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 history of price‐dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend growth rates are predictable with values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% 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 decompose corporate bond and equity index returns into duration-matched government bond returns and the excess returns over this duration-matched counterfactual, which we term duration-adjusted returns. Compared with previously used excess return definitions (ie, returns in excess of Treasury bills), our decomposition leads to markedly different return patterns and asset pricing implications. In particular, we find that investment-grade bonds earn a small credit risk premium, comparable in magnitude to the convenience yield, and that duration adjustment resolves the CAPM’s failure to price corporate bonds. These findings highlight the importance of adjusting for nonstationary interest rate environments in asset pricing tests.

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.

We estimate risk-free interest rates unaffected by convenience yields on safe assets. We infer them from risky asset prices without relying on any specific model of risk. We obtain interest rates and implied convenience yields with maturities up to three years at a minutely frequency. Our estimated convenience yield on Treasuries equals about 40 basis points, is larger below three months maturity, and quadruples during the financial crisis. In high-frequency event studies, conventional and unconventional monetary stimulus reduces our rates more than the corresponding Treasury yields, thus broadly affecting rates even outside the narrow confines of the fixed-income market.

We decompose mutual fund value added by the length of funds' holdings using transaction‐level data. We motivate our decomposition with a model featuring horizon‐specific investment ideas, where short‐term ideas are less scalable because the associated trades cannot be spread over time. Fund turnover correlates negatively with the horizon over which value is added and positively with price impact costs. As predicted, holdings of high‐turnover funds add a substantial amount of value in the first two weeks, of which more than 80% is earned on Federal Open Market Committee (FOMC) and earnings announcement days. Holdings of low‐turnover funds add value only over longer horizons.

We establish an important role for the firm by studying capital reallocation decisions of mutual fund firms. The firm's decision to reallocate capital among its mutual fund managers adds at least $474,000 a month, which amounts to over 30% of the total value added of the industry. We provide evidence that this additional value added results from the firm's private information about the skill of its managers. The firm captures this value because investors reward the firm following a capital reallocation decision by allocating additional capital to the firm's funds.

We use exogenous variation in tax benefit functions to estimate firm‐specific cost of debt functions that are conditional on company characteristics such as collateral, size, and book‐to‐market. By integrating the area between the benefit and cost functions, we estimate that the equilibrium net benefit of debt is 3.5% of asset value, resulting from an estimated gross benefit (cost) of debt equal to 10.4% (6.9%) of asset value. We find that the cost of being overlevered is asymmetrically higher than the cost of being underlevered and that expected default costs constitute only half of the total ex ante costs of debt.

We introduce a real-time measure of conditional biases to firms’ earnings forecasts. The measure is defined as the difference between analysts’ expectations and a statistically optimal unbiased machine-learning benchmark. Analysts’ conditional expectations are, on average, biased upward, a bias that increases in the forecast horizon. These biases are associated with negative cross-sectional return predictability, and the short legs of many anomalies contain firms with excessively optimistic earnings forecasts. Further, managers of companies with the greatest upward-biased earnings forecasts are more likely to issue stocks. Commonly used linear earnings models do not work out-of-sample and are inferior to those analysts provide.

We exploit heterogeneity in decreasing returns to scale (DRS) parameters across mutual funds to analyze the importance of scalability for investors’ capital allocation decisions. We find strong evidence that steeper DRS attenuate flow sensitivity to performance. We calibrate a rational model of active fund management and show that a large fraction of cross-sectional variation in assets-under-management is due to investors anticipating the effects of scale on return performance. We conclude that DRS play a key role in achieving equilibrium in the intermediated investment management market.

We classify asset pricing anomalies into those exacerbating mispricing (build-up anomalies) and those resolving it (resolution anomalies). We estimate the dynamics of price wedges for well-known anomaly portfolios and map them to firm-level mispricings. We find that several prominent anomalies like momentum and profitability further dislocate prices. Multi-factor models designed to eliminate one-month alphas still produce large price wedges. Our estimates yield a novel decomposition of Tobin’s q, revealing that q’s mispricing component has substantial explanatory power for firm investment. Overall, our results suggest that financial intermediaries chasing build-up anomalies negatively affect price efficiency and associated real capital allocation.