Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for August 2020 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for August 2020 (Final)
1st ETF 2nd ETF 3rd ETF

Open Source Stock Predictor Data and Code

| | Posted in: Big Ideas, Equity Premium

Are published studies that predict higher returns for some U.S. stocks and lower for others based on firm accounting, stock trading and other data reproducible? In their May 2020 paper entitled "Open Source Cross-Sectional Asset Pricing", Andrew Chen and Tom Zimmermann make available data and code that reproduce many published cross-sectional stock return predictors, allowing other researchers to modify and extend past studies. They commit to annual updates of their study. Defining statistical significance as achieving at least 95% confidence in predictive power, they include:

  • 180 clear predictors that exhibit statistical significance in original studies and are easily reproducible.
  • 30 likely predictors that exhibit statistical significance in original studies but are not precisely reproducible.
  • 315 additional predictors covered in past studies that were not clearly tested or failed, or are variations of these predictors. They further extend this group by separately testing 1-month, 3-month, 6-month and 12-month portfolio reformation frequencies (1,260 total tests).

They compute all predictors on a monthly basis and create for each a long-short portfolio based on the specifications and the sample period of its original study. They check predictive power of each using data available at the end of each month to evaluate long-short portfolio returns the next month. They assume a 6-month lag for availability of annual accounting data and a 1-quarter lag for quarterly accounting data. They make no attempt to account for portfolio reformation frictions or to winnow predictors based on similarity. Using data and sample periods for U.S. firms/stocks as specified in original published studies as described above, they find that:

Please or subscribe to continue reading...
Gain access to hundreds of premium articles, our momentum strategy, full RSS feeds, and more!  Learn more

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