We show that corporate investment decisions can explain conditional dynamics in expected asset returns. Our approach is similar in spirit to Berk, Green, and Naik (1999), but we introduce to the investment problem operating leverage, reversible real options, fixed adjustment costs, and finite growth opportunities. Asset betas vary over time with historical investment decisions and current product market demand. Book-to-market effects emerge and relate to operating leverage, while size captures the residual importance of growth options relative to as-sets in place. We estimate and test the model using simulation methods and reproduce portfolio excess returns comparable to the data. Corporate investment decisions are often evaluated in a real options context, 1 and option exercise can change the riskiness of a firm in various ways. For example, if growth opportunities are finite, the decision to invest changes the ratio of growth options to assets in place. Additionally, the resulting increase

I survey work on the intersection between macroeconomics and finance. The challenge is to find the right measure of “bad times,” rises in the marginal value of wealth, so that we can understand high average returns or low prices as compensation for assets’ tendency to pay off poorly in “bad times.” I survey the literature, covering the time-series and cross-sectional facts, the equity premium, consumption-based models, general equilibrium models, and labor income/idiosyncratic risk approaches.

Growth in capital expenditures conditions subsequent classification of firms to portfolios based on size and book-to-market ratios, as in the widely used Fama and French (1992, 1993) methods. Growth in capital expenditures also explains returns to portfolios and the crosssection of future stock returns. These findings are consistent with recent theoretical models (e.g., Berk, Green, and Naik (1999)) in which the exercise of investment growth options results in changes in both valuation and expected stock returns.

In this paper we revisit the theoretical relation between financial leverage and stock returns in a dynamic world where both the corporate investment and finance decisions are endogenous. We find that the link between leverage and stock returns is more complex than the static textbook examples suggest and will usually depend on the investment opportunities available to the firm. In the presence of financial market imperfections leverage and investment are generally correlated so that highly levered firms are also mature firms with relatively more (safe) book assets and fewer (risky) growth opportunities. We use a quantitative version of our model to generate empirical predictions concerning the empirical relationship between leverage and returns. We test these implications in actual data and find support for them.

This paper provides new evidence on the empirical success of structural models in explaining corporate credit risk changes. A parsimonious set of common factors and firm-level fundamentals, inspired by structural models, explains more than 54 % (67%) of the variation in credit spread changes for medium (low) grade bonds. No dominant latent factor is present in the unexplained variation. While our set of variables has lower explanatory power among high-grade bonds, it does capture most of the systematic variation of credit-spread changes in that category as well. It also subsumes the explanatory power of the Fama and French (1993) factors among all grade classes. This paper assesses the success of structural models in empirical studies of changes in corporate credit spreads. Our focus is set on the change in the credit spread, not its level. 1 The difference in studying credit spreads vs. changes in credit spreads is equiv-alent to the difference in studying equity prices vs. equity expected returns. Indeed, there is a one-to-one correspondence between the spread level and the bond price, while

We solve a model with two i.i.d. Lucas trees. While the corresponding one-tree model produces a constant price-dividend ratio and i.i.d. returns, the two-tree model produces interesting asset-pricing dynamics. Investors want to rebalance their portfolios after any change in value. Since the size of the trees is fixed, prices must adjust to offset this desire. As a result, expected returns, excess returns, and return volatility all vary through time. Returns display serial correlation and are predictable from price-dividend ratios. Return volatility differs from cash-flow volatility and return shocks can occur without news about cash flows. 2 Returns that are independent over time are the standard benchmark for theory and empirical work in asset pricing. Yet, on reflection, i.i.d. returns seem impossible with multiple positive net supply assets. If a stock or a sector rises in value, investors will try to rebalance away from it. But we cannot all rebalance, as the average investor must hold the market portfolio. It seems that the successful asset’s expected returns must

and participants in seminars at UCLA, the University of Alberta, and in the Liquidity Conference

There is an urgent need for improved measurement and benchmarking of size and book-tomarket (style) performance. Given the proliferation of choice, a potentially serious problem is that existing style indexes can provide a somewhat confusing picture of the return on these factors. In this paper, we present detailed evidence of strong heterogeneity in the information conveyed by competing indexes. We also report disturbing evidence that this heterogeneity poses serious problems, not only for modern portfolio analysis, but also for empirical tests of asset pricing theory. This is somewhat reminiscent of Roll’s (1977) critique of CAPM: if the “true ” book-to-market and size factors are not observable, and if there is very little robustness with respect to the choice of the proxy used in empirical tests, then the relevance of these factors in asset pricing theory may never be empirically testable. As an attempt to address some of these problems, in exploratory analysis we suggest various methodologies designed to help extract a “pure style index ” from competing index returns.

We study own and rival risk in a dynamic duopoly with a homogeneous output good, stochastic industry demand, real options to expand or contract capacity, and potentially different adjustment costs across firms. In general, a competitor’s options to adjust capacity reduce own-firm risk through a simple hedging channel. Intuitively, product market improvements increase the probability of near-term rival expansion, and negative demand shocks induce competitor contraction. As the rival moves closer to its expansion or contraction boundaries, these hedging effects become more important, and generally differ from the own-firm effects of real options. As a consequence, when a leader and a follower emerge in equilibrium, risk dynamics differ substantially from the simultaneous move benchmark. In leader-follower equilibria ownfirm and competitor required returns tend to move together through

referee, and participants at the Eastern Finance and Midwest Finance Association Meetings, 2003, and at the Washington Area Finance Association Meetings, 2002. All remaining errors are ours. Bayesian Analysis of Stochastic Betas This paper proposes a mean-reverting stochastic process for the market beta. In a simulation study, the proposed model generates significantly more precise beta estimates relative to competing GARCH betas, betas scaled by aggregate or firm-level variables, and betas based on rolling regressions, even when the true betas are generated based on these competing specifications. Applying our model to US industry portfolios, we document significant improvement in out-of-sample hedging effectiveness relative to the traditional OLS beta estimate. In asset-pricing tests, our model provides substantially stronger support for the conditional CAPM relative to competing beta models. It also helps resolve asset-pricing anomalies such as the size, book-to-market, and idiosyncratic volatility effects in the cross-section of stock returns. Beta estimates are essential for many areas of modern finance, such as asset pricing, cash flow valuation, risk management, and performance evaluation. The modeling and