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 [?] |
|---|---|---|---|---|---|---|---|
| Chinese Yuan | 6.8005 | -0.0% | -0.0% | -0.0% | +2.8% | +4.5% | — |
| Indonesian Rupiah | 17705.8200 | -0.0% | -0.0% | -0.0% | -6.2% | -7.5% | — |
| Indian Rupee | 96.3500 | -0.0% | -0.0% | -0.0% | -7.1% | -9.2% | — |
| Korean Won | 1504.5800 | -0.0% | -0.0% | -0.0% | -4.3% | -8.3% | — |
| Malaysian Ringgit | 3.9720 | -0.0% | -0.0% | -0.0% | +2.0% | +5.6% | — |
| Philippine Peso | 61.6300 | -0.0% | -0.0% | -0.0% | -4.7% | -8.1% | — |
| Singapore Dollar | 1.2796 | -0.0% | -0.0% | -0.0% | +0.5% | +0.3% | — |
| Thai Baht | 32.6000 | -0.0% | -0.0% | -0.0% | -3.5% | -2.7% | — |
| Taiwan Dollar | 31.6150 | -0.0% | -0.0% | -0.0% | -0.9% | -4.1% | — |
| Vietnamese Dong | 26357.0000 | -0.0% | -0.0% | -0.0% | -0.2% | +0.1% | — |
| Czech Koruna | 20.8710 | -0.0% | -0.0% | -0.0% | -1.4% | -0.4% | — |
| Egyptian Pound | 53.2700 | -0.0% | -0.0% | -0.0% | -11.7% | -10.6% | — |
| Hungarian Forint | 309.1800 | -0.0% | -0.0% | -0.0% | +5.5% | +7.9% | — |
| Israeli Shekel | 2.8973 | -0.0% | -0.0% | -0.0% | +9.1% | +13.4% | — |
| Nigerian Naira | 1371.0200 | -0.0% | -0.0% | -0.0% | +5.2% | +9.0% | — |
| Polish Zloty | 3.6420 | -0.0% | -0.0% | -0.0% | -1.4% | -0.9% | — |
| Romanian Leu | 4.4737 | -0.0% | -0.0% | -0.0% | -3.3% | -3.9% | — |
| Russian Ruble | 72.4500 | -0.0% | -0.0% | -0.0% | +8.0% | +13.4% | — |
| Turkish Lira | 45.5644 | -0.0% | -0.0% | -0.0% | -6.1% | -10.4% | — |
| South African Rand | 16.6388 | -0.0% | -0.0% | -0.0% | -0.8% | +5.2% | — |
| Australian Dollar | 0.7142 | +0.0% | +0.0% | +0.0% | +7.0% | +8.4% | — |
| Euro | 1.1643 | +0.0% | +0.0% | +0.0% | -0.9% | -0.5% | — |
| British Pound | 1.3415 | +0.0% | +0.0% | +0.0% | -0.4% | -0.8% | — |
| New Zealand Dollar | 0.5829 | +0.0% | +0.0% | +0.0% | +0.7% | -1.6% | — |
| Canadian Dollar | 1.3743 | -0.0% | -0.0% | -0.0% | -0.1% | +0.7% | — |
| Swiss Franc | 0.7853 | -0.0% | -0.0% | -0.0% | +1.0% | +1.5% | — |
| Japanese Yen | 158.9400 | -0.0% | -0.0% | -0.0% | -1.4% | -7.8% | — |
| Norwegian Krone | 9.2649 | -0.0% | -0.0% | -0.0% | +8.1% | +7.2% | — |
| Swedish Krona | 9.4006 | -0.0% | -0.0% | -0.0% | -2.0% | -0.2% | — |
| Argentine Peso | 1396.0000 | -0.0% | -0.0% | -0.0% | +3.8% | +1.4% | — |
| Brazilian Real | 4.9907 | -0.0% | -0.0% | -0.0% | +8.9% | +8.3% | — |
| Chilean Peso | 900.4000 | -0.0% | -0.0% | -0.0% | -0.0% | +7.0% | — |
| Colombian Peso | 3797.7200 | -0.0% | -0.0% | -0.0% | -1.5% | +3.2% | — |
| Mexican Peso | 17.2914 | -0.0% | -0.0% | -0.0% | +3.9% | +7.2% | — |
| Peruvian Sol | 3.4223 | -0.0% | -0.0% | -0.0% | -1.8% | +2.0% | — |
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 momentum standout is concentrated in stocks with strong relative strength, with BE (98), NBIS (96), COHU (90), BVC (90), VBNK (89), and GORO (89), while the ETFs include more leveraged and thematic products, such as NVDU.US (69), NVDX.US (69), and MSTU.US (68), along with EMXC.US, EEM.US, and AVEM.US (66). In institutional flows, money is pouring strongly into broad U.S. equity ETFs, with iShares Core S&P 500 ETF (+1664M), SPDR S&P 500 ETF Trust (+1524M), and iShares Russell 2000 ETF (+911M), and flowing out of crypto and tactical semiconductor bets, such as iShares Bitcoin Trust (-1318M), Direxion Daily Semiconductor B (-608M), and iShares Ethereum Trust ETF (-527M). The overall picture suggests a market regime favorable to risk assets, but with rotation toward broad index exposure and a simultaneous search for names and ETFs with elevated momentum. The 3-month fuzzy backtest reinforces this bias, with an average return of 10.0%, a 5.4% excess return versus SPY, and a win rate of 69%.
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 |
|---|---|---|---|
| +$1664.5M | +36.8% | $6192.4M | |
| +$1524.2M | +5.6% | $28615.8M | |
| +$910.9M | +19.2% | $5666.3M | |
| +$894.3M | +47.7% | $2768.7M | |
| +$609.9M | +2.8% | $22255.6M |
▸ ▼ Top Outflows (7 days)
| ETF | Flow 7d | Change | Vol/day |
|---|---|---|---|
| $1318.5M | -34.4% | $2514.8M | |
| $608.5M | -9.3% | $5942.1M | |
| $526.7M | -43.7% | $678.6M | |
| $507.4M | -30.0% | $1184.3M | |
| $469.6M | -18.3% | $2099.9M |
Source: EODHD (ETF prices, AUM, holdings)
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)
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 |
|---|---|---|---|
| Mortgage (22) | +0.9% | -9.2% | 14.2% |
| Specialty (15) | -2.2% | -0.4% | 4.2% |
| Residential (20) | -2.4% | -3.0% | 6.8% |
| Office (18) | -3.1% | +404.0% | 5.2% |
| Retail (24) | -3.1% | +1.1% | 4.2% |
| Diversified (13) | -3.3% | +1.4% | 6.4% |
| Healthcare Facilities (16) | -3.4% | +3.5% | 4.3% |
| Industrial (16) | -4.0% | +6.1% | 4.7% |
| Hotel & Motel (12) | -4.2% | +49.8% | 3.9% |
🇧🇷 Brazil
| Sector | Ret 1M | Ret 6M | Yield |
|---|---|---|---|
| Diversified (34) | +209.1% | +146.7% | 0.5% |
| Office (2) | +0.4% | +3.8% | 0.0% |
| Specialty (4) | +0.2% | -0.9% | 0.0% |
| Residential (2) | -1.3% | -4.1% | 0.0% |
| Retail (3) | -2.2% | -27.7% | 0.0% |
| Industrial (2) | -3.4% | -22.0% | 0.0% |
Source: EODHD (fundamentals_enrichment — REITs)
Energy leads the dashboard by a wide margin, with +105.6% YTD, reflecting the combination of geopolitical tensions in the Middle East, risk around the Strait of Hormuz, and recent strength in Brent and gas, while Grains (+12.6% YTD) and Industrial Metals (+11.2% YTD) remain firm on tighter supply and lean inventories. Among commodities, Gas Oil (+132.3% YTD), Heating Oil (+117.7% YTD), and Brent Crude (+99.0% YTD) were the positive standouts, in line with the energy shock; in agriculture, Cotton (+18.5% YTD) and Sugar (+2.5% YTD) appear at the top of the monthly gainers, while Cocoa (-37.6% YTD) and Coffee (-23.1% YTD) remain among the biggest decliners, consistent with crop adjustments and partial supply normalization. Natural Gas is still at -7.8% YTD, despite the backdrop of seasonal demand and tighter European inventories, and 1M changes were 0.0% across all categories, suggesting that the recent move is concentrated in the year-to-date performance and in specific divergences between energy, grains, and softs.
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)
The DI curve is still pricing in high interest rates, consistent with the Selic at 14.00% after the Copom cut on 08/05 and with the market expecting 13.75% at the end of 2026, while the Focus survey on 09/08 showed IPCA at 5.00% for 2026, above target. In this environment, the implied inflation of the ETTJ tends to remain under pressure, because reading real rates requires an additional premium over inflation that is still unanchored. Since the dashboard did not include the IPCA+ bonds with the largest spread versus the ETTJ, it is not possible to identify which securities are the most stretched, but the theme of elevated spreads remains the central point in the relative value assessment. The recent Copom backdrop and the deterioration/stability of expectations in Focus reinforce a curve that is still sensitive to inflation risk and to the potential fiscal impact.
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 | 5.01% [4.30 — 5.81] |
4.28% [3.17 — 6.00] |
||
| Selic | 13.75% a.a. [12.75 — 14.00] |
12.00% a.a. [9.75 — 14.00] |
||
| FX Rate (BRL/USD) | 5.20 [4.80 — 5.60] |
5.30 [4.70 — 5.68] |
||
| GDP | 1.92% [1.33 — 2.40] |
1.50% [0.67 — 2.50] |
||
| IGP-M | 4.38% [2.36 — 5.51] |
4.10% [1.78 — 7.09] |
||
| Gross Debt / GDP | 83.22% PIB [81.90 — 86.24] |
87.20% PIB [83.80 — 92.19] |
||
| Primary Balance / GDP | -0.50% PIB [-0.90 — 0.00] |
-0.40% PIB [-1.00 — 0.50] |
||
| IPCA Administered | 4.69% [3.70 — 6.42] |
3.83% [2.73 — 5.52] |
||
| IPCA Services | 5.59% [4.33 — 6.70] |
5.06% [2.62 — 7.10] |
||
| IPCA Market Prices | 5.14% [3.79 — 6.32] |
4.44% [2.22 — 5.98] |
||
| Unemployment | 5.40% [4.68 — 6.00] |
5.90% [4.70 — 7.00] |
||
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 09/09/2026 21:46
The backdrop of the week is clearly one of greater geopolitical tension, with a concentrated focus on the Middle East and Eastern Europe, but still without spilling over into systemic stress in risk prices. The VADER sentiment scores show quite negative signals in Qatar (-0.823), Iran (-0.477), Israel and Lebanon (both -0.553), reflecting the combination of risk around the Strait of Hormuz – with a warning of “industrial catastrophe” if the blockade persists – and military escalation between Israel and Hezbollah in southern Lebanon, with dozens of deaths in recent days. At the same time, the Russia-Ukraine conflict remains intense, with Russian attacks causing civilian casualties and reports of strategic pressure on cities in Donbas, generating negative sentiment in Russia (-0.406), Ukraine (-0.310) and also impact in European countries close to the conflict such as the United Kingdom (-0.354) and Australia (-0.421, through the commodities and security channel). Even so, the aggregate tone for major developed economies is more balanced: the United States is still slightly negative (-0.132), while Japan shows a positive score (+0.233), supported by expectations of monetary normalization and firmer employment data; Germany appears practically neutral-positive (+0.054), with the news flow concentrated on European politics and inflation above 3% sustaining expectations of additional ECB tightening.
On the index map, the absolute highlight is Asia and some specific emerging markets, with strong compression of idiosyncratic risk in technology and exporters. South Korea leads with YTD return of +93.3%, accumulating +50.0% in 3 months and +27.8% in 1 month, in an environment of moderate GDP (1.25%), inflation of 3.34% and interest rates of only 2.91%, which implies a slightly negative real rate (-0.4%) and monetary policy still accommodative relative to the asset boom. Taiwan also shows very strong performance (YTD +39.0%, 3M +24.7%, 1M +9.4%), reinforcing the semiconductor and technology theme in the region, even though the macro data are incomplete in the panel. Singapore appears with solid fundamentals – GDP at 3.75%, low inflation at 0.9% – and an index up +25.7% in the year, with +20.6% in 3M and +6.2% in 1M, suggesting a combination of stable growth and perception of a regional safe haven. On the emerging-market side, Nigeria surprises with YTD of +60.9%, 3M of +40.5% and 1M of +18.1%, even with very high inflation (23.01%) and no yield-curve data, possibly indicating a repricing of assets after prior stress, in an still fragile macroeconomic environment. Romania, meanwhile, shows a relevant rise of +28.6% YTD, but with negative GDP (-1.97%) and elevated inflation (7.19%), evidencing a dissociation between weak economic cycle and market performance, possibly supported by regional flows and expectations of a future reversal of the contraction.
On the opposite side, the “bottom 8” picture reveals concentrated pressure in some emerging markets with high rates and/or external challenges, as well as developed markets more sensitive to commodities. Brazil stands out negatively with -10.5% in 1M and -6.7% in 3M, despite still accumulating +9.8% in the year. The country combines moderate GDP (2.47%), inflation at 5.53% and very high nominal rates (14.0%), which results in the highest real rate in the panel, around 8.5% – alongside an inverted yield curve (10Y-2Y spread of -0.13%), suggesting intense monetary tightening and expectations of a future slowdown. This contrast between very high real rates and weak recent index performance reinforces the reading that the local stock market has been suffering from a risk-premium adjustment, possibly interacting with exchange rates and political perception. The ex-core Asia block – Indonesia (-25.5% YTD, -19.8% in 3M, -15.1% in 1M) and Australia (-1.4% YTD, -4.0% in 1M and 3M) – shows high sensitivity to the commodities channel and to the “Russian pincer” narrative in Donbas, which keeps uncertainty over energy and metal supply and feeds back concerns about global growth, especially for ore and coal exporters. In Europe, the Czech Republic posts -5.6% YTD, -6.8% in 3M and -5.2% in 1M, with negative GDP (-0.79%) and moderate inflation (1.85%), as well as rates at 3.68% and a slightly positive curve (1.02%), indicating a more classic picture of a weak cycle and a market following low dynamism.
In foreign exchange, the return data are flat over the 1-week, 1-month and 3-month horizons, both for the Asian “winners” (Chinese yuan, Indonesian rupiah, Indian rupee, South Korean won, Malaysian ringgit, Philippine peso, Singapore dollar, Thai baht) and for the “losers” in G10 and Latin America (Norwegian krone, Swedish krona, Argentine peso, Brazilian real, Chilean peso, Colombian peso, Mexican peso, Peruvian sol). This neutrality of variation relative to the dollar suggests that, in aggregate, the recent FX impact on indices was not the main driver in the last month, and the stock moves that appear in the panel seem much more linked to internal factors (real rates, yield curve, growth and political risk) than to direct currency moves in the observed period. Even so, in countries with very high real rates such as Brazil (8.5%) and Indonesia (3.5%), the historical FX sensitivity tends to matter: currencies under pressure usually increase the premium demanded in stocks, especially when the FX→equity loading is negative, amplifying the decline in assets during depreciation episodes. In Asian economies with relatively more balanced fundamentals and real rates close to zero or slightly negative (South Korea, Singapore), the short-term currency neutrality helps support the reading that the recent equity rally is more micro/theme-driven (technology, reopening, global flows) than defensive via FX.
From the perspective of risk perception, volatility data show a clear compression scenario compared with 3 months ago, despite the heavy geopolitical news flow. In the U.S., S&P 500 VIX fell from 19.9 to 14.3 (-27.9%), Dow Jones volatility fell from 17.1 to 13.2 (-22.5%), Nasdaq 100 from 30.5 to 20.2 (-33.8%) and Russell 2000 from 26.3 to 18.5 (-29.7%). This signals an environment of greater complacency or confidence regarding traditional macro risks, even with conflicts underway. In emerging markets, the drop is even more pronounced: the VXEEM volatility index plunged from 39.8 to 21.8, a reduction of 45.1%, suggesting strong repricing of risk and a shift toward a more