Equity Returns

Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings

Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model

Stephen Jones

String Advisors, Inc


Analyses of P/E10 and Market Value/GDP (MV/GDP) market valuation ratios reveal P/E10’s reliance on misconceptions of the differences between micro and macro earnings. Kalecki’s profit function is used to identify and avoid these problems, contest P/E10’s theoretical support, reveal MV/GDP as the metric providing better theoretical and statistical support, introduce the concept of “macro-earnings negativity”, and provide other important implications for economic theory. Based on the MV/GDP metric, we develop a multi-variable forecasting model utilizing both new and prior-researched variables, the most effective of which is a demographic measure. The resulting composite model is much more accurate than popular benchmark metrics, and, relative to popular benchmarks, forecasts considerably lower returns for the coming decade.

Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model – Introduction

1. Literature Review

For over a century, researchers have developed strategies to forecast equity market returns, only to see others conclude that such strategies do not outperform the market. Thorough surveys of the history of these studies can be found in Huang and Zhou (2013); Scholz, Nielsen, and Sperlich (2013); Rapach and Zhou (2012); and Campbell and Thompson (2008). An early notable strategy is the approximately 255 Wall Street Journal editorials written by Charles H. Dow (1851-1902). Though Dow never used the expression “Dow Theory,” that term typically refers to these works. Later, Cowles (1933), in “Can Stock Market Forecasters Forecast?” tracked Dow Theory forecasts and found that they underperformed the market by about 3.5% a year. Cowles also found that recommendations by 24 other publications underperformed by 4% a year. From Cowles (1933) through the mid-1980s, the efficient market hypothesis dominated, and market returns were generally considered to be unpredictable. Major research supporting this view includes those of Godfrey, Granger and Morgenstern (1964); Fama (1965); Malkiel and Fama (1970); and Malkiel’s (1973) book, A Random Walk Down Wall Street.

The 1980’s, however, saw a surge of research backing up the claim that market returns could be forecasted. The research supported a variety of variables:

  • Book to Market: Kothari and Shanken (1997), Pontiff and Schall (1998), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Consumption Wealth Ratio: Lettau and Ludvigson (2000), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Corporate Activities: Lamont (1988), Baker and Wurgler (2000), Boudoukh, Michaely, Richardson, and Roberts (2007), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Dividend Yields: Hodrick (1982), Rozeff (1984), Fama and French (1988), Campbell and Shiller (1988a, 1988b), Nelson and Kim (1993), Kothari and Shanken (1997), Lamont (1998), Lettau and Van Nieuwerburgh (2008), Cochrane (2008), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Economic Combined with Technical: Huang and Zhou (2013);
  • Earnings: Fama and French (1988), Campbell and Shiller (1988a, 1988b), Lamont (1998), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Inflation Rate: Nelson (1976), and Fama and Schwert (1977), Campbell and Vuolteenaho (2004), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Interest Rates & Bond Yields: Fama and Schwert (1977), Keim and Stampaugh (1986), Campbell (1987), Breen, Glosten, and Jaganathan (1989), Fama and French (1989), Campbell (1991), Ang and Bekaert, (2007), Welch and Goyal (2008), Campbell and Thompson (2008);
  • Relative Valuations of High and Low Beta Stocks: Polk, Thompson, and Vuolteenaho (2006);
  • Stock Volatility: French, Schwert, and Stambaugh (1987), Guo (2000), Goyal and Santa-Clara (2003), Welch and Goyal (2008), Campbell and Thompson (2008).

However, after claims that several variables were able to forecast market returns, arguments disputing those claims returned, the most prominent of which comes from Goyal and Welch (2007). Their study reexamined “the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium,” and, based on extensive out-of-sample testing, they found that these models “would not have helped an investor with access only to available information to profitably time the market.” Goyal and Welch also brought out-of-sample testing to widespread, if not universal, acceptance as a benchmark for testing investment strategies. Goyal and Welch’s findings brought a response from Campbell and Thompson (2008), which accepted the use of out-of-sample results, but “show that many predictive regressions beat the historical average return once weak restrictions are imposed on the signs of coefficients and return forecasts.” Campbell and Thompson’s response appeared to accelerate research into alternative methods of identifying and testing forecasting variables. Rapach and Zhou (2012) covered this topic thoroughly, and, in brief, show that “recent studies provide forecasting strategies that deliver statistically and economically significant out-of-sample gains, including strategies based on:

  • economically motivated model restrictions (e.g., Campbell and Thompson, 2008; Ferreira and Santa-Clara, 2011);
  • forecast combination (e.g., Rapach et al., 2010);
  • diffusion indices (e.g., Ludvigson and Ng, 2007; Kelly and Pruitt, 2012; Neely, Rapach, Tu, and Zhou, 2012);
  • regime shifts (e.g., Guidolin and Timmermann, 2007; Henkel, Martin, and Nadari, 2011; Dangl and Halling, 2012).”

 Equity Returns

 Equity Returns

See full PDF here via SSRN.


Saved Articles

The Life and Career of Charlie Munger

Charlie is more than just Warren Buffett’s friend and Berkshire Hathaway’s Vice Chairman – Buffett has actually credited him with redefining how he looks at investing. Now you can learn from Charlie firsthand via this incredible ebook and over a dozen other famous investor studies by signing up below:

  • Learn from the best and forever change your investing perspective
  • One incredible tidbit of knowledge after another in the page-turning masterpiece of a book
  • Discover the secrets to Charlie’s success and how to apply it to your investing
Never Miss A Story!
Subscribe to ValueWalk Newsletter. We respect your privacy.

Congrats! Are you a smart person?

We have an exclusive targeted & limited time offer for being a sophisticated and loyal reader.

ValueWalkPremium is a website and newsletter on the latest industry news much of which is not in the public domain and obtained via our sources.

We also have 10 years of resources on how to use this information to better your investment process.

Sign up for  today and get our exclusive content for 40% off. This is our second biggest discount ever!!

Use coupon code VIP20 or click on the button below

Limited time offer only ENDS 3/31/2020 or after the next 45 subscribers take advantage whichever comes first – please do not share this discount with others