We show that firms with more illiquid real assets have a higher cost of capital. This effect is stronger when real illiquidity arises from lower within-industry acquisition activity. Real asset illiquidity increases the cost of capital more for firms that face more competition, have less access to external capital, or are closer to default, and for those facing negative demand shocks. The effect of real asset illiquidity is distinct from that of firms’ stock illiquidity or systematic liquidity risk. These results suggest that real asset illiquidity reduces firms’ operating flexibility and through this channel their cost of capital.

This paper develops a multi-asset mixture distribution hypothesis model to investigate commonality in stock returns and trading volume. The model makes two main predictions: First, the factor structures of returns and trading volume are independent although they stem from the same valuation fundamentals and jointly depend on a latent information flow; second, cross-sectional positive volatility-volume relations arise solely from the dynamic features of the information flow. Empirical analyses at the market level support these predictions. Furthermore, the results indicate that removing the information flow significantly reduces the return volatility persistence and the extent of the reduction exhibits a size pattern.

Innovating firms pay more bribes than noninnovators across 25,000 firms in 57 countries. The difference is larger in countries with more bureaucratic regulation and weaker governance. Innovators that pay bribes do not receive better services and do not have greater propensity to engage in other illegal activities such as tax evasion. Thus, innovators are more likely to be victims of corruption than perpetrators. Our findings point to the challenges facing entrepreneurs in developing countries and are consistent with the view that rent seeking by government officials unlike private criminal activity is more likely to target innovators.

This paper examines the role of local attitudes toward gambling on corporate innovative activity. Using a county’s Catholics-to-Protestants ratio as a proxy for local gambling preferences, we find that firms located in gambling-prone areas tend to undertake riskier projects, spend more on innovation, and experience greater innovative output. We contrast the local gambling effect with chief executive officer (CEO) overconfidence, another behavioral effect reported to influence innovation. We find that local gambling preferences are a stronger determinant of innovative activity, with CEO overconfidence being more relevant to innovation in areas where gambling attitudes are strong.

Does the momentum effect arise naturally from the determination of asset prices in market equilibrium? We calibrate a standard endowment model of multiple assets under recursive preferences. The momentum effect partly comes from investors’ aversion to consumption risks. An unexpected dividend increase generates a positive return and increases the asset’s proportion of consumption, raising the correlation between its future dividend growth and consumption growth. This is compensated by a higher expected return, generating the momentum effect. The cross-sectional difference in expected returns is also a key contributor. The quantified model produces sizable momentum profits, often close to the observed profits.

Using data on over 4,000 individual residential addresses, we find that geographic distance between directors and corporate headquarters is related to information acquisition and board decisions. The fraction of a board’s unaffiliated directors who live near headquarters is higher when information-gathering needs are greater. When the fraction of unaffiliated directors living near headquarters is lower, nonroutine chief executive officer (CEO) turnover is more sensitive to stock performance. Also, the level, intensity, and sensitivity of CEO equity-based pay increase with board distance. Overall, our results suggest that geographic location is an important dimension of board structure that influences directors’ costs of gathering information.

Although stock returns of intangibles-intensive firms tend to exceed physical assets-intensive firms, risk-adjusted returns of actively managed mutual funds significantly decrease (increase) with their portfolios’ exposure to intangibles-intensive (physical assets-intensive) firms. Fund managers tend to exhibit skill when they focus on difficult-to-value (e.g., small) firms, except when the firms are intangibles-intensive. In sum, the worst-performing funds are in areas of the market that seem to offer ample opportunities for professional investors due to exacerbated mispricing. The negative impact of investments in intangibles-intensive firms on fund performance appears to be driven by extrapolation bias and decreases with learning from experience.

Rather than assuming a fixed recovery rate in estimation, we estimate recovery rates from credit default swap spreads, using 3 years of daily data on 152 corporations. We use a quadratic pricing model, which ensures nonnegative default probabilities and recovery rates. The estimated cross section of recovery rates is plausible, with an average recovery rate of 54% and substantial cross-sectional variation. Estimated 5-year default probabilities are on average 67% higher than default probabilities obtained using the standard 40% recovery assumption. This finding critically impacts the valuation of structured credit products. Larger firms and firms with more tangible assets have higher recovery rates.

This paper examines whether one of the most important participants in the takeover market, the institutional investors of target companies, suffers from the disposition effect and, if so, how this selling bias influences the takeover outcomes. I report robust evidence that target institutional investors are reluctant to realize losses. This bias further allows their sunk cost to affect both the takeover price and the deal success. My results are explained by neither the undervalued targets nor the 52-week-high price effect. They are most pronounced among targets whose investors have a strong propensity to hold on to loser stocks.

A growing number of studies use dollar-weighted (DW) returns as evidence that bad timing substantially reduces investor returns, and that consequently the equity risk premium must be considerably lower than previously thought. This paper demonstrates that this method is subject to a hindsight effect (as prior returns influence levels of new investment) and derives a technique that corrects it. The results show that for mainstream U.S. equities, DW returns are low because of this hindsight effect (bad investor timing had very little impact). Thus, low DW returns do not imply that the risk premium is correspondingly low.

A spurious positive relation between exponential generalized autoregressive conditional heteroskedasticity (EGARCH) estimates of expected month t idiosyncratic volatility and month t stock returns arises when the month t return is included in estimation of model parameters. We illustrate via simulations that this look-ahead bias is problematic for empirically observed degrees of stock return skewness and typical monthly return time series lengths. Moreover, the empirical idiosyncratic risk-return relation becomes negligible when expected month t idiosyncratic volatility is estimated using returns only up to month t − 1.