Performance Evaluation with Transactions Data: the Stock Selection of Investment Newsletters

Abstract

This paper analyzes the equity-portfolio recommendations made by investment newsletters. The dataset spans 17 years, is free of survivor and back-fill biases, and includes the complete recommendations for 153 different newsletters. Overall, there is no significant evidence of superior stock-picking ability for this sample of newsletters. Some individual letters do have superior performance records, but this does not occur more often than would be expected by chance, and these records are never more extreme than would be expected for the sample size. In addition, a strategy of buying past winners does not earn positive abnormal returns. The comprehensive and bias-free transactions database also allows for insights into several popular models of performance evaluation. The transactions-based approach of Daniel, Grinblatt, Titman and Wermers (1997) yields a median improvement in precision of 10 percent over the 4-factor model of Carhart (1997a), with the former approach providing more precise estimates of abnormal performance for more than 80 percent of the newsletters. This compares with a median improvement of less than 1 percent for the 4-factor model over the CAPM.