Attempts to characterize stock return predictability have resulted in little consensus on the important conditioning variables, giving rise to model uncertainty and data snooping fears. We introduce a new methodology that explicitly incorporates model uncertainty by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful information. Our approach minimizes data snooping given the information set and the priors. We compare the prior views of a skeptic and a confident investor. The data imply posterior probabilities that are in general more supportive of stock return predictability than the priors for both types of investors.

Attempts to characterize stock return predictability have resulted in little consensus on the important conditioning variables, giving rise to model uncertainty and data snooping fears. We introduce a new methodology that explicitly incorporates model uncertainty by comparing all possible models simultaneously and in which the priors are calibrated to reflect economically meaningful information. Our approach minimizes data snooping given the information set and the priors. We compare the prior views of a skeptic and a confident investor. The data imply posterior probabilities that are in general more supportive of stock return predictability than the priors for both types of investors.

[We introduce a new measure of active portfolio management, Active Share, which represents the share of portfolio holdings that differ from the benchmark index holdings. We compute Active Share for domestic equity mutual funds from 1980 to 2003. We relate Active Share to fund characteristics such as size, expenses, and turnover in the cross-section, and we also examine its evolution over time. Active Share predicts fund performance: funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence. Nonindex funds with the lowest Active Share underperform their benchmarks.]

We introduce a new measure of active portfolio management, Active Share, which represents the share of portfolio holdings that differ from the benchmark index holdings. We compute Active Share for domestic equity mutual funds from 1980 to 2003. We relate Active Share to fund characteristics such as size, expenses, and turnover in the cross-section, and we also examine its evolution over time. Active Share predicts fund performance: funds with the highest Active Share significantly outperform their benchmarks, both before and after expenses, and they exhibit strong performance persistence. Nonindex funds with the lowest Active Share underperform their benchmarks.

[This article applies the methodology of Bai and Ng (2002, 2004) for decomposing panel data into systematic and idiosyncratic components to both stock returns and turnover panels. This approach works well for both returns and turnover, despite the presence of severe heteroscedasticity and nonstationarity of individual stocks' turnover. We test the mutual fund separation model of Lo and Wang (2000). Trading due to systematic risk in returns can account for 66% of systematic turnover. Thus, portfolio rebalancing due to systematic risk is a very important motive for stock trading. Finally, several common turnover measures may understate the impact of stock trading.]

[We analyze proprietary internal capital allocation data from a large retail banking group consisting of member banks and a headquarters organization. We find that capital allocations from headquarters compensate for deposit shortfalls on the bank level, suggesting that the headquarters offers deposit smoothing to member banks. We then analyze how the distribution of influence within the group relates to capital allocations and lending behavior. More influential banks are allocated more funds from headquarters, and their loan growth is less sensitive to their deposit base. The effects of influence are stronger if banks have a greater demand for deposit smoothing.]

[We study whether option-implied jump risk premia can explain the high observed level of credit spreads. We use a structural jump-diffusion firm value model to assess the level of credit spreads generated by option-implied jump risk premia. Prices and returns of equity index and individual options are used to estimate the jump parameters. We further calibrate the model to historical information on default risk and the equity premium. The results show that incorporating option-implied jump risk premia brings predicted credit spread levels much closer to observed levels. The introduction of jumps also helps to improve the fit of the volatility of credit spreads and equity returns.]

This paper considers the impact of the takeover likelihood on firm valuation. If firms are more likely to acquire when there is more free cash or lower required rates of return, the targets become more sensitive to shocks to cash flows or the price of risk. Ceteris paribus, firms exposed to takeovers have different rates of return than protected firms. Using takeover likelihood estimates, we create a “takeover factor, ” buying (selling) firms with a high (low) takeover likelihood, which generates “abnormal ” returns. Several tests confirm that the takeover factor helps explaining cross-sectional differences in equity returns and is related to takeover activity. This paper considers the impact of the takeover channel on valuation. While it is well known that target shareholders receive a large premium on a takeover, how expectations about takeover premiums affect firm valuation has not been investigated. One possible reason for this lack of interest may be the assumption that differences in takeover exposure are purely idiosyncratic, and hence do not affect a firm’s cost of capital. In that case, the issue of incorporating the takeover channel into valuation is solved by simply adding the expected

[This paper considers the impact of the takeover likelihood on firm valuation. If firms are more likely to acquire when there is more free cash or lower required rates of return, the targets become more sensitive to shocks to cash flows or the price of risk. Ceteris paribus, firms exposed to takeovers have different rates of return than protected firms. Using takeover likelihood estimates, we create a "takeover factor," buying (selling) firms with a high (low) takeover likelihood, which generates "abnormal" returns. Several tests confirm that the takeover factor helps explaining cross-sectional differences in equity returns and is related to takeover activity.]

[We investigate the effects of shareholder governance mechanisms on bondholders and document two new findings. First, the impact of shareholder control (proxied by large institutional blockholders) on credit risk depends on takeover vulnerability. Shareholder control is associated with higher (lower) yields if the firm is exposed to (protected from) takeovers. In the presence of shareholder control, the difference in bond yields due to differences in takeover vulnerability can be as high as 66 basis points. Second, event risk covenants reduce the credit risk associated with strong shareholder governance. Therefore, without bond covenants, shareholder governance, and bondholder interests diverge.]