Economic Indicators

Are many accepted equity factors/return anomalies artifacts of the secular decline in interest rates during their discovery sample periods? In their September 2022 paper entitled “The Factor Multiverse: The Role of Interest Rates in Factor Discovery”, Jules van Binsbergen, Liang Ma and Michael Schwert study the role of the secular decline in interest rates since the early 1980s in the discovery of equity factors/return anomalies. They use value-weighted long-short portfolios and monthly reformation for all factors/anomalies. They apply duration-matched fixed income portfolio return adjustments to returns for each anomaly portfolio to model returns for the latter if there had been no interest rate decline. They then classify each anomaly as false positive (present for unadjusted returns, but not adjusted returns), false negative (present for adjusted returns, but not unadjusted returns) or robust to the effect of interest rates (present for both unadjusted and adjusted returns). Using monthly returns for 153 accepted factors/anomalies over respective original test periods and for 1,395 potential undiscovered factors/anomalies based on firm accounting variables during July 1962 through December 2020, along with contemporaneous yield data for zero coupon U.S. Treasury bonds and notes, they find that:

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Does the U.S. stock market respond predictably to Federal Open Market Committee (FOMC) announcements, typically released between 14:00 and 14:20 EST? In the August 2022 version of their paper entitled “The FOMC Announcement Reversal”, Tommaso Baglioni and Ruy Ribeiro examine the relationship between pre-FOMC announcement returns and post-FOMC announcement returns. Specifically, they test a reversal strategy that buys (sells) E-mini S&P 500 just before announcement at 13:50 EST when the return during the 24 hours before the announcement is negative (positive) and closes the position at the end of the trading day. They buy at the ask and sell at the bid to account for trading frictions. They compute average cumulative return per round trip transaction and Sharpe ratio as average return divided by standard deviation (standardized to reflect one trading day and the number of hours the position is open). They consider two subperiods (October 1997 through March 2011 and April 2011 through January 2020). They also look at interactions of strategy performance with four measures of economic conditions: market uncertainty (VIX), economic policy uncertainty, monetary policy uncertainty and consumer sentiment. Using intraday E-mini S&P 500 prices, exact FOMC announcement release data and measures of economic conditions on FOMC announcement dates during mid-October 1997 through January 2020 (a total of 180 scheduled FOMC announcements), they find that:

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Is the gap between the yield on the 10-year constant maturity U.S. Treasury note (T-note) and the 10-Year breakeven inflation rate (a measure of expected inflation over the next 10 years derived from T-note yield and 10-Year Treasury inflation-indexed constant maturity securities yield) indicative of future stock market or U.S. Treasury bond yields? To investigate, we relate monthly values of this gap (the expected real T-note gap) and changes in the gap to future monthly returns for SPDR S&P 500 ETF Trust (SPY) and iShares 20+ Year Treasury Bond ETF (TLT). Using monthly values for the four series during January 2003, limited by the breakeven inflation rate series, through July 2022, we find that: Keep Reading

Which variables deserve greatest focus when predicting stock market returns? In their July 2022 paper entitled “Searching for the Best Conditional Equity Premium Model”, Hui Guo, Saidat Sanni and Yan Yu exhaustively explore combinations of 18 previously identified potential stock market return predictors to isolate the most powerful subset. They focus on a best subset selection method with a penalty on complexity, thereby suppressing data snooping bias and selecting a manageable subset. For robustness, they consider four alternative variable selection methods. Using quarterly U.S. data for these 18 variables and S&P 500 Index levels/returns during 1947 through 2020, they find that: Keep Reading

Why have inflation, economic uncertainty and geopolitical uncertainty not driven up the price of gold? In their brief May 2022 commentary entitled “Why Isn’t the Gold Price Higher?”, Paul Gambles and James Fraser review the times when gold is, and is not, a good investment. Based on historical gold prices, with focus on recent decades, they conclude that: Keep Reading

How should investors reposition portfolios across inflationary regimes (deflation, low inflation, mild inflation, high inflation)? In their July 2022, paper entitled “Investing in Deflation, Inflation, and Stagflation Regimes”, Guido Baltussen, Laurens Swinkels and Pim van Vliet examine asset class and factor returns across inflationary regimes. They first construct long monthly return histories for asset classes (global equities, bonds and cash) and four factors (value, momentum, low-risk and quality/carry) as applied to equities, bonds and a multi-asset portfolio. They then segment the long sample into four global inflation regimes: (1) below 0% (deflation); (2) 0% to 2% (low); (3) 2% to 4% (mild); and, (4) above 4% (high). They next define sub-regimes (most notably for high inflation) according to other economic variables, with focus on stagflation (high inflation and economic downturn as measured by recessions, weakening earnings or falling equity markets). They further divide sub-regimes based on increasing/decreasing long-term interest rates or inflation rates. Using monthly inflation and asset price data as specified during 1875 through 2021 (147 years), they find that:

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Is the government debt-to-Gross Domestic Product (GDP) ratio a useful predictor of stock and bond market returns? In his May 2021 paper entitled “Government Debt and Risk Premia”, Yang Liu examines relationships between future stock and bond market excess returns (relative to short term government bills) and government debt-to-GDP ratio. He measures government debt as market value of the federal government debt held by the public. For the U.S., this means aggregate market value of all Treasury bonds, Treasury notes, Treasury bills, TIPS, etc. across maturities, excluding government accounts and Federal Reserve holdings. Using debt and GDP data for the U.S. during 1926 through the mid-2010s, associated U.S. stock and bond market return data during 1926 through 2020 and comparable data for 19 major developed countries spanning 1970 through 2018, he finds that:

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A subscriber requested confirmation of the following relationship between U.S. M2 Money Stock and gold offered in “Why Gold May Be Looking Cheap”: “[O]ne measure I’ve found useful is the ratio of the price of gold to the U.S. money supply, measured by M2, which includes cash as well as things like money market funds, savings deposits and the like. The logic is that over the long term the price of gold should move with the change in the supply of money… That equilibrium level is also relevant for future price action. When the ratio is low, defined as 25% below equilibrium, the medium 12-month return has been over 12%. Conversely, when the ratio is high, defined as 25% above equilibrium, the 12-month median return has been -6%. …This measure can be refined further. [G]old tends to trade at a higher ratio to M2 when inflation is elevated.” Because it retrospectively defines specific valuation thresholds using the full sample, this approach impounds lookahead bias/data snooping bias in threshold selection. We consider an alternative setup that relates monthly change in M2 to monthly gold return. We also consider the effect of inflation on this relationship. Using monthly seasonally adjusted M2 and end-of-month London PM gold price fix during January 1976 (to ensure a free U.S. gold market) through March 2022, we find that: Keep Reading

A subscriber, citing an assertion (without explanation) from an interview with a hedge fund manager, asked for confirmation that negative real yields on U.S. Treasury instruments predict poor returns for emerging market equities.  To investigate, we look at interactions between the real return on 10-year U.S. Treasury notes (T-notes) and return on iShares MSCI Emerging Markets ETF (EEM). We calculate the real yield on T-notes by substracting from its nominal yield the inflation rate for the last 12 months as indicated by the U.S. Consumer Price Index, All Items (CPI). We measure T-note yield and dividend-adjusted EEM price at the close on monthly CPI release dates. Using the specified monthly data during April 2003 (limited by EEM history) through March 2022, we find that: Keep Reading

How should investors think about surprises in economic data? In their March 2022 paper entitled “Caught by Surprise: How Markets Respond to Macroeconomic News”, Guido Baltussen and Amar Soebhag devise and investigate a real-time aggregate measure of surprises in economic (not financial) variables around the world. Each measurement for each variable consists of release date/time, initial as-released value, associated consensus (median) forecast, number and standard deviation of individual forecasts and any revision to the previous as-released value across U.S., UK, the Eurozone and Japan markets from the Bloomberg Economic Calendar. They classify variables as either growth-related or inflation-related. They apply recursive principal component analysis to aggregate individual variable surprises separately into daily nowcasts of initial growth-related and inflation-related announcement surprises and associated revision surprises. They investigate the time series behaviors of these nowcasts and then examine their interactions with returns for four asset classes:

1. Stocks via prices of front-month futures contracts rolled the day before expiration for S&P 500, FTSE 100, Nikkei 225 and Eurostoxx 50 indexes.
2. Government bonds via prices of front-month futures contracts rolled the day before first notice on U.S., UK, Europe and Japan 10-year bonds.
3. Credit via returns on 5-year credit default swaps for U.S. and Europe investment grade and high yield corporate bond indexes.
4. Commodities via excess returns for the Bloomberg Commodity Index.

Specifically, they test an investment strategy that takes a position equal to the 1-day lagged value of the growth surprise nowcast or the inflation surprise nowcast on the last trading day of each month. They pool regions within an asset class by equally weighting regional markets. Using daily as-released data for 191 economic variables across global regions and the specified monthly asset class price inputs during March 1997 through December 2019, they find that: Keep Reading