Smoothing Out Momentum and Reversal
Soros Chitsiripanich,
Marc S. Paolella,
Pawel Polak and
Patrick S. Walker
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Soros Chitsiripanich: University of Zurich - Department Finance
Marc S. Paolella: University of Zurich - Department Finance; Swiss Finance Institute
Pawel Polak: Stony Brook University-Department of Applied Mathematics and Statistics; Institute for Advanced Computational Science
Patrick S. Walker: University of Zurich, Department of Banking and Finance; OLZ AG
No 24-47, Swiss Finance Institute Research Paper Series from Swiss Finance Institute
Abstract:
We introduce new path-dependent constraints within a sequential portfolio optimization framework designed to reduce turnover in frequently rebalanced investment strategies, such as momentum and short-term reversal. This method classifies individual assets into distinct groups based on their attractiveness from signal and rebalancing perspectives, effectively managing the trade-off between anomaly-based predictability and the required trading volume for exploitation. These constraints function independently from the ℓ 1 portfolio turnover regularization, which manages reallocation at the aggregated portfolio level, proving more effective in enhancing net profitability. The combined turnover management mechanisms reduce the turnover of daily-rebalanced momentum and reversal portfolios by 95-99%, aligning closely with traditional monthly-rebalanced strategies. Furthermore, our method captures signals more promptly, resulting in more stable portfolios, a substantial reduction in maximum drawdown from 76-99% to 22-49%, and an improvement in risk-adjusted net returns by 38-149%, all under realistic transaction cost assumptions.
Keywords: Fractional Differencing; Momentum Crashes; Momentum Factor; Portfolio Optimization; Regularization; Reversal Strategy (search for similar items in EconPapers)
JEL-codes: C32 C53 C61 G11 G17 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2024-09
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2447
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