We recover prices of dividend strips on the aggregate stock market using data from derivatives markets. The price of a k-year dividend strip is the present value of the dividend paid in k years. The value of the stock market is the sum of all dividend strip prices across maturities. We study the properties of strips and find that expected returns, Sharpe ratios, and volatilities on short-term strips are higher than on the aggregate stock market, while their CAPM betas are well below one. Short-term strip prices are more volatile than their realizations, leading to excess volatility and return predictability.

Web Appendix for "On the Timing and Pricing of Dividends", AER forthcoming. Our response to a recent paper by Boguth et al. can be found here.

We study optimal capital structure by first estimating firm-specific cost and benefit functions for debt. The benefit functions are downward sloping reflecting that the incremental value of debt declines as more debt is used. The cost functions are upward sloping, reflecting the rising costs that occur as a firm increases its use of debt. The cost functions vary by firm to reflect the firm’s characteristics such as asset collateral and redeployability, asset size, the book-to-market ratio, profitability, and whether the firm pays dividends.

We use these cost and benefit functions to produce a firm-specific recommendation of the optimal amount of debt that a given company should use. In textbook economics, equilibrium occurs where supply equals demand. Analogously, optimal capital structure occurs where the marginal benefit equals the marginal cost of debt. We illustrate optimal debt choices for specific firms such as Barnes & Noble, Coca-Cola, Six Flags, and Performance Food Group, among others. We also calculate the cost of being underlevered for companies that use too little debt, the cost of being overlevered for companies that use too much debt, and the net benefit of debt usage for those that are correctly levered. Finally, we provide formulas that can be easily used to approximate the cost of debt function, and in turn to determine the optimal amount of debt, for any given firm.

We estimate firm-specific marginal cost of corporate debt functions for several thousand companies between 1980 and 2006. The positively sloped marginal cost curves are identified by observing exogenous shifts in simulated tax benefit curves. By integrating the area between the benefit and cost functions we estimate that the net benefit of debt is about 3% of asset value, resulting from a gross benefit of debt of about 10% of asset value and a cost of debt of about 7%. Our findings are consistent over time and when accounting for fixed adjustment costs of debt. The location of a given company’s cost of debt function varies with characteristics such as asset collateral, size, book-to-market, intangibility, cash flows, and whether the firm pays dividends. We provide easy to use algorithms that allow others to construct firm-specific cost of debt curves. Our framework allows us to make recommendations about firm-specific optimal debt ratios and to approximate the cost of being under- or overlevered. We find that the cost of being overlevered is asymmetrically higher than the cost of being underlevered and that the default cost of debt amounts to approximately half of total ex ante cost of debt.

We propose a latent-variables approach within a present-value model to estimate the expected returns and expected dividend growth rates of the aggregate stock market. This approach aggregates information contained in the whole history of the price-dividend ratio and dividend growth rates to obtain predictors for future returns and dividend growth rates. We find that both returns and dividend growth rates are predictable with R-squared values ranging from 8.2-8.9 percent for returns and 13.9-31.6 percent for dividend growth rates. Both expected returns and expected dividend growth rates have a persistent component, but expected returns are more persistent than expected dividend growth rates.

We study an institutional investment problem in which a centralized decision maker, the Chief Investment Officer (CIO), for example, employs multiple asset managers to implement investment strategies in separate asset classes. The CIO allocates capital to the managers who, in turn, allocate these funds to the assets in their asset class. This two-step investment process causes several misalignments of objectives between the CIO and his managers and can lead to large utility costs for the CIO. We focus on (1) loss of diversification, (2) unobservable managerial appetite for risk, and (3) different investment horizons. We derive an optimal unconditional linear performance benchmark and show that this benchmark can be used to better align incentives within the firm. We find that the CIO's uncertainty about the managers' risk appetites increases both the costs of decentralized investment management and the value of an optimally designed benchmark.

Many authors have argued that proper modeling of the strategic interaction between players requires a game theoretic approach as opposed to a decision theoretic approach. We argue in this paper, however, that there are many environments in which decision analysis can deal with strategic interactions just as well and we present equivalence results for such environments. These equivalence results allow the prescriptive decision analyst to use the standard tools that a sound decision analysis requires, including decision trees and sensitivity analysis, even when confronted with strategic settings.

Most dynamic programming methods deployed in the portfolio choice literature involve recursions on an approximated value function. The simulation-based method proposed recently by Brandt, Goyal, Santa-Clara, and Stroud (Review of Financial Studies, 18, 831---873, 2005), relies instead on recursive uses of approximated optimal portfolio weights. We examine the relative numerical performance of these two approaches. We show that when portfolio weights are constrained by short sale restrictions for example, iterating on optimized portfolio weights leads to superior results. Value function iterations result in a lower variance but disproportionately higher bias of the solution, especially when risk aversion is high and the investment horizon is long.

Using the dollar-value a mutual fund manager adds as the measure of skill, we find that not only does skill exist (the average mutual fund manager adds about $2 million per year), but this skill is persistent, as far out as 10 years. We further document that investors recognize this skill and reward it by investing more capital with skilled managers. Higher skilled managers are paid more and there is a strong positive correlation between current managerial compensation and future performance. Finally, we find no evidence of a superstar effect - managers are compensated proportionately in the value they add.

We analyze the cross-section of firm-level equity yields using a new proprietary database of dividend futures contracts of the individual constituents of the Eurostoxx 50 index as well as the 50 largest constituents of the S&P 500 index. We use these yields to compute expected dividend growth rates and risk premia at the firm level with maturities up to three years. We find that the comovement of equity yields across firms is much stronger at short horizons, which appears puzzling for a general class of models that allows for time variation in expected returns and expected dividend growth rates. Our dataset also allows us to decompose the cross-section of expected stock returns by maturity and to directly test duration-based theories of stock prices and expected returns.

We study a new data set of prices of traded dividends with maturities up to 10 years across three world regions: the US, Europe, and Japan. We use these asset prices to construct equity yields, analogous to bond yields. We decompose these yields to obtain a term structure of expected dividend growth rates and a term structure of risk premia, which allows us to decompose the equity risk premium by maturity. We find that both expected dividend growth rates and risk premia exhibit substantial variation over time, particularly for short maturities. In addition to predicting dividend growth, equity yields help predict other measures of economic growth such as consumption growth. We relate the dynamics of growth expectations to recent events such as the financial crisis and the earthquake in Japan.

I study the cross-section of expected stock returns in a general equilibrium framework in which agents form habits over individual varieties of goods as opposed to over a composite consumption good. Goods are produced by monopolistically competitive firms whose income and price elasticities of demand depend on the habit formation of the consumers. Firms who produce goods with a low consumption surplus ratio, that is a high habit level relative to consumption, earn low expected returns and set high prices because their income and price elasticities of demand are low. By sorting firms in portfolios based on consumption surplus ratios or based on their recent price setting behavior, I find a return spread of 4-6 percent that cannot be explained by the Capital Asset Pricing Model or by commonly used multi-factor models.

We study the impact of regulations on the investment decisions of a defined benefits pension plan. We assess the influence of ex ante (preventive) and ex post (punitive) risk constraints on the gains to dynamic, as opposed to myopic, decision making. We find that preventive measures, such as Value-at-Risk constraints, tend to decrease the gains to dynamic investment. In contrast, punitive constraints, such as mandatory additional contributions from the sponsor when the plan becomes underfunded, lead to very large utility gains from solving the dynamic program. We also show that financial reporting rules have real effects on investment behavior. For example, the current requirement to discount liabilities at a rolling average of yields, as opposed to at current yields, induces grossly suboptimal investment decisions.

We study the investment problem of a pension fund, which employs multiple asset managers to implement investment strategies in separate asset classes. The Chief Investment Officer (CIO) of the fund allocates capital to the managers taking into account the liabilities of the fund. The managers subsequently allocate these funds to the assets in their asset class. This organizational structure relies on the premise that managers have specific stock selection skills that allow them to outperform passive benchmarks. However, by decentralizing asset allocation decisions, the fund introduces several inefficiencies due to (i) reduced opportunities to manage mismatch risk, (ii) a loss in diversification, and (iii) imperfectly observable appetites for managerial risk taking. If the CIO compensates the managers using cash benchmarks, the information ratio required for each active manager to justify this organizational structure ranges from 0.4 to 1.3. However, we derive optimal benchmarks that substantially mitigate the inefficiencies, while preserving the potential benefits of active management. The optimal performance benchmarks reflect the risk in the liabilities. We therefore develop a novel framework to implement liabilities-driven investment (LDI) strategies.