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Dynamic Trading with Predictable Returns and Transaction Costs




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    • Gârleanu is at Haas School of Business, University of California, Berkeley, NBER, and CEPR, and Pedersen is at New York University, Copenhagen Business School, AQR Capital Management, NBER, and CEPR. We are grateful for helpful comments from Kerry Back; Pierre Collin-Dufresne; Darrell Duffie; Andrea Frazzini; Esben Hedegaard; Ari Levine; Hong Liu (discussant); Anthony Lynch; Ananth Madhavan (discussant); Mikkel Heje Pedersen; Andrei Shleifer; and Humbert Suarez; as well as from seminar participants at Stanford Graduate School of Business, AQR Capital Management, University of California at Berkeley, Columbia University, NASDAQ OMX Economic Advisory Board Seminar, University of Tokyo, New York University, University of Copenhagen, Rice University, University of Michigan Ross School, Yale University School of Management, the Bank of Canada, and the Journal of Investment Management Conference. Pedersen gratefully acknowledges support from the European Research Council (ERC grant no. 312417) and the FRIC Center for Financial Frictions (grant no. DURF102).


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.