Economic Indicators

The “Simple Asset Class ETF Value Strategy” (SACEVS) seeks diversification across a small set of asset class exchanged-traded funds (ETF), plus a monthly tactical edge from potential undervaluation of three risk premiums:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

Premium valuations are relative to historical averages. How might this strategy react to changes in the Effective Federal Funds Rate (EFFR)? Using end-of-month values of the three risk premiums, EFFR, total 12-month U.S. inflation and core 12-month U.S. inflation during March 1989 (limited by availability of operating earnings data) through February 2025, we find that: Keep Reading

Does the M2 measure of money supply reliably drive bitcoin and/or gold prices at a monthly horizon? To investigate we relate monthly change in M2 to future monthly bitcoin and SPDR Gold Shares (GLD) returns. Using monthly data for M2, bitcoin and GLD from September 2014 (inception of bitcoin price series) through February 2025, we find that:

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Does economic history rhyme in that similar economic/financial conditions precede similar equity factor performance? In their March 2025 paper entitled “Regimes”, Amara Mulliner, Campbell Harvey, Chao Xia, Ed Fang and Otto Van Hemert present a way to characterize the current economic/financial regime and relate this characterization to future factor returns. They consider seven input variables: (1) S&P 500 Index level; (2) 10-year U.S. Treasury note yield minus 3-month U.S. Treasury bill (T-bill) yield; (3) WTI crude oil price; (4) copper price; (5) T-bill yield; (6) VIX level; and, (7) U.S. stocks-bonds 3-year daily correlation. Specifically, they each month:

1. Compute for each input variable its annual change and its z-score relative to a rolling 10-year history, constraining the z-score to a range of -3 to +3.
2. For each variable, measure the similarity of its current z-score to its past z-scores for all dates based on squared difference.
3. For each date, add the squared differences across the seven input variables to compute a global similarity score for that date (lowest values are most similar to now).
4. For each past date, measure next-month gross performance of each of six long-short equity factors (market, size, value, profitability, investment and momentum).
5. Sort past dates into fifths (quintiles) based on global similarity scores relative to the current date.
6. For each quintile of past dates and each factor, take for the current date a long (short) position in the factor if its average next-month past performance is positive (negative) and reform an equal-weighted portfolio of the resulting six factor positions for the next month.

They focus on the quintiles of past dates with the most and least global similarities to the current date. Using daily and monthly data for the seven economic/financial input variables and for the six equity factors during 1985 through 2024, they find that:

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The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for February 2025. The actual total (core) inflation rate is a little lower than (a little lower than) forecasted.

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:

• Equities:
  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
• Bonds:
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
• Real assets:
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)

Using monthly total CPI values and monthly dividend-adjusted prices for the 10 specified ETFs during December 2007 (limited by EMB) through January 2025, we find that: Keep Reading

Does commercial and industrial (C&I) credit fuel business growth and thereby drive the stock market? To investigate, we relate changes in credit standards from the Federal Reserve Board’s quarterly Senior Loan Officer Opinion Survey on Bank Lending Practices to future U.S. stock market returns. Presumably, loosening (tightening) of credit standards is good (bad) for stocks. The Federal Reserve publishes survey results a few days after the end of the first month of each quarter (January, April, July and October). Using as-released “Net Percentage of Domestic Respondents Tightening Standards for C&I Loans” for large and medium businesses from the Senior Loan Officer Opinion Survey on Bank Lending Practices Chart Data for the second quarter of 1990 through the fourth quarter of 2024 (140 surveys), and contemporaneous S&P 500 Index quarterly returns (aligned to survey months), we find that: Keep Reading

Given anxiety among investors about the rapid rise of U.S. public debt, are U.S. Treasuries fairly valued? In his July 2024 paper entitled “A Historical Perspective on US Treasuries Risk Premia”, Olivier Davanne describes factors driving the U.S. Treasuries yield curve and explains how to gauge beliefs of market participants. He extracts investor rate expectations for various horizons from the monthly Consensus Economics surveys and the quarterly Surveys of Professional Forecasters to support a model of U.S. Treasury premiums based on eight variables, four related to monetary policy (current and expected) and four related to risk pricing (current and expected), as follows:

1. Current short-term yield.
2. Expected equilibrium short-term yield.
3. Short-term yield expected in one year.
4. Short-term yield expected in three years.
5. Current short-term risk premium for 10-year U.S. Treasury notes (T-note).
6. Expected long-term equilibrium for the T-note risk premium.
7. As an indication on the expected speed of convergence between them, expected T-note risk premium in three years.
8. As a measure of monetary policy risk premium, current risk premium on 1-year U.S. Treasury notes.

Applying a standard statistical procedure to the current Federal Funds Rate, yields for 3-month, 6-month, 1-year, 2-year, 5-year and 10-year U.S. Treasury instruments and responses to the above-cited surveys during 1994 through early 2024, he finds that:

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Since 1990, the Federal Reserve Bank of Philadelphia has conducted a quarterly Survey of Professional Forecasters. The American Statistical Association and the National Bureau of Economic Research conducted the survey from 1968-1989. Among other things, the survey solicits from experts probabilities of U.S. economic recession (negative GDP growth) during each of the next four quarters. The survey report release schedule is mid-quarter. For example, the release date of the third quarter 2024 report is August 9, 2024, with forecasts through the third quarter of 2025. The “Anxious Index” is the probability of recession during the next quarter. Are these forecasts meaningful for future U.S. stock market returns? Rather than relate the probability of recession to stock market returns, we instead relate one minus the probability of recession (the probability of good times). If forecasts are accurate, a relatively high (low) forecasted probability of good times should indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels (on survey release dates as available, and mid-quarter before availability of release dates) from the fourth quarter of 1968 through the third quarter of 2024 (224 surveys), we find that:

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When the U.S. government runs substantial deficits, some experts proclaim the dollar’s inevitable inflationary debasement and bad times for stocks. Other experts say that deficits are no cause for alarm, because government spending stimulates the economy, and the country can bear more debt. Who is right? Using quarterly nominal level of the U.S. public debt, interest expense on the debt, U.S. Gross Domestic Product (GDP), S&P 500 Index level (SP500) and consumer price index (CPI) as available during January 1966 (limited by public debt data) through September 2024 (about 59 years), we find that: Keep Reading

What variables best explain increases and decreases in Cyclically Adjusted Price-to-Earnings ratio (CAPE or P/E10)? In their August 2024 paper entitled “Analyzing Changing ‘Investor Exuberance’: The Determinants of S&P Composite Index Total Return CAPE Changes”, C. Krishnan, Jiemin Yang and Xiyao Tan apply the following three techniques to investigate which of 42 potentially explanatory variables relate most strongly to changes in CAPE:

1. Linear regression with principal component analysis.
2. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, which shrinks some regression coefficients to zero, thereby identifying the most important independent variables.
3. Elastic net, which combine approaches of LASSO and Ridge regression to distill the most important independent variables.

Using monthly values for CAPE and the 42  potentially explanatory variables during February 2000 through December 2019, they find that: Keep Reading