Economic Indicators

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 about the end of the first month of each quarter (January, April, July and October). Using the “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 first quarter of 2019 (117 surveys), and contemporaneous S&P 500 Index quarterly returns (aligned to survey months), we find that: Keep Reading

Each month, the Institute for Supply Management (ISM) compiles results of a survey “sent to more than 375 purchasing executives working in the non-manufacturing industries across the country.” Based on this survey, ISM computes the Non-Manufacturing Index (NMI), “a composite index based on the diffusion indexes for four…indicators with equal weights: Business Activity (seasonally adjusted), New Orders (seasonally adjusted), Employment (seasonally adjusted) and Supplier Deliveries.” ISM releases NMI for a month on the third business day of the following month. Does the monthly level of NMI or the monthly change in NMI predict U.S. stock market returns? Using monthly seasonally adjusted NMI data during January 2008 through January 2016 from the Federal Reserve Bank of St. Louis and from press releases thereafter through December 2018, and contemporaneous monthly S&P 500 Index closes (132 months), we find that: Keep Reading

According to the Institute for Supply Management (ISM), their Manufacturing Report On Business, published since 1931, “is considered by many economists to be the most reliable near-term economic barometer available.” The manufacturing summary component of this report is the Purchasing Managers’ Index (PMI), aggregating monthly inputs from purchasing and supply executives across the U.S. regarding new orders, production, employment, deliveries and inventories. ISM releases PMI for a month at the beginning of the following month. Does PMI predict stock market returns? Using monthly seasonally adjusted PMI data during January 1950 through January 2016 from the Federal Reserve Bank of St. Louis (discontinued and removed) and from press releases thereafter through December 2018, and contemporaneous monthly S&P 500 Index closes (828 months), we find 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 economic 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 fourth quarter 2018 report is November 13, 2018, with forecasts for the four quarters of 2019. 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 fourth quarter of 2018 (201 surveys), we find that:

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Do investors reliably react over short and intermediate terms to changes in the U.S. Consumer Price Index (CPI), a logical measure of the wealth discount rate? Using monthly total and core (excluding food and energy) CPI releases (for all items, not seasonally adjusted) from the Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index open and close data for the period mid-January 1994 (the earliest for which CPI release dates are available) through mid-November 2018 (299 releases), we find that: Keep Reading

Which economic and market variables are most effective in predicting U.S. stock market returns? In his October 2018 paper entitled “Forecasting US Stock Returns”, David McMillan tests 10-year rolling and recursive (inception-to-date) one-quarter-ahead forecasts of S&P 500 Index capital gains and total returns using 18 economic and market variables, as follows: dividend-price ratio; price-earnings ratio; cyclically adjusted price-earnings ratio; payout ratio; Fed model; size premium; value premium; momentum premium; quarterly change in GDP, consumption, investment and CPI; 10-year Treasury note yield minus 3-month Treasury bill yield (term structure); Tobin’s q-ratio; purchasing managers index (PMI); equity allocation; federal government consumption and investment; and, a short moving average. He tests individual variables, four multivariate combinations and and six equal-weighted combinations of individual variable forecasts. He employs both conventional linear statistics and non-linear economic measures of accuracy based on sign and magnitude of forecast errors. He uses the historical mean return as a forecast benchmark. Using quarterly S&P 500 Index returns and data for the above-listed variables during January 1960 through February 2017, he finds that: Keep Reading

Each month, the Census Bureau announces and the financial media report U.S. new home sales as a potential indicator of future U.S. stock market returns. Release date is about three weeks after the month being reported. Moreover, new releases may substantially revise recent past releases, so that the Census Bureau historical data set effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index (SP500) and for the FTSE NAREIT All REITs total return index (REITs) to changes in new home sales at the monthly release frequency. Using monthly data for SP500 and for seasonally adjusted annualized new homes sales starting January 1963, and for REITs starting December 1971, all through September 2018, we find that: Keep Reading

Each month, the Census Bureau announces and the financial media report U.S. housing starts as a potential indicator of future U.S. stock market returns. Release date is about two weeks after the month being reported. New releases may substantially revise recent past releases, so that the Census Bureau historical data set effectively has a longer lag. Does this economic indicator convey useful information about future returns for the broad U.S. stock market or for Real Estate Investment Trusts (REIT)? To investigate, we relate returns for the S&P 500 Index (SP500) and for the FTSE NAREIT All REITs total return index (REITs) to changes in housing starts at the monthly release frequency. Using monthly data for SP500 and for seasonally adjusted annualized housing starts starting January 1959, and for REITs starting December 1971, all through September 2018, we find that:

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Which economic variables are most important for predicting stock returns? In their October 2018 paper entitled “Sparse Macro Factors”, David Rapach and Guofu Zhou apply machine learning to isolate via sparse principal component analysis (PCA) which of 120 economic variables from the FRED-MD database most influence stocks. These variables span output/income, labor market, housing, consumption, orders/inventories, money/credit, yields/exchange rates and inflation. As a preliminary step, they adjust raw economic variables by, where necessary: (1) transforming them to produce stationary series; (2) adjusting for reporting lags of one or two months. They next execute sparse PCA, which sets small component weights to zero, thereby facilitating interpretation of results without sacrificing much predictive power. For comparison, they also extract the first 10 conventional principal components from the same variables. Finally, they use 202 stock portfolios to estimate the influence of sparse and conventional principal components on the cross section of stock returns. Using monthly data for the 120 economic variables and 202 stock portfolios during February 1960 through June 2018, they find that:

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A reader requested review of the Decision Moose asset allocation framework. Decision Moose is “an automated stock, bond, and gold momentum model developed in 1989. Index Moose uses technical analysis and exchange traded index funds (ETFs) to track global investment flows in the Americas, Europe and Asia, and to generate a market timing signal.” The trading system allocates 100% of funds to the index projected to perform best. The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for dividend-adjusted SPDR S&P 500 (SPY) over the same intervals. Using Decision Moose signals/performance data and contemporaneous SPY prices during 8/30/96 through 10/19/18 (22+ years), we find that: Keep Reading