Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2020 (Final)
1st ETF 2nd ETF 3rd ETF

Economic Indicators

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.

KCFSI as a Stock Market Return Predictor

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

Usefulness of Published Stock Market Predictors

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:

Keep Reading

Short-term Equity Risk More Political Than Economic?

How does news flow interact with short-term stock market return? In their April 2019 paper entitled “Forecasting the Equity Premium: Mind the News!”, Philipp Adämmer and Rainer Schüssler test the ability of a machine learning algorithm, the correlated topic model (CTM), to predict the monthly U.S. equity premium based on information in news articles. Their news inputs consist of about 700,000 articles from the New York Times and the Washington Post during June 1980 through December 2018, with early data used for learning and model calibration and data since January 1999 used for out-of-sample testing. They measure the U.S. stock market equity premium as S&P 500 Index return minus the risk-free rate. Specifically, they each month:

  1. Update news time series arbitrarily segmented into 100 topics (with robustness checks for 75, 125 and 150 topics).
  2. Execute a linear regression to predict the equity premium for each of the 100 topical news flows.
  3. Calculate an average prediction across the 100 regressions.
  4. Update a model (CTMSw) that switches between the best individual topic prediction and the average of 100 predictions, combining the flexibility of model selection with the robustness of model averaging.

They use the inception-to-date (expanding window) average historical equity premium as a benchmark. They include mean-variance optimal portfolio tests that each month allocate to the stock market and the risk-free rate based on either the news model or the historical average equity premium prediction, with the equity return variance computed from either 21-day rolling windows of daily returns or an expanding window of monthly returns. They constrain the equity allocation for this portfolio between 50% short and 150% long, with 0.5% trading frictions. Using the specified news inputs and monthly excess return for the S&P 500 Index during June 1980 through December 2018, they find that:

Keep Reading

Expert Estimates of 2019 Country Equity Risk Premiums and Risk-free Rates

What are current estimates of equity risk premiums (ERP) and risk-free rates around the world? In their March 2019 paper entitled “Market Risk Premium and Risk-free Rate Used for 69 Countries in 2019: A Survey”, Pablo Fernandez, Mar Martinez and Isabel Acin summarize results of a February-March 2019 email survey of international finance/economic professors, analysts and company managers “about the Market Risk Premium (MRP or Equity Premium) and Risk-Free Rate that companies, analysts, regulators and professors use to calculate the required return on equity in different countries.” Results are in local currencies. Based on 5,096 specific and credible premium estimates spanning 69 countries with more than eight such responses, they find that: Keep Reading

EFFR and the Stock Market

Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over the intermediate term? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through February 2019, we find that: Keep Reading

ISM PMI and Future Junk Bond Returns?

A subscriber asked about the validity of the assertion in “The Daily Shot” of February 26, 2019 (The Wall Street Journal) that “recent weakness in the ISM [Institute for Supply Management] Manufacturing PMI [Purchasing Managers’ Index] index points to downside risks for high-yield debt.” Such a relationship might support a strategy of switching between high-yield bonds and cash, or high-yield bonds and U.S. Treasuries, based on PMI data. To investigate, we consider the following two pairs of funds:

  1. Vanguard High-Yield Corporate (VWEHX) and Vanguard Long-Term Treasury (VUSTX) since May 1986 (limited by VUSTX).
  2. iShares iBoxx High Yield Corp Bond (HYG) and iShares 7-10 Year Treasury Bond (IEF) since April 2007 (limited by HYG).

We consider both statistical tests and strategies that each month (per the PMI release frequency) holds high-yield bonds or cash, or high-yield bonds or Treasuries, according to whether the prior-month change in PMI is positive or negative. We use the 3-month U.S. Treasury bill (T-bill) yield as a proxy for return on cash. Using fund monthly total returns as available and monthly seasonally adjusted PMI data for January 1950 through January 2016 from the Federal Reserve Bank of St. Louis (discontinued and removed) and from press releases thereafter, all through February 2019, we find that: Keep Reading

Consumer Inflation Expectations Predictive?

A subscriber noted and asked: “Michigan (at one point) claimed that the inflation expectations part of their survey of consumers was predictive. That was from a paper long ago. I wonder if it is still true.” To investigate, we relate “Expected Changes in Prices During the Next Year” (expected annual inflation) from the monthly final University of Michigan Survey of Consumers and actual U.S. inflation data based on the monthly non-seasonally adjusted consumer price index (U.S. All items, 1982-84=100). The University of Michigan releases final survey data near the end of the measured month, and the long-turn historical expected inflation series presents a 3-month simple moving average (SMA3) of monthly measurements. We consider two relationships:

  • Expected annual inflation versus one-year hence actual annual inflation.
  • Monthly change in expected annual inflation versus monthly change in actual annual inflation.

As a separate (investor-oriented) test, we relate monthly change in expected annual inflation to next-month total returns for SPDR S&P 500 (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT). Using monthly survey/inflation data since March 1978 (limited by survey data) and monthly SPY and TLT total returns since July 2002 (limited by TLT), all through January 2019, we find that: Keep Reading

ISM NMI and Stock Market Returns

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

ISM PMI and Stock Market Returns

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:

Keep Reading

Most Effective U.S. Stock Market Return Predictors

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

Daily Email Updates
Filter Research
  • Research Categories (select one or more)