Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for August 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for August 2021 (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.

Exploiting Chicago Fed NFCI Predictive Power

“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 since the beginning of 1973, providing an initial 20-year calculation interval.
  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 the beginning of 1973.

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 since January 1973 and weekly dividend-adjusted opens of SPY and T-bills since February 1993 (limited by SPY), all through April 2020, we find that: Keep Reading

Chicago Fed NFCI as U.S. Stock Market Predictor

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 April 2020, we find that: Keep Reading

Weekly Economic Index and Asset Returns

The Weekly Economic Index (WEI) is a composite of weekly year-over-year percentage changes in 10 economic indicators: Redbook same-store sales; Rasmussen Consumer Index; new claims for unemployment insurance; continued claims for unemployment insurance; adjusted income/employment tax withholdings; railroad traffic originated; the American Staffing Association Staffing Index; steel production; wholesale sales of gasoline, diesel and jet fuel; and, weekly average US electricity load. Does WEI usefully predict U.S. stock market and government bond returns? To investigate, we relate WEI to performance data for SPDR S&P 500 (SPY) as a proxy for the stock market and iShares Barclays 20+ Year Treasury Bond (TLT) as a proxy for government bonds. Since WEI measurements are through Saturdays, we align its values with SPY and TLT prices at the first daily close of the following week. Using weekly data as described during January 2008 (limited by WEI) through most of April 2020, we find that: Keep Reading

Federal Reserve Holdings and the U.S. Stock Market

Using quarterly data in their April 2013 preliminary paper entitled “Analyzing Federal Reserve Asset Purchases: From Whom Does the Fed Buy?” Seth Carpenter, Selva Demiralp, Jane Ihrig and Elizabeth Klee find that some categories of investors appear to sell U.S. Treasuries to the Federal Reserve and rebalance toward riskier assets (corporate bonds, commercial paper, and municipal debt). Are stocks a part of this process? To investigate, we relate weekly, monthly and quarterly U.S. stock market returns to changes in the Federal Reserve’s System Open Market Account (SOMA) holdings, comprised of U.S. Treasury bills, U.S. Treasury notes and bonds, U.S. Treasury Inflation-Protected Securities (TIP) and Mortgage-Backed Securities (MBS). The Federal Reserve reports these holdings with a small lag. Using weekly (Wednesday close) dividend-adjusted prices for SPDR S&P 500 (SPY) as a stock market proxy and total SOMA holdings during early July 2003 through mid-April 2020, we find that: Keep Reading

Testing Zweig’s Combined Super Model

A subscriber requested testing Martin Zweig’s Combined Super Model, which each month specifies an equity allocation based on a system that assigns up to eight points from his Monetary Model and 0 or 2 points from his Four Percent Model. We consider two versions of the Combined Super Model:

  1. Zweig-Cash – Allocate to Fidelity Fund (FFIDX) as equities, with the balance in cash earning the 3-month U.S. Treasury bill (T-bill) yield.
  2. Zweig-FGOVX – Allocate to FFIDX as equities, with the balance in Fidelity Government Income Fund (FGOVX)

The benchmark is buying and holding FFIDX. We focus on compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio, with average monthly T-bill yield during a year as the risk-free rate for that year. We ignore impediments to mutual fund trading and any issues regarding timeliness of allocation changes for end-of-month rebalancing. Using monthly Combined Super Model allocations and monthly fund returns/T-bill yield during December 1986 through March 2020, we find that: Keep Reading

Expert Estimates of 2020 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 2020 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 81 countries in 2020”, Pablo Fernandez, Eduardo de Apellániz and Javier Acín summarize results of a February-March 2020 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 5,235 specific and credible premium estimates spanning 81 countries, they find that: Keep Reading

Evolving Equity Index Earnings-returns Relationship

Why does the coincident relationship between U.S. aggregate corporate earnings growth and stock market return change from negative in older research to positive in recent research? In their January 2020 paper entitled “Assessing the Structural Change in the Aggregate Earnings-Returns Relation”, Asher Curtis, Chang‐Jin Kim and Hyung Il Oh examine when the change in the aggregate earnings growth-market returns relationship occurs. They then examine factors explaining the change based on asset pricing theory (expected cash flow and expected discount rate). They calculate aggregate earnings growth as the value-weighted average of year-over-year change in firm quarterly earnings scaled by beginning-of-quarter stock price. They consider only U.S. firms with accounting years ending in March, June, September or December, and they exclude firms with stock prices less than $1 and firms in the top and bottom 0.5% of quarterly earnings growth. They calculate corresponding quarterly stock market returns from one month prior to two months after fiscal quarter ends to capture earnings announcement effects. Using quarterly earnings and returns data as specified for a broad sample of U.S. public firms from the first quarter of 1970 through the fourth quarter of 2016, they find that:

Keep Reading

Should the “Anxious Index” Make Investors Anxious?

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 fourth quarter 2019 report is November 15, 2019, with forecasts for the four quarters of 2020. 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 2019 (205 surveys), we find that:

Keep Reading

Cyclical Consumption as Stock Market Return Predictor

Do investors drive stocks to overvaluation (undervaluation) in good (bad) economic times, such that corresponding expectations for future returns are therefore relatively low (high). In the August 2019 update of their paper entitled “Consumption Fluctuations and Expected Returns”, flagged by a subscriber, Victoria Atanasov, Stig Møller and Richard Priestley introduce the cyclical consumption economic variable and examine its power to predict stock market returns. They hypothesize that in good (bad) economic times:

  1. Marginal utility of present consumption is low (high).
  2. Investors are willing (unwilling) to sacrifice current consumption for investment.
  3. This investment pushes stock prices up (down) and expected returns therefore down (up).

Their principal measure of consumption is quarterly seasonally adjusted real per capita consumption expenditures on non-durables and services from the National Income and Product Accounts (NIPA) Table 7.1 maintained by the U.S. Bureau of Economic Analysis. They extract its cyclical component (detrend) by regressing the logarithm of real per capita consumption on a constant and four lagged values of consumption from about six years prior. They conduct both in-sample and out-of-sample (expanding window regressions, with 2-quarter lag for release delay) tests of the quarterly relationship between cyclical consumption and future U.S. stock market returns. Using the specified consumption data and quarterly returns for the S&P 500 Index and the broad value-weighted U.S. stock market from the first quarter of 1947 through the fourth quarter of 2017, they find that: Keep Reading

CPI-to-PPI Ratio and the Stock Market

In response to “PPI and the Stock Market”, a subscriber hypothesized that increases and decreases in the ratio of the Consumer Price Index (CPI) to the Producer Price Index (PPI) are bullish and bearish for the stock market, respectively. The reasoning for the hypothesis is that CPI reflects aggregate corporate revenue, while PPI reflects aggregate costs. The ratio CPI/PPI therefore relates to aggregate profitability, which should translate to stock market level. To test this hypothesis, we construct U.S. CPI/PPI monthly from non-seasonally adjusted CPI and non-seasonally adjusted PPI. We then relate changes in this ratio to S&P 500 Index returns. Using CPI and PPI values and S&P 500 Index levels during December 1927 through November 2019, we find that: Keep Reading

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