The U.S. economy is a very complex system, with indicators therefore ambiguous and difficult to interpret. To what degree do macroeconomics and the stock market go hand-in-hand, if at all? Do investors/traders: (1) react to economic readings; (2) anticipate them; or, (3) just muddle along, mostly fooled by randomness? These blog entries address relationships between economic indicators and the stock market.
Does bitcoin (BTC) return exhibit any exploitable leading or lagging roles with respect to gold (SPDR Gold Shares – GLD) return, change in the all-items consumer price index (CPI) or change in the effective federal funds rate (EFFR) for a monthly measurement interval? To investigate, we compute correlations between monthly BTC return and each of monthly GLD return, change in CPI and change in EFFR for various lead-lag relationships, ranging from BTC return leads other variables by six months (-6) to other variables lead BTC return by six months (6). Using monthly BTC, GLC, CPI and EFFR levels during September 2014 (limited by BTC) through July 2026, we find that:Keep Reading
How do returns of different asset classes recently interact with inflation as measured by monthly change in the not seasonally adjusted, all-items consumer price index (CPI) from the U.S. Bureau of Labor Statistics? To investigate, we look at lead-lag relationships between change in CPI and returns for each of the following 10 exchange-traded fund (ETF) asset class proxies:
Using monthly total CPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through mid-August 2026, we find that:Keep Reading
The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for August 2026. The actual total (core) inflation rate is a little higher than (slightly lower than) forecasted.
Do U.S. business applications, based on Employer Identification Number (EIN) applications associated with new businesses, predict future economic activity and therefore stock market returns? To investigate, we relate changes in both seasonally adjusted (SA) and non-seasonally adjusted (NSA) business applications to S&P 500 Index (SP500) returns at monthly and annual frequencies. In case the relationships are non-linear, we also look at average SP500 returns for negative/positive and extreme changes in business applications. Monthly business application data are released 11-12 days after the end of the observed month. Using monthly SA and NSA business application counts and contemporaneous SP500 levels during July 2004 (limited by the business applications series) through June 2026, we find that:Keep Reading
How do different asset classes interact with the Japanese yen-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Japanese Yen (FXY) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXY and the asset class proxies since March 2007 as available through June 2026, we find that:Keep Reading
How do different asset classes interact with euro-U.S. dollar exchange rate? To investigate, we consider relationships between Invesco CurrencyShares Euro Currency (FXE) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for FXE and the asset class proxies since February 2006 as available through June 2026, we find that:Keep Reading
How do different asset classes interact with U.S. dollar valuation? To investigate, we consider relationships between Invesco DB US Dollar Index Bullish Fund (UUP) and the exchange-traded fund (ETF) asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) or the Simple Asset Class ETF Value Strategy (SACEVS) at a monthly measurement frequency. Using monthly dividend-adjusted closing prices for UUP and the asset class proxies since March 2007 as available through June 2026, we find that:Keep Reading
Is the Buffett Indicator, the ratio of total U.S. stock market capitalization (proxied by Wilshire 5000 Total Market Index W5000) to U.S. Gross Domestic Product (GDP), a useful indicator of future U.S. stock market performance? W5000/GDP clearly has no stable average value over its available history (see the first chart below), so the level of the ratio is likely not a useful predictor. We therefore also consider the following variables based on W5000/GDP as predictors of W5000 returns at horizons up to two years:
Quarterly change in W5000/GDP.
Average quarterly change in W5000/GDP over the past two years (eight quarters).
Average quarterly change in W5000/GDP over the past five years (20 quarters).
Slope of W5000/GDP over the past two years.
Slope of W5000/GDP over the past five years.
We consider two kinds of tests: (1) a linear test relating these variables to future W5000 returns up to two years; and, (2) a non-linear test calculating average next-quarter W5000 returns by ranked fifths (quintiles) of in these variables. Using quarterly levels of W5000 (with extension), Shiller’s P/E10 lagged by one quarter (for comparison) and quarterly GDP lagged by one quarter during the first quarter of 1971 through the first quarter of 2026, we find that:Keep Reading
Can investors extract an edge from prediction markets such as Kalshi, the first (2021) Commodity Futures Trading Commission (CFTC)-regulated exchange that trades on outcomes of future events including consumer price index (CPI), federal funds rate (FFR), gross domestic product (GDP) and unemployment? In their February 2026 paper entitled “Kalshi and the Rise of Macro Markets”, Anthony Diercks, Jared Katz and Jonathan Wright evaluate the accuracy of Kalshi forecasts, with comparisons to outputs of traditional survey and market-implied forecasts. They further examine how Kalshi forecast distributions respond to economic news. Using high-frequency Kalshi prediction contract data for a broad set of economic variables during 2022-2025, they find that:Keep Reading
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