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
Sentiment Index
Positive
Negative
Neutral
Positive
5%
Pos
95%
Neu
0%
Neg
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.
How to read this map: Each bubble is a country. Color indicates the selected indicator's value (green/warm = high, blue/cool = low). Size reflects relative GDP. Use the layer buttons to switch between indicators and periods. Click a country to see all data.
▸Methodology Note — Macro Map
What is this map?
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.
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.
Taylor Rule data not available.
How to read this chart: Each bar is a country. Gold bars to the right = rates below prescribed (loose policy). Blue bars to the left = rates above prescribed (tight policy). Longer bars indicate greater misalignment between actual rates and what economic conditions suggest.
▸Methodology Note — Taylor Rule
What is the Taylor Rule?
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.
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% | +0.6% | +13.6% | +8.0% | +7.8% | — |
| Hungarian Forint | 309.1800 | -0.0% | +0.6% | +10.1% | +5.5% | +11.2% | — |
| Israeli Shekel | 2.8973 | -0.0% | +0.6% | +7.2% | +9.1% | +17.2% | — |
| Brazilian Real | 4.9907 | -0.0% | +1.3% | +5.3% | +8.9% | +9.1% | — |
| Norwegian Krone | 9.2649 | -0.0% | +0.3% | +3.6% | +8.1% | +6.6% | — |
| Mexican Peso | 17.2914 | -0.0% | +0.2% | +3.2% | +3.9% | +8.6% | — |
| Czech Koruna | 20.8710 | -0.0% | +0.1% | +2.4% | -1.4% | +2.7% | — |
| South African Rand | 16.6388 | -0.0% | +0.4% | +2.1% | -0.8% | +6.6% | — |
| Chilean Peso | 900.4000 | -0.0% | +1.0% | +2.0% | -0.0% | +3.8% | — |
| Australian Dollar | 0.7142 | +0.0% | -0.1% | +1.7% | +7.0% | +9.7% | — |
| Euro | 1.1643 | +0.0% | +0.2% | +1.6% | -0.9% | +0.8% | — |
| Polish Zloty | 3.6420 | -0.0% | +0.2% | +1.3% | -1.4% | +1.4% | — |
| Taiwan Dollar | 31.6150 | -0.0% | -0.1% | +1.3% | -0.9% | -7.3% | — |
| British Pound | 1.3415 | +0.0% | +0.8% | +1.1% | -0.4% | -1.1% | — |
| Chinese Yuan | 6.8005 | -0.0% | +0.1% | +1.1% | +2.8% | +5.3% | — |
| Swiss Franc | 0.7853 | -0.0% | +0.2% | +1.0% | +1.0% | +3.5% | — |
| Thai Baht | 32.6000 | -0.0% | +0.2% | +0.6% | -3.5% | -0.5% | — |
| Japanese Yen | 158.9400 | -0.0% | -0.1% | +0.6% | -1.4% | -9.8% | — |
| Peruvian Sol | 3.4223 | -0.0% | +0.3% | +0.5% | -1.8% | +5.0% | — |
| Singapore Dollar | 1.2796 | -0.0% | +0.0% | +0.3% | +0.5% | +0.1% | — |
| Korean Won | 1504.5800 | -0.0% | -0.4% | +0.3% | -4.3% | -10.7% | — |
| Swedish Krona | 9.4006 | -0.0% | -0.1% | +0.2% | -2.0% | +0.9% | — |
| Canadian Dollar | 1.3743 | -0.0% | +0.0% | -0.1% | -0.1% | -1.3% | — |
| Argentine Peso | 1396.0000 | -0.0% | -0.1% | -0.1% | +3.8% | -18.1% | — |
| Vietnamese Dong | 26357.0000 | -0.0% | -0.1% | -0.2% | -0.2% | -1.1% | — |
| New Zealand Dollar | 0.5829 | +0.0% | -0.1% | -0.5% | +0.7% | -3.8% | — |
| Nigerian Naira | 1371.0200 | -0.0% | -0.0% | -1.1% | +5.2% | +11.2% | — |
| Malaysian Ringgit | 3.9720 | -0.0% | -0.5% | -1.4% | +2.0% | +6.3% | — |
| Romanian Leu | 4.4737 | -0.0% | +0.1% | -1.5% | -3.3% | -3.0% | — |
| Egyptian Pound | 53.2700 | -0.0% | -0.0% | -2.0% | -11.7% | -7.2% | — |
| Colombian Peso | 3797.7200 | -0.0% | -0.0% | -2.7% | -1.5% | +7.3% | — |
| Turkish Lira | 45.5644 | -0.0% | -0.1% | -3.0% | -6.1% | -15.8% | — |
| Philippine Peso | 61.6300 | -0.0% | -0.1% | -3.2% | -4.7% | -9.4% | — |
| Indian Rupee | 96.3500 | -0.0% | -0.4% | -3.5% | -7.1% | -12.2% | — |
| Indonesian Rupiah | 17705.8200 | -0.0% | -1.4% | -4.3% | -6.2% | -8.9% | — |
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.
▸Methodology Note — Global FX
What does this section show?
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.
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.
▸Methodology Note — FX → Equity Panel
What is this analysis?
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.
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.
37
Elevated Fear
How to read this chart: The stacked area chart shows systemic risk evolution over time. Each colored band represents a risk category (equity volatility, credit, EM, etc.). When a band expands, that dimension is dominating stress. The radar on the right compares the current profile with 3 months ago.
▸Methodology Note — Systemic Risk Perception
What is systemic risk?
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.
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)
Weekly Reading
In the week of 06/16/2026, momentum is concentrated in technology and semiconductor stocks, with WDC (93), LRCX (92), ARM (92), AMAT (92), ACLS (92), and ONTO (91) among the highlights, while the Technology sector leads with 6 names. In ETFs, institutional flow is mainly going into broad exposure to U.S. equities, with inflows into Invesco QQQ Trust (+21748M), SPDR S&P 500 ETF Trust (+17145M), and iShares Core S&P 500 ETF (+4335M), while there are outflows from EAFE Value, Quality Factor, and bond total return. The current regime is clearly risk-on, with prob_risk_on at 90%, prob_neutral at 9%, and prob_risk_off at 0%. The 3-month fuzzy backtest reinforces this picture, with an average return of 14.3%, an excess return of 8.9% vs. SPY, and a win rate of 70%.
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
WDC
Technology · Computer Hardware
Score 93 · P100
▸
Western Digital Corporation desenvolve, fabrica e vende dispositivos e soluções de armazenamento de dados baseados em tecnologia de hard disk drive (HDD) nos Estados Unidos, Ásia, Europa, Oriente Médio e África. A empresa oferece HDDs internos, unidades para data center, plataformas para data cen...
Perf 1M
+26.7%
Perf 6M
+308.5%
Sharpe 1Y
16.98
P/E25.8
Margem35.6%
ROE41.1%
D/E0.63
Mkt Cap$147.5B
LRCX
Technology · Semiconductor Equipment & Materials
Score 92 · P100
▸
A Lam Research Corporation projeta, fabrica, comercializa, recondiciona e presta serviços de equipamentos de processamento de semicondutores usados na fabricação de circuitos integrados nos Estados Unidos, China, Korea, Taiwan, Japan, Southeast Asia e Europe. A empresa oferece sistemas ALTUS para...
Perf 1M
+31.4%
Perf 6M
+145.9%
Sharpe 1Y
6.58
P/E48.5
Margem30.9%
ROE66.8%
Div Yield0.39%
D/E0.44
Mkt Cap$321.0B
ARM
Technology · Semiconductors
Score 92 · P100
▸
Arm Holdings plc projeta, desenvolve e licencia produtos de unidade central de processamento e tecnologias relacionadas para empresas de semicondutores e fabricantes de equipamentos originais. A empresa atua no licenciamento, marketing, pesquisa e desenvolvimento de microprocessadores, propriedad...
Perf 1M
+94.0%
Perf 6M
+205.6%
Sharpe 1Y
2.81
P/E277.9
Margem17.2%
ROE11.3%
D/E0.11
Mkt Cap$224.3B
AMAT
Technology · Semiconductor Equipment & Materials
Score 92 · P100
▸
Applied Materials, Inc. fornece soluções de engenharia de materiais, equipamentos, serviços e software para a indústria de semicondutores e indústrias relacionadas nos Estados Unidos, China, Korea, Taiwan, Japan, Southeast Asia, Europe e internacionalmente.
Perf 1M
+32.0%
Perf 6M
+129.9%
Sharpe 1Y
5.18
P/E39.8
Margem27.8%
ROE38.9%
Div Yield0.47%
D/E0.33
Mkt Cap$308.8B
ACLS
Technology · Semiconductor Equipment & Materials
Score 92 · P100
▸
Axcelis Technologies, Inc. projeta, fabrica e presta serviços para equipamentos de implantação iônica e outros equipamentos de processamento usados na fabricação de chips semicondutores nos Estados Unidos, Europa e Ásia-Pacífico.
Perf 1M
+16.1%
Perf 6M
+135.1%
Sharpe 1Y
3.40
P/E36.9
Margem14.3%
ROE11.8%
D/E0.04
Mkt Cap$4.3B
ONTO
Technology · Semiconductor Equipment & Materials
Score 91 · P100
▸
Onto Innovation Inc. atua no projeto, desenvolvimento, fabricação e suporte de ferramentas de controle de processo que realizam inspeção de macrodefeitos e metrologia nos Estados Unidos, Taiwan, Coreia do Sul, Japão, China, Sudeste Asiático, Ásia e Europa.
Perf 1M
+16.8%
Perf 6M
+118.1%
Sharpe 1Y
4.22
P/E105.4
Margem13.6%
ROE6.8%
Mkt Cap$14.6B
MKSI
Technology · Scientific & Technical Instruments
Score 91 · P100
▸
MKS Inc. fornece soluções de tecnologia fundamentais para fabricação de semicondutores, eletrônicos e embalagens, e aplicações industriais especiais nos Estados Unidos, China, Coreia do Sul, Japão, Taiwan, Cingapura e internacionalmente.
Perf 1M
+17.4%
Perf 6M
+133.2%
Sharpe 1Y
6.28
P/E63.9
Margem7.5%
ROE11.7%
Div Yield0.31%
D/E0.11
Mkt Cap$18.8B
TWST
Healthcare · Diagnostics & Research
Score 91 · P100
▸
Twist Bioscience Corporation fabrica e vende produtos sintéticos à base de DNA. A companhia oferece genes sintéticos e fragmentos de genes usados no desenvolvimento de produtos para terapêuticos, diagnósticos, químicos/materiais, alimentos/agricultura, armazenamento de dados e diversas aplicações...
Perf 1M
+43.7%
Perf 6M
+172.0%
Sharpe 1Y
2.43
Margem-19.6%
ROE-16.8%
D/E0.40
Mkt Cap$3.7B
▸ Top Brazilian Stocks — Fuzzy Momentum
TEND3.SA
Real Estate · Real Estate - Development
Score 82 · P98
▸
Construtora Tenda S.A. atua como uma empresa de construção de casas no Brasil.
Perf 1M
+15.9%
Perf 6M
+36.7%
Sharpe 1Y
0.90
P/E6.8
Margem12.1%
ROE44.8%
D/E0.94
Mkt Cap$3.4B
EUCA4.SA
Industrials · Building Products & Equipment
Score 79 · P98
▸
Eucatex S.A. Indústria e Comércio produz e vende hardboard e painéis de madeira no Brasil.
Perf 1M
+17.5%
Perf 6M
+57.5%
Sharpe 1Y
1.09
P/E6.2
Margem10.3%
ROE12.0%
Div Yield5.41%
D/E0.44
Mkt Cap$2.0B
BRKM6.SA
Basic Materials · Chemicals
Score 77 · P97
▸
Braskem S.A., juntamente com suas subsidiárias, atua na fabricação, venda, importação e exportação de produtos químicos, petroquímicos e combustíveis no Brasil. Ela produz e vende polietileno (PE), polipropileno, éter etílico terc-butílico, gasolina, benzeno, tolueno, xileno, etileno, propeno, cl...
Perf 1M
+17.0%
Perf 6M
+22.9%
Sharpe 1Y
0.01
Margem-14.0%
ROE-548.8%
D/E17.90
Mkt Cap$10.9B
ALPA4.SA
Consumer Cyclical · Footwear & Accessories
Score 74 · P96
▸
Alpargatas S.A. fabrica e vende calçados e acessórios no Brasil e internacionalmente.
Perf 1M
+16.0%
Perf 6M
+17.5%
Sharpe 1Y
0.95
P/E15.2
Margem12.5%
ROE15.4%
D/E0.19
Mkt Cap$8.2B
PINE4.SA
Financial Services · Banks - Regional
Score 73 · P96
▸
Banco Pine S.A., juntamente com suas subsidiárias, fornece produtos e serviços bancários no Brasil. A companhia atua nas áreas comercial, de investimento, crédito, financiamento, câmbio e arrendamento mercantil.
Perf 1M
-3.4%
Perf 6M
+32.8%
Sharpe 1Y
3.24
P/E7.5
Margem37.9%
ROE33.8%
Div Yield4.74%
D/E0.55
Mkt Cap$3.7B
▸ Top ETFs — Fuzzy Momentum
PSI.US
ETF · ETF
Score 83 · P99
▸
O ETF PSI.US (Invesco Dynamic Semiconductors ETF) investe pelo menos 90% de seus ativos em ações de cerca de 30 empresas americanas do setor de semicondutores, incluindo fabricação de chips, equipamentos e tecnologia da informação, com foco em companhias de grande capitalização nos EUA. Seu objet...
Perf 1M
+14.8%
Perf 6M
+120.2%
Sharpe 1Y
1.92
FTXL.US
ETF · ETF
Score 82 · P99
▸
O ETF FTXL.US (First Trust Nasdaq Semiconductor ETF) investe em ações de empresas dos setores de tecnologia da informação, semicondutores e equipamentos para semicondutores, com foco em companhias listadas nos mercados de ações públicos dos Estados Unidos. Ele prioriza ações de valor, crescimento...
Perf 1M
+16.5%
Perf 6M
+122.0%
Sharpe 1Y
2.10
PTF.US
ETF · ETF
Score 82 · P98
▸
O ETF PTF.US (Invesco DWA Technology Momentum ETF) investe principalmente em ações de empresas de grande capitalização do setor de tecnologia da informação, listadas nos mercados de ações dos Estados Unidos. Ele seleciona e pondera essas ações com base em momentum de preço, identificando companhi...
Perf 1M
+10.0%
Perf 6M
+74.7%
Sharpe 1Y
1.24
EWY.US
ETF · ETF
Score 80 · P98
▸
O ETF EWY.US (iShares MSCI South Korea ETF) é um fundo passivo gerido pela BlackRock que investe em ações de empresas de grande e médio porte listadas na Coreia do Sul, com exposição significativa a companhias como a Samsung e diversificação em setores variados. Ele rastreia o índice MSCI Korea 2...
Perf 1M
+11.0%
Perf 6M
+128.2%
Sharpe 1Y
2.54
FLKR.US
ETF · ETF
Score 80 · P98
▸
O ETF FLKR (Franklin FTSE South Korea ETF) investe principalmente em ações de grandes e médias empresas sul-coreanas, com pelo menos 80% de seus ativos em componentes do FTSE South Korea RIC Capped Index, priorizando setores como tecnologia (cerca de 46%), industriais e financeiros. Ele foca na r...
Perf 1M
+10.5%
Perf 6M
+120.0%
Sharpe 1Y
2.48
SCO.US
ETF · ETF
Score 80 · P98
▸
O ProShares UltraShort Bloomberg Crude Oil (SCO) é um ETF de commodities que investe principalmente em contratos futuros de petróleo bruto WTI, assumindo posições vendidas (short) com uso de derivativos. Ele atua no setor de energia, com foco global em petróleo, buscando oferecer o retorno diário...
Perf 1M
+366.4%
Perf 6M
+49.2%
Sharpe 1Y
0.64
EDC.US
ETF · ETF
Score 79 · P98
▸
O Direxion Daily MSCI Emerging Markets Bull 3X Shares (EDC) é um ETF alavancado que busca entregar 300% do desempenho diário do índice MSCI Emerging Markets, que reúne ações de grandes e médias empresas de países emergentes. Ele investe principalmente em derivativos e outros instrumentos vinculad...
Perf 1M
+7.5%
Perf 6M
+88.7%
Sharpe 1Y
1.61
TECL.US
ETF · ETF
Score 79 · P98
▸
O ETF TECL.US (Direxion Daily Technology Bull 3X Shares) busca resultados diários de investimento equivalentes a 300% do desempenho do Technology Select Sector Index, antes de taxas e despesas, oferecendo alavancagem tripla ao setor de tecnologia de grandes empresas dos Estados Unidos. Ele invest...
Perf 1M
+16.3%
Perf 6M
+104.0%
Sharpe 1Y
1.21
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).
Average Return 3M
+14.3%
Excess vs S&P 500
+8.9%
Win Rate vs S&P 500
+69.6%
% of weeks that beat SPY
View Backtest Details
Source: EODHD (historical prices)
▸Methodological Note — Fuzzy Logic Momentum
What is fuzzy logic?
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.
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 |
|---|---|---|---|
| +$21747.8M | +82.6% | $48092.0M | |
| +$17144.8M | +51.3% | $50543.8M | |
| +$4334.7M | +64.2% | $11087.4M | |
| +$4329.5M | +35.9% | $16376.0M | |
| +$3649.2M | +81.4% | $8134.6M |
▸ ▼ Top Outflows (7 days)
| ETF | Flow 7d | Change | Vol/day |
|---|---|---|---|
| $1065.3M | -81.8% | $236.9M | |
| $980.4M | -73.4% | $355.9M | |
| $969.2M | -83.7% | $188.1M | |
| $944.1M | -37.5% | $1575.0M | |
| $853.4M | -46.5% | $981.3M |
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 | IGR | +0.1428 | -3.7% | -7.5% | -9.5% | +0.8628 | +0.0041 | -0.0000 | +0.4272 | +0.1950 | +0.3579 | |
| ▸2 | DNP | +0.0984 | +0.2% | +17.1% | +34.2% | +0.5731 | -0.1666 | -0.0000 | +0.2860 | +0.1011 | +0.3655 | |
| ▸3 | NAD | -0.0300 | -0.2% | +4.4% | +8.2% | +0.2718 | +0.1008 | 0.0000 | +0.1952 | -0.0384 | +0.2087 | |
| ▸4 | NEA | -0.0321 | -0.7% | +4.7% | +9.0% | +0.3106 | +0.1330 | 0.0000 | +0.3073 | -0.0381 | +0.2287 | |
| ▸5 | JQC | -0.0708 | -1.6% | -10.5% | -6.0% | +0.5133 | -0.1433 | -0.0000 | +0.1926 | -0.0395 | +0.3518 | |
| ▸6 | CLM | -0.0731 | -2.6% | -9.1% | +12.2% | +0.7691 | -0.2171 | -0.0000 | +0.0523 | -0.3255 | +0.4262 | |
| ▸7 | USA | -0.0741 | +0.9% | -15.2% | -5.5% | +0.7947 | -0.2240 | -0.0000 | +0.1035 | -0.1277 | +0.6903 | |
| ▸8 | JPC | -0.0872 | -2.5% | +2.2% | +13.2% | +0.5009 | -0.1873 | -0.0000 | +0.1701 | -0.0301 | +0.3945 | |
| ▸9 | JFR | -0.0912 | +1.1% | -6.7% | -3.9% | +0.4703 | -0.2259 | -0.0000 | +0.1277 | -0.0185 | +0.3702 | |
| ▸10 | CRF | -0.1076 | -3.0% | -14.3% | +5.3% | +0.8108 | -0.1230 | 0.0000 | +0.1853 | -0.3056 | +0.3371 |
▸ Top 10 Funds — Brazil
| # | Ticker | Name | Alpha (α) | 1M | 6M | 12M | β Mkt | β SMB | β HML | β RMW | β CMA | R² |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ▸1 | MXRF11.SA | -0.0170 | -2.2% | +9.8% | +6.2% | +0.2584 | +0.0945 | 0.0000 | -0.0046 | -0.0816 | +0.0643 | |
| ▸2 | HGLG11.SA | -0.0266 | -2.6% | +3.4% | +0.2% | +0.2425 | +0.1727 | 0.0000 | +0.0622 | -0.1300 | +0.0788 | |
| ▸3 | VRTA11.SA | -0.0503 | -3.2% | +1.3% | -6.6% | +0.4144 | +0.3160 | 0.0000 | -0.0650 | -0.3048 | +0.1217 | |
| ▸4 | NSDV11.SA | -0.0550 | -4.0% | +37.7% | +26.4% | +0.5314 | -0.7487 | -0.0000 | +0.2324 | +0.4594 | +0.6613 | |
| ▸5 | NDIV11.SA | -0.0794 | -6.0% | +27.1% | +16.6% | +0.4888 | -0.7429 | -0.0000 | +0.2657 | +0.4695 | +0.5783 | |
| ▸6 | CRAA11.SA | -0.0952 | -4.3% | +19.8% | +2.9% | +0.2554 | +0.0352 | 0.0000 | -0.1305 | -0.2601 | +0.0443 | |
| ▸7 | CXAG11.SA | -0.0976 | -1.6% | +6.3% | +8.5% | +0.1523 | +0.1636 | 0.0000 | +0.0761 | -0.1723 | +0.0466 | |
| ▸8 | APTO11.SA | -0.1333 | -1.1% | +1.7% | -2.9% | +0.1590 | +0.6108 | -0.0000 | +0.4063 | -0.1445 | +0.0804 | |
| ▸9 | HGBS11.SA | -0.1427 | -3.7% | +12.7% | -0.6% | +0.3620 | +0.1758 | 0.0000 | -0.0345 | -0.2575 | +0.1114 | |
| ▸10 | AJFI11.SA | -0.1434 | +0.9% | +26.4% | +7.7% | +0.1801 | -0.0042 | 0.0000 | +0.0281 | -0.0784 | +0.0298 |
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.
▸Methodological Note — Fama-French 5-Factor Model
The origin of the model
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
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.
How to read this chart: each bar represents the portfolio's exposure to a Fama-French risk factor. Bars to the right (positive) indicate the portfolio benefits from that factor. Bars to the left (negative) indicate opposite exposure. For example, a high "Market" value means the portfolio tends to rise when the market rises; a negative "Size" value indicates a preference for large companies over small ones.
▸Methodological Note — Thematic Portfolios and Correlation Network
What is a correlation network?
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
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 |
|---|---|---|---|
| Hotel & Motel (10) | +15.0% | +34.2% | 4.0% |
| Office (12) | +11.9% | +0.9% | 6.8% |
| Retail (17) | +3.4% | +13.5% | 2.9% |
| Industrial (11) | +1.0% | +13.7% | 4.4% |
| Specialty (11) | -0.3% | +21.5% | 3.9% |
| Residential (12) | -0.8% | -9.1% | 5.6% |
| Diversified (6) | -1.9% | -0.7% | 5.1% |
| Healthcare Facilities (9) | -3.3% | +11.4% | 3.2% |
| Mortgage (20) | -5.5% | -6.9% | 13.0% |
🇧🇷 Brazil
| Sector | Ret 1M | Ret 6M | Yield |
|---|---|---|---|
| Office (2) | +0.9% | +43.1% | 0.0% |
| Residential (2) | -2.5% | -0.4% | 0.0% |
| Diversified (33) | -3.4% | +12.8% | 0.5% |
| Retail (3) | -4.0% | +3.2% | 0.0% |
| Industrial (2) | -8.4% | -0.3% | 0.0% |
| Specialty (4) | -12.6% | -10.9% | 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.
Where We Are Now
Current reading of the 4 principal components
PC1
Risk Appetite
Neutral 86%
Neutral risk appetite — market without clear direction, no strong bias toward risk-on or risk-off.
PC2
Duration / Rates
Easing 89%
Yields falling, bonds rallying — monetary easing or flight-to-quality environment.
PC3
Cyclical Rotation
Balanced 73%
Balanced rotation — no clear bias between cyclicals and defensives.
PC4
Tail Risk
Alert 92%
Tail risk alert — elevated VIX and gold demand as refuge. Protection recommended.
How regime identification works
We track 11 ETFs representing the main market forces: equities (SPY), value vs. growth (IWD−IWF), momentum (MTUM), quality (QUAL), long-term bonds (TLT), investment grade credit (LQD), high yield (HYG), emerging markets (EEM), volatility (VIXY), commodities (DBC) and gold (GLD). Each week, these assets move together or in opposite directions — and hidden in those patterns are market regimes.
We use Principal Component Analysis (PCA) to extract the four dominant patterns from these 11 assets. Instead of analyzing each ETF separately, PCA finds the "invisible axes" that explain most of the joint movement. These axes are called Principal Components.
Together, these four components explain 77% of all weekly variation across the 11 factors — capturing the essential market dynamics in four complementary dimensions.
On each component, we run a Markov-Switching model that automatically selects the optimal number of regimes (K) by BIC, allowing us to detect regime changes in real time with the ideal granularity for each dimension.
PC1
Risk Appetite
The market thermometer
42% of variance
When this component rises, nearly everything rises together: equities, momentum, quality, credit and EM all move in the same direction, while volatility (VIXY) falls. It's the dominant force — the classic risk-on / risk-off axis.
Neutral 86%
Projection →
Neutral
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| SPY | +0.438 | +1.8% | +1.3% |
| QUAL | +0.431 | +1.1% | +2.2% |
| MTUM | +0.409 | +2.9% | +9.8% |
| HYG | +0.399 | +0.1% | +0.2% |
| EEM | +0.365 | +2.8% | +6.1% |
How to read it
▲ high = risk-on (equities, momentum and credit rallying)
▼ low = risk-off (broad selloff, flight to safety)
▼ 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 |
PC2
Duration / Rates
The rates channel
16% of variance
Captures the rates and safe-haven world, independent of risk appetite. TLT, LQD and gold dominate; commodities and equities on the opposite side. Distinguishes panic from rising rates.
Easing 89%
Projection →
Easing
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| TLT | +0.677 | -0.1% | +1.2% |
| LQD | +0.497 | -0.0% | +0.6% |
| GLD | +0.347 | +2.6% | -4.1% |
| VIXY | +0.220 | -6.8% | -15.1% |
| DBC | −0.216 | -1.2% | -7.8% |
How to read it
▲ high = yields falling, bonds rallying (easing or flight-to-quality)
▼ low = yields rising, bonds falling (monetary tightening)
▼ 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 |
PC3
Cyclical Rotation
Value, commodities and cycle
11% of variance
Captures rotation between cyclical assets (value, commodities, gold) and defensive/growth. When it rises, the market favors sectors tied to the economic cycle and inflation.
Balanced 73%
Projection →
Balanced
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| IWD − IWF | +0.643 | -1.6% | +3.5% |
| DBC | +0.590 | -1.2% | -7.8% |
| GLD | +0.398 | +2.6% | -4.1% |
| QUAL | −0.170 | +1.1% | +2.2% |
| SPY | −0.138 | +1.8% | +1.3% |
How to read it
▲ high = rotation to value and commodities (cycle expanding, reflation trade)
▼ low = rotation to growth and defensives (cycle slowing)
▼ 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 |
| SPY | S&P 500 total return — broad US equity market exposure |
| MTUM | MSCI USA Momentum Factor — stocks with strong recent price trends |
| EEM | iShares MSCI Emerging Markets — broad EM equity exposure (China, Taiwan, India, Korea, Brazil) |
| 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 |
| 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 |
PC4
Tail Risk
Fear and protection
9% of variance
Dominated by volatility (VIXY) and gold — captures fear spikes and demand for protection that don't necessarily show in equity or bond prices.
Alert 92%
Projection →
Alert
| ETF | Peso | 1W | 1M |
|---|---|---|---|
| IWD − IWF | +0.639 | -1.6% | +3.5% |
| GLD | −0.587 | +2.6% | -4.1% |
| DBC | −0.297 | -1.2% | -7.8% |
| LQD | +0.245 | -0.0% | +0.6% |
| HYG | +0.202 | +0.1% | +0.2% |
How to read it
▲ high = fear spike (VIX rising, gold as refuge, elevated tail risk)
▼ low = calm market (complacency, compressed VIX)
▼ 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 |
| TLT | 20+ Year US Treasury bonds — long duration, rises when yields fall |
| VIXY | ProShares VIX Short-Term Futures — direct proxy for market fear/volatility (VIX) |
| 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
K=3 Markov-Switching on PC1+PC2 of 11 factor-ETFs (SPY, IWD−IWF, MTUM, QUAL, TLT, LQD, HYG, EEM, VIXY, DBC, GLD)
PCA: PC1 42% + PC2 16% + PC3 11% + PC4 9% = 77% variance · AIC: 2563 · BIC: 2631
PCA: PC1 42% + PC2 16% + PC3 11% + PC4 9% = 77% variance · AIC: 2563 · BIC: 2631
▸Methodological Note — Market Regime Model
What is a market regime?
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.
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
Weekly Reading
In the week of 16/06/2026, the clear standout was Energy, with +105.6% YTD and +1.0% over 1M, driven by WTI Crude Oil (+3.3% over 1M; +106.2% YTD), in line with the recent rise in geopolitical risk in the Middle East and the additional pressure that oil prices place on inflation and the currency. Next, Grains also showed strength, with +12.6% YTD and +2.2% over 1M, led by Wheat (+5.4%; +28.0% YTD), Kansas Wheat (+3.3%; +31.6% YTD), Corn (+4.7%; +4.3% YTD), and Soybeans (+3.1%; +13.0% YTD), a move consistent with weather-related noise and tighter agricultural supply. On the weak side, Cocoa had the largest decline, at -5.2% over 1M and -37.6% YTD, followed by Orange Juice (-2.5%; -17.3% YTD) and Platinum (-0.7%; -7.3% YTD), while Copper fell -0.6% in the month despite still being +7.8% YTD; this suggests a more selective market across softs, precious metals, and industrial metals. Among the baskets, the divergence between very strong energy and weaker precious metals points to potential out-of-balance spreads in Energy versus Precious Metals relationships, but the dominant signal remains the repricing of geopolitical risk and 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 to read this panel: Categories are sorted by YTD return (year-to-date). Within each category, each commodity shows returns across different windows (1W, 1M, 3M, YTD). Green = up, red = down. Click a commodity to see the 5 global stocks with the highest correlation over the last 30 days.
▸Methodology Note — Commodities
What is it? The commodities panel shows the recent return of each commodity grouped by category (energy, precious metals, industrial metals, grains, softs, and livestock), using Bloomberg Commodity indices as reference.
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.
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.
Cointegration analysis unavailable.
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[?].
Trade data not available.
▸Methodology Note — Trade Flow
What is it? An interactive map of the largest bilateral commodity trade corridors, based on official UN 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.
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)
Weekly Reading
The **DI curve** remains at a **high plateau**, consistent with a Selic that is still restrictive for longer, while the latest **Focus** readings have started to embed a **13.50% Selic at the end of 2026** and inflation that is still under pressure, which helps explain the **IPCA+ above 8%** seen in the **Tesouro IPCA+ 2032**. In this environment, the **implied inflation** in the **ETTJ** tends to stay higher and limit room for the curve to compress, especially after readings of sovereign yields at **2026 highs** and the deterioration in the inflation backdrop cited in the latest analyses. The higher **spreads** in **IPCA+ bonds** continue to stand out as a risk premium above the curve, but whether there are real “opportunities” depends on comparing that spread with **implied inflation** and with **recent volatility**. The recent backdrop remains one of **higher rates for longer**, with the market reacting to **Focus revisions** and signs of **more resilient inflation**.
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.
Dados indisponíveis
▸Methodology Note — Fixed Income
What is it? An integrated view of the Brazilian government bond market. It combines actual Tesouro Direto prices, the ANBIMA-estimated term structure (ETTJ), B3-traded futures curves, and the Central Bank's Focus Survey market projections.
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.
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 curve data unavailable
▸Methodology Note — Yield Curves
What is it? Yield curves show the rate the market expects for each maturity. Two sets:
• 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.
• 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 | 5.30% [4.20 — 5.86] |
4.10% [3.00 — 6.00] |
||
| Selic | 13.75% a.a. [11.50 — 14.50] |
12.00% a.a. [9.50 — 14.50] |
||
| FX Rate (BRL/USD) | 5.20 [4.75 — 6.00] |
5.25 [4.50 — 6.00] |
||
| GDP | 1.96% [1.18 — 2.36] |
1.70% [0.72 — 2.59] |
||
| IGP-M | 6.22% [4.20 — 10.02] |
4.04% [2.00 — 6.00] |
||
| Gross Debt / GDP | 83.30% PIB [79.74 — 86.00] |
87.00% PIB [80.25 — 90.00] |
||
| Primary Balance / GDP | -0.50% PIB [-1.00 — 0.00] |
-0.40% PIB [-1.13 — 0.50] |
||
| IPCA Administered | 5.00% [3.26 — 6.94] |
3.81% [2.34 — 6.18] |
||
| IPCA Services | 5.78% [3.60 — 6.80] |
5.03% [2.65 — 6.56] |
||
| IPCA Market Prices | 5.47% [2.80 — 6.43] |
4.26% [2.17 — 5.54] |
||
| Unemployment | 5.46% [4.71 — 6.51] |
6.00% [4.80 — 8.00] |
||
Source: BCB / Focus Survey (2026-06-16)
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
|
MAE = mean absolute error. Bias: ▲ = overestimates, ▼ = underestimates (|bias| > 0.3)
How to read this table: Each row is a macro indicator (Selic, IPCA, GDP, etc.) with the market median projection for this year and next. Trend arrows (▲ / ▼) show whether projections are being revised up or down in recent weeks. Sparklines show the evolution of projections over time.
▸Methodology Note — Focus Survey
What is it? The Focus Survey is a weekly poll by Brazil's Central Bank collecting projections from ~130 financial institutions for key macroeconomic indicators: Selic, IPCA, GDP, FX, trade balance, and others.
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? 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.
▸Methodology Note — Monte Carlo Simulations
What is it? Monte Carlo simulation generates thousands of possible paths for an indicator, allowing you to visualize the distribution of future scenarios instead of a single point forecast.
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.
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 16/06/2026 02:04
The geopolitical sentiment block presents a mixed backdrop, but with relevant events for global risk. On one hand, Australia shows strongly negative sentiment (-0.673), in line with the RBA’s June bulletin discussing geopolitical shock scenarios and financial stability—this reinforces the institutional perception that wars and trade disruptions remain at the center of the risk map. In contrast, Israel (+0.557), Pakistan (+0.526), and Iran (+0.259) show positive sentiment linked to the announcement of an agreement between the U.S. and Iran, with a ceremony scheduled in Geneva and Islamabad playing a diplomatic leading role; this kind of détente tends to ease risk premiums tied to the Middle East and energy. Russia (+0.181), China (+0.120), Mexico (+0.153), the U.S. (+0.087), and Canada (+0.077) have slightly positive or neutral sentiment, highlighted by the proximity of the 2026 World Cup in North America and by the G7 in France (neutral sentiment), which puts discussions of economic policy coordination and security on the agenda. Meanwhile, Ukraine, Palestine, the United Kingdom, and India show negative scores (-0.382), keeping tension points that still anchor part of the global geopolitical risk premium.
In the index map, the major standout is South Korea, with a YTD return of 93.3%, up 27.8% in 1 month and 50.0% in 3 months, despite relatively low nominal rates (2.82%), inflation of 3.34%, and a negative real rate of -0.5%. This combination suggests strong euphoria around earnings growth and/or structural themes (such as technology) that outweigh the still moderate GDP figure (1.25%), and it also shows that, despite a negative real rate, the local market is seen as a “growth play” rather than a safe-haven asset. Nigeria is another outlier: YTD of 60.9% and 18.1% in 1 month, with inflation of 33.24% and GDP of 1.92%, indicate that the stock market is, in part, reflecting nominal effects and a tactical search for discounted assets in a very high-inflation environment. Taiwan (+39.0% YTD, +9.4% in 1M, +24.7% in 3M) maintains strong performance even with neutral-to-negative geopolitical sentiment due to Chinese military pressure; there are no GDP/CPI data, but the pattern is consistent with a narrative of technology and semiconductors offsetting political risk.
In Latin America, there is strong divergence: Colombia is up 23.3% YTD and 22.6% in 1 month, with GDP still modest (0.51%) and inflation elevated (6.61%), indicating aggressive risk repricing after a period of pessimism, while the Colombian peso is stable to slightly weaker over 3 months (-2.7% vs USD), suggesting that part of the equity inflow is not accompanied by proportional currency strength. Peru and Romania show solid but less explosive performance: Peru at +21.0% YTD and +4.3% in 1M, supported by GDP of 2.18% and well-behaved inflation (2.01%), while Romania is up 28.6% YTD and 8.9% in 1M even with GDP contraction (-1.12%) and inflation still at 5.72%, a classic case of weak domestic activity decoupling from market optimism (potentially due to expectations of rate cuts or fiscal anchoring). Japan, with +20.0% YTD, +4.3% in 1M, and +4.8% in 3M, combines weak GDP (0.32%), negative inflation (-0.4%), and still low rates (1.24%), supporting the thesis of a “structural rerating” of equities after decades of valuation compression, despite an still anemic macro environment.
On the underperformer side, Brazil is the recent negative highlight: +9.8% YTD, but down -10.5% in 1 month and -6.7% in 3 months, in a context of robust GDP (2.47%), inflation of 5.53%, very high interest rates (14.5%), and a real rate of approximately 9.0%, one of the highest in the world. The yield curve is slightly inverted (-0.48 pp), suggesting a perception of high monetary tightening and/or doubts about future growth, which contrasts with the relatively strong real (+1.3% in 1M and +5.3% in 3M against the USD), painting a picture in which the currency serves as an anchor and the stock market suffers from domestic concerns (political, fiscal, or earnings-related). Indonesia stands out with -25.5% YTD, down -15.1% in 1 month and -19.8% in 3 months, despite solid GDP (4.93%), low inflation (1.95%), and a positive real rate (3.5%); combined with rupiah depreciation (-1.4% in 1M, -4.3% in 3M), this disconnect suggests foreign capital outflows and country-risk repricing, possibly more linked to the global emerging-market sentiment than to domestic fundamentals. Vietnam and the UAE also appear among the worst in 1 month (-7.0% and -6.3%, respectively), while Australia falls -4.0% in 1 month and -4.1% in 3 months in an environment of decent GDP (2.05%), inflation of 3.17%, and elevated rates (4.43%), coinciding with the RBA’s focus on geopolitical risk scenarios and financial stability.
In foreign exchange, there is a group of emerging-market currencies that strengthened against the dollar over 1 month and especially over 3 months: the Russian ruble (+0.6% in 1M, +13.6% in 3M), Hungarian forint (+0.6%, +10.1%), Israeli shekel (+0.6%, +7.2%), Brazilian real (+1.3%, +5.3%), Norwegian krone (+0.3%, +3.6%), Mexican peso (+0.2%, +3.2%), Czech koruna (+0.1%, +2.4%), and South African rand (+0.4%, +2.1%). This performance suggests a context of a weaker dollar at the margin and a rebuilding of positions in some high-beta currencies and/or those with high real rates (Brazil, Indonesia, South Africa) and in divi