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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.

Misery Index and Future U.S. Stock Market Returns

Does the Misery Index, the sum of the U.S. total inflation rate and the U.S. unemployment rate, predict U.S. stock market returns? To investigate, we relate monthly Misery Index and monthly change in Misery Index to monthly S&P 500 Index (SP500) returns. Using monthly Misery Index level and monthly SP500 level during January 1948 (limited by the Misery Index) through June 2021, we find that: Keep Reading

U.S. Stock Market Returns Around Scheduled FOMC Meetings

A subscriber requested testing of a strategy that buys SPDR S&P 500 (SPY) at the open on the day before each scheduled Federal Open Market Committee (FOMC) meeting and sells at the close. Using daily dividend-adjusted SPY open and close prices and dates of FOMC meetings during January 2016 through June 2021 (43 meetings), we find that: Keep Reading

Unemployment Rate and Stock Market Returns

Financial media and expert commentators often cite the U.S. unemployment rate as an indicator of economic and stock market health, generally interpreting a jump (drop) in the unemployment rate as bad (good) for stocks. Conversely, investors may interpret a falling unemployment rate as a trigger for increases in the Federal Reserve target interest rate (and adverse stock market reactions). Is this variable in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted unemployment rate from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1948 (limited by unemployment rate data) through June 2021, we find that: Keep Reading

Employment and Stock Market Returns

U.S. job gains or losses receive prominent coverage in the monthly financial news cycle, with media and expert commentators generally interpreting employment changes as an indicator of future economic and stock market health. One line of reasoning is that jobs generate personal income, which spurs personal consumption, which boosts corporate earnings and lifts the stock market. Are employment changes in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted non-farm employment data from the U.S. Bureau of Labor Statistics (BLS) and monthly S&P 500 Index levels during January 1939 (limited by employment data) through June 2021, we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for June 2021. The actual total (core) inflation rate is much higher than (much higher than) forecasted.

Credit Spread as an Asset Return Predictor

A reader commented and asked: “A wide credit spread (the difference in yields between Treasury notes or Treasury bonds and investment grade or junk corporate bonds) indicates fear of bankruptcies or other bad events. A narrow credit spread indicates high expectations for the economy and corporate world. Does the credit spread anticipate stock market behavior?” To investigate, we define the U.S. credit spread as the difference in yields between Moody’s seasoned Baa corporate bonds and 10-year Treasury notes (T-note), which are average daily yields for these instruments by calendar month (a smoothed measurement). We use the S&P 500 Index (SP500) as a proxy for the U.S. stock market. We extend the investigation to bond market behavior via:

Using monthly Baa bond yields, T-note yields and SP500 closes starting April 1953 and monthly dividend-adjusted closes of VUSTX, VWESX and VWEHX starting May 1986, January 1980 and January 1980, respectively, all through June 2021, we find that: Keep Reading

Predicting Stock Market Crashes with Interpretable Machine Learning

Can machine learning-generated stock market crash predictions be amenable to human interpretation? In their June 2021 paper entitled “Explainable AI (XAI) Models Applied to Planning in Financial Markets”, Eric Benhamou, Jean-Jacques Ohana, David Saltiel and Beatrice Guez apply a gradient boosting decision tree (GBDT) to 150 technical, fundamental and macroeconomic inputs to generate daily predictions of short-term S&P 500 Index crashes. They define a crash as a 15-day S&P 500 Index return below its historical fifth percentile within the training dataset. The 150 model inputs encompass:

  1. Risk aversion metrics such as asset class implied volatilities and credit spreads.
  2. Price indicators such as returns, major stock index Sharpe ratios, distance from a long-term moving average and and equity-bond correlations.
  3. Financial metrics such as 12-month sales growth and price-to-earnings ratio forecasts.
  4. Macroeconomic indicators such Citigroup regional and global economic surprise indexes.
  5. Technical indicators such as market breath and index put-call ratio.
  6. Interest rates such as 10-year and 2-year U.S. Treasury yields and break-even inflation level.

They first rank and filter the 150 inputs based on GBDT to discard about two thirds of the variables. They then apply the Shapley value solution concept to identify the most important of the remaining variables and thereby support interpretation of methodology outputs. Using daily values of the 150 model inputs and daily S&P 500 Index roll-adjusted futures prices from the beginning of January 2003 through mid-January 2021 (with data up to January 2019 used for training, the next year for validation and the rest for testing), they find that:

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Expert Estimates of 2021 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 June 2021 paper entitled “Survey: Market Risk Premium and Risk-Free Rate used for 88 countries in 2021″, Pablo Fernandez, Sofia Bañuls and Pablo Acin summarize results of a May 2021 email survey of international economic professors, analysts and company managers “about the Risk-Free Rate and the Market Risk Premium (MRP) used ‘to calculate the required return to equity in different countries.'” Results are in local currencies. Based on 4,607 specific and credible premium estimates spanning 88 countries, they find that: Keep Reading

Negative 30-year Real Yield as Gold Buy Signal

A subscriber asked for corroboration of an assertion that a negative 30-year U.S. Treasury real yield indicates a good time to buy gold. To investigate, we employ the following monthly data:

Each month, we subtract the 12-month past change in CPI (lagged one month for release delay) from the 30-year yield. When this real yield turns negative, we buy spot gold at the end of the same month and sell it the at the end of the month when the real yield turns positive. Using monthly data as specified through May 2021, we find that: Keep Reading

Real Interest Rates and Asset Returns

How sensitive are returns of stocks, bonds and gold to levels real interest rates (nominal rates minus inflation)? To investigate, we consider three nominal interest rates and two measures of inflation, as follows:

These choices offer six alternative real interest rates. We use end-of-month interest rates and inflation measures lagged by one month to account for release delay. We use the S&P 500 Index (SP500) capital gain only, the 10-year yield (with bond prices moving inversely) and spot gold price, all measured end-of-month, to represent returns for stocks, bonds and gold. We then relate monthly changes in real interest rates to asset class monthly returns in two ways: (1) calculate correlations of monthly real interest rates to asset class returns for each of the next 12 months to get a sense of how real rates lead asset returns; and, (2) calculate average asset class monthly returns by ranked tenths (deciles) of prior-month real interest rates to discover any non-linear relationships. Using monthly PCEPI and Core PCEPI since January 1961, interest rates since January 1962, SP500 level since December 1961 and spot gold price since December 1974 (when controls are removed), all through May 2021, we find that:

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