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).
Dynamic Trading with Predictable Returns and Transaction Costs
Article first published online: 12 NOV 2013
© 2013 The American Finance Association
The Journal of Finance
Volume 68, Issue 6, pages 2309–2340, December 2013
How to Cite
GÂRLEANU, N. and PEDERSEN, L. H. (2013), Dynamic Trading with Predictable Returns and Transaction Costs. The Journal of Finance, 68: 2309–2340. doi: 10.1111/jofi.12080
- Issue published online: 12 NOV 2013
- Article first published online: 12 NOV 2013
- Accepted manuscript online: 26 JUL 2013 10:16AM EST
- Manuscript Accepted: 14 JUN 2013
- Manuscript Received: 21 JUN 2009
- European Research Council. Grant Number: 312417
- FRIC Center for Financial Frictions. Grant Number: 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.