Every research call we make at Tara Capital is filtered through a single quantitative engine: the Tara Capital Investment Framework, or TCIF. This is a plain-English guide to how it works, what it measures, and why we built it the way we did.
The fundamental problem with macro research is subjectivity. Every analyst has a mental model of the world. Those models are shaped by experience, by temperament, by recency bias, and by whatever happened to be the dominant narrative when they first entered markets. Left unchecked, those biases produce outputs that feel rigorous but are ultimately anchored to prior conviction rather than current evidence. The TCIF is our answer to that problem.
It does not replace judgement. It structures it. By forcing every input category to be scored on a consistent, 0–100 scale — every single week — it creates an auditable, comparable record of where the macro regime actually sits, stripped of whatever narrative happens to be dominating the news cycle.
"The framework does not tell you what to do. It tells you what kind of world you are operating in. That distinction matters enormously."
What the TCIF is
The TCIF is a quantitative macro regime classification engine. It scores ten input categories on a 0–100 scale, produces a weighted composite score, and classifies the current environment into one of six regimes — from Risk-On Expansion at the top of the cycle to Recessionary Environment at the bottom.
It runs every Monday morning at 07:45 UTC. It fetches current data from FRED and Yahoo Finance, scores each category, computes the weighted composite, classifies the regime, and delivers the full report automatically via Telegram and email — including year-to-date charts showing composite score and category breakdowns. The entire process, from data pull to delivered report, takes under two minutes.
The ten categories are not weighted equally. The weights reflect our analytical conviction about which inputs have the highest signal-to-noise ratio for predicting risk asset conditions. Liquidity leads. Real yields follow. Credit conditions and the dollar are significant. Growth indicators — updated to 1.5× in v2.1 — carry above-neutral weight, reflecting their forward-looking accuracy when combined with the CFNAI and Philly Fed signals added in July 2026.
The ten categories
The Fed balance sheet, the Reverse Repo facility (RRP), the Treasury General Account (TGA), aggregate bank reserves, and M2 money supply growth (added v2.1). The core calculation is deceptively simple:
Net liquidity proxy = Fed balance sheet − RRP − TGA
The RRP and TGA are subtracted because they represent money that sits inside the Federal Reserve system rather than circulating through financial markets. When the RRP drains — as it did dramatically through 2023 and 2024 — that money flows back into money market funds, then into risk assets. When the TGA is drawn down by the Treasury, the same dynamic occurs. Rising net liquidity is the single most important signal for risk asset conditions.
M2 year-on-year growth was added in v2.1 as a slower-moving but powerful liquidity confirmation signal. M2 contracting on a YoY basis has historically been a reliable recession precursor; accelerating M2 above 6% YoY is among the most supportive macro conditions for risk assets.
A score above 70 indicates abundant and expanding net liquidity — historically the most supportive environment for equities, credit, and digital assets. Below 30 indicates active contraction.
The real 10-year yield (nominal 10-year Treasury minus the 10-year breakeven inflation rate), the 2s10s yield curve slope, and the trend in breakeven inflation.
Real yields are the price of money after adjusting for inflation expectations. When real yields are deeply negative, holding cash is a guaranteed loss in real terms — money flows aggressively into risk assets. When they are significantly positive, safe assets offer attractive real returns and the incentive to take risk diminishes.
Real yields above 2% are considered restrictive. Below 0% are highly accommodative. The 2s10s slope matters because an inverted curve signals that the market expects the Fed to cut — and historically precedes economic contraction by six to eighteen months.
The DXY level and its 20-day momentum, the EMFX basket (added v2.1), and EM equity relative performance via EEM vs SPY (added v2.1). The dollar is the world's reserve currency and the primary funding currency for global trade and capital flows. A rising dollar tightens financial conditions globally — it raises the cost of dollar-denominated debt for emerging market borrowers, compresses commodity prices (which are denominated in dollars), and tends to reduce risk appetite across asset classes.
The DXY captures dollar strength against developed-market currencies (EUR, JPY, GBP, CHF, SEK, CAD) but entirely misses the EM currency universe. In v2.1, an EMFX basket covering MXN, BRL, KRW, INR, ZAR, and other major EM pairs was added to address this gap — blended 60% DXY / 40% EMFX. EM equity relative performance (EEM vs SPY on a 20-day basis) further validates the EM dollar signal: when both EM currencies and EM equities simultaneously underperform, the EM stress reading is substantially more reliable than currency alone.
The relationship is not symmetric. Dollar strength can be driven by flight-to-safety (bearish for risk) or by US growth outperformance (less bearish). The framework captures direction and momentum rather than attempting to decompose the cause.
DXY above 105 is considered a stress signal for global financial conditions. Below 93 represents a meaningful tailwind for risk assets, emerging markets, and commodity prices.
US High Yield option-adjusted spreads (OAS), US Investment Grade OAS, Emerging Market High Yield spreads, the VIX, the TED spread (added v2.1), the HYG/LQD ratio (added v2.1), and the Senior Loan Officer Opinion Survey (SLOOS, added v2.1). Credit spreads are the market's real-time assessment of default risk and liquidity stress. Unlike equity markets, which can remain elevated on narrative momentum, credit markets tend to respond earlier and more honestly to deteriorating fundamentals.
The TED spread (3-month LIBOR/SOFR vs 3-month T-bill) measures interbank funding stress — historically one of the earliest indicators of systemic credit strain. The HYG/LQD ratio (high-yield vs investment-grade ETF relative performance) provides a real-time, market-priced read on risk appetite within credit markets — independent of spread levels, which can lag. SLOOS captures the demand-side of credit: when senior loan officers are tightening standards, the credit contraction is already underway before spreads reflect it.
The VIX — the implied volatility of the S&P 500 — is included because it captures market anxiety more directly than spread levels alone, and because the relationship between rising volatility and tightening credit is consistent and well-documented.
Tight spreads and low VIX signal genuine risk-on conditions. Widening spreads — particularly in high yield — are among the most reliable early-warning indicators of a regime shift from expansion to contraction. A rising TED spread or a deteriorating SLOOS reading can precede spread widening by weeks.
Non-farm payrolls, industrial production, the unemployment rate, the copper/gold ratio, ISM Manufacturing PMI, ISM Services PMI, the Philadelphia Fed Manufacturing Survey, and the Chicago Fed National Activity Index (CFNAI). The CFNAI is a broad composite of 85 economic indicators — its inclusion in v2.1 significantly improves the category's timeliness. The Philly Fed provides an additional regional manufacturing pulse that leads the national ISM by several weeks.
Growth indicators range from lagging to genuinely leading. The CFNAI and Philly Fed are among the most forward-looking inputs in the framework. The copper/gold ratio is a real-time, market-derived growth proxy — rising when industrial demand outpaces defensive positioning. ISM Services PMI captures the ~70% of the US economy that manufacturing surveys miss entirely.
Updated to 1.5× in v2.1 (July 2026), reflecting the improved forward-looking composition of the category after the addition of CFNAI and Philly Fed.
Weight increased from 1.0× to 1.5× in v2.1. The expanded signal set reduces the historical lag and gives Growth a meaningful leading edge. The category now carries above-neutral weight — second only to Liquidity in the framework's hierarchy.
The Chicago Fed National Financial Conditions Index (NFCI), the St. Louis Fed Financial Stress Index (STLFSI), and the VIX term structure (added v2.1). The NFCI and STLFSI are weekly, freely available, and composite — they aggregate dozens of financial market variables into a single standardised number.
The VIX term structure (contango vs backwardation) was added in v2.1. When the VIX curve is in backwardation — near-term implied vol higher than longer-dated — the market is pricing acute near-term stress. Persistent backwardation historically coincides with the most adverse financial conditions readings, providing an independent real-time signal that complements the weekly NFCI.
A high-yield credit spread slope signal was added in v2.2 (August 2026). The rate of change in US high-yield spreads over the prior five trading days — not the level — provides a leading edge that the NFCI and STLFSI, as weekly composites, do not capture. Spread widening tends to precede NFCI deterioration by one to four weeks; spread narrowing tends to confirm before the composite moves. This approach draws on the analytical frameworks of Jordi Visser (Weiss Multi-Strategy) and Alfonso Peccatiello, both of whom treat credit spread direction rather than level as the more informative real-time signal.
The NFCI incorporates money markets, debt and equity markets, and the traditional and shadow banking systems. The STLFSI captures rates, spreads, and other market indicators. Together they provide a robust, independently-constructed cross-check on what the individual category inputs are telling us.
Both the NFCI and STLFSI are centred at zero, with values below zero indicating looser-than-average financial conditions and values above zero indicating tighter-than-average. A known limitation of these indices is that they measure domestic US financial conditions and do not fully capture cross-border liquidity dynamics — offshore dollar funding, Japanese carry flows, or the balance-sheet capacity of non-US shadow banks. These cross-border forces are partially captured by the Dollar category (1.2×) and Liquidity category (1.6×), which together cover the transmission mechanisms that US-centric FC indices miss. The JPY/USD rate and yen carry dynamics are monitored as a stress canary: sustained yen carry unwinds have historically preceded NFCI deterioration, because forced deleveraging in carry trades transmits to global credit conditions before the composite indices register it.
The AI capital expenditure cycle has become a structurally significant macro force — one of the few investment themes large enough to move aggregate economic data. This category scores that cycle using a two-layer composite designed to separate genuine infrastructure spend from equity market narrative.
Layer 1 — Market sentiment (40% weight): NVDA and SMH 20-day price momentum relative to QQQ. A fast-moving, tactical signal that captures how the market is pricing the AI buildout in real time. Useful for detecting inflection points, but prone to false readings during sector rotation events where AI infrastructure names underperform the index for reasons unrelated to actual spending.
Layer 2 — Structural capex (60% weight): Actual quarterly capital expenditure from the five largest hyperscalers — Microsoft, Alphabet, Amazon, Meta, and Oracle — drawn from SEC filings. Year-on-year growth in reported capex. Slow-moving (quarterly cadence) and immune to market noise. This is the ground truth: the physical dollars flowing into data centres, GPU clusters, and network infrastructure.
The two layers are designed to diverge usefully. When Layer 1 is weak but Layer 2 is strong — semiconductor equities underperforming while real hyperscaler spend accelerates — the model flags a structural buy signal: the market is mispricing the cycle. When Layer 1 is strong but Layer 2 is flat, the model flags sentiment excess: equity prices are running ahead of actual capital deployment.
The original version of this category used Layer 1 only. In August 2026 it scored 38.6/100 — labelling the AI capex cycle as contraction — at a moment when Microsoft, Alphabet, Amazon, Meta, and Oracle were collectively deploying capital at a $686B annualised run rate, each growing year-on-year between 77% and 109%. A 20-day stock momentum reading had overwhelmed the structural reality. The two-layer composite scored the same period at 71.4/100 — expansion — which correctly reflected the underlying cycle. Layer 2 is the anchor. Layer 1 is the early-warning sensor.
Headline CPI (year-on-year), core CPI (excluding food and energy), the 10-year breakeven inflation rate, and the four-week trend in breakeven inflation. Together these provide both the backward-looking realised inflation signal and the forward-looking market expectation.
Inflation is scored on a goldilocks model rather than a linear scale. The framework does not assume that lower inflation is always better for equities — deflation and near-zero inflation are historically associated with weak nominal growth and compressed corporate revenue. The optimal zone is moderate and stable inflation (approximately 2–2.5%), which supports pricing power without eroding real purchasing power or forcing an aggressive monetary response.
A score above 60 indicates inflation is in or approaching the goldilocks zone and is a tailwind for the regime. A score below 40 indicates inflation is either too high (eroding margins and purchasing power, triggering rate pressure) or too low (deflationary signal, weak nominal demand). The directional trend in breakeven inflation carries significant weight: falling breakevens are treated as a constructive signal even when the absolute level remains elevated.
The 10-year breakeven rate and 5Y5Y forward inflation expectation are tracked within Real Yields (Category 2) as inputs to the real yield calculation. In Category 8, they are evaluated independently for their signal value about the inflation regime itself, not as a component of the real yield level.
Corporate profits year-on-year (FRED CPATAX), the equity risk premium (earnings yield minus the real 10-year yield), the earnings beat rate across the top-ten S&P 500 constituents, and the profit margin trend measured as after-tax profits relative to GDP.
This category corrects a historical blind spot in macro regime frameworks: the possibility that liquidity and credit conditions read as cautious while corporate profits are compounding at double-digit rates. When earnings are growing and margins are expanding, the framework gives equities credit for that fundamental support — even when the macro backdrop is ambiguous.
The equity risk premium sub-signal is particularly important. It measures whether equities are cheap or expensive relative to bonds on an earnings basis. A high ERP — where the earnings yield is well above the real bond yield — indicates that equities offer genuine value and that the hurdle rate for holding cash or bonds over equities is high. A compressed or negative ERP, where equities yield less than bonds in real terms, is a late-cycle warning signal regardless of earnings momentum.
Weight: 1.0× — equal to Inflation. The neutral weight reflects two structural constraints: earnings data is backward-looking by one to two months (quarterly reporting cycle), and the signals are predominantly US-centric, reflecting the S&P 500 corporate sector rather than global macro conditions.
The interest-to-revenue ratio (federal interest payments as a share of tax receipts), the deficit as a percentage of GDP, the NY Fed ACM term premium on the 10-year Treasury, yen carry trade unwind risk (USD/JPY 20-day momentum), and M2 velocity as a proxy for QE transmission effectiveness.
Tara Capital is a liquidity-first house. Quantitative easing has been the dominant countercyclical policy tool since the Global Financial Crisis. This category asks a specific and increasingly urgent question: is QE still viable, and what trajectory of fiscal conditions determines when it stops working?
The 30-year Treasury yield, the term premium, and the interest-to-revenue ratio collectively describe the state of the government's fiscal room. When interest payments consume a rising share of tax revenues — and when the term premium rises to reflect markets demanding additional compensation for fiscal uncertainty — the government's ability to backstop growth through bond issuance becomes constrained. QE in a high-term-premium environment risks being interpreted by markets as fiscal monetisation, which can push yields higher rather than lower.
The yen carry sub-signal addresses a specific source of systemic risk. The yen is the predominant funding currency for global carry trades, in which investors borrow at low Japanese interest rates to finance positions in higher-yielding currencies and risk assets. A rapid yen appreciation raises the cost of repaying that funding and can force leveraged investors to sell assets and buy yen simultaneously. In August 2024, a Bank of Japan rate increase, weaker US employment data and expectations of a narrowing US–Japan interest-rate differential triggered just such an unwind. Margin calls, volatility controls and crowded positioning amplified the move: Japanese equities fell by approximately 12% on 5 August, global equities sold off and volatility surged. The episode was brief, but it demonstrated that a sharp yen reversal can turn an otherwise modest macroeconomic catalyst into an acute, cross-asset regime shift.
Weight: 1.2× — above Inflation (1.0×) and AI CapEx (0.8×), below Growth (1.5×) and Liquidity (1.6×). The above-neutral weight reflects Tara Capital's view that fiscal sustainability is the most underappreciated macro risk of the current cycle, and that QE viability is a necessary input for any framework that takes the policy response function seriously.
Regime classifications
The ten input categories are scored, weighted, and combined into a single composite score between 0 and 100. That score maps to one of six macro regimes:
Why liquidity is the primary lens
Every analytical framework makes a claim about what matters most. The TCIF makes a clear, empirically-grounded claim: net system liquidity is the single highest-correlation leading indicator for risk asset conditions.
This is not a novel idea. Michael Howell at CrossBorder Capital has written extensively on global liquidity cycles. Lyn Alden has documented the relationship between Fed balance sheet expansion and risk asset performance across multiple cycles. The data is consistent and compelling: when net liquidity — defined as the Fed balance sheet minus the RRP minus the TGA — expands, risk assets tend to perform. When it contracts, they struggle. This holds regardless of what growth or earnings are doing in the short term.
The intuition is straightforward. Liquidity is the fuel for the financial system. It determines how much capital is chasing a given set of assets, and therefore at what price equilibrium is reached. When the Fed expands its balance sheet, that expansion flows through primary dealers into money markets, into bond markets, and ultimately into equities, credit, and digital assets. The transmission is not instant — it operates with lags that vary by asset class — but it is consistent.
"When net liquidity expands, risk assets tend to perform. When it contracts, they struggle — regardless of what growth or earnings are doing in the short term. This is the core of the Tara Capital analytical framework."
The RRP and TGA adjustments are critical and frequently overlooked. The gross Fed balance sheet is widely reported and widely misunderstood. What matters is not how large the balance sheet is in absolute terms, but how much of that liquidity is available to circulate through financial markets. Money parked in the reverse repo facility or sitting in the Treasury's account at the Fed does not support risk asset prices. Only the net figure matters.
This is why the liquidity category carries the highest weight in the TCIF — not because the other nine categories are unimportant, but because the evidence across multiple cycles suggests that net liquidity is the factor most likely to be right when everything else is giving mixed signals.
A note on the weightings
Liquidity carries the highest weight (1.6×) because it is the first mover — changes in Fed balance sheet, RRP, and reserve dynamics typically precede shifts in real yields, credit, and risk appetite by weeks. Real yields follow at 1.4×, because they are the most direct transmission mechanism: real yield direction drives the dollar, which drives EM financial conditions, which drives risk appetite. Credit conditions (1.3×) are confirmatory — they validate what liquidity and yields are signalling, and tend to lead equities. Dollar (1.2×) reflects the singular importance of dollar direction as a cross-asset transmission variable, particularly for real assets and emerging markets. Growth indicators carry 1.5× — raised from 1.0× in v2.1 — because the addition of CFNAI and Philly Fed meaningfully improved the category's forward-looking accuracy. Inflation (1.1×) sits just above neutral weight — it is a regime-shaping force, but its impact on equities depends heavily on whether we are in a demand-pull or cost-push environment, so it warrants analytical weight without dominance. Financial Conditions carries 0.9× — raised from 0.7× in v2.2 — because the expanded signal set (VIX term structure added in v2.1, HY credit spread slope added in v2.2) gives the category a meaningful real-time edge during stress periods that pure confirmation framing understated. NFCI backwardation signals and acute STLFSI spikes have historically preceded Credit Conditions moving by a week or more; the spread slope signal adds a further leading edge of one to four weeks. That partial leading property justifies a weight above AI CapEx. The category's sub-1.0× ceiling reflects a genuine limitation: the NFCI and STLFSI measure domestic US financial conditions and miss cross-border liquidity dynamics — offshore dollar funding, yen carry flows, and the balance-sheet capacity of non-US shadow banks. Those forces are better captured by the Liquidity (1.6×) and Dollar (1.2×) categories, which is why those categories carry higher weights. The FC weight will be reviewed as the scoring history at 0.9× accumulates. AI CapEx carries 0.8× — the lowest weight — because it is the newest and least battle-tested category. The two-layer composite (40% equity momentum, 60% hyperscaler SEC filings) corrects the original single-layer scorer that mislabelled the August 2026 cycle as contraction. Even with Layer 2 anchoring the score, the category is structurally less comparable across cycles than Liquidity or Credit Conditions, which have decades of consistent data. The weight will be reviewed as the scoring history accumulates. Corporate Earnings & Profitability carries 1.0× — neutral weight — because earnings are backward-looking by one to two months and predominantly US-centric; meaningful as a confirming signal, but not a leader. Fiscal Sustainability & QE Viability carries 1.2× — above-neutral — because the viability of the policy response function is the most underappreciated macro risk of the current cycle. A framework that ignores the fiscal constraints on QE is a framework that will misread the next crisis.
These weights reflect analytical judgement about sequencing in the cycle, not a formal optimisation. They will be reviewed over time as history accumulates.
What the framework is — and is not
The TCIF is not a trading signal. It does not produce buy or sell recommendations. What it produces is a structured, consistent characterisation of the macro regime — and a regime classification changes how we interpret everything else.
In a Risk-On Expansion regime, we interpret pullbacks in risk assets as buying opportunities and weight our analysis towards assets that benefit most from abundant liquidity: high-beta equities, digital assets, emerging market risk. In a Credit Contraction regime, the same pullback is treated as an early warning and we look for confirmation from credit spreads and the financial conditions indices before drawing conclusions.
The framework also prevents the most common analytical error in macro research: changing your framework to fit the current narrative. Because the TCIF scores every category on a fixed methodology every single week, it creates a running record that is immune to hindsight revision. The scores from January are the scores from January. They cannot be quietly updated to fit February's news.
In practice, the Monday morning report is the foundation on which the week's research is built. It does not replace the analytical work — it structures it. Every subsequent piece of research we publish at Tara Capital, whether on digital assets, emerging markets, or geopolitics, is written with a clear understanding of the current regime score and what that means for the risk environment.
The Convergence Principle
A regime score on its own answers one question: what kind of macro environment are we in? The Convergence Principle answers the next question: given that environment, where are the specific opportunities — and how confident should we be in them?
The framework runs four outputs from the same composite and regime reading. The first — the Risk Budget and Cash Reserve assessment — answers a prior question: how much risk should the portfolio carry before asking where it should be allocated? Three independent implementation engines then follow in parallel:
- Country allocation — where should capital be deployed, given policy divergence, currency direction, and capital flows?
- Sector allocation — which types of business does the current cyclical position support?
- Theme allocation — which structural transformation forces are being accelerated by the current environment?
The three implementation engines are parallel, not sequential. None is upstream of the others. Each receives the same regime classification as its input and runs its own logic entirely independently. The Risk Budget layer, by contrast, sits sequentially between the regime and the implementation engines — it sets the quantity of risk before the framework determines where that risk should be taken.
Risk is part of every decision
TCIF does not stop at identifying what may benefit from the prevailing regime. It assesses how much risk may be appropriate, where that risk may be rewarded and what evidence would justify changing the position.
Every conclusion is recorded alongside its confidence, principal risks and reversal conditions. When the supporting evidence weakens, the framework identifies which exposures are most vulnerable and what should be considered for reduction, exit or rotation.
The system disciplines the evidence; the investor retains the judgement.
The Convergence Principle states: when all three engines independently arrive at the same conclusion, that alignment is the signal — not any one engine alone. A Country OW call on its own is a view. A Sector OW call on its own is a view. When both are OW on a specific country-sector pair, and the Theme engine is simultaneously OW on the structural force that drives that pair, the three independent processes have triangulated on the same opportunity from three different analytical starting points. That is not a view. That is a high-conviction call.
When they conflict — when Country says underweight a region but Theme says overweight the structural force that dominates it — the conflict is surfaced explicitly rather than resolved by averaging. Tension in the signals is information. It tells you that the macro environment is sending mixed messages about that opportunity, and that a position there requires additional analytical justification.
This architecture grew out of years managing emerging market portfolios, where no single signal was ever sufficient. Dollar direction, local real yields, political risk, and global liquidity all mattered simultaneously — and you had to know which one was dominant at any given moment. At CLIG, the discipline was country allocation alone. At Ashmore, country and sector combined. The TCIF adds the third lens — structural theme — because AI, industrial policy, digital assets, and geopolitics now cut across both countries and sectors in ways that neither geography nor cyclical rotation can capture independently.
"Three independent processes, same conclusion. That alignment is the signal."
The convergence table in practice
Each Monday, after the three engines have run, the Convergence Engine maps their outputs against each other. It looks for combinations where country, sector, and theme are all simultaneously OW or Strong OW — and where there is a genuine analytical reason those three belong together, not just mathematical coincidence.
A worked example illustrates the principle:
| Country | Sector | Theme | Conviction |
|---|---|---|---|
| Taiwan | Information Technology | Digital Economy | ⚡ High |
| Country Strong OW (AI CapEx score + market confirmation); Sector Strong OW; Theme OW. The pair is only eligible if the country-specific sector series (here, TSMC relative to the Taiwan 50) passes the market-confirmation gate. Illustrative only. | |||
| United States | Information Technology | Digital Economy | ⚡ High |
| US OW (NFCI -0.51, loose financial conditions); Tech Strong OW; Digital Economy OW driven by AI CapEx momentum. US AI infrastructure — semiconductors, data centres, software platforms — remains the primary engine of the current capex cycle. | |||
| Japan | Industrials | Industrial Sovereignty | · Moderate |
| Japan OW (dollar/yen dynamics aid exporters); Industrials OW (mid-cycle base); Industrial Sovereignty OW (Japan defence spending acceleration + semiconductor reshoring). Convergence on Japan's defence and industrial complex. | |||
| China | Industrials | Industrial Sovereignty | · Moderate |
| China OW (credit conditions loose + liquidity); Industrials OW; Industrial Sovereignty OW. Beijing's domestic manufacturing self-sufficiency drive is the dominant policy force — the macro environment is supportive of that structural theme within China. | |||
Convergence is not a buy signal. It is a prioritisation tool. It identifies where the macro environment is simultaneously supportive across geography, cyclical positioning, and structural theme. Investment judgement, valuation, and position sizing remain human decisions — made with the benefit of knowing that three independent analytical lenses are pointing in the same direction.
The framework does not tell you what to do. It tells you what kind of world you are operating in — and where, within that world, the evidence is strongest. That distinction matters enormously.
Allocation
The regime classification is translated into opportunity sets across three independent lenses: country allocation identifies where capital is best deployed given policy divergence, currency direction, and capital flows; sector allocation identifies which types of business the current cyclical environment supports; and theme allocation identifies which structural transformation forces are active and supported by the macro environment. Together, they form the bridge between macro analysis and portfolio construction. Read the design philosophy →
Sector Allocation — Mid-Cycle Growth
Composite 58.7/100 · TCIF Eight-Category Framework
| Sector | Signal | Rationale |
|---|---|---|
| Energy | Neutral | Energy neutral — inflation moderating, growth signal insufficient to confirm demand-pull commodity bid. |
| Materials | Neutral | Materials neutral — mixed inflation and dollar signals; no directional conviction. |
| Industrials | ● OW | Industrials constructive; mid-cycle capex and credit conditions support earnings. |
| Consumer Discretionary | ● OW | Discretionary constructive on solid growth and liquidity backdrop; inflation not yet a headwind. |
| Consumer Staples | ● UW | Staples underperform in expansion — opportunity cost of defensives high when risk-on is running. |
| Health Care | Neutral | Health Care neutral — neither the growth rotation nor defensive rotation is dominant here. |
| Financials | ● OW | Financials constructive on loose liquidity and credit backdrop; yield curve supportive. |
| Information Technology | ●● OW | Tech leads in goldilocks regime — moderate inflation keeps real yields in check; AI CapEx supports. |
Key macro themes:
Strong OW: Information Technology. Also OW: Industrials, Consumer Discretionary, Financials. UW: Consumer Staples.
Seven structural themes scored against the live TCIF sub-scores. Each theme operates independently of the regime cycle — the macro environment modulates conviction, but structural forces are not switched on and off by a composite number.
| Theme | Signal | Key Drivers |
|---|---|---|
|
AI Infrastructure & Physical Buildout
AI Compute & Semiconductors · Data Centre & Power Infrastructure
|
●● Strong OW | Ai Capex supportive (76); Financial Conditions supportive (73) |
|
Defence & Aerospace
Prime Contractors · Electronic Systems & Sensors
|
●● Strong OW | Financial Conditions supportive (73); Dollar supportive (50) |
|
Robotics & Automation
Industrial Robots & Automation · Collaborative Robots & AI Integration
|
●● Strong OW | Ai Capex supportive (76); Financial Conditions supportive (73) |
|
Longevity & Healthcare Innovation
GLP-1 & Metabolic Health · AI Drug Discovery & Genomics
|
●● Strong OW | Financial Conditions supportive (73); Inflation supportive (52) |
|
Digital Economy
Semiconductors & Foundries · Data Centre Infrastructure
|
● OW | Ai Capex supportive (76); Financial Conditions supportive (73) |
|
Industrial Sovereignty
Defence & Security · Reshoring & Domestic Manufacturing
|
● OW | Inflation supportive (52) |
|
Energy & Power Systems
Nuclear & Uranium · Natural Gas & LNG
|
Neutral | Mixed signals — regime-neutral |
|
Real Assets & Commodities
Gold & Silver · Copper & Base Metals
|
Neutral | Mixed signals — regime-neutral |
|
Fiscal Infrastructure
Construction & Engineering · Transport Infrastructure
|
Neutral | Mixed signals — regime-neutral |
⚡ Three or more themes aligned OW: AI Infrastructure & Physical Buildout, Defence & Aerospace, Robotics & Automation, Longevity & Healthcare Innovation, Digital Economy, Industrial Sovereignty — elevated conviction.
|
AI Semiconductor Foundry & Packaging
Advanced Packaging · Specialty Materials & Gases
|
Tier 2 — Watch | Advanced packaging (CoWoS, HBM stacking, chiplet integration), leading-edge foundry capacity, and th… |
|
Tokenised RWA & Stablecoin Rails
Stablecoin Issuers · Tokenised Treasuries & RWA
|
Tier 2 — Watch | Stablecoin issuers, tokenised real-world assets (e.g. tokenised Treasuries) and the on-chain settlem… |
|
Space Infrastructure
|
Tier 3 — Horizon | Satellite broadband, launch economics, space-based observation. Macro signal too thin currently. Pro… |
Updated every Monday at 07:45 UTC. Ten primary themes actively scored; Tier 2 themes monitored for promotion to primary status; Tier 3 themes on the horizon watch list. Read the Phase 2 design philosophy →
DM vs EM tilt: Neutral — Dollar and liquidity signals mixed — no strong DM/EM tilt.
| Country / Region | Signal | Key Drivers |
|---|---|---|
|
Taiwan
AI hardware supply chain: foundry, advanced packaging, servers
|
●● Strong OW
score 70 · macro 66 · market 96
|
Ai Capex supportive; Financial Conditions supportive; market confirms — Taiwan vs world: 100th pct of 5y range, 19.7% above 200d MA, +28.9% 6m / +56.9% 12m relative |
|
United States
|
● OW
score 66 · macro 66 · market 70
|
Financial Conditions supportive; Ai Capex supportive; market confirms — US vs world: 83rd pct of 5y range, 1.9% above 200d MA, +2.8% 6m / +0.4% 12m relative |
|
South Korea
Memory (HBM/DRAM), semiconductor equipment, power equipment
|
●● Strong OW
score 66 · macro 65 · market 73
|
Ai Capex supportive; Financial Conditions supportive; market confirms — South Korea vs world: 92nd pct of 5y range, 1.2% above 200d MA, +5.2% 6m / +67.3% 12m relative |
|
Israel
AI / semiconductor / cyber / defence-technology hub
|
●● Strong OW
score 65 · macro 69 · market 47
|
Ai Capex supportive; Financial Conditions supportive; market neutral — Israel vs world: 69th pct of 5y range, 9.4% below 200d MA, -14.7% 6m / +3.3% 12m relative |
|
Technology-Metals Exporters
Copper, tin and other technology metals: Indonesia, South Africa, Canada, Australia (Chile and Peru not yet in universe)
|
● OW
score 61 · macro 59 · market 76
|
Ai Capex supportive; market confirms — Technology-metals basket (copper, tin) (COPX+TINS.JK+AFM.V+HG=F) vs world: 94th pct of 5y range, 2.3% above 200d MA, -0.3% 6m / +24.9% 12m relative |
|
Japan
|
● OW
score 58 · macro 58 · market 55
|
Financial Conditions supportive; market neutral — Japan vs world: 56th pct of 5y range, 1.2% above 200d MA, +1.4% 6m / +6.1% 12m relative |
|
Hong Kong
Separate from China — HKD peg, English common law, HKSCC custody
|
● OW
score 55 · macro 61 · market 21
|
Financial Conditions supportive; market does NOT confirm — Hong Kong vs world: 15th pct of 5y range, 8.5% below 200d MA, -11.4% 6m / -7.0% 12m relative |
|
Oil & Gas Exporters
Saudi Arabia, Norway, Brazil, Mexico. Driven by geopolitics (Iran, Ukraine) as much as by the macro regime
|
Neutral
score 52 · macro 51 · market 62
|
market confirms — Oil & gas exporters basket (PBR+EQNR+KSA+CL=F) vs world: 47th pct of 5y range, 10.6% above 200d MA, +6.0% 6m / +32.9% 12m relative |
|
India
|
Neutral
score 50 · macro 58 · market 10
|
Financial Conditions supportive; market does NOT confirm — India vs world: 0th pct of 5y range, 11.9% below 200d MA, -15.8% 6m / -27.5% 12m relative |
|
Europe
|
Neutral
score 48 · macro 53 · market 18
|
market does NOT confirm — Europe vs world: 5th pct of 5y range, 6.6% below 200d MA, -8.5% 6m / -6.4% 12m relative |
|
China
|
Neutral
score 47 · macro 54 · market 9
|
market does NOT confirm — China vs world: 0th pct of 5y range, 12.9% below 200d MA, -17.6% 6m / -29.1% 12m relative |
⚡ Multiple countries / regions aligned OW: Taiwan, United States, South Korea, Israel, Technology-Metals Exporters, Japan, Hong Kong.
Updated every Monday at 07:45 UTC alongside the regime score. Each country / region score is 85% macro inputs and 15% market confirmation: relative strength versus world equities (5-year percentile and distance from the 200-day average). Market confirmation is descriptive, not predictive. A historical test (1993-2026) found it does not forecast future relative returns, so it is used as a consistency and risk check. Country-sector convergences are gated on the same measure.
Where Country, Sector, and Theme independently arrive at the same conclusion. Each engine runs from the same regime input but asks a different question — country allocation asks where, sector asks what type of business, theme asks what structural force. When all three answer the same way, that alignment is the signal.
| Country | Sector | Theme | Conviction |
|---|---|---|---|
| Israel | Information Technology | Digital Economy | ⚡ High |
| Israel Strong OW, Information Technology Strong OW, Digital Economy OW; market-confirmed: Israel tech ETF vs Israel (EIS): 30th pct of 5y range, 6.5% above 200d MA, +9.1% 6m / +2.9% 12m relative | |||
| Israel | Information Technology | Industrial Sovereignty | ⚡ High |
| Israel Strong OW, Information Technology Strong OW, Industrial Sovereignty OW; market-confirmed: Israel tech ETF vs Israel (EIS): 30th pct of 5y range, 6.5% above 200d MA, +9.1% 6m / +2.9% 12m relative | |||
| Japan | Information Technology | Industrial Sovereignty | ⚡ High |
| Japan OW, Information Technology Strong OW, Industrial Sovereignty OW; market-confirmed: TOPIX-17 electric & precision vs TOPIX: 96th pct of 5y range, 7.9% above 200d MA, +12.2% 6m / +15.3% 12m relative | |||
| Japan | Information Technology | Digital Economy | ⚡ High |
| Japan OW, Information Technology Strong OW, Digital Economy OW; market-confirmed: TOPIX-17 electric & precision vs TOPIX: 96th pct of 5y range, 7.9% above 200d MA, +12.2% 6m / +15.3% 12m relative | |||
| South Korea | Information Technology | Digital Economy | ⚡ High |
| South Korea Strong OW, Information Technology Strong OW, Digital Economy OW; market-confirmed: Samsung Electronics vs Korea (EWY): 92nd pct of 5y range, 0.8% below 200d MA, +2.9% 6m / +34.1% 12m relative | |||
| South Korea | Information Technology | Industrial Sovereignty | ⚡ High |
| South Korea Strong OW, Information Technology Strong OW, Industrial Sovereignty OW; market-confirmed: Samsung Electronics vs Korea (EWY): 92nd pct of 5y range, 0.8% below 200d MA, +2.9% 6m / +34.1% 12m relative | |||
| Taiwan | Information Technology | Digital Economy | ⚡ High |
| Taiwan Strong OW, Information Technology Strong OW, Digital Economy OW; market-confirmed: TSMC vs Taiwan 50 (0050): 69th pct of 5y range, 5.7% below 200d MA, -8.3% 6m / -5.6% 12m relative | |||
| Taiwan | Information Technology | Industrial Sovereignty | ⚡ High |
| Taiwan Strong OW, Information Technology Strong OW, Industrial Sovereignty OW; market-confirmed: TSMC vs Taiwan 50 (0050): 69th pct of 5y range, 5.7% below 200d MA, -8.3% 6m / -5.6% 12m relative | |||
| United States | Information Technology | Digital Economy | ⚡ High |
| United States OW, Information Technology Strong OW, Digital Economy OW; market-confirmed: US tech vs S&P 500: 98th pct of 5y range, 9.9% above 200d MA, +16.3% 6m / +17.1% 12m relative | |||
| United States | Information Technology | Industrial Sovereignty | ⚡ High |
| United States OW, Information Technology Strong OW, Industrial Sovereignty OW; market-confirmed: US tech vs S&P 500: 98th pct of 5y range, 9.9% above 200d MA, +16.3% 6m / +17.1% 12m relative | |||
| Israel | Industrials | Digital Economy | ⚡ High |
| Israel Strong OW, Industrials OW, Digital Economy OW; no country-specific market series (unconfirmed) | |||
| Israel | Industrials | Industrial Sovereignty | ⚡ High |
| Israel Strong OW, Industrials OW, Industrial Sovereignty OW; no country-specific market series (unconfirmed) | |||
12 convergence opportunities identified. Broad alignment across engines — regime supports diversified risk-on positioning.
Gated out — market not confirming:
- United States Financials: US financials vs S&P 500: 2nd pct of 5y range, 4.6% below 200d MA, -4.3% 6m / -9.4% 12m relative (score 20 < 35)
- United States Industrials: US industrials vs S&P 500: 0th pct of 5y range, 9.4% below 200d MA, -11.1% 6m / -5.7% 12m relative (score 12 < 35)
- Japan Industrials: TOPIX-17 machinery vs TOPIX: 32nd pct of 5y range, 9.9% below 200d MA, -13.3% 6m / -13.7% 12m relative (score 27 < 35)
No country-specific market series (not gated): Israel Industrials, South Korea Industrials, Taiwan Industrials
Convergence is not a buy signal — it is a prioritisation tool. It identifies where the macro environment is simultaneously supportive across geography, cyclical positioning, and structural theme. Investment judgement, valuation, and position sizing remain human decisions.
The framework is reading 59/100 — the environment is balanced and sustainable, with no dominant stress signal. Corporate Earnings & Profitability is the standout tailwind at 83, with Corp profits +18.2% YoY. The notable tension is between Financial Conditions (73 — aggregate conditions accommodative) and Real Yields (53 — still restrictive), a combination that typically favours carry trades and quality credit over long-duration rate exposure. The key watch-point is whether Real Yields continue rising — that is the variable most likely to break the current equilibrium.