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Trend-following with better adaptation to large downside risks.

Teruko Takada1, Takahiro Kitajima1,2

  • 1Graduate School of Business, Osaka Metropolitan University, Osaka, Japan.

Plos One
|October 18, 2022
PubMed
Summary

Trend-following strategies face challenges with profitability due to market conditions. Machine learning integration helps optimize trend-following by balancing averaging windows, improving performance in equity markets.

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Area of Science:

  • Quantitative Finance
  • Algorithmic Trading
  • Machine Learning Applications

Background:

  • Trend-following strategies are popular but have shown diminished profitability in recent equity markets.
  • Identifying and mitigating losses from long-term trend reversals remains a significant challenge for traders.
  • The effectiveness of trend-following is questioned due to recent performance issues.

Purpose of the Study:

  • To examine the impact of market conditions and averaging windows on the profitability of trend-following rules.
  • To compare trend-following rules (moving average, momentum) with a machine-classification-based non-trend-following rule.
  • To propose remedies for the diminished profitability of trend-following strategies.

Main Methods:

  • Out-of-sample experiment using four major stock indices.
  • Comparison of moving average and momentum trend-following rules against a machine-classification rule.
  • Analysis of the effect of market conditions and averaging window size on strategy performance.

Main Results:

  • Trend-following rules demonstrate a significant advantage in avoiding downside risks.
  • A discrepancy exists in optimal averaging window sizes between different trend direction phases, worsened by high positive trend ratios.
  • Machine learning incorporation effectively alleviates the dilemma of choosing averaging windows, leading to profit maximization.

Conclusions:

  • The sluggishness of trend-following is attributed to insufficient trend reversal opportunities, not a loss of inherent profitability.
  • Integrating machine learning into trend-following can mitigate performance dilemmas by optimizing averaging window selection.
  • A simple guideline for selecting the optimum averaging window is suggested to improve trend-following performance.