Fractional Momentum Strategy II

Introduction

The FD-II framework (Fractional Differencing with Turnover Management) advances momentum and reversal trading by embedding sequential decision-making into a scalable convex optimization. Instead of smoothing predictive signals, FD-II preserves their high-frequency informativeness and constraints only the portfolio's reaction through two innovations:

  1. ℓ1 turnover penalization, which directly controls trading intensity, and
  2. Path-Dependent Constraints (PDCs), which enforce realistic trading rules such as inertia, bounded entry, and side-switching delays.

This design substantially reduces transaction costs while maintaining signal responsiveness. In doing so, FD-II transforms high-turnover anomaly strategies—long thought impractical for daily rebalancing—into robust performers. Empirical results demonstrate 95-99% turnover reduction, 76-99% drawdowns cut to 22-49%, and net Sharpe gains of 38-149%, enabling daily trading of factor portfolios at net performance levels previously associated only with monthly horizons.

Performance Teaser

The plots below highlight the dramatic effect of turnover control:

Turnover: Unmanaged strategies exhibit daily turnovers of 70-240%, while FD-II reduces this to 1-6%.

Gross vs. Net Sharpe: While turnover management can reduce raw signal exploitation, the net effect after 10bps costs is strongly positive—e.g., fractional momentum reaches a net Sharpe ratio of 2.69.

Wealth trajectories: With FD-II, both long-short and long-only implementations deliver stable volatility-adjusted wealth growth, surpassing traditional benchmarks such as Markowitz optimization, equal-weighted portfolios, and unmanaged anomaly sorts.

Key Performance Results

The table illustrates the net effects of turnover management. A few highlights:

  • Long-short portfolios (Panel A):
    • FRACMOM (D) improves from -2.70 net Sharpe (unmanaged) to +2.69 with FD-II.
    • STR (D) rises from -0.33 to +1.39 net Sharpe.
    • Across anomalies, drawdowns shrink from near-100% to ~20-40%.
  • Long-only portfolios (Panel B):
    • MOM, STR, and STRMOM all stabilize with Sharpe ratios in the 1.1-1.2 range, competitive with institutional long-only mandates.
    • Maximum drawdowns fall by 15-30 percentage points relative to unmanaged baselines.

Methodology Notes

  • Investment universe: Daily CRSP data (NYSE/AMEX/NASDAQ), 1973-2020, filtered to 500-1000 mid/large-cap stocks. Microcaps excluded.
  • Transaction cost model: Linear, 10bps per turnover unit.
  • Strategies tested: 12-month momentum (MOM), 1-month short-term reversal (STR), double-sorted MOM-STR (STRMOM), and fractional momentum (FRACMOM).
  • Optimization: Minimum variance with QIS covariance shrinkage.
  • Turnover controls: ℓ1 penalties + PDCs (no smoothing of predictive signals).

Cite this work

This work relies on our paper Smoothing Out Momentum and Reversal. This paper can be cited as:


            Chitsiripanich, Soros and Paolella, Marc S. and Polak, Pawel and Walker, Patrick S., Smoothing Out Momentum and Reversal (Semptember 13, 2024). Swiss Finance Institute Research Paper No. 24-47, Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4955388
            

BibTeX citation


            @article{smoothing-out-momentum-and-reversal,
                author = {Soros Chitsiripanich and Marc S. Paolella and Pawel Polak and Patrick S. Walker},
                title = {Smoothing Out Momentum and Reversal},
                journal = {Swiss Finance Institute Research Paper No. 24-47},
                year = {2024},
                note = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4955388}
            }