2022Journal of Futures MarketsRequires access

Understanding intraday momentum strategies

Carlo Rosa

Open publisher page 8 citations

Abstract

Abstract This paper studies the out‐of‐sample performance of the intraday momentum strategy where the overnight return predicts the return of the last half‐hour of trading. The predictability disappears in the out‐of‐sample period. A Markov‐switching model endogenously identifies two distinct regimes and suggests that the predictability depends on the strength of the signal. Hence, assessing return predictability in calendar time may lead to false conclusions when anomalies feature time‐varying returns. The paper documents that understanding the return dynamics is important for an effective strategy implementation. A strategy with thresholds delivers higher returns than a strategy that is always active.

About this research paper

What this paper is about

Abstract This paper studies the out‐of‐sample performance of the intraday momentum strategy where the overnight return predicts the return of the last half‐hour of trading. The predictability disappears in the out‐of‐sample period. A Markov‐switching model endogenously identifies two distinct regimes and suggests that the predictability depends on the strength of the signal. Hence, assessing return predictability in calendar time may lead to false conclusions when anomalies feature time‐varying returns. The paper documents that understanding the return dynamics is important for an effective strategy implementation. A strategy with thresholds delivers higher returns than a strategy that is always active.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Abstract This paper studies the out‐of‐sample performance of the intraday momentum strategy where the overnight return predicts the return of the last half‐hour of trading. The predictability disappears in the out‐of‐sample period. A Markov‐switching model endogenously identifies two distinct regimes and suggests that the predictability depends on the strength of the signal. Hence, assessing return predictability in calendar time may lead to false conclusions when anomalies feature time‐varying returns. The paper documents that understanding the return dynamics is important for an effective strategy implementation. A strategy with thresholds delivers higher returns than a strategy that is always active.

Key concepts: Predictability, Momentum (technical analysis), Trading strategy, Econometrics, Sample (material), Computer science, Markov chain, Economics

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