Several studies reveal variables that predict cross-sectional differences in stock returns but mainly rely on a sample of U.S. stocks, mostly covering the post-1963 period. These studies are often criticized for potential data mining issues since the database never changes, but “new” findings crop up all the time.
This paper studies the cross-section of U.S. stock returns using a novel constructed database of out-of-sample data from 1866-1926. This ‘pre-CRSP’ sample period is of about similar length as existing CRSP-based studies (61 years), and covers an economically important period independent of existing datasets. This large new set provides new grounds for independent tests to understand stock prices and drivers of return better.
What are the Academic Insights?
The authors find:
In line with Black, Jensen and Scholes, and Fama-MacBeth we find that market beta is not priced in the cross-section, and the CAPM, on average fails to explain asset prices: low-beta stocks have positive alpha and high-beta stocks have negative alpha over the 1866-1926 sample
Size has no significant slope in Fama-MacBeth regression and no significant return spread in portfolio sorts
Short-term reversal is only significant in Fama-MacBeth regression tests
Price momentum and dividend yield carry significant cross-sectional premiums or return spreads
Combined, the six stock characteristics can explain 28% of the variation in stock returns
Why does it matter?
This study serves two main contributions: 1) the creation of a novel database covering 61-years including the major stocks traded on the U.S. exchanges during the second half of the 19th and early 20th century; 2) the examination of the cross-section of stock returns out-of-sample in a robust and rigorous way. Overall, findings on stock factors are largely similar over the pre-1926 and post-1926 era’s.
The Most Important Chart from the Paper:
We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of additional and independent data. Our database contains data on stock prices, dividends and hand-collected market capitalizations for 1,488 major stocks between 1866-1926. Results over this ‘pre-CRSP’ era reveal a flat relation between market beta and returns, an insignificant size premium, and significant momentum, value, and low-risk premiums that are of similar size as over the post-1926 period. Overall, stock characteristics can explain over 25% of variation in stock returns. Further, recent machine learning methods are successful in predicting cross-sectional returns out-of-sample. These results show strong out-of-sample robustness of traditional factor models and novel machine learning methods.
Dr. Elisabetta Basilico is a seasoned investment professional with an expertise in "turning academic insights into investment strategies." Research is her life's work and by combing her scientific grounding in quantitative investment management with a pragmatic approach to business challenges, she’s helped several institutional investors achieve stable returns from their global wealth portfolios. Her expertise spans from asset allocation to active quantitative investment strategies. Holder of the Charter Financial Analyst since 2007 and a PhD from the University of St. Gallen in Switzerland, she has experience in teaching and research at various international universities and co-author of articles published in peer-reviewed journals. She and co-author Tommi Johnsen published a book on research-backed investment ideas, titled Smarte(er) Investing. How Academic Insights Propel the Savvy Investor. You can find additional information at Academic Insights on Investing.
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