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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.

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

New Home Sales and Future Stock Market/REIT Returns

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

Housing Starts and Future Stock Market/REIT Returns

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 Really Matter for Stocks?

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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Mojena Market Timing Model

The Mojena Market Timing strategy (Mojena), developed and maintained by professor Richard Mojena, is a method for timing the broad U.S. stock market based on a combination of many monetary, fundamental, technical and sentiment indicators to predict changes in intermediate-term and long-term market trends. He adjusts the model annually to incorporate new data. Professor Mojena offers a hypothetical backtest of the timing model since 1970 and a live investing test since 1990 based on the S&P 500 Index (with dividends). To test the robustness of the strategy’s performance, we consider a sample period commencing with inception of SPDR S&P 500 (SPY) as a liquid, low-cost proxy for the S&P 500 Index. As benchmarks, we consider both buying and holding SPY (Buy-and-Hold) and trading SPY with crash protection based on the 10-month simple moving average of the S&P 500 Index (SMA10). Using the trade dates from the Mojena Market Timing live test, daily dividend-adjusted closes for SPY and daily yields for 13-week Treasury bills (T-bills) from the end of January 1993 through August 2018 (over 25 years), we find that: Keep Reading

Gold Return vs. Change in M2

A subscriber requested testing of the relationship between U.S. M2 Money Stock and gold, offered in one form via “Why Gold May Be Looking Cheap”: “[O]ne measure I’ve found useful is the ratio of the price of gold to the U.S. money supply, measured by M2, which includes cash as well as things like money market funds, savings deposits and the like. The logic is that over the long term the price of gold should move with the change in the supply of money… That equilibrium level is also relevant for future price action. When the ratio is low, defined as 25% below equilibrium, the medium 12-month return has been over 12%. Conversely, when the ratio is high, defined as 25% above equilibrium, the 12-month median return has been -6%. …This measure can be refined further. [G]old tends to trade at a higher ratio to M2 when inflation is elevated.” Because it defines specific valuation thresholds, this approach is susceptible to data snooping bias in threshold selection. We consider an alternative setup that relates monthly change in M2 to monthly gold return. We also consider the effect of inflation on this relationship. Using monthly seasonally adjusted M2 and end-of-month London gold price fix during January 1976 (to ensure a free U.S. gold market) through June 2018 (510 months), we find that: Keep Reading

Unemployment Claims Reports and Near-term Stock Market Returns

Each week the media report U.S. initial and continued unemployment claims (seasonally adjusted) as a potential indicator of future U.S. stock market returns. Do these indicators move the market? To investigate, we focus on weekly changes in unemployment claims during a period of “modern” information dissemination to release-day and next-week stock market returns. By modern period, we mean the history of S&P Depository Receipts (SPY), a proxy for the U.S. stock market. Using relevant news releases and archival data as available from the Department of Labor (DOL) and dividend-adjusted weekly and daily opening and closing levels for SPY during late January 1993 through mid-July 2018 (1,330 weeks), we find that:

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Expert Estimates of 2018 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 April 2018 paper entitled “Market Risk Premium and Risk-free Rate Used for 59 Countries in 2018: A Survey”, Pablo Fernandez, Vitaly Pershin and Isabel Acin summarize results of a March 2018 email survey of international finance/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 5,173 specific and credible responses spanning 59 countries with more than five such responses, they find that: Keep Reading

Chemical Activity Barometer as Stock Market Trend Indicator

A subscriber proposed: “It would be interesting to do an analysis of the Chemical Activity Barometer [CAB] to see if it has predictive value for the stock market. Either [look] at stock prices when [CAB makes] a two percent pivot down [from a preceding 6-month high] as a sell signal and one percent pivot up as a buy signal…[or when CAB falls] below its x month moving average.” The American Chemistry Council claims that CAB “determines turning points and likely future trends of the wider U.S. economy” and leads other commonly used economic indicators. To investigate its usefulness for U.S. stock market timing, we consider the two proposed strategies, plus two benchmarks, as follows:

  1. CAB SMAx Timing – hold stocks (the risk-free asset) when monthly CAB is above (below) its simple moving average (SMA). We consider SMA measurement intervals ranging from two months (SMA2) to 12 months (SMA12).
  2. CAB Pivot Timing – hold stocks (the risk-free asset) when monthly CAB most recently crosses 1% above (2% below) its maximum value over the preceding six months. We look at a few alternative pivot thresholds.
  3. Buy and Hold (B&H) – buy and hold the S&P Composite Index.
  4. Index SMA10 – hold stocks (the risk-free asset) when the S&P Composite Index is above (below) its 10-month SMA (SMA10), assuming signal execution the last month of the SMA measurement interval.

Since CAB data extends back to 1912, we use Robert Shiller’s S&P Composite Index to represent the U.S. stock market. For the risk-free rate, we use the 3-month U.S. Treasury bill (T-bill) yield since 1934. Prior to 1934, we use Shiller’s long interest rate minus 1.59% (the average 10-year term premium since 1934). We assume a constant 0.25% friction for switching between stocks and T-bills as signaled. We focus on number of switches, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly data for CAB, the S&P Composite Stock Index, estimated dividends for the stocks in this index (for calculation of total returns) and estimated long interest rate during January 1912 through December 2017 (about 106 years), and the monthly T-bill yield since January 1934, we find that: Keep Reading

Combining Market, Unemployment and Interest Rate Trends

In reaction to “Combine Market Trend and Economic Trend Signals?”, a subscriber suggested adding an interest rate trend signal to those for the U.S. stock market and U.S. unemployment rate for the purpose of timing the S&P 500 Index (SP500). To investigate, we look at combining:

We consider scenarios when the SP500 trend is positive, the UR trend is positive, the T-bill trend is positive, at least one trend is positive (>=1), at least two trends are positive (>=2) or all three trends are positive (All). For total return calculations, we adjust the SP500 monthly with estimated dividends from the Shiller dataset. When not in the index, we assume return on cash from the broker is the specified T-bill yield. We focus on gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics. We use the average monthly T-bill yield during a year as the risk-free rate for that year in Sharpe ratio calculations. While we do not apply any stocks-cash switching frictions, we do calculate the number of switches for each scenario. Using the specified monthly data through October 2017, we find that: Keep Reading

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