While recent studies document increasing idiosyncratic volatility over the past four decades, an explanation for this trend remains elusive. We establish a theoretical link between growth options available to managers and the idiosyncratic risk of equity. Empirically both the level and variance of corporate growth options are significantly related to idiosyncratic volatility. Accounting for growth options eliminates or reverses the trend in aggregate firm-specific risk. These results are robust for different measures of idiosyncratic volatility, different growth option proxies, across exchanges, and through time. Finally, our results suggest that growth options explain the trend in idiosyncratic volatility beyond alternative explanations. Campbell, Lettau, Malkiel, and Xu (2001) document increasing firm-level return volatility but stable market and industry return volatilities over the last four decades. Subsequently, there has been a flurry of work attempting to characterize the upward trend in idiosyncratic volatility. 1 We now know that increasing idiosyncratic volatility is: (1) related to the level and variance of profitability (Pastor and Veronesi 2003 and Wei and Zhang 2006); (2) positively related to institutional ownership and expected earnings growth (Malkiel and Xu 2003); (3) negatively related to firm age We are grateful to Choong

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two e¡ects reinforce each other, because more highly persistent series are more likely to be found signi¢cant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious. PREDICTIVE MODELS FOR COMMON STOCK RETURNS have long been a staple of ¢nancial economics. Early studies, reviewed by Fama (1970), used such models to examine market e⁄ciency. Stock returns are assumed to be predictable, based on lagged instrumental variables, in the current conditional asset pricing literature. Standard lagged variables include the levels of short-term interest rates, payout-toprice ratios for stock market indexes, and yield spreads between low-grade and high-grade bonds or between long-and short-term bonds. Many of these variables behave as persistent, or highly autocorrelated, time series. This paper studies the ¢nite sample properties of stock return regressions with persistent lagged regressors. We focus on two issues. The ¢rst is spurious regression, analogous to Yule (1926) and Granger and Newbold (1974). These studies warned that spurious relations may be found between the levels of trending time series that are actually independent. For example, given two independent random walks, it is likely that a regression of one on the other will produce a ''signi¢cant'' slope coe⁄cient, evaluated by the usual t-statistics.In this paper, the dependent variables are asset rates of return, which are not highly persistent. Thus, one may think that spurious regression problems

Even though stock returns are not highly autocorrelated, there is a spurious regression bias in predictive regressions for stock returns related to the classic studies of Yule (1926) and Granger and Newbold (1974). Data mining for predictor variables interacts with spurious regression bias. The two effects reinforce each other, because more highly persistent series are more likely to be found significant in the search for predictor variables. Our simulations suggest that many of the regressions in the literature, based on individual predictor variables, may be spurious

Because of upward trends in research and development activity, accounting measures of financial distress have become less accurate. We document that (1) higher research and development spending increases the likelihood of misclassifying solvent firms, (2) adjusting for conservative accounting of research and development increases the number of correctly identified distressed firms, and (3) adjusted measures of distress alleviate previously documented anomalously low returns of large, high distress risk, low book-to-market firms. The results hold after updating stale parameters and under various tax assumptions. Our evidence raises concerns about interpretation of extant literature that relies on accounting measures of distress. Copyright 2007 by The American Finance Association.