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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Assessing Goodness‐of‐Fit of Asset Pricing Models: The Distribution of the Maximal R2
Published: 04/18/2012 | DOI: 10.1111/j.1540-6261.1997.tb04814.x
F. DOUGLAS FOSTER, TOM SMITH, ROBERT E. WHALEY
The development of asset pricing models that rely on instrumental variables together with the increased availability of easily‐accessible economic time‐series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness‐of‐fit are inappropriate. This study investigates the distribution of the maximal R2 when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical R2 values to account for selecting variables by searching among potential regressors.
Assessing Goodness‐of‐Fit of Asset Pricing Models: The Distribution of the Maximal R2
Published: 04/18/2012 | DOI: 10.1111/j.1540-6261.1997.tb04814.x
F. DOUGLAS FOSTER, TOM SMITH, ROBERT E. WHALEY
The development of asset pricing models that rely on instrumental variables together with the increased availability of easily‐accessible economic time‐series have renewed interest in predicting security returns. Evaluating the significance of these new research findings, however, is no easy task. Because these asset pricing theory tests are not independent, classical methods of assessing goodness‐of‐fit are inappropriate. This study investigates the distribution of the maximal R2 when k of m regressors are used to predict security returns. We provide a simple procedure that adjusts critical R2 values to account for selecting variables by searching among potential regressors.