This panel gathers global macroeconomic indicators — growth, inflation, rates, FX, and risk perception. Together, they form the backdrop that influences all asset classes.
Sentiment & News of the Week
Source: VADER (sentiment), Perplexity AI (geopolitical), Claude API (commentary)
Global Macro Map
Interactive map with macroeconomic indicators by country — GDP, inflation, rates, FX, and bond yields[?]. Toggle between layers (type and period) and click any country to open the detail panel.
A global view of macroeconomic indicators by country, updated weekly. Each bubble represents a country, colored and sized according to the selected layer (stock index, FX, inflation, etc.).
Data sources
• Stock indices — 38 countries via EODHD (1M, 3M, YTD, 12M returns)
• FX — ~40 pairs vs USD from forex.db (more precise than FRED)
• Macro — GDP, inflation, rates, yields, unemployment, debt/GDP via FRED (~45 countries)
• Geopolitical — AI-generated sentiment (Perplexity) per country
How to read?
Use the layer buttons (type and period) to switch between indicators. Click any country to open a detail panel with all available indicators. Warm colors = high values, cool colors = low values.
Source: FRED, EODHD, forex.db, Perplexity AI
Taylor Rule Monitor
Deviation between the Taylor Rule[?] prescribed rate and the actual policy rate for 29 economies. Bars to the right (gold) indicate looser monetary policy than prescribed; to the left (blue), tighter.
A formula created by economist John Taylor (1993) that calculates what a country's interest rate should be based on two variables: how much inflation is above or below the target, and how much the economy is above or below its potential (the so-called output gap).
How does it work?
The formula is: i = r* + π + 0.5·(π − π*) + 0.5·gap
• r* — neutral real interest rate (when the economy is in equilibrium)
• π — current inflation (annual CPI)
• π* — central bank's inflation target
• gap — output gap: how much GDP is above (+) or below (−) potential
The gap is estimated using the Hodrick-Prescott filter (λ=1600) on quarterly real GDP since 1995. The HP trend represents potential output; the percentage deviation is the gap.
What does the deviation mean?
• Positive deviation (gold) — real rates are below prescribed → looser monetary policy than recommended
• Negative deviation (blue) — rates are above prescribed → tighter monetary policy
What is it for?
Identifying which countries have rates misaligned with economic conditions — which can anticipate changes in monetary policy or movements in FX and equities.
Source: FRED (GDP, CPI), BCB SGS 432 (Selic)
Global FX
Performance of major currencies against the US dollar across multiple periods. Positive returns indicate currency appreciation vs USD. The scatter plot shows the correlation[?] between FX and equities by country.
▸ Currency Details
| Currency | Rate [?] | 1S [?] | 1M | 3M | YTD | 12M | Loading [?] |
|---|---|---|---|---|---|---|---|
| Russian Ruble | 72.4500 | -0.0% | -0.0% | +3.1% | +8.0% | +9.2% | — |
| Peruvian Sol | 3.4223 | -0.0% | -0.0% | +1.2% | -1.8% | +4.0% | — |
| Egyptian Pound | 53.2700 | -0.0% | -0.0% | +0.6% | -11.7% | -10.2% | — |
| Norwegian Krone | 9.2649 | -0.0% | -0.0% | +0.4% | +8.1% | +9.8% | — |
| Israeli Shekel | 2.8973 | -0.0% | -0.0% | +0.3% | +9.1% | +15.3% | — |
| Chinese Yuan | 6.8005 | -0.0% | -0.0% | +0.2% | +2.8% | +5.3% | — |
| Vietnamese Dong | 26357.0000 | -0.0% | -0.0% | -0.1% | -0.2% | -0.7% | — |
| Romanian Leu | 4.4737 | -0.0% | -0.0% | -0.2% | -3.3% | -2.3% | — |
| Mexican Peso | 17.2914 | -0.0% | -0.0% | -0.2% | +3.9% | +8.5% | — |
| Polish Zloty | 3.6420 | -0.0% | -0.0% | -0.6% | -1.4% | +1.0% | — |
| Argentine Peso | 1396.0000 | -0.0% | -0.0% | -0.6% | +3.8% | -2.8% | — |
| Euro | 1.1643 | +0.0% | +0.0% | -0.6% | -0.9% | +0.5% | — |
| Taiwan Dollar | 31.6150 | -0.0% | -0.0% | -0.7% | -0.9% | -5.7% | — |
| Turkish Lira | 45.5644 | -0.0% | -0.0% | -0.7% | -6.1% | -12.1% | — |
| Czech Koruna | 20.8710 | -0.0% | -0.0% | -0.8% | -1.4% | +1.7% | — |
| Japanese Yen | 158.9400 | -0.0% | -0.0% | -0.8% | -1.4% | -8.3% | — |
| Canadian Dollar | 1.3743 | -0.0% | -0.0% | -0.8% | -0.1% | +0.3% | — |
| Swiss Franc | 0.7853 | -0.0% | -0.0% | -0.8% | +1.0% | +2.9% | — |
| Singapore Dollar | 1.2796 | -0.0% | -0.0% | -0.9% | +0.5% | +0.7% | — |
| Chilean Peso | 900.4000 | -0.0% | -0.0% | -0.9% | -0.0% | +6.8% | — |
| Nigerian Naira | 1371.0200 | -0.0% | -0.0% | -1.0% | +5.2% | +10.1% | — |
| Malaysian Ringgit | 3.9720 | -0.0% | -0.0% | -1.2% | +2.0% | +6.3% | — |
| Thai Baht | 32.6000 | -0.0% | -0.0% | -1.2% | -3.5% | -0.7% | — |
| Australian Dollar | 0.7142 | +0.0% | +0.0% | -1.3% | +7.0% | +10.3% | — |
| British Pound | 1.3415 | +0.0% | +0.0% | -1.3% | -0.4% | +0.9% | — |
| Hungarian Forint | 309.1800 | -0.0% | -0.0% | -1.4% | +5.5% | +10.3% | — |
| Brazilian Real | 4.9907 | -0.0% | -0.0% | -1.4% | +8.9% | +9.2% | — |
| South African Rand | 16.6388 | -0.0% | -0.0% | -1.5% | -0.8% | +7.2% | — |
| Philippine Peso | 61.6300 | -0.0% | -0.0% | -1.5% | -4.7% | -7.5% | — |
| Swedish Krona | 9.4006 | -0.0% | -0.0% | -1.8% | -2.0% | +2.7% | — |
| Indian Rupee | 96.3500 | -0.0% | -0.0% | -1.8% | -7.1% | -9.9% | — |
| Indonesian Rupiah | 17705.8200 | -0.0% | -0.0% | -1.9% | -6.2% | -8.0% | — |
| New Zealand Dollar | 0.5829 | +0.0% | +0.0% | -2.1% | +0.7% | -1.3% | — |
| Colombian Peso | 3797.7200 | -0.0% | -0.0% | -2.3% | -1.5% | +7.3% | — |
| Korean Won | 1504.5800 | -0.0% | -0.0% | -4.1% | -4.3% | -8.6% | — |
Positive returns = currency appreciated vs USD. 90d sparkline shows cumulative % change.
Loading: FX→equity transmission coefficient estimated via PanelOLS with country fixed effects and Driscoll-Kraay standard errors. Negative values indicate currency depreciation is associated with local stock market decline.
The performance of major world currencies against the US dollar (USD), grouped by region. Positive returns mean the currency appreciated against the dollar.
Data sources
Daily quotes for ~40 currency pairs via EODHD, stored in forex.db. Returns calculated for 1-week, 1-month, 3-month, YTD, and 12-month periods. Sparklines show cumulative change over the last 90 days.
FX × equity correlation
The scatter plot crosses FX return (3M) with local equity index return. Pearson correlation (ρ) shows the degree of association: values near +1 indicate that when the currency appreciates, the stock market tends to rise as well.
What is it for?
Mapping which currencies are strengthening or weakening, and how that relates to local equity markets.
Source: EODHD forex.db
When Currencies Fall, What Happens to Stocks?
We measure the daily impact of currency depreciation on each country's stock market using panel regression[?]. The more negative the score, the greater the vulnerability of local stocks to currency shocks.
It measures how much each country's stock market reacts when its currency weakens. The idea is simple: in many countries, when the currency weakens, foreign capital leaves and stocks fall together. But the intensity of this reaction varies widely across countries.
How does it work?
We use panel regression with entity and time fixed effects, analyzing daily returns from 38 countries. Controls isolate global factors (S&P 500, VIX, gold, oil) to measure the pure effect of currency on local stocks. Four robustness models confirm results:
• Base model (FX → equity)
• With global controls (SPY, VIX, GLD, CL)
• With structural interactions (real rates, export profile)
• Full model (all factors)
What amplifies the effect?
Countries with high real rates or heavy commodity export dependence tend to suffer more: speculative capital flees at the same time the currency weakens, amplifying the stock decline.
How to read?
Bars to the left (red) = stocks fall when currency weakens. Longer bars mean higher sensitivity. Stars (★) indicate statistical significance.
Source: EODHD (indices, FX), FRED (global factors)
Risk Perception
How much stress is in the financial system right now? This composite index combines 20 volatility and credit indicators (VIX, commodity volatility, credit spreads, risk ETFs) into a unified view of systemic risk[?]. The chart shows which dimension (equities, credit, EM) is dominating stress.
Risk that affects the financial system as a whole — not just a single asset or sector. When systemic risk rises, all risky assets tend to fall together.
How do we measure it?
We combine 20 series in a unified analysis via PCA (Principal Component Analysis):
• 8 CBOE volatility indices — VIX (US equities), OVX (oil), GVZ (gold), VXEEM (EM), VXFXI (China), VXEFA (developed ex-US), MOVE (bonds), TYVIX (treasuries)
• 12 credit proxy ETFs — HYG, JNK (high yield), LQD, VCIT (investment grade), KRE, KBE (banks), EMB, PCY (emerging), TLT, IEF (treasuries), SHY (short-term), BKLN (loans)
PCA extracts the "common factor" that explains most of the co-movement across these series — that factor is our systemic risk index.
How to read the chart?
The stacked area chart decomposes each category's contribution (equity volatility, commodities, credit, emerging markets, etc.) to total risk. When a slice expands, that dimension is dominating market stress.
Source: FRED (VIX, VXN, VXEEM, GVZ, OVX), EODHD (credit ETFs)
The standout momentum assets are mainly **technology and growth stocks**, with PGEN (93), AEHR (91), FSLY (90), TWST (86), SNOW (83), and AMBA (82), while among ETFs the names are leveraged and tech-linked, such as WEBL.US (75), FNGU.US (72), and CWEB.US/GGLL.US/MAGX.US (68). Institutional ETF flow is concentrated in large-beta and broad-index products, with inflows into Invesco QQQ Trust (+US$ 8.683 billion), SPDR S&P 500 ETF Trust (+US$ 6.136 billion), and VanEck Semiconductor ETF (+US$ 4.213 billion), alongside outflows from ProShares Bitcoin Strategy ETF (-US$ 2.882 billion), SPAC and New Issue ETF (-US$ 1.783 billion), and iShares Core S&P 500 ETF (-US$ 599 million). The current regime is clearly **risk-on**, with prob_risk_on at 90%, prob_neutral at 10%, and prob_risk_off at 0%; the 3-month fuzzy backtest supports that view, showing an average return of 13.1%, 7.9% excess return versus SPY, and a 72% win rate.
This panel provides insight into which types of assets are performing better or worse — and why. All analyses are based on robust quantitative methodologies widely used in academic and institutional settings.
Assets in Momentum (Uptrend)
The system uses fuzzy logic[?] to evaluate each asset: instead of rigid rules (e.g., "above 20-day moving average → bullish"), it assigns membership degrees to various bullish indicators. 11 rules combine these degrees to generate a signal (strong or moderate) with a confidence between 0 and 1. A momentum score is generated by weighting each evaluated indicator. The 6 assets with the highest score are shown in each group below. Click "View Details" to see the recent price chart. Below, we present a backtest of the methodology to assess whether the score predicted positive returns retrospectively.
▸ Top International Stocks — Fuzzy Momentum
▸ Top Brazilian Stocks — Fuzzy Momentum
▸ Top ETFs — Fuzzy Momentum
Search Fuzzy Score
Search any stock or ETF in the universe to see its composite score, percentile, and position in the distribution.
Walk-Forward Backtest
To test whether the system really works, we went back in time: each Friday over the last 52 weeks, we recalculated scores using only data available on that date (no peeking into the future). The top 6 stocks + 6 ETFs were selected and then we measured what actually happened with those assets in the following 1, 2, and 3 months. The 3 indicators below summarize the 3-month result: the average return of the picks, how much they beat the S&P 500, and in how many weeks the picks beat the index (Win Rate — above 50% means the system got it right most weeks).
Source: EODHD (historical prices)
In traditional logic, a statement can only be true or false. In fuzzy logic, things can be partially true. For example: an asset priced 2% above its 20-day moving average is not "completely above" or "completely below" — it has an intermediate degree of membership in the "above average" group. This allows the system to capture nuances that binary rules would miss.
How does the momentum score work?
The system evaluates 5 indicators for each asset, each receiving a degree between 0 and 1:
• Weekly momentum — is the asset rising, falling, or flat?
• Position vs. 20-day moving average — is the price above or below the recent trend?
• Relative volume (10 days) — is trading volume above normal? (more people buying/selling)
• RSI (14 days) — is the asset overbought, oversold, or in a neutral zone?
• Trend type — is the trend consistently bullish, a reversal, or undefined?
11 rules combine these degrees to generate a buy signal (strong or moderate) with a confidence level. The final score weights each indicator according to its predictive importance.
Illustrative example:
Imagine an asset with the following readings: weekly momentum = 0.85 (strong rise), position vs. MA20 = 0.70 (well above average), relative volume = 0.60 (above normal), RSI = 0.55 (neutral-high zone), trend type = 0.90 (consistent uptrend). The 11 rules evaluate these combinations — for example, "if momentum is high AND position vs. MA20 is high, then the signal is strong with high confidence". The weighted final score would be approximately: 0.85×35% + 0.70×25% + 0.60×15% + 0.55×10% + 0.90×5% + confidence×10% ≈ 0.74. This score is compared against all other assets to form the ranking.
Source: EODHD (prices, fundamentals, 4K symbols)
ETFs — Largest Investment Inflows and Outflows (Last Week)
Shows the ETFs that received the most and lost the most capital in the last week, measured by the change in average daily trading volume. Useful for identifying where institutional money is flowing.
▸ ▲ Top Inflows (7 days)
| ETF | Flow 7d | Change | Vol/day |
|---|---|---|---|
| +$8683.2M | +33.8% | $34403.7M | |
| +$6135.5M | +17.7% | $40791.0M | |
| +$4212.9M | +70.9% | $10153.7M | |
| +$1988.0M | +39.5% | $7026.8M | |
| +$1889.7M | +19.2% | $11715.9M |
▸ ▼ Top Outflows (7 days)
| ETF | Flow 7d | Change | Vol/day |
|---|---|---|---|
| $2882.5M | -73.8% | $1022.3M | |
| $1783.1M | -20.1% | $7084.4M | |
| $598.8M | -16.3% | $3078.6M | |
| $449.7M | -65.9% | $233.1M | |
| $449.1M | -16.9% | $2207.4M |
Source: EODHD (ETF prices, AUM, holdings)
Fund Performance and Alpha
Evaluates investment funds using the academic Fama-French model. The goal is to answer: does this fund truly generate value, or does it just ride known risks? Alpha (α)[?] measures the annualized return not explained by the model's 5 risk factors.
▸ Top 10 Funds — Global
| # | Ticker | Name | Alpha (α) | 1M | 6M | 12M | β Mkt | β SMB | β HML | β RMW | β CMA | R² |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ▸1 | CLM | -0.0343 | -1.2% | +0.9% | +11.7% | +0.7543 | -0.3311 | -0.0000 | -0.1701 | -0.1738 | +0.3979 | |
| ▸2 | DNP | -0.0527 | -0.7% | +15.2% | +19.4% | +0.5654 | -0.1684 | -0.0000 | +0.3035 | +0.0329 | +0.3582 | |
| ▸3 | JFR | -0.0560 | +0.3% | -5.6% | -5.3% | +0.4464 | -0.1573 | -0.0000 | +0.1324 | -0.0742 | +0.3373 | |
| ▸4 | NAD | -0.0681 | -3.1% | +0.4% | +2.4% | +0.2585 | +0.0243 | -0.0000 | +0.0232 | +0.0264 | +0.2111 | |
| ▸5 | USA | -0.0777 | +1.7% | -6.8% | -7.0% | +0.7664 | -0.3233 | -0.0000 | -0.0354 | -0.0381 | +0.6293 | |
| ▸6 | NEA | -0.0785 | -3.7% | +0.1% | +2.4% | +0.2947 | +0.0585 | -0.0000 | +0.1394 | -0.0315 | +0.2059 | |
| ▸7 | JQC | -0.0949 | -1.0% | -11.0% | -11.3% | +0.4615 | -0.1766 | -0.0000 | +0.0652 | -0.0106 | +0.3026 | |
| ▸8 | JPC | -0.1038 | -1.4% | -0.8% | +6.6% | +0.4853 | -0.2352 | -0.0000 | +0.0181 | -0.0148 | +0.3946 | |
| ▸9 | IGR | -0.1058 | +1.1% | -6.9% | -18.8% | +0.8080 | -0.1072 | -0.0000 | +0.2528 | +0.1248 | +0.3550 | |
| ▸10 | CRF | -0.1107 | -0.4% | -1.6% | +2.7% | +0.7525 | -0.3027 | -0.0000 | -0.1216 | -0.1814 | +0.3155 |
▸ Top 10 Funds — Brazil
| # | Ticker | Name | Alpha (α) | 1M | 6M | 12M | β Mkt | β SMB | β HML | β RMW | β CMA | R² |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ▸1 | NSDV11.SA | -0.0088 | +2.2% | +36.4% | +28.6% | +0.5111 | -0.8440 | -0.0000 | -0.0712 | +0.4063 | +0.5972 | |
| ▸2 | NDIV11.SA | -0.0270 | +2.0% | +25.5% | +18.4% | +0.4887 | -0.8526 | -0.0000 | -0.0713 | +0.4055 | +0.5306 | |
| ▸3 | HGLG11.SA | -0.0788 | -2.2% | -0.0% | -2.3% | +0.2108 | +0.1279 | 0.0000 | +0.0076 | -0.1788 | +0.0755 | |
| ▸4 | MXRF11.SA | -0.0843 | -4.2% | +6.7% | +2.3% | +0.2308 | +0.0863 | 0.0000 | -0.0045 | -0.1309 | +0.0677 | |
| ▸5 | CXAG11.SA | -0.1295 | +2.0% | +12.3% | +9.2% | +0.2048 | +0.0941 | 0.0000 | +0.0890 | -0.2365 | +0.0724 | |
| ▸6 | VRTA11.SA | -0.1618 | +2.8% | -6.4% | -10.3% | +0.2731 | +0.2465 | 0.0000 | +0.0359 | -0.2879 | +0.0719 | |
| ▸7 | AJFI11.SA | -0.1720 | +0.9% | +21.4% | +8.2% | +0.1808 | -0.0588 | -0.0000 | +0.1149 | -0.0450 | +0.0347 | |
| ▸8 | HGBS11.SA | -0.1939 | -1.9% | +9.4% | -3.0% | +0.2312 | +0.1413 | 0.0000 | +0.0565 | -0.1823 | +0.0691 | |
| ▸9 | APTO11.SA | -0.1947 | -1.7% | -1.8% | -5.7% | +0.1852 | +0.5106 | 0.0000 | +0.2506 | -0.3286 | +0.0688 | |
| ▸10 | CRAA11.SA | -0.2069 | -5.5% | +5.7% | -3.1% | +0.1326 | +0.1067 | 0.0000 | +0.0907 | -0.1861 | +0.0322 |
Search Alpha for Any Asset
Search any stock, ETF, or fund in the ~4,000-asset universe to see its alpha, Fama-French risk factor exposure, and position in the distribution.
In 1992, economists Eugene Fama and Kenneth French published a study that revolutionized how we evaluate investments. They discovered that a stock's return depends not only on "did the market go up or down", but on other predictable patterns. Eugene Fama received the Nobel Prize in Economics in 2013 for this and other work on financial markets.
The core idea — in plain language
Imagine you want to evaluate whether a fund manager is truly skilled. If their fund returned 15% this year, it sounds great — but what if the entire market rose 20%? In that case, the manager actually underperformed the market. The Fama-French model goes further: it checks whether the fund's return can be explained not just by the market, but by 5 patterns (called factors) that historically generate returns:
• Market — the extra return for investing in stocks instead of risk-free bonds (how much the market as a whole went up or down)
• Size (SMB) — smaller companies tend to outperform giant ones over time, because they are riskier
• Value (HML) — "cheap" stocks (low price relative to company assets) tend to beat "expensive" ones (trendy companies with inflated prices)
• Profitability (RMW) — more profitable companies tend to deliver better returns
• Investment (CMA) — companies that invest conservatively (without overspending on expansion) tend to outperform
What is Alpha (α)?
After discounting these 5 effects, what remains is Alpha. If a fund has positive alpha, it means it generates returns beyond what would be expected given the risks it takes. This suggests genuine manager skill. If alpha is negative, the fund is destroying value — likely due to high fees, bad decisions, or poor timing.
How to read the table
• Alpha (α) — the annualized extra return (positive = good, negative = bad)
• β Mkt, β SMB, β HML, β RMW, β CMA — the fund's exposure to each factor (higher values = more exposed to that type of risk)
• R² — how much of the fund's behavior is explained by the model (0% = nothing, 100% = fully). Low R² may mean the fund has a very different strategy from the traditional stock market
Source: EODHD (4K stocks), Fama-French 5-factor model
Thematic Portfolios
The universe's assets are grouped into thematic portfolios (momentum, diversified, defensive, dollar, gold, oil, etc.) based on how they behave together. Assets that rise and fall in similar patterns are placed in the same group. Select a portfolio from the menu to see its constituent assets. Click any point in the network to see asset details and its most related peers — if the asset belongs to another portfolio, the view switches automatically.
Imagine each asset (stock or ETF) as a dot. When two assets tend to rise and fall together, we draw a line between them. The more similar their behavior, the thicker the line. The result is a visual map where similarly-behaving assets are close together, and assets with different behavior are far apart.
How are portfolios formed?
From this network, the system automatically identifies natural clusters — groups of assets that move in similar ways. Each cluster receives a thematic name describing the dominant behavior of its members: "momentum" (assets in uptrend), "defensive" (more stable assets), "dollar" (assets sensitive to exchange rates), etc.
What is it for?
This map helps you understand the real diversification of a portfolio. If all your assets are in the same group, they will likely fall together during stress. Assets from different groups tend to offset each other, reducing overall risk.
How to read the chart:
• Dots = individual assets (stocks or ETFs)
• Lines = correlation between two assets (thicker = more correlated)
• Colors = each color represents a different thematic portfolio
• Proximity = nearby assets behave similarly
• Distance = distant assets offer diversification from each other
Source: EODHD (returns, correlations), Fama-French 5-factor
REITs — Real Estate Investment Trusts
REIT market overview: performance by sub-sector, geographic comparison, and recent top performers.
▸ Performance by Sub-Sector
🇺🇸 United States
| Sector | Ret 1M | Ret 6M | Yield |
|---|---|---|---|
| Office (13) | +159.9% | +397.2% | 6.4% |
| Hotel & Motel (10) | +2.7% | +68.4% | 3.7% |
| Industrial (11) | +1.6% | +12.5% | 4.3% |
| Specialty (11) | +1.5% | +7.7% | 4.3% |
| Healthcare Facilities (9) | -1.0% | +19.2% | 3.5% |
| Retail (17) | -1.2% | +12.3% | 3.6% |
| Residential (12) | -2.8% | -4.7% | 6.9% |
| Diversified (6) | -4.6% | -6.4% | 5.5% |
| Mortgage (20) | -5.4% | -16.5% | 13.7% |
🇧🇷 Brazil
| Sector | Ret 1M | Ret 6M | Yield |
|---|---|---|---|
| Office (2) | +0.5% | +16.5% | 0.0% |
| Specialty (3) | -0.3% | +2.0% | 0.0% |
| Residential (2) | -1.4% | -1.6% | 0.0% |
| Industrial (2) | -3.5% | -11.6% | 0.0% |
| Diversified (33) | -4.5% | -8.6% | 0.5% |
| Retail (3) | -5.8% | -17.4% | 0.0% |
Source: EODHD (fundamentals_enrichment — REITs)
Market Regime
Identifies the current market state by analyzing 11 asset classes weekly: equities (SPY), value vs. growth (IWD−IWF), momentum (MTUM), quality (QUAL), long-term bonds (TLT), investment-grade credit (LQD), high-yield credit (HYG), emerging markets (EEM), volatility (VIXY), commodities (DBC), and gold (GLD). The model automatically detects the market's current "mood" — whether it is optimistic and accepting risk, cautious, or in protective mode.
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| SPY | +0.438 | +1.4% | +0.4% |
| QUAL | +0.431 | +1.1% | +0.9% |
| MTUM | +0.408 | +0.7% | -6.2% |
| HYG | +0.399 | -0.2% | -0.5% |
| EEM | +0.365 | +0.4% | -3.8% |
▼ low = risk-off (broad selloff, flight to safety)
▶ What each ETF represents
| SPY | S&P 500 total return — broad US equity market exposure |
| QUAL | MSCI USA Quality Factor — stocks with high ROE, stable earnings, low leverage |
| MTUM | MSCI USA Momentum Factor — stocks with strong recent price trends |
| HYG | High-yield corporate bonds (below BBB) — higher credit risk, correlated with equities in stress |
| EEM | iShares MSCI Emerging Markets — broad EM equity exposure (China, Taiwan, India, Korea, Brazil) |
| LQD | Investment-grade corporate bonds (BBB and above) — credit risk with moderate spread |
| DBC | Invesco DB Commodity Index — diversified basket (energy, metals, agriculture) |
| VIXY | ProShares VIX Short-Term Futures — direct proxy for market fear/volatility (VIX) |
| GLD | SPDR Gold Shares — gold price proxy, safe haven and inflation hedge |
| IWD − IWF | Russell 1000 Value minus Growth — spread between value and growth stocks (positive = value outperforms) |
| TLT | 20+ Year US Treasury bonds — long duration, rises when yields fall |
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| TLT | +0.678 | -0.1% | -2.7% |
| LQD | +0.499 | -0.1% | -1.3% |
| GLD | +0.341 | +0.1% | -1.4% |
| VIXY | +0.221 | -0.8% | +0.3% |
| DBC | −0.219 | -1.9% | +5.1% |
▼ low = yields rising, bonds falling (monetary tightening)
▶ What each ETF represents
| TLT | 20+ Year US Treasury bonds — long duration, rises when yields fall |
| LQD | Investment-grade corporate bonds (BBB and above) — credit risk with moderate spread |
| GLD | SPDR Gold Shares — gold price proxy, safe haven and inflation hedge |
| VIXY | ProShares VIX Short-Term Futures — direct proxy for market fear/volatility (VIX) |
| DBC | Invesco DB Commodity Index — diversified basket (energy, metals, agriculture) |
| SPY | S&P 500 total return — broad US equity market exposure |
| IWD − IWF | Russell 1000 Value minus Growth — spread between value and growth stocks (positive = value outperforms) |
| EEM | iShares MSCI Emerging Markets — broad EM equity exposure (China, Taiwan, India, Korea, Brazil) |
| QUAL | MSCI USA Quality Factor — stocks with high ROE, stable earnings, low leverage |
| HYG | High-yield corporate bonds (below BBB) — higher credit risk, correlated with equities in stress |
| MTUM | MSCI USA Momentum Factor — stocks with strong recent price trends |
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| IWD − IWF | +0.643 | -1.2% | +5.4% |
| DBC | +0.594 | -1.9% | +5.1% |
| GLD | +0.393 | +0.1% | -1.4% |
| QUAL | −0.167 | +1.1% | +0.9% |
| MTUM | −0.137 | +0.7% | -6.2% |
▼ low = rotation to growth and defensives (cycle slowing)
▶ What each ETF represents
| IWD − IWF | Russell 1000 Value minus Growth — spread between value and growth stocks (positive = value outperforms) |
| DBC | Invesco DB Commodity Index — diversified basket (energy, metals, agriculture) |
| GLD | SPDR Gold Shares — gold price proxy, safe haven and inflation hedge |
| QUAL | MSCI USA Quality Factor — stocks with high ROE, stable earnings, low leverage |
| MTUM | MSCI USA Momentum Factor — stocks with strong recent price trends |
| SPY | S&P 500 total return — broad US equity market exposure |
| HYG | High-yield corporate bonds (below BBB) — higher credit risk, correlated with equities in stress |
| LQD | Investment-grade corporate bonds (BBB and above) — credit risk with moderate spread |
| EEM | iShares MSCI Emerging Markets — broad EM equity exposure (China, Taiwan, India, Korea, Brazil) |
| VIXY | ProShares VIX Short-Term Futures — direct proxy for market fear/volatility (VIX) |
| TLT | 20+ Year US Treasury bonds — long duration, rises when yields fall |
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| IWD − IWF | +0.631 | -1.2% | +5.4% |
| GLD | −0.592 | +0.1% | -1.4% |
| DBC | −0.294 | -1.9% | +5.1% |
| LQD | +0.247 | -0.1% | -1.3% |
| HYG | +0.206 | -0.2% | -0.5% |
▼ low = calm market (complacency, compressed VIX)
▶ What each ETF represents
| IWD − IWF | Russell 1000 Value minus Growth — spread between value and growth stocks (positive = value outperforms) |
| GLD | SPDR Gold Shares — gold price proxy, safe haven and inflation hedge |
| DBC | Invesco DB Commodity Index — diversified basket (energy, metals, agriculture) |
| LQD | Investment-grade corporate bonds (BBB and above) — credit risk with moderate spread |
| HYG | High-yield corporate bonds (below BBB) — higher credit risk, correlated with equities in stress |
| VIXY | ProShares VIX Short-Term Futures — direct proxy for market fear/volatility (VIX) |
| TLT | 20+ Year US Treasury bonds — long duration, rises when yields fall |
| EEM | iShares MSCI Emerging Markets — broad EM equity exposure (China, Taiwan, India, Korea, Brazil) |
| MTUM | MSCI USA Momentum Factor — stocks with strong recent price trends |
| QUAL | MSCI USA Quality Factor — stocks with high ROE, stable earnings, low leverage |
| SPY | S&P 500 total return — broad US equity market exposure |
Technical details
PCA: PC1 42% + PC2 16% + PC3 11% + PC4 9% = 77% variance · AIC: 2590 · BIC: 2657
Financial markets do not behave the same way all the time. There are periods of optimism, where most assets rise in coordinated fashion, and periods of stress, where everything falls together and investors seek protection. Between these extremes, there are transition moments with no clear direction. Identifying which "regime" we are in helps understand the current investment environment.
How does the identification work?
We track 11 asset classes weekly that represent the market's main forces: US equities, value vs. growth, momentum, quality, long-term bonds, corporate credit (high and low quality), emerging markets, volatility, commodities, and gold. Each week, these assets move together or in opposite directions — and in these co-movement patterns lie the regime signals.
What is the Markov-Switching model?
The Markov-Switching model (or regime-switching model) is a statistical technique that assumes the market can be in different "states" and switches between them over time. The name comes from Russian mathematician Andrei Markov, who studied processes where the next state depends only on the current state (not the entire past history).
In practice, the model does the following:
• Assumes distinct states exist (in our case: optimistic, neutral, and stressed)
• In each state, asset returns behave in statistically different ways (different means and volatilities)
• The model calculates, week by week, the probability of being in each state
• When one state's probability exceeds the others, a "regime change" occurs
Why is this useful for investors?
Different asset types perform better in different regimes. Growth stocks tend to shine in optimistic regimes. Gold and government bonds tend to protect in stress regimes. Knowing which regime we are in helps calibrate risk exposure — not as a crystal ball, but as a thermometer of the current situation.
Principal Component Analysis (PCA)
Since there are 11 assets, analyzing them individually would be complex. We use a technique called PCA that extracts the 4 most important movement patterns from these 11 assets. Each pattern (principal component) captures a different market dimension: risk appetite, rates/duration, cyclical rotation, and tail risk. For each dimension, we run a separate Markov-Switching model, allowing a richer and more granular reading of the current regime.
Source: EODHD (weekly ETFs), PCA + Markov-Switching
Energy was the strongest category, with **+105.6% YTD**, far above Grains (+12.6%), Industrial Metals (+11.2%), Precious Metals (+5.4%), and Livestock (+4.3%), while all of them were flat at **0.0%** in the month, suggesting a consolidation phase after the sharp rally in the energy complex. Among the individual standouts, Sugar led the monthly gains among the cited commodities, but at **0.0% over 1M** and only **+2.5% YTD**, while Cotton (+18.5% YTD) remained relatively firm; on the weak side, Cocoa (-37.6% YTD), Coffee (-23.1% YTD), and Orange Juice (-17.3% YTD) remain under pressure, in line with more favorable supply and weather conditions in parts of the agricultural belt. In energy, Brent Crude (+99.0% YTD), Gas Oil (+132.3% YTD), and Heating Oil (+117.7% YTD) continue to lead the ranking, reflecting a combination of geopolitical risk in oil and tighter refined products, although more recent news points to some relief after a sharp drop in Brent and WTI as tensions in the Middle East eased and OPEC+ signaled higher supply.
This panel tracks the performance of major global commodities, their statistical equilibrium relationships, and bilateral trade flows between countries. Together, these indicators reveal supply and demand pressures that affect FX, inflation, and producer stocks.
Commodities — Bloomberg Commodity Indices
Returns panel by category (click to filter). Data from Bloomberg Commodity[?] sub-indices (BCOM). For each commodity, we show the 5 stocks with the highest correlation[?] over the last 30 days.
How does it work? Returns are calculated from daily closing prices. For each commodity, we identify the 5 global stocks with the highest correlation over the last 30 days — stocks whose prices moved in the same direction and intensity.
Why is it useful? It helps identify which commodities are trending up or down, and which producer or consumer stocks may be affected.
How to read? Categories are sorted by YTD return. Within each category, check returns across different windows (1W, 1M, 3M, YTD). Click a commodity to see the most correlated stocks.
Source: EODHD — Bloomberg Commodity Indices (BCOM)
Commodity Cointegration — Basket Equilibrium
Monitors historical relationships between commodities using cointegration[?] tests. When two assets that normally move together decouple, the z-score[?] indicates the deviation intensity. The half-life[?] estimates the expected correction time.
Source: EODHD commodities.db — Engle-Granger / Johansen
Global Trade Flow Map
Visualization of major bilateral trade[?] corridors, 2014–2025. Gold nodes are net exporters; blue are net importers. Data: UN Comtrade[?].
How does it work? For each selected commodity, the map shows the largest export and import flows between countries. Curved lines represent trade routes — thicker lines indicate higher traded value. Gold nodes are net exporters; blue nodes are net importers.
Why is it useful? It reveals trade dependencies between countries and how shocks to a producer (crop, sanctions, logistics) can affect global prices.
How to read? Select the commodity, exporter, and importer from the menus. Use the year buttons to compare evolution. Click a country to see origin and destination details.
Source: UN Comtrade (bilateral trade, 2014–2025)
With the DI curve still elevated and the market demanding a high premium on Tesouro IPCA+ bonds, the view is that long real rates are at historically stretched levels, with issues such as the IPCA+ 2032 reaching IPCA+ 8.51% per year and the 2040 bond at 7.61% after the latest round of stress. Focus had been pointing to a higher Selic for longer and inflation still above target, which also supports a still-pressured ETTJ implied inflation, with the long end pricing in gradual disinflation, but without quick relief. This backdrop strengthened after the Copom’s recent decision to maintain a hard line in the face of resilient inflation and fiscal noise, while the R$ 258 billion maturity of the Tesouro IPCA+ 2026 in August adds reinvestment flow to the market. In this environment, securities with a high spread versus the ETTJ continue to draw relative attention, but the premium reflects more uncertainty about rates and fiscal conditions than a simple “bargain” in price.
This panel covers the Brazilian fixed income market — government bonds, yield curves, market expectations, and stochastic simulations. It helps evaluate bond opportunities, track inflation and rate expectations, and understand the term structure.
Fixed Income
How much do government bonds yield today — and are they paying above or below fair value? The table compares each IPCA+[?] bond's real rate with the theoretical ETTJ[?] curve from ANBIMA. Positive spreads indicate opportunity — the bond pays above the curve. Compare Monte Carlo scenarios with CDI[?] returns.
How does it work?
• ETTJ Table: Compares each IPCA+ bond's real rate with the theoretical ANBIMA curve, calculating the spread in basis points and projected IRR.
• B3 Curves: Shows DI futures (nominal interest rate) and FX-hedged rate (FRC) curves, extracted daily from B3.
• ANBIMA ETTJ: Term structure estimated via Svensson model for 13 maturities (1M to 15Y), decomposed into nominal rate, real rate, and break-even inflation.
• Focus: Market median projections for 11 macro indicators, with historical accuracy analysis.
• Monte Carlo: Stochastic simulations of future IPCA and Selic paths using Vasicek and Brownian Bridge models, calibrated with Focus data and DI curve.
Why is it useful? It helps identify bonds trading above fair value (positive spread vs ETTJ), understand market expectations for rates and inflation, and simulate probabilistic scenarios.
How to read? In the table, positive spreads (green) indicate the bond offers a rate above the theoretical curve. In the curves, compare slopes to assess expectations for rate increases or decreases. In Focus, watch the direction of revision arrows.
Source: Tesouro Direto, ANBIMA (ETTJ), BCB SGS (IPCA, CDI)
• B3 Curves: Extracted from futures contracts traded on B3 — DI1 reflects the expected nominal interest rate, and FRC (FX-hedged rate) reflects the cost of FX hedging in USD.
• ANBIMA ETTJ: Theoretical curves estimated by ANBIMA using the Svensson model (6 parameters) for 13 maturities (1 month to 15 years). Decomposed into: nominal rate (Prefixado), real rate (IPCA+), and break-even inflation (difference between the two).
Why is it useful? Curve slope reveals expectations: an upward-sloping curve suggests the market expects higher future rates; inverted, a decrease. Dashed curves show the previous week for comparison — shifts indicate recent changes in expectations.
How to read? Compare solid curves (current) with dashed (previous week). If the solid curve is above the dashed, rates have opened (market more pessimistic on rates). Break-even inflation (yellow) is the difference between Prefixado and IPCA+ — shows how much inflation the market prices for each maturity.
Source: B3 Derivatives (DI1, FRC)
Source: ANBIMA via pyettj (Svensson model)
Focus Survey — Market Expectations
| Indicator | 2026 | 2027 | ||
|---|---|---|---|---|
| Median | Trend | Median | Trend | |
| IPCA | 5.03% [4.30 — 5.62] |
4.22% [3.17 — 6.00] |
||
| Selic | 13.75% a.a. [12.50 — 14.25] |
12.00% a.a. [9.50 — 14.00] |
||
| FX Rate (BRL/USD) | 5.20 [4.85 — 5.60] |
5.28 [4.70 — 5.82] |
||
| GDP | 1.99% [1.33 — 2.51] |
1.57% [0.70 — 2.50] |
||
| IGP-M | 4.54% [1.97 — 6.01] |
4.16% [1.83 — 5.90] |
||
| Gross Debt / GDP | 83.30% PIB [82.00 — 86.22] |
87.20% PIB [83.80 — 92.31] |
||
| Primary Balance / GDP | -0.50% PIB [-1.00 — 0.00] |
-0.40% PIB [-1.00 — 0.50] |
||
| IPCA Administered | 4.93% [3.08 — 6.82] |
3.90% [2.34 — 6.13] |
||
| IPCA Services | 5.59% [4.30 — 6.24] |
5.08% [2.68 — 7.12] |
||
| IPCA Market Prices | 5.15% [4.13 — 5.99] |
4.44% [2.17 — 5.98] |
||
| Unemployment | 5.40% [4.70 — 6.40] |
6.00% [4.70 — 6.60] |
||
Focus Survey — Historical Error & Bias (2016–2025)
| Indicator | 6M MAE | 12M MAE | 24M MAE |
|---|---|---|---|
| IPCA |
1.36
bias -0.3 · n=10
|
1.38 ▼
bias -0.6 · n=10
|
1.59 ▼
bias -1.0 · n=10
|
| Selic |
0.85
bias -0.1 · n=10
|
2.29
bias -0.1 · n=10
|
4.55 ▼
bias -0.8 · n=10
|
| FX Rate |
0.27
bias -0.1 · n=10
|
0.61
bias -0.1 · n=10
|
0.76 ▼
bias -0.5 · n=10
|
| GDP |
0.98 ▼
bias -0.9 · n=10
|
1.86
bias -0.2 · n=10
|
2.07 ▲
bias +0.5 · n=10
|
| IGP-M |
3.84 ▼
bias -1.5 · n=10
|
5.81 ▼
bias -3.1 · n=10
|
6.14 ▼
bias -3.6 · n=10
|
| Unemployment |
1.44 ▲
bias +1.4 · n=4
|
2.20 ▲
bias +2.2 · n=4
|
3.36 ▲
bias +3.4 · n=3
|
How does it work? Every Friday the BCB publishes the projection medians for the current and next year. The table shows these medians along with sparklines revealing the recent revision trend. Arrows indicate whether projections are being revised up or down.
Accuracy Analysis: Below the table, we analyze Focus's track record since 2016 — measuring mean absolute error (MAE), bias (whether the market tends to be optimistic or pessimistic), and how accuracy varies with horizon (December projections are more precise than January ones).
Why is it useful? Shows market consensus — and whether that consensus is being revised. When many projections shift in the same direction, it may signal a changing macro outlook.
How does it work? Two distinct models:
• IPCA (Vasicek): Mean-reverting process — inflation tends to converge to the Focus target, with speed calibrated by historical persistence. Volatility is estimated from past Focus forecast errors.
• Selic (Brownian Bridge): Path guided by the B3 DI1 futures curve as a "backbone", connecting the current value to the Focus target. Uncertainty grows then shrinks approaching the anchor point.
Why is it useful? Instead of asking "what will Selic be?", it shows "what is the probability of Selic being above X%?". Allows assessing tail risks and extreme scenarios.
How to read? The dark band (P25–P75) covers the 50% most likely scenarios. The light band (P5–P95) covers 90% of scenarios. The center line is the median. The probability card summarizes the chance of exceeding a specific threshold.
Source: BCB Focus (targets), B3 DI1 (curve), historical Focus errors (volatility)
Weekly Analysis 04/08/2026 01:35
The geopolitical sentiment of the week shows a predominance of negative headlines, with emphasis on Sudan, Libya, Yemen/Red Sea, Iran, and Venezuela, all with strongly negative scores, signaling an environment of elevated risk in parts of Africa, the Middle East, and Latin America. In Sudan’s case, reports of drone attacks in North Kordofan with dozens of deaths reinforce humanitarian risks and institutional instability. In Libya, the invasion of Mellitah Oil and Gas facilities and the cut in gas supplies to power plants illustrate energy and institutional vulnerability in North Africa. On the maritime security front, Houthi attacks on Saudi oil tankers in the Red Sea increase the risk of logistical disruption on a critical energy route. The situation worsens with the conflict involving Iran, including a missile attack against U.S. forces, and with complex political negotiations in Venezuela, which begin in August amid low trust between the government and the opposition. This geopolitical backdrop, although concentrated in specific areas, helps explain the rise in volatility in emerging markets and the recent move in high-yield credit.
On the global index map, absolute leadership is concentrated in Asia and in some frontier emerging markets. South Korea shows the highest YTD performance, with a gain of 93.3% and increases of 27.8% in the last month and 50.0% in 3 months, combining moderate GDP of 1.25% with inflation of 3.34% and relatively low interest rates of 2.91%, resulting in a slightly negative real rate of -0.4% and a moderately steep yield curve of 1.27. This set suggests a moderate growth environment, still relatively low capital costs, and strong risk appetite in equities, possibly driven by technology and semiconductors. Taiwan reinforces this technology beta reading: YTD of 39.0%, 1M of 9.4%, and 3M of 24.7%, even without GDP, inflation, or interest rate data on the panel, indicating that the flow into Asian technology remains intense. Singapore, with robust GDP of 3.75%, inflation of only 0.9%, and YTD of 17.7% (1M of 7.4%, 3M of 12.0%), shows a classic case of an open economy with solid fundamentals and a stock market tracking the growth cycle.
Among emerging markets, Nigeria stands out with YTD of 60.9%, 1M of 18.1%, and 3M of 40.5%, despite extremely high inflation of 23.01% and unavailable interest rates, suggesting a repricing of domestic assets in an environment of strong nominal growth and possible expectations of stabilization ahead. Romania combines strong equity performance (YTD 28.6%, 1M 8.9%, 3M 14.7%) with negative GDP of -1.13% and high inflation of 7.19%, an important divergence: the market seems to be pricing in more of a future recovery cycle and/or regional flows in Central Europe than the current growth picture. Japan, despite recent adverse events such as the magnitude 7.1 earthquake in Kumamoto and factory shutdowns, shows YTD of 20.0% and gains of 4.3% in the month and 4.8% in 3 months, with weak GDP (0.32%), negative inflation (-0.4%), rates at 1.27%, and a very steep curve at 1.40. This suggests that gradual monetary normalization and the exit from deflation are coexisting with temporary shocks, without yet reversing the structural trend of repricing Japanese assets.
On the laggard side, there is a clear block of underperformance in Australia, Southeast Asia, and some Gulf markets. Australia posts a YTD decline of -1.4%, a drop of -4.0% in 1M and -4.1% in 3M, despite reasonable GDP of 2.05%, moderate inflation of 2.87%, and relatively high rates of 4.46%, with a slightly positive curve (0.37). The combination of tighter monetary policy and a less favorable global commodity cycle weighs on local equities. Vietnam also appears among the worst performers, with YTD of -8.3%, 1M -5.3%, and 3M -8.3%, with no macro data on the panel, indicating likely profit-taking after a period of strong structural optimism. In the United Arab Emirates, the index falls -7.2% on the year, -6.3% in the month, and -16.5% in 3 months, in a context where the conflict involving Iran and production adjustments among OPEC+ members create noise around the oil cycle and prospects for regional assets. Indonesia is the most extreme case: YTD of -25.5%, 1M -15.1%, and 3M -19.8%, despite strong GDP of 4.93%, low inflation of 1.95%, and a nominal rate of 5.46%, which generates a relatively high real rate of 3.5%. This disconnect indicates specific pressure on the local market, possibly associated with currency depreciation, foreign outflows, and greater sensitivity to global risk.
In Brazil, the data show a clearly more volatile and defensive dynamic in the short term. The Brazilian index accumulates YTD of 9.8%, but records a significant decline of -10.5% in the last month and -6.7% in 3 months, in an environment of GDP of 2.47%, inflation of 5.53%, and high nominal rates of 14.25%, which translate into the highest real rate in the sample, about 8.7%. The yield curve is inverted at -0.39, which is consistent with prolonged monetary tightening and concern about future slowdown, even though current growth is not weak. This combination of high real rates, an inverted curve, and a recent correction in stocks signals a more risk-averse domestic environment and a high opportunity cost for risky assets. In contrast, the U.S. presents a more balanced picture: YTD of 8.1%, 1M of 3.9%, 3M of 8.2%, with GDP of 2.1%, inflation of 3.46%, rates of 3.63%, and a positively sloped curve at 1.12, suggesting a solid growth cycle with monetary policy that is less restrictive in real terms and no strong signs of excessive tightening through the curve.
In FX, the picture is one of short-term stability versus the dollar and marginal adjustments over 3 months. In the group of currencies that appreciated the most in 3M, the Russian ruble appears with a gain of 3.1%, followed by the Peruvian sol (1.2%), Egyptian pound (0.6%), Norwegian krone (0.4%), Israeli shekel (0.3%), Chinese yuan (0.2%), and small variations close to zero for the Vietnamese dong and Romanian leu. This indicates that, despite the adverse geopolitical environment in Russia and Egypt and the challenging backdrop in Israel and China, the currencies did not suffer acute stress over the 3-month horizon, possibly reflecting capital controls, exchange-rate