Momentum trading strategies exploit return persistence, but conventional past-year cumulative-return measures generate a noisy signal that attenuates performance. We propose a pure momentum strategy that identifies trends directly from daily returns. Our method reduces volatility, tail risk, and transaction costs while enhancing profitability. Performance is primarily driven by long positions in winners, supporting theories postulating overconfidence. The bad news, instead, appears to be incorporated more slowly. In particular, we show that omitting the most recent month is unnecessary to identify winners.
Momentum is one of the most widely studied and implemented trading strategies in financial economics. Its effectiveness relies on the tendency for past trends in asset returns to persist into the future. Since the seminal contribution of Jegadeesh and Titman (1993), momentum is measured using the cumulative past returns of individual stocks over a formation period of roughly 12 months. The cross-sectional strategy is then executed by buying stocks with the highest past returns (“winners”) and selling those with the lowest past returns (“losers”).
This paper begins with a simple observation: large past returns are often a noisy in dicator of the true underlying trend. Substantial price movements may reflect sudden jumps due to new information quickly absorbed by the market, or periods of elevated volatility driven by uncertainty about the asset’s value. Neither scenario is inherently predictive, and there is little economic rationale for expecting the sign of such returns to persist. We refer to these instances as “illusory momentum.” Stocks exhibiting illusory momentum also carry higher risk, so including them in momentum portfolios is likely to lower risk-adjusted performance.
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