Economic Indicators

Is the level, or changes in the level, of Federal Reserve (Fed) holdings of U.S. Treasuries (bills, notes, bonds and TIPS, measured weekly as of Wednesday) an indicator of future stock market and/or Treasuries returns? To investigate, we take dividend-adjusted SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT) as tradable proxies for the U.S. stock and Treasuries markets, respectively. Using weekly Fed holdings of Treasuries, SPY and TLT during mid-December 2002 through mid-July 2019, we find that: Keep Reading

How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through June 2019 (over 148 years), and estimated monthly yield on 1-year U.S. Treasury bills (T-bills) since January 1951, we find that:

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The Conference Board “publishes leading, coincident, and lagging indexes designed to signal peaks and troughs in the business cycle for major economies around the world,” including the widely cited Leading Economic Index (LEI) for the U.S. Does the LEI predict stock market behavior? Using the as-released monthly change in LEI from archived Conference Board press releases and contemporaneous dividend-adjusted daily levels of SPDR S&P 500 (SPY) for June 2002 through mid-July 2019 (206 monthly LEI observations), we find that: Keep Reading

Is the Office of Financial Research Financial Stress Index (OFR FSI), described in “The OFR Financial Stress Index”, useful as a U.S. stock market return predictor? OFR FSI is a daily snapshot of global financial market stress, distilling more than 30 indicators via a dynamic weighting scheme. The index drops and adds indicators over time as some become obsolete and new ones become available. Unlike some other financial stress indicators, past OFR FSI series values do not change due to any periodic renormalization and are therefore suitable for backtesting. To investigate OFR FSI power to predict U.S. stock market returns, we relate level of and change in OFR FSI to SPDR S&P 500 (SPY) returns. Using daily and monthly values of OFR FSI and SPY total returns during January 2000 (OFR FSI inception) through June 2019, we find that:

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The monthly Cass Freight Index is a “measure of North American freight volumes and expenditures… Data within the Index includes all domestic freight modes and is derived from $28 billion in freight transactions processed by Cass annually on behalf of its client base of hundreds of large shippers. These companies represent a broad sampling of industries including consumer packaged goods, food, automotive, chemical, OEM, retail and heavy equipment… The diversity of shippers and aggregate volume provide a statistically valid representation of North American shipping activity. …Volumes represent the month in which transactions are processed by Cass, not necessarily the month when the corresponding shipments took place. The January 1990 base point is 1.00. …Each month’s volumes are adjusted to provide an average 21-day work month. Adjustments also are made to compensate for business additions/deletions to the volume figures.” Cass typically publishes the index level for a month about the middle of the following month. Does this index usefully anticipate economic trend and thereby U.S. stock market returns? To investigate, we relate index changes to SPDR S&P 500 (SPY) returns. Using monthly Cass Freight Index levels and monthly dividend-adjusted SPY returns during January 1999 (limited by the freight index) through mid-June 2019, we find that: Keep Reading

Financial media often cite Bureau of Labor Statistics (BLS) productivity growth news releases as relevant to investment outlook. Does the quarter-to-quarter change in U.S. labor force productivity predict U.S. stock market behavior? Specifically, does a relatively weak (strong) change in productivity portend strong (weak) earnings and therefore an advance (decline) for stocks? Using annualized quarterly changes in non-farm labor productivity from BLS and end-of quarter S&P 500 Index levels during January 1950 through March 2019, we find that: Keep Reading

A subscriber suggested the Kansas City Financial Stress Index (KCFSI) as a potential U.S. stock market return predictor. This index “is a monthly measure of stress in the U.S. financial system based on 11 financial market variables. A positive value indicates that financial stress is above the long-run average, while a negative value signifies that financial stress is below the long-run average. Another useful way to assess the current level of financial stress is to compare the index to its value during past, widely recognized episodes of financial stress.” The paper “Financial Stress: What Is It, How Can It Be Measured, and Why Does It Matter?” describes the 11 financial inputs for KCFSI and its methodology, which involves monthly demeaning of inputs, monthly normalization of the overall indicator to have historical standard deviation one and principal component analysis. This process changes past values in the series, perhaps even changing their signs. Is KCFSI useful for U.S. stock market investors? To investigate, we relate monthly S&P 500 Index returns to monthly values of, and changes in, KCFSI. Per the KCFSI release schedule, we use the market close on the first trading day of the month after the 7th for calculations. Using monthly data for KCFSI and the S&P 500 Index during February 1990 (limited by KCFSI) through May 2019, we find that: Keep Reading

“Chicago Fed NFCI as U.S. Stock Market Predictor” suggests that weekly change in the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) may be a useful indicator of future U.S. stock market returns. We test its practical value via two strategies that are each week in SPDR S&P 500 (SPY) when prior change in NFCI is favorable and in cash (U.S. Treasury bills, T-bills) when prior change in NFCI is unfavorable, as follows:

1. Change in NFCI < Mean [aggressive]: hold SPY (cash) when prior-week change in NFCI is below (above) its mean since inception in January 1971.
2. Change in NFCI < Mean+SD [conservative]: hold SPY (cash) when prior-week change in NFCI is below (above) its mean plus one standard deviation of weekly changes in NFCI since inception in January 1971.

The return week is Wednesday open to Wednesday open (Thursday open when the market is not open on Wednesday) per the NFCI release schedule. We assume SPY-cash switching frictions are a constant 0.1% over the sample period. We use buying and holding SPY as the benchmark. Using weekly levels of NFCI as of May 2019 since January 1971 and weekly dividend-adjusted opens of SPY and T-bills since February 1993 (limited by SPY), all through May 2019, we find that: Keep Reading

A subscriber suggested that the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI) may be a useful U.S. stock market predictor. NFCI “provides a comprehensive weekly update on U.S. financial conditions in money markets, debt and equity markets, and the traditional and ‘shadow’ banking systems.” It consists of 105 inputs, including the S&P 500 Implied Volatility Index (VIX) and Senior Loan Officer Survey results. Positive (negative) values indicate tight (loose) financial conditions, with degree measured in standard deviations from the mean. The Chicago Fed releases NFCI each week as of Friday on the following Wednesday at 8:30 a.m. ET (or Thursday if Wednesday is a holiday), renormalized such that the full series always has a mean of zero and a standard deviation of one (thereby each week changing past values, perhaps even changing their signs). To investigate its usefulness as a U.S. stock market predictor, we relate NFCI and changes in NFCI to future S&P 500 Index returns. Using weekly levels of NFCI and weekly closes of the S&P 500 Index during January 1971 (limited by NFCI) through May 2019, we find that: Keep Reading

Are variables determined in published papers to be statistically significant predictors of stock market returns really useful to investors? In their November 2018 paper entitled “On the Economic Value of Stock Market Return Predictors”, Scott Cederburg, Travis Johnson and Michael O’Doherty assess whether strength of in-sample statistical evidence for 25 stock market predictors published in top finance journals translates to economic value after accounting for some realistic features of returns and investors. Predictive variables include valuation ratios, volatility, variance risk premium, tail risk, inflation, interest rates, interest rate spreads, economic variables, average correlation, short interest and commodity prices. Their typical investor makes mean-variance optimal allocations between the stock market and a risk-free security (yielding a fixed 2% per year) via Bayesian inference based on a vector autoregression model of market return-predictor dynamics. The investor has moderate risk aversion and a 1-month or longer investment horizon (reallocates monthly). Stock market returns and predictors exhibit randomly varying volatility. They focus on annual certainty equivalent return (CER) gain, which incorporates investor risk aversion, to quantify economic value of market predictability. Using monthly U.S. stock market returns and data required to construct the 25 predictive variables as available (starting as early as January 1927 and as late as June 1996 across variables) through December 2017, they find that:

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