The strong bias in favor of domestic securities is a well-documented characteristic of international investment portfolios, yet we show that the preference for investing close to home also applies to portfolios of domestic stocks. Specifically, U.S. investment managers exhibit a strong preference for locally headquartered firms, particularly small, highly levered firms that produce nontraded goods. These results suggest that asymmetric information between local and nonlocal investors may drive the preference for geographically proximate investments, and the relation between investment proximity and firm size and leverage may shed light on several well-documented asset pricing anomalies. THE STRONG PREFERENCE FOR DOMESTIC EQUITIES exhibited by investors in international markets, despite the well-documented gains from international diversification, 1 remains an important yet unresolved empirical puzzle in financial economics. As French and Poterba ~1991! document, U.S. equity

This paper estimates recent default risk premia for U.S. corporate debt, based on a close relationship between default probabilities, as estimated by Moody's KMV EDFs, and default swap (CDS) market rates. The default-swap data, obtained through CIBC from 22 banks and specialty dealers, allow us to establish a strong link between actual and risk-neutral default probabilities for the 69 firms in the three sectors that we analyze: broadcasting and entertainment, healthcare, and oil and gas. We find dramatic variation over time in risk premia, from peaks in the thrid quarter of 2002, dropping by roughly 50% to late 2003.

I use a vector autoregressive model (VAR) to decompose an individual firm’s stock return into two components: changes in cash-flow expectations (i.e., cash-flow news) and changes in discount rates (i.e., expected-return news). The VAR yields three main results. First, firm-level stock returns are mainly driven by cash-flow news. For a typical stock, the variance of cash-flow news is more than twice that of expected-return news. Second, shocks to expected returns and cash flows are positively correlated for a typical small stock. Third, expected-return-news series are highly correlated across firms, while cash-flow news can largely be diversified away in aggregate portfolios.

Many studies have documented portfolio strategies that provide returns in excess of those expected, given the level of risk of the portfolio. Variables that seem to have predictive power for equity returns include the market capitalization of the firm’s equity and the ratio of the firm’s book equity to market equity (BE/ME). Firms with low market capitalization and high book-tomarket values seem to earn high returns. With respect to the book-to-market anomaly, it has been argued that the apparent superior performance is due to a subtle selection bias in a typical data source used to implement the tests of asset pricing models, the COMPUSTAT data. We use a sample of COMPUSTAT data that is free from this bias to investigate whether the previous evidence on the book-to-market anomaly is an artifact of this selection bias. The postulated selection bias does not seem to be important for samples restricted to NYSE/AMEX firms. There is some difference when NASDAQ firms are included in the standard COMPUSTAT sample. This may be due to a truly stronger BE/ME effect or to a more severe selection bias in that sample. Our data do not allow us to disentangle these two possible explanations.

Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jump-diffusion firm value model to assess the level of credit spreads that is generated by option-implied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low out-of-sample prediction for credit spreads. Adding jumps and jump risk premia improves the fitofthe model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.

Four decades later, the CAPM is still widely used in applications, such as estimating the cost of capital for firms and evaluating the performance of managed portfolios. It is the centerpiece of MBA investment courses. Indeed, it is often the only asset pricing model taught in these courses. 1 The attraction of the CAPM is that it offers powerful and intuitively pleasing predictions about how to measure risk and the relation between expected return and risk. Unfortunately, the empirical record of the model is poor – poor enough to invalidate the way it is used in applications. The CAPM’s empirical problems may reflect theoretical failings, the result of many simplifying assumptions. But they may also be caused by difficulties in implementing valid tests of the model. For example, the CAPM says that the risk of a stock should be measured relative to a comprehensive “market portfolio ” that in principle can include not just traded financial assets, but also consumer durables, real estate, and human capital. Even if we

We show that the abnormal returns on high-default risk stocks documented by Vassalou and Xing (2004) are driven by short-term return reversals rather than systematic default risk. These abnormal returns occur only during the month after portfolio formation and are concentrated in a small subset of stocks that had recently experienced large negative returns. Empirical evidence supports the view that the short-term return reversal arises from a liquidity shock triggered by a clientele change.

The fragility of the CAPM has led to a resurgence of research that frequently uses trading strategies based on sorting procedures to uncover relations between firm characteristics (such as “value ” or “glamour”) and equity returns. We examine the propensity of these strategies to generate statistically and economically significant profits due to our familiarity with the data. Under plausible assumptions, data-snooping can account for up to 50 percent of the insample relations between firm characteristics and returns uncovered using single (one-way) sorts. The biases can be much larger if we simultaneously condition returns on two (or more) characteristics.

This paper develops and tests a method of extracting expectations about default losses on corporate debt from yield spreads. It is based on calibrating the Merton (1974) model to yield spread, leverage and equity volatility. For rating classes, the approach generates forward-looking expected default loss estimates similar to historical losses, and is also applicable for individual bonds. The information content of the estimate is superior to linear ex ante functions of the variables it uses as inputs. We also find that estimates of equity risk premia consistent with historical default experiences range from 3.1 % to 8.5 % depending on rating.

This paper evaluates the capacity of five structural creditrisk models to forecast default rates. In contrast to previous studies with similar objectives, the paper employs firm-level data and finds that model-based forecasts of default rates tend to be unbiased and to deliver point-in-time errors that are small in both statistical and economic terms. In addition, inand out-of-sample regression analysis reveals that the models account for a significant portion of the variability of credit risk over time but fail to fully reflect its dependence on macroeconomic cycles.