Trend-following with better adaptation to large downside risks

Abstract

Avoiding losses from long-term trend reversals is challenging, and trend-following is one of the few trading approaches to explore it. While trend-following is popular among investors and has gained increased attention in academia, the recent diminished profitability in equity markets casts doubt on its effectiveness. To clarify its cause and suggest remedies, we thoroughly examine the effect of market conditions and averaging window on recent profitability using four major stock indices in an out-of-sample experiment comparing trend-following rules (moving average and momentum) and a machine-classification-based non-trend-following rule. In addition to the significant advantage of trend-following rules in avoiding downside risks, we find a discrepancy in the optimum averaging window size between trend direction phases, which is exacerbated by a higher positive trend direction ratio. A higher positive trend direction ratio leads to poor performance relative to buy-and-hold returns. This discrepancy creates the dilemma of choosing which trend direction phase to emphasize. Incorporating machine-learning into trend-following is effective for alleviating this dilemma. We find that the profit-maximizing averaging window realizes the level that best balances the dilemma and suggest a simple guideline for selecting the optimum averaging window. We attribute the sluggishness of trend-following in recent equity markets to the insufficient chances of trend reversals rather than their loss of profitability. Our results contribute to improving the performance of trend following by mitigating the dilemma.