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The portfolio optimization model has limited impact in practice because of estimation issues when applied to real data. To address this, we adapt two machine learning methods, regularization and cross-validation, for portfolio optimization. First, we introduce performance-based regularization (PBR), where the idea is to constrain the sample variances of the estimated portfolio risk and return, which steers the solution toward one associated with less estimation error in the performance. We consider PBR for both mean-variance and mean-conditional value-at-risk (CVaR) problems. For the mean-variance problem, PBR introduces a quartic polynomial constraint, for which we make two convex approximations: one based on rank-1 approximation and another based on a convex quadratic approximation. The rank-1 approximation PBR adds a bias to the optimal allocation, and the convex quadratic approximation PBR shrinks the sample covariance matrix. For the mean-CVaR problem, the PBR model is a combinatorial optimization problem, but we prove its convex relaxation, a quadratically constrained quadratic program, is essentially tight. We show that the PBR models can be cast as robust optimization problems with novel uncertainty sets and establish asymptotic optimality of both sample average approximation (SAA) and PBR solutions and the corresponding efficient frontiers. To calibrate the right-hand sides of the PBR constraints, we develop new, performance-based k -fold cross-validation algorithms. Using these algorithms, we carry out an extensive empirical investigation of PBR against SAA, as well as L1 and L2 regularizations and the equally weighted portfolio. We find that PBR dominates all other benchmarks for two out of three Fama–French data sets. This paper was accepted by Yinyu Ye, optimization .

Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, investment, and betting-against-beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.

Responsible investors require data to underpin their stock and sector selections. Regardless of the rating agency, bond ratings for a particular issuer are broadly similar. This is not the case for ESG ratings. Companies with a high score from one rater often receive a middling or low score from another rater. This article examines the extent of, and reasons for, disagreement among the leading suppliers of ESG ratings. The weightings given to each pillar of an ESG rating also vary across agencies. Many asset managers contend that ESG ratings can help investors to select assets with superior financial prospects, and the authors therefore review the investment performance of portfolios and of indexes screened for their ESG credentials. In the authors' opinion, ESG ratings, used in isolation, are unlikely to make a material contribution to portfolio returns.

We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many \"miles to go\" before the gains promised by optimal portfolio choice can actually be realized out of sample.

We present a new model of investors delegating portfolio management to professionals based on trust. Trust in the manager reduces an investor's perception of the riskiness of a given investment, and allows managers to charge fees. Money managers compete for investor funds by setting fees, but because of trust, fees do not fall to costs. In equilibrium, fees are higher for assets with higher expected return, managers on average underperform the market net of fees, but investors nevertheless prefer to hire managers to investing on their own. When investors hold biased expectations, trust causes managers to pander to investor beliefs.

We investigate how conventional asset managers account for environmental, social, and governance (ESG) factors in their investment process. We do so on the basis of an international survey among fund managers. We find that many conventional managers integrate responsible investing in their investment process. Furthermore, we find that ESG information in particular is being used for red flagging and to manage risk. We find that many conventional fund managers have already adopted features of responsible investing in the investment process. Furthermore, we argue and show that ESG investing is highly similar to fundamental investing. We also reveal that there is a substantial difference in the ways in which U.S. and European asset managers view ESG.

We survey institutional investors to better understand their role in the corporate governance of firms. Consistent with a number of theories, we document widespread behind-the-scenes intervention as well as governance-motivated exit. These governance mechanisms are viewed as complementary devices, with intervention typically occurring prior to a potential exit. We further find that long-term investors and investors that are less concerned about stock liquidity intervene more intensively. Finally, we find that most investors use proxy advisors and believe that the information provided by such advisors improves their own voting decisions.

We derive a closed-form optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean-reversion speeds. The optimal strategy is characterized by two principles: (1) aim in front of the target, and (2) trade partially toward the current aim. Specifically, the optimal updated portfolio is a linear combination of the existing portfolio and an \"aim portfolio,\" which is a weighted average of the current Markowitz portfolio (the moving target) and the expected Markowitz portfolios on all future dates (where the target is moving). Intuitively, predictors with slower mean-reversion (alpha decay) get more weight in the aim portfolio. We implement the optimal strategy for commodity futures and find superior net returns relative to more naive benchmarks.

Our study demonstrates empirically that the choice of resource allocation strategy affects innovation performance. Allocating resources to a broader range of innovation projects increases new product sales, an effect that appears to outweigh that of resource intensity. In addition, we find that the performance benefit of breadth is higher for firms that allocate resources selectively at later stages of the innovation process. This breadth-selectiveness effect is greatest for firms intending to create relatively more novel products, departing further from their knowledge base. Based on these results, we theorize that breadth increases performance because it spreads firms' bets on unproven innovative endeavors. Limiting resource commitments by selecting out deteriorating projects prevents an escalation in the costs of breadth. This advantage increases with the uncertainty implicit in greater innovative intent. The paper thus contributes to theory of how resource allocation strategies influence performance outcomes of innovation project portfolios.

We show how to conduct asymptotically valid tests of model comparison when the extent of model mispricing is gauged by the squared Sharpe ratio improvement measure. This is equivalent to ranking models on their maximum Sharpe ratios, effectively extending the Gibbons, Ross, and Shanken (1989) test to accommodate the comparison of nonnested models. Mimicking portfolios can be substituted for any nontraded model factors, and estimation error in the portfolio weights is taken into account in the statistical inference. A variant of the Fama and French (2018) 6-factor model, with a monthly updated version of the usual value spread, emerges as the dominant model.