 Export

📁 Step 1: Import Current Portfolio

Enter your current holdings to calculate rebalancing trades.

💰 Portfolio Value
Enter manually or let CSV import calculate from holdings
✏️
Manual Entry
Add holdings one by one
📄
CSV Import ?
Upload portfolio file
📥
Need a CSV template?
Download, fill in Excel/Sheets, then upload
💡 Quick Start
Load a sample portfolio to explore the wizard
💡 About Portfolio Import
Enter your current holdings to compare against your target SAA and TAA allocations. The wizard will calculate the drift from your targets and recommend rebalancing trades.

🎯 Step 2: Load Target Allocation

Select your target allocation — either strategic (SAA) or tactical (TAA Portfolio with tilts & satellites).

📊
Strategic (SAA)
Long-term strategic allocation only
✓ Base asset class weights
✓ No tactical adjustments
✓ Stable benchmark
📈
Tactical (TAA Portfolio)
SAA + tactical tilts + satellites
✓ SAA base with tactical tilts
✓ Sector rotations applied
✓ Satellite positions included
💡 SAA vs TAA Portfolio
Strategic (SAA): Your long-term target allocation without tactical adjustments — best for passive rebalancing.
Tactical (TAA Portfolio): SAA base + tactical tilts + satellite positions — best for active management with current market views.

🗺️ Step 3: Map Holdings to Asset Classes

Verify and adjust how your holdings map to target asset classes for accurate drift analysis.

Holdings
0
Mapped
0
Unmapped
0
Asset Classes
0
💡 Holdings are auto-detected based on name/ticker patterns
🗺️ Holdings Mapping
Assign each holding to a target asset class
Holding Value Weight Maps To Asset Class Status
📊 Current Allocation by Asset Class
Aggregated from mapped holdings
Asset Class Current % Target % Difference Holdings
💡 About Mapping
Each holding needs to be mapped to a target asset class so we can compare your current allocation to your SAA/TAA targets. Auto-mapping uses ticker and name patterns to guess the asset class. You can override any mapping using the dropdown.

📉 Step 4: Drift Analysis

See how far your current portfolio has drifted from targets and identify rebalancing priorities.

Total Drift
0%
Sum of absolute deviations
Largest OW
--
--
Largest UW
--
--
Out of Band
0
Assets exceeding threshold
🥧 Allocation Comparison
Current vs Target (hover for details)
📊 Current Allocation
Rebalance
🎯 Target Allocation
Drift Threshold:
Assets outside threshold are flagged for rebalancing
🎚️ Rebalancing Bands
Visual tolerance bands by asset class
Within Tolerance
Approaching Threshold
Outside Threshold
Target
Current
📊 Drift Visualization
Current vs Target Allocation
📋 Drift Details
Sorted by absolute drift (largest first)
Asset Class Current Target Drift Value Diff Status
🎯 Rebalancing Priority
Recommended actions based on drift analysis
💡 About Drift Analysis
Drift measures how far your current allocation has moved from your target (SAA + TAA tilts). Assets outside the threshold band are candidates for rebalancing. In the next step, you can choose your rebalancing strategy and generate specific trade recommendations.

⚙️ Step 5: Rebalancing Strategy

Choose how you want to rebalance your portfolio and configure trade parameters.

🎯
Full Rebalance
Bring all assets to exact targets
Rebalances every asset class to its target allocation. Best for annual reviews or significant market moves.
📊
Threshold Only
Only fix out-of-band assets
Only rebalances assets that exceed the drift threshold. Minimizes trading costs and tax events.
💵
Cash Flow
Deploy new cash strategically
Uses available cash to buy underweight assets. No selling required - ideal for regular contributions.
📤
Sell Only
Raise cash from overweight
Only sells overweight positions. Useful when you need to raise cash or reduce risk exposure.
⚙️ Trade Settings
£
Trades below this amount will be skipped
For threshold-based rebalancing
£
Additional cash available for purchases
Round trade amounts for cleaner orders
📋 Strategy Preview
Full Rebalance
💡 Strategy Tips
Full Rebalance: Most comprehensive but may generate more trades and tax events.
Threshold Only: Balances trading costs vs. drift tolerance.
Cash Flow: Best for regular contributions without selling existing positions.

📋 Step 6: Trade Recommendations

Review, adjust, and finalize the recommended trades to rebalance your portfolio.

Sell Orders
0
Buy Orders
0
Total Sells
£0
Total Buys
£0
Net Cash Flow
£0
💡 Click amounts to adjust, or use checkboxes to skip trades
📋 Trade List
Full Rebalance
Asset Class Holding(s) Action Amount From % To %
📊 Before & After Comparison
Current Allocation
After Rebalance
Ready to Execute?
Review the trades above, then proceed to export your trade list.
💡 Trade Execution Tips
• Execute sell orders first to generate cash for buys
• Consider market conditions and liquidity when timing trades
• Use limit orders for better price control on larger trades

📄 Step 7: Generate Report & Export

Create professional rebalancing reports and export your trade list.

⚖️ Rebalancing Summary
Portfolio Value
-
Total Trades
-
Total Volume
-
Net Cash Flow
-
Sell Orders -
Buy Orders -
Strategy -
📄 Title Slide
📑 Slide Deck Sections
Click to toggle
Portfolio value, strategy, key metrics
Current portfolio positions & weights
SAA targets with TAA tilts applied
Current vs target with drift metrics
Sell & buy orders with amounts
Allocation comparison visualization
Rebalancing approach & calculations
User settings & parameters used
🎨 Branding & Style
No file selected
📄 How to Create PDF / PowerPoint
Step 1: Click "Generate Report" — opens slide deck in a new tab
Step 2: Press Ctrl+P (or Cmd+P on Mac)
Step 3: Set print options:
  • Destination: Save as PDF
  • Layout: Landscape
⚠️ CRITICAL: Click "More settings" and enable "Background graphics" — without this, colours won't print!
📋
Trade List Export
Download implementation trades as CSV for your broker
📄
Rebalancing Factsheet
One-page summary of portfolio rebalancing
💡 About Rebalancing Reports
The report includes your current holdings, target allocation (SAA + TAA), drift analysis, and an actionable trade list. Use the slide deck for presentations or convert to PDF/PPTX for sharing.

📈 TAA Views Wizard

Generate tactical asset allocation views step by step

1 Sources
2 Signals
3 Risks
4 Ideas
5 Views
6 Report

⚖️ Step 1: Source Weightings

Control how each perspective contributes to your tactical views. Choose a preset or customize the weightings manually.

🎯
Consensus
H:40 AI:25 D:25 U:10
👥
Human-Led
H:60 AI:15 D:15 U:10
📊
Quant-Led
H:20 AI:20 D:50 U:10
🤖
AI-Led
H:20 AI:50 D:20 U:10
👤
User-Led
H:20 AI:20 D:20 U:40
👥 Human Strategists
40%
50+ professional sources consensus
🤖 AI Models
25%
Claude, ChatGPT, Perplexity synthesis
📊 Data / Quantitative
25%
Regime indicators and cycle signals
👤 User Override
10%
Your own views (input in Step 2)
Total Weight
100%
Must equal 100% to proceed
💡 How Source Weights Work
Source weights determine how much each perspective influences your final tactical views. Human = strategist consensus, AI = LLM synthesis, Data = cycle/regime signals, User = your signal overrides (Step 2). Higher User weight means your overrides have more impact on the final output.

📊 Step 2: Signal Weightings

Configure how much weight each macro signal has, and review how different sources assess each signal.

💡
How This Step Works ▼ Show details
Signal Weight = importance of this factor • Source values get blended into Weighted Signal • Learn more
⚖️
Equal Weight
25% each signal
Balanced view across all macro factors
🎯
Risk Sentiment-Led
50% Risk Sentiment
Prioritise market risk appetite signals
📈
Growth-Led
50% Growth Cycle
Prioritise economic expansion/contraction
🔥
Inflation-Led
50% Inflation Cycle
Prioritise price pressure trajectory
🏛️
Policy-Led
50% Monetary Policy
Prioritise central bank stance signals
🎯 Risk Sentiment
25%
Signal Weight:
📊 Signal Weight
How much this macro signal contributes to your overall TAA framework.
All signal weights should sum to 100%
25%
Market risk appetite: 0% = Max Risk-Off, 100% = Max Risk-On
Source Assessments
📈 Source Assessments
Each source's assessment of this signal, blended using your Step 1 weights to produce the Weighted Signal.
(blended using Step 1 weights)
Human --
👥 Human Strategists
--
Loading...
AI --
🤖 AI Models
--
Loading...
Data --
📊 Data Signals
--
Loading...
Weighted Signal:
⚖️ Weighted Signal
The blended signal value after combining all sources using your Step 1 weights.
This is what gets used in the final TAA calculation
--
--
View relative to neutral risk appetite
0-25
Very Risk-Off
25-40
Risk-Off
40-60
Neutral
60-75
Risk-On
75-100
Very Risk-On
📈 Growth Cycle
25%
Signal Weight:
📊 Signal Weight
How much this macro signal contributes to your overall TAA framework.
All signal weights should sum to 100%
25%
Economic activity: 0% = Recession, 100% = Strong Expansion
Source Assessments
📈 Source Assessments
Each source's assessment of this signal, blended using your Step 1 weights to produce the Weighted Signal.
(blended using Step 1 weights)
Human --
👥 Human Strategists
--
Loading...
AI --
🤖 AI Models
--
Loading...
Data --
📊 Data Signals
--
Loading...
Weighted Signal:
⚖️ Weighted Signal
The blended signal value after combining all sources using your Step 1 weights.
This is what gets used in the final TAA calculation
--
--
View relative to trend growth
0-25
Recession
25-40
Weak
40-60
Trend
60-75
Strong
75-100
Very Strong
🔥 Inflation Cycle
25%
Signal Weight:
📊 Signal Weight
How much this macro signal contributes to your overall TAA framework.
All signal weights should sum to 100%
25%
Price pressures: 0% = Deep Deflation, 100% = High Inflation
Source Assessments
📈 Source Assessments
Each source's assessment of this signal, blended using your Step 1 weights to produce the Weighted Signal.
(blended using Step 1 weights)
Human --
👥 Human Strategists
--
Loading...
AI --
🤖 AI Models
--
Loading...
Data --
📊 Data Signals
--
Loading...
Weighted Signal:
⚖️ Weighted Signal
The blended signal value after combining all sources using your Step 1 weights.
This is what gets used in the final TAA calculation
--
--
View relative to target inflation
0-25
Deflation
25-40
Disinflation
40-60
At Target
60-75
Inflationary
75-100
High Inflation
🏛️ Monetary Policy
25%
Signal Weight:
📊 Signal Weight
How much this macro signal contributes to your overall TAA framework.
All signal weights should sum to 100%
25%
Central bank stance: 0% = Very Tight, 100% = Very Loose
Source Assessments
📈 Source Assessments
Each source's assessment of this signal, blended using your Step 1 weights to produce the Weighted Signal.
(blended using Step 1 weights)
Human --
👥 Human Strategists
--
Loading...
AI --
🤖 AI Models
--
Loading...
Data --
📊 Data Signals
--
Loading...
Weighted Signal:
⚖️ Weighted Signal
The blended signal value after combining all sources using your Step 1 weights.
This is what gets used in the final TAA calculation
--
--
View relative to neutral policy
0-25
Very Tight
25-40
Tight
40-60
Neutral
60-75
Loose
75-100
Very Loose
Total Signal Weight
100%
Must equal 100% to proceed
💡 How Signal Weights Work
Weighted Signal combines Human, AI, and Data views using your Step 1 source weights (H:40% / AI:25% / D:25% / You:10%).

Add my view: When checked, your input becomes the 4th source weighted at 10%. When unchecked, the remaining weights are renormalized among Human/AI/Data only.

⚠️ Step 3: Macro Risk Integration

Incorporate the top macro risks into your tactical positioning. Selected risks will influence the final asset and sector views.

📈 Impact Scale (-3 to +3)
-3
Very Negative
-2
Negative
-1
Slightly Neg
0
Neutral
+1
Slightly Pos
+2
Positive
+3
Very Positive
Selected Risks: 0
Avg Likelihood: --
💡 How Risk Integration Works
Each selected risk adjusts asset and sector views based on its Impact Score and Likelihood.

Calculation:
View Adjustment (bps) = Impact Score × 10 × Likelihood Weight
Likelihood Weights:
● High = 0.75
● Medium = 0.50
● Low = 0.25
Example:
Risk with -3 Impact on Gold + High likelihood
= -3 × 10 × 0.75 = -22.5 bps adjustment to Gold view
All selected risk adjustments are summed to produce the final risk-adjusted view for each asset/sector.

💡 Step 4: Non-Core Ideas Integration

Include tactical investment themes beyond standard asset allocation. Selected ideas will be highlighted in your final views.

Filter by conviction:
Selected Ideas: 0
Avg Conviction: --
Custom Overrides: 0
💡 How Non-Core Ideas Work
Showing tactical themes beyond standard asset allocation identified from strategist research.

⭐ Score
Popularity/consensus measure based on how many strategists mention this theme. Higher = more widely recommended.
Conviction
High/Medium from source research. Click badge to override. Affects how much this idea influences views.
🌍 Regional Tilt
Country/region bets (e.g., China, India). Shown for implementation guidance - they do NOT affect core view BPS calculations (already captured in Steps 1-2).
Idiosyncratic
FX positions, private markets, etc. Don't map to core asset/sector views but appear in report recommendations.
Calculation (for non-Regional Tilt ideas):
View Adjustment (bps) = Impact Score × 5 × Conviction Weight (High=1.5×, Med=1.0×, Low=0.5×)
📈 Impact Scale Reference
-3
Very Neg
-2
Negative
-1
Slight Neg
0
Neutral
+1
Slight Pos
+2
Positive
+3
Very Pos

📊 Step 5: Generated Views

Your tactical views calculated from all configured inputs.

Click to generate views from your configured inputs
📊
Ready to Generate Your Views
Click the button above to calculate your tactical asset allocation views based on your configured source weightings, signals, risks, and ideas from Steps 1-4.
Asset Classes
16
Sectors
11
Data Sources
4
📊 How Views Are Calculated
Base Signal = Human × H% + AI × A% + Data × D% + User × U% (from Steps 1-2)
Risk Adjustment = Σ [Risk Impact × Likelihood × Multiplier] (from Step 3)
Idea Adjustment = Σ [Idea Impact × Conviction Weight] (from Step 4)
Final View = Base Signal + Risk Adjustment + Idea Adjustment (clamped to ±100)

📄 Step 6: Generate Report

Configure and generate your tactical asset allocation report and data exports.

📊 Full Presentation
Multi-slide deck with executive summary, methodology, asset views, sector views, and configuration details. Best for investment committees.
📄 TAA Factsheet NEW
Single-page summary with risk gauge, key views with rationales, and sector positioning. Perfect for quick reference and client updates.
💼 Portfolio Factsheet NEW
Complete 1-page portfolio summary with SAA→TAA tilts, implementation details, sector views, and trade list. Ideal for portfolio managers.
📄 Title Slide
🎨 Branding & Style
📑 Slide Order & Selection
Drag to reorder slides. Uncheck to exclude from report.
⋮⋮ 📄 Title Slide 1
⋮⋮ 📋 Executive Summary 2
⋮⋮ 🏦 Asset Class Views 3
⋮⋮ 📊 Sector Views 4
⋮⋮ 💡 Idiosyncratic Opportunities 5
⋮⋮ ⚠️ Top Macro Risks 6
⋮⋮ 📐 Methodology Overview 7
⋮⋮ ⚙️ Configuration Summary 8
📄 How to Create PDF / PowerPoint
Step 1: Click "Generate Report" — opens slide deck in a new tab
Step 2: Press Ctrl+P (or Cmd+P on Mac)
Step 3: Set print options:
  • Destination: Save as PDF
  • Layout: Landscape
⚠️ CRITICAL: Click "More settings" and enable "Background graphics" — without this, colours won't print!
To create editable PowerPoint: Open the saved PDF in PowerPoint (File → Open → select PDF) — it converts automatically to editable slides.