๐Ÿ”ฅ Key Ideas

Strategic and Tactical Allocation Insights

January 2026 | Human + AI + Data
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1. Executive Summary
Your dashboard home base
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2. Long Term Views
CMAs, SAA, Risk Premia, Themes
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3. Short Term Views
Macro, Assets, Sectors, Risks, Ideas
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Build, analyze & optimize allocations
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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 โ€ข 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 divergenceโ€”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 challengeโ€”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.

Loading summary...

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 rangeโ€”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 uncertaintyโ€”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 evolutionโ€”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 dominatesโ€”premature easing reigniting inflation versus delayed easing triggering recessionโ€”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 ๐Ÿ“Š 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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๐ŸŽฏ 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 โ€ข +1: Mildly bullish
0: Neutral โ€ข -1: Mildly bearish โ€ข -2: Strongly bearish
OVERALL ALIGNMENT SCORE โ“˜
0%
Significant divergence from signals
๐Ÿ“Š Alignment Score Methodology
How Signal Weights are calculated:
1. Start with base allocation (balanced portfolio benchmark)
2. Apply dashboard signal adjustments (ยฑ5% max per asset)
How Delta is calculated:
Delta = Your Weight โˆ’ Signal Weight
  • Positive = OVERWEIGHT vs consensus
  • Negative = UNDERWEIGHT vs consensus
Alignment Score:
Score = 100 โˆ’ (ฮฃ|Deltas|) ร— 1.5
Interpretation:
  • 80-100%: Strong alignment
  • 50-79%: Moderate divergence
  • 0-49%: Significant divergence
Your % โ† Underweight | Overweight โ†’
Signal % โ“˜
๐Ÿ“Š Signal % Calculation
How target allocations are calculated:
Signal % = Neutral Weight + (Signal Adj ร— 0.5)
Step 1: Neutral Weights (60/40 baseline)
  • Equities: 60% รท 6 = 10% each
  • Fixed Income: 30% รท 5 = 6% each
  • Alternatives: 10% รท 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 0.5ร— to moderate impact.
Example: DM Large Cap
Neutral: 10% + (Signal +12 ร— 0.5) = 16%
Complete Step 1 to see delta analysis
๐Ÿ“ˆ

Portfolio Analytics (Based on Long-Term CMAs)

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

๐Ÿ“‹ Score Interpretation Guide

โœ…
80-100%: Strong Alignment
Portfolio closely matches consensus signals
โš ๏ธ
50-79%: Moderate Divergence
Some intentional tilts from consensus
๐Ÿ”ด
0-49%: Significant Divergence
Portfolio differs significantly from signals

โšก Suggested Actions

Based on largest divergences from signals

๐Ÿ’ก Actionable suggestions will appear here

๐Ÿ”ง Optimize Allocation

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

๐ŸŽš๏ธ Tilt Sensitivity โ“˜

How aggressively should dashboard signals adjust your base weights?

Conservative Aggressive
Moderate (ยฑ3% per signal)

โš–๏ธ Portfolio Weights

โ“˜
Total: 100%
Asset Signal Base Tilt New
Weight

โšก Quick Actions โ“˜

๐Ÿ“Š Live Metrics โ“˜

Metric
Before
After
ฮ”
Expected Return โ€” โ€” โ€”
Volatility โ€” โ€” โ€”
Sharpe Ratio โ€” โ€” โ€”
Max Drawdown โ€” โ€” โ€”
Equity Beta โ€” โ€” โ€”
Tactical Alignment โ€” โ€” โ€”

๐Ÿฉ Allocation Breakdown โ“˜

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

๐Ÿ“ˆ Risk/Return Positioning โ“˜

๐Ÿ’ก Your portfolio is near the efficient frontier โ€” good risk/return tradeoff.

โš–๏ธ vs Benchmark โ“˜

Metric You Benchmark Delta
Return 6.3% 6.5% -0.2% โ–ผ
Volatility 9.9% 10.2% -0.3% โ–ผ
Sharpe 0.33 0.34 -0.01 โ–ผ
Max DD -18.1% -14.5% -3.6% โ–ผ
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%
โš ๏ธ Summary: You're taking -0.3% less risk for -0.2% less return. Risk-adjusted, this is worse than benchmark.

๐Ÿ“‹ Review & Export

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

๐Ÿ“„ Final Portfolio โ“˜

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

๐Ÿ”„ Before & After Comparison

๐Ÿ“Š Current Portfolio
Return
โ€”
Vol
โ€”
Sharpe
โ€”
Alignment
โ€”
โœจ Proposed Portfolio
Return
โ€”
Vol
โ€”
Sharpe
โ€”
Alignment
โ€”

๐Ÿ“ Portfolio Rationale โ“˜

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

๐Ÿ“ค Export Options โ“˜

๐Ÿ“Š
Excel
Trade list & weights
๐Ÿ“„
PDF
Summary report
๐Ÿ“‘
Add to IC Report
Include in deck
๐Ÿ’พ
Save Portfolio
For comparison
โš ๏ธ

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.

๐Ÿ’ท British Pounds (GBP)
Display all return expectations, volatilities, and risk metrics in GBP terms for UK-based investors.
โœ“ 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.
๐Ÿ’ต US Dollars (USD)
Display all return expectations, volatilities, and risk metrics in USD terms for US-based or dollar-denominated investors.
โœ“ 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.
๐ŸŽฏ Conviction View
Displays return forecasts with visual opacity indicating conviction level. High conviction = solid colors, low conviction = faded.
โœ“ 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.
๐Ÿ“Š Risk Adjusted View
Penalises low-conviction forecasts by inflating their displayed volatility. This better reflects the true uncertainty in less-confident estimates.
โœ“ 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.
๐ŸŽฏ Theme Adjustments
Reveals how long-term macro themes (AI, deglobalisation, demographics, etc.) impact each asset class's return expectations.
โœ“ 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.
โš™๏ธ Customize CMAs
Manually override individual asset class return, volatility, and correlation assumptions to reflect your own views or stress test scenarios.
โœ“ 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.
โšก Quick Presets
One-click macro scenario adjustments. Apply pre-configured CMA shifts for scenarios like "Stagflation", "Risk-Off", "Goldilocks", etc.
โœ“ 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.
๐Ÿ“ฅ Export CMAs
Download your current CMA assumptions (including any customisations) as a CSV file for use in external tools or documentation.
โœ“ Use CMAs in Excel, Python, or other portfolio tools
How to use
Click to download. Exports returns, volatilities, correlations, and Sharpe ratios.
๐Ÿ’พ Scenario Manager
Save, load, and compare multiple CMA scenarios. Store your customised assumptions for different market regimes or investment theses.
โœ“ Compare outcomes across scenarios side-by-side
How to use
Save current settings as a scenario. Load saved scenarios. Use "Compare" to see differences.
๐Ÿ† Highest Return
Loading...
--%
Click to highlight
โš–๏ธ Best Risk-Adjusted
Loading...
--
Sharpe Ratio
๐Ÿ›ก๏ธ Best Diversifier
Loading...
--%
Lowest Covariance
๐Ÿ’Ž Highest Conviction
Loading...
--%
Source Agreement
๐Ÿ’ผ

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.
--%
2ฯƒ 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 ร— Asset Volatility ร— Global Equity Volatility (15.5%)

Asset Class Data

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

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 โ€” 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 โ€” more recent sources contribute more to the coverage score, and (2) forecast dispersion โ€” tighter agreement between sources yields higher conviction. Data older than 12 months is flagged as potentially stale.

๐ŸŽฏ

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.

๐Ÿ“ˆ

Upward Return Bias

๐Ÿ“‰

Downward Return Bias

โšก

Elevated Volatility Considerations

๐Ÿ’ก 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 โ‰ค10%
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% โ†‘
Volatility
9.86%
Optimized
vs
12.20%
60/40
-2.34% โ†“
Sharpe Ratio
0.42
Optimized
vs
0.14
60/40
+0.28 โ†‘
Max Drawdown
-35.3%
Optimized
vs
-18.3%
60/40
-17.0% worse
Verdict
โœ…
Higher Return
Similar Risk

๐Ÿ“ˆ 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
โš ๏ธ 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

๐Ÿ“Š 12-Month Scenario Analysis

Testing portfolio sensitivity to different market environments over the next 12 months
๐Ÿ“Š Base Case
Current CMA consensus assumptions with normal market conditions
Historical analog: Long-term average conditions
Assumptions:
Equity mkt: 0%
Volatility: ร—1.00
Correlation: +0.00
Inflation: 2.50%
Effective ฮฒ: 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 ๐Ÿป Bear ๐Ÿ“Š Base ๐Ÿ‚ Bull

Drawdown Probability Distribution

Return Outcome Distribution

Portfolio Holdings

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

Allocation by Asset Class

Equities68.91%
Fixed Income31.09%

Performance Metrics

๐Ÿ“ˆ 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%

โš ๏ธ 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%

โš–๏ธ 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

๐Ÿ”ด
MAX STRESS
60% Equity Fall
Portfolio Drawdown
-35.3%
Probability
2%/yr
Exp Loss
0.71%
๐ŸŸ 
BAD MARKET
35% Equity Fall
Portfolio Drawdown
-20.6%
Probability
8%/yr
Exp Loss
1.65%
๐ŸŸก
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
โ†’
-3.82%
Expected Losses
โ†’
-2.50%
Inflation
โ†’
0.86%
Real Return (After Losses)

Portfolio Builder Calculator

ยฃ
ETF Target % Amount to Invest Est. Annual Cost
Total 100.00% ยฃ100,000 ยฃ190
* Amounts rounded to nearest ยฃ1. 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 ร— 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.

๐Ÿ“‹
Executive Summary
Risk Premia Analysis โ€ข January 2026

โš ๏ธ Risk premia at 50-year lows โ€” 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 โ€” defensive portfolios currently offer better risk-adjusted returns than growth portfolios.

Composite Reading
21%
โ–ผ Below Average
Recommended
Very
Defensive
Target Vol: 5-6%
Risk-Reward Curve
Inverted
โš ๏ธ More risk โ‰  more return
Thesis Confidence
83%
High conviction
Last Updated: January 4, 2026
Current Composite Portfolio Risk Premia Attractiveness โ€”
Average of All Risk Profiles
Source: Author's dataset
21%
Max Unattractive
Unattractive
Neutral
Attractive
Max Attractive
โ„น๏ธ 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 0โ€“100 score. It translates 50 years of portfolio data into one signal โ€” 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 โ€” 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 (%)
โ–ฒ Market Peak
โ–ผ Market Trough
Long-run average
Current Composite Reading
21%
Below Average
Long-run Average: 50%
Historical Range: 9% - 97%
โš ๏ธ 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 โ€” 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
๐Ÿ” 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) โ€” 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.

๐Ÿ“Š Why Very Defensive? The Data Speaks
Return per Unit Vol
Very Defensive
0.087
โ–ฒ 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 โ‰  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 โ€” patience is the most undervalued alpha.

๐ŸŽฏ Action: Tilt strategic allocation toward the Defensive end of the spectrum. This isn't market timing โ€” 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.

โ„น๏ธ 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 0โ€“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 โ€” 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) โ€ข 50% = Neutral โ€ข 100% = Max Growth signal (best risk premia)
โš–๏ธ
How We Could Be Wrong
Risks to the defensive thesis โ€” 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.

๐Ÿ“Š Component Breakdown

Component Importance Weight Current Z-Score Percentile Signal
Expected Nominal Return (10Y) Low 10% -1.15 16% โฌ‡ Bearish
Expected Real Return (10Y) Medium 15% -1.37 12% โฌ‡ Bearish
Real Return (Adj. for Drawdown) High 20% -1.39 8% โฌ‡ Bearish
Portfolio Sharpe Ratio (10Y) Medium 15% -0.70 24% โฌ‡ Bearish
Sharpe Ratio (Adj. for Drawdown) High 20% -0.78 20% โฌ‡ Bearish
Return per Unit Beta High 20% -1.33 12% โฌ‡ 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 โ€” is more risk being rewarded?
โš ๏ธ
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 โœ“ 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
๐Ÿ“‰ What This Means

The risk profile curve is inverted โ€” 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 รท 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
๐Ÿ”ด
Severe Crisis
60% equity fall โ€ข 2% annual prob.
๐ŸŸ 
Bad Market
35% equity fall โ€ข 8% annual prob.
๐ŸŸก
Normal Correction
10% equity fall โ€ข 40% annual prob.
Scenario / Risk Profile Very Defensive Defensive Balanced Growth High Growth
๐Ÿ”ด 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%
๐ŸŸ  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%
๐ŸŸก 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%
๐Ÿ“Š Total Expected 12m Loss
Probability-weighted sum
-2.2% -3.1% -3.8% -4.2% -4.2%
๐Ÿ’ก 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 โ€” without sacrificing much adjusted return.

โš ๏ธ 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 ร— 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
๐Ÿ“‰
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
๐Ÿ”ฎ 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 โ€” or overshoot of โ€” 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 โ‰ฅ threshold.
Historical Major Market Drawdowns
12 significant equity market declines since 1976 โ€” 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)
๐Ÿ“š What These Events Teach Us

Causes vary, drawdowns are constant. From geopolitical shocks to bubbles to pandemics โ€” 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 โ†’ 19
2
AI Check
6 Criteria
3
Reconcile
Human + AI
4
Portfolio
5 ETFs
148
Universe
โ†’
19
Screened
โ†’
5
Portfolio
1
HIGH
4
MEDIUM
14
EXCLUDED
0.65%
Wtd TER
~17%
Target Vol
๐Ÿ“š
What is Thematic Investing 2.0?
AI + Human hybrid approach to capture megatrends via ETF portfolio
โ–ผ

Investment Process Steps

Click any step to expand details

1
Fundamental Theme Screen
Barclays Thematic Roadmap โ€ข 148 โ†’ 19 themes
๐Ÿ”ต Human โ–ผ
2
AI Sanity Check
6 criteria analysis โ€ข 1 High, 6 Medium, 12 Low
๐ŸŸข AI โ–ผ
3
Final Rating Reconciliation
Human + AI โ†’ Final โ€ข 1 High, 4 Medium included
๐ŸŸก Hybrid โ–ผ
4
Portfolio Construction
5 ETFs โ€ข 17% vol target โ€ข Equal weight
๐ŸŸฃ AI-Opt โ–ผ

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.

๐Ÿ“Š
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.

๐ŸŽš๏ธ
Theme Weighting: โ„น๏ธ
๐ŸŽš๏ธ 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.
โ†“ FLOWS THROUGH TO
Asset Class Heatmap โ€ข Cycle Impact Scores โ€ข Theme Rankings โ€ข Portfolio Recommendations โ€ข All weighted calculations
โš™๏ธ Customize Theme Weights
Adjust how much each structural theme influences the overall portfolio view. Increase weights for themes you believe are most important.
โœ“ 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
๐Ÿ”„ Reset All Settings
Restore all theme weights to their default values and clear any impact overrides or disabled themes.
โœ“ Start fresh with a clean slate
What gets reset
  1. All theme weights โ†’ Default (100%)
  2. All impact overrides โ†’ Removed
  3. All disabled themes โ†’ Re-enabled
  4. Preset selection โ†’ Equal Weight
๐Ÿ“ค Export Theme Settings
Save your custom theme weights, impact overrides, and disabled themes to a JSON file on your computer.
โœ“ 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
๐Ÿ“ฅ Import Theme Settings
Restore your previously exported theme settings from a JSON file. Instantly applies all saved customisations.
โœ“ 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!
โ“ Save & Restore Guide
Learn how to preserve your theme customisations across browser sessions and dashboard updates.
โœ“ 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

๐ŸŒŠ

Theme โ†’ Impact Flow

Visualize how macro themes flow to impacts โ€ข Line thickness = impact magnitude ร— weight

๐Ÿ”ฅ 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
๐Ÿ“Š Hybrid View

๐Ÿ’ผ 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
๐Ÿ“Š Hybrid View
โ—€ Scroll to see all 16 asset classes โ–ถ

Speed Read: Long Term Macro Themes

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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

โ— Equities +
โ— Fixed Income +
โ— 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 โ†’ High Growth) +

Return Decomposition

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

Forecast Accuracy Statistics

Correlation (Forecast vs Realized) +
R-Squared (Rยฒ) +
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.

๐Ÿ“Š Portfolio Construction

Asset Allocation โ–ผ

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) โ–ผ

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) โ–ผ

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 โ–ผ

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 (ยฑ1% per signal) to 5 (ยฑ5% per signal) in this dashboard.

๐Ÿ“‰ Risk Metrics

Volatility (Standard Deviation) โ–ผ

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) โ–ผ

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) โ–ผ

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 โ–ผ

A measure of risk-adjusted return. Calculated as (Portfolio Return - Risk-Free Rate) / Portfolio Volatility. Higher is betterโ€”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 โ–ผ

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.

๐ŸŽฏ Signals & Alignment

Signal Strength โ–ผ

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 โ–ผ

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 goalโ€”you may intentionally diverge from signals based on your own views.

Divergence Alert โ–ผ

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.

๐Ÿ’ผ Asset Classes

DM Equities (Developed Markets) โ–ผ

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) โ–ผ

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) โ–ผ

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 โ–ผ

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)

๐Ÿ“Š My Portfolio (OLD) LEGACY

โš ๏ธ
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.

๐Ÿ“Š Current Holdings

Total: 0%

๐Ÿ“ˆ Equities 0%

๐Ÿ“‰ Fixed Income 0%

๐Ÿ’Ž Alternatives 0%

๐Ÿ“‹ Quick Templates

๐Ÿ” 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.

๐Ÿ’ก
Why Divergence Matters
When Human and AI agree, conviction is high. When they diverge, that's the signal โ€” 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)

๐Ÿ—บ๏ธ Divergence Heatmap

Click any cell to see detailed comparison

AI VIEW โ†’
HUMAN VIEW โ†’
UW
Neutral
OW
UW
โœ“ Agree
0
โš  Mild
0
๐Ÿ”ฅ MAX
0
Neutral
โš  Mild
0
โœ“ Agree
0
โš  Mild
0
OW
๐Ÿ”ฅ MAX
0
โš  Mild
0
โœ“ Agree
0
Agreement
Mild Divergence
Max Divergence

๐ŸŽฏ Actionable Insights

๐Ÿ”ฅ High Conviction Divergences
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๐Ÿ‘€ Watch List โ€” Emerging Divergences
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โœ… Strong Consensus โ€” High Conviction
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๐Ÿ“Š All Divergences โ€” Deep Dive

Asset / Sector ๐Ÿ‘ค Human ๐Ÿค– AI Gap Severity Action

Idea Details

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Risk Details

×

Speed Read: Critical Insights Hybrid View

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Speed Read: Risk On/Off View

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Speed Read: Growth View

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Speed Read: Inflation View

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Speed Read: Monetary Policy View

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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 expectationsโ€”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 concernsโ€”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

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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 pointsโ€”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

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Speed Read: Non-Core Investment Ideas

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Speed Read: Core Sector Views

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Speed Read: Mega Trends

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๐Ÿ“Š Multi-Dimensional Factor Analysis
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