Recent years have seen increased demand from institutional investors for passive replication products that track the performance of hedge fund strategies using liquid investable assets such as futures contracts. In practice, linear replication methods suffer from poor tracking performance and high turnover. We propose a model combination approach to index replication that pools information from a diverse set of pre-specified factor models. Compared with existing methods, the pooled clone strategies yield consistently lower tracking errors, generate less severe portfolio drawdowns, and require substantially smaller trading volume. The pooled hedge fund clones also provide economic benefits in a portfolio allocation context.

Model selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.

Prior studies find that a strategy that buys high‐beta stocks and sells low‐beta stocks has a significantly negative unconditional capital asset pricing model (CAPM) alpha, such that it appears to pay to “bet against beta.” We show, however, that the conditional beta for the high‐minus‐low beta portfolio covaries negatively with the equity premium and positively with market volatility. As a result, the unconditional alpha is a downward‐biased estimate of the true alpha. We model the conditional market risk for beta‐sorted portfolios using instrumental variables methods and find that the conditional CAPM resolves the beta anomaly.

We characterize the distribution of long-term equity returns based on the historical record of stock market performance in a broad cross section of 39 developed countries over the period from 1841 to 2019. Our comprehensive sample mitigates concerns over survivor and easy data biases that plague other work in this area. A bootstrap simulation analysis implies substantial uncertainty about long-horizon stock market outcomes, and we estimate a 12% chance that a diversified investor with a 30-year investment horizon will lose relative to inflation. The results contradict the conventional advice that stocks are safe investments over long holding periods.

We study the effects of time-varying volatility and investment horizon on the economic significance of stock market return predictability from the perspective of Bayesian investors. Using a vector autoregression framework with stochastic volatility (SV) in market returns and predictor variables, we assess a broad set of twenty-six predictors with both in-sample and out-of-sample designs. Volatility and horizon are critically important for assessing return predictors, as these factors affect how an investor learns about predictability and how she chooses to invest based on return forecasts. We find that statistically strong predictors can be economically unimportant if they tend to take extreme values in high volatility periods, have low persistence, or follow distributions with fat tails. Several popular predictors exhibit these properties such that their impressive statistical results do not translate into large economic gains. We also demonstrate that incorporating SV leads to substantial utility gains in real-time forecasting.

We investigate the optimal savings decisions for investors with access to pre-tax (traditional) and post-tax (Roth) versions of tax-advantaged retirement accounts. The model features a progressive tax schedule and uncertainty over future tax rates. Traditional accounts are valuable for hedging retirement account performance and managing current income near tax-bracket cutoffs, whereas Roth accounts allow investors to mitigate uncertainty over future tax schedules. The optimal asset location policy for most households involves diversifying between traditional and Roth vehicles. Contrary to conventional advice, the substantial economic benefits from Roth investments are not limited to investors with low current income.

Using a comprehensive set of 103 equity strategies, we analyze the value of volatility-managed portfolios for real-time investors. Volatility-managed portfolios do not systematically outperform their corresponding unmanaged portfolios in direct comparisons. Consistent with Moreira and Muir (2017), volatility-managed portfolios tend to exhibit significantly positive alphas in spanning regressions. However, the trading strategies implied by these regressions are not implementable in real time, and reasonable out-of-sample versions generally earn lower certainty equivalent returns and Sharpe ratios than do simple investments in the original, unmanaged portfolios. This poor out-of-sample performance for volatility-managed portfolios stems primarily from structural instability in the underlying spanning regressions.