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Return signal momentum

Journal: Journal of Banking & Finance

Date: 2021

Author: Fotis Papailias, Jiadong Liu, Dimtrios D. Thomakos

Abstract:
A new type of momentum based on the signs of past returns is introduced. This momentum is driven primarily by sign dependence, which is positively related to average return and negatively related to return volatility. An empirical application using a universe of commodity and financial futures offers supporting evidence for the existence of such momentum. Investment strategies based on return signal momentum result in higher returns and Sharpe ratios and lower drawdown relative to time series momentum and other benchmark strategies. Overall, return signal momentum can benefit investors as an effective strategy for speculation and hedging.

Link: Google Scholar

Background and Context

Research Focus

This study introduces a new type of momentum trading strategy based on the signs of past returns (positive/negative) rather than just using return magnitudes.

Data Coverage

Analysis covers 55 of the world's most liquid futures contracts across commodities, currencies, equity indices and government bonds from 1985 to 2015.

Methodology

Returns are analyzed using probability thresholds of positive signs over 12-month periods to generate trading signals, with both fixed and time-varying thresholds tested.

RSM Strategies Outperform Traditional Methods

  • RSM strategy with 0.4 threshold achieves highest Sharpe ratio compared to traditional strategies
  • Demonstrates superior risk-adjusted returns over benchmark approaches
  • Shows effectiveness of using sign-based momentum signals

Cumulative Profits Show Long-Term Outperformance

  • RSM 0.4 strategy generates significantly higher cumulative profits
  • Demonstrates consistent outperformance over 30-year study period
  • Shows superior profitability compared to traditional momentum approaches

Lower Maximum Drawdown Indicates Better Risk Management

  • RSM strategy demonstrates lowest maximum drawdown among all approaches
  • Indicates better downside risk protection
  • Shows improved risk management capabilities of sign-based signals

Strategy Performance Across Different Time Horizons

  • Strategy performance improves with longer investment horizons
  • Shows consistently high winning rates across different timeframes
  • Demonstrates robustness of the strategy over time

Risk Factor Exposure Analysis

  • Shows moderate exposure to market and momentum factors
  • Negative exposure to size factor indicates tilt toward larger stocks
  • Demonstrates unique risk factor profile compared to traditional strategies

Contribution and Implications

  • Introduces a novel momentum strategy based on return signs that outperforms traditional approaches
  • Provides better risk-adjusted returns and lower drawdowns compared to existing strategies
  • Offers practical applications for both speculation and hedging in financial markets

Data Sources

  • Performance comparison chart based on Table 5 of the article
  • Cumulative profits visualization derived from Table 5 maximum drawdown statistics
  • Time horizon analysis based on Table 8 winning rate statistics
  • Risk factor exposure chart constructed from Table 6 Panel A data
  • Strategy comparison metrics sourced from Tables 5 and 7