Research

Portfolio Choice with Target Horizon (Job Market Paper)

Finalist for one of the three Best Paper Awards at the 2026 Northern Finance Association conference

Presentations: Northern Finance Association (2026, scheduled), Southern Finance Association (2026, scheduled), Southwestern Finance Association (2026), Financial Management Association Annual Meeting (2025), University of Arizona (2025)

Abstract: I propose a new approach to multi-period portfolio choice in high-dimensional settings that considers important aspects of the joint distribution of holding-period returns while being agnostic about the dynamics of investment opportunities. I characterize optimal final payoffs through factor principal components of holding-period returns and form tradable factor-tracking portfolios that deliver these horizon-specific payoffs. The resulting optimal portfolio is a utility-maximizing combination of the factor-tracking portfolios. Using broad samples of equity portfolios as investable test assets, I show economically large utility gains for investors compared to important benchmarks, both in-sample and out-of-sample. More importantly, the estimated portfolio rules are horizon-specific: optimal short-horizon strategies cost investors with long horizons, while optimal long-horizon strategies appear deeply unattractive when evaluated by their short-term returns. The findings provide compelling evidence for the economic value of investment horizon in portfolio decisions and challenge the conventional focus on short-term performance metrics for evaluating portfolios of long-horizon investors.

The Efficient Cross-Section Is Sparse: Factor Timing and the Dimension of the SDF (Working Paper)

Abstract: Out-of-sample Sharpe ratios in the cross-section of anomaly returns keep rising as principal components with negligible variance are added, an apparent violation of the near-arbitrage restriction that expected returns concentrate on a few systematic sources of risk. I argue that this pattern is a property of the payoff space empirical asset pricing studies rather than of the market it describes. Academic anomaly portfolios are held at constant weights despite predictable variation in both their premia and their risk, and are therefore unconditionally mean-variance inefficient. Applying volatility timing and mean timing to each of 212 anomalies, using only each portfolio's own return history, raises the Sharpe ratio of the large majority. In the resulting efficient cross-section, out-of-sample performance is exhausted by nine principal components, whereas the original cross-section continues to improve through 48. The discount factor built from those few components also prices the cross-section more accurately than the celebrated factor models and than its original-space counterpart, in sample and out-of-sample. Factor proliferation appears to measure unharvested timing rents rather than a large number of distinct sources of compensated risk.

Time-Series Efficient Factors and the Cross-Section of Returns (Working Paper)

Abstract: A practical mean-timing approach systematically enhances asset pricing models and generates higher Sharpe ratios across a broad range of assets. Evaluating 206 anomaly portfolios, I document a significant improvement in models that incorporate time-series efficient (TSE) factors for pricing returns. On average, models using TSE factors correctly price 17.6% more anomaly portfolios than original models. This improvement extends beyond renowned asset pricing models; randomly constructed three-factor models using TSE factors outperform their counterparts that employ original factors. Notably, time-series efficient trading strategies exhibit an average 21.7% increase in out-of-sample Sharpe ratios, underscoring their potential value for real-time investors.

How AI Affects the Future Relationship Between Corporate Governance and Financial Markets: A Note on Impact Capitalism (Published)

Managerial Finance (2022, with R. McBride and A. Dastan)

Abstract: We discuss how artificial intelligence (AI) can facilitate and incentivize reform in corporate social responsibility (CSR), i.e. governance with regard to pledge for socially responsible investments (SRIs). In a closed system, there is a reinforcing feedback loop between SRI and CSR. AI is a moderator to increase SRI and a mediator to incentivize CSR. If the legal and ethical provisions are involved in the AI systems, they could act as catalyst for corporate governance reform towards sustainability.

The Term Structure of Factor Structure (Work in Progress)

(with Scott Cederburg and Austin Sabotka)

Abstract: This paper asks whether the systematic risks facing a long-horizon investor are the same ones that dominate monthly returns. We develop a benchmark that distinguishes real changes in structure from the noise that long-horizon estimates generate. Early results show long-horizon risk is less concentrated and differently composed than short-horizon risk.