The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
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Firm Size, Book‐to‐Market Ratio, and Security Returns: A Holdout Sample of Financial Firms
Published: 04/18/2012 | DOI: 10.1111/j.1540-6261.1997.tb04826.x
BEAD M. BARBER, JOHN D. LYON
Fama and French (1992) document a significant relation between firm size, book‐to‐market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book‐to‐market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book‐to‐market premiums in returns. Our results indicate data‐snooping and selection biases do not explain the size and book‐to‐market patterns in returns.
Improved Methods for Tests of Long‐Run Abnormal Stock Returns
Published: 05/06/2003 | DOI: 10.1111/0022-1082.00101
John D. Lyon, Brad M. Barber, Chih‐Ling Tsai
We analyze tests for long‐run abnormal returns and document that two approaches yield well‐specified test statistics in random samples. The first uses a traditional event study framework and buy‐and‐hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness‐adjusted t‐statistic or the empirically generated distribution of long‐run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar‐time portfolios and a time‐series t‐statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long‐run abnormal returns is treacherous.