Up and down together? On the linkage of momentum and reversal

Hofmann, Daniel; Keiber, Karl Ludwig; Luczak, Adalbert

doi:10.1016/j.gfj.2022.100754

Cited by 2 publications

(5 citation statements)

References 31 publications

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“…Jegadeesh and Titman [13] have emphasized that momentum and reversal phenomena are intimately linked. Over a longer time horizon, they find that return reversal often follows return continuation, consist with findings of Hofmann et al [17].…”

Section: Introductionsupporting

confidence: 84%

“…We examine the momentum, reversal, and momentum-reversal strategies and follow Onishchenko et al [23], Herberger et al [25] and Hofmann et al [17] by using thirty minutes as an interval for high-frequency data collection. For the momentum strategy, we select winners by ranking the first half-hour return of all assets.…”

Section: Plos Onementioning

confidence: 99%

“…The proposed model overcomes the drawback that intraday trading frequently erodes profits, resulting in negative returns [25]. The M_R_Omega model suggests that performing the momentum and reversal strategies on the same day is not recommended because the frequent trading significantly erodes market value even though return reversal often follows return momentum [17].…”

Section: Plos Onementioning

confidence: 99%

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Extending the Omega model with momentum and reversal strategies to intraday trading

Yu,

Wei,

Lai

et al. 2023

PLoS ONE

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This study develops the Omega model integrated with momentum and reversal strategies using high-frequency data on the component stocks of the S&P 500 Index and the NASDAQ 100. The Omega model based on the momentum strategy (M_Omega), the reversal strategy (R_Omega), and both strategies (M_R_Omega) are designed to simulate trading over three periods. The portfolio is rebalanced every transaction day to optimize asset allocation by incorporating intraday winners or losers’ information and trading cost. The study finds that the proposed models generate positive returns (net of trading costs), in spite of fact that intraday trading frequently erodes profits. The M_Omega and R_Omega models produce a higher return than that of the S&P 500 index or NASDAQ 100 index, considering the intraday trading cost. The performance of the Omega model integrated with the momentum or reversal strategy is more profitable in a volatile market or period. The M_Omega and R_Omega reach the highest final market value from 2020 to 2021, when COVID 19 pandemic emerged. The rebalancing of the momentum or reversal strategy is suitable for the short term but not recommended in the long term for intraday trading as the trading costs become increasingly significant over time.

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Section: Introductionsupporting

confidence: 84%

Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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Extending the Omega model with momentum and reversal strategies to intraday trading

Yu,

Wei,

Lai

et al. 2023

PLoS ONE

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“…Momentum and contrarian strategies are two of the most hotly discussed profitgenerating investment methods [44][45][46]. Momentum trading can result in short-term profits depending on recent share price moves [47].…”

Section: Investing Strategiesmentioning

confidence: 99%

“…As such, in addition to the existing literature on momentum, contrarian strategies, and the January effect [44,59,85], relevant research has indicated that stock market overreaction could explain contrarian profits [86], as well as the relationship between sentiment and technical indicator performance [87]. However, we found that there is a gap in the literature regarding the impact of trading signals triggered by contrarian regulations of SOIs and the RSI in a particular month on subsequent performance ("monthly effects"), which differs significantly from the better monthly performance in a particular month (e.g., the January effect that has been extensively investigated in the relevant literature [2,3,69,71,74].…”

Section: January Effectsmentioning

confidence: 99%

Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period

Chiu,

Huang,

Day

et al. 2024

Mathematics

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In contrast to finding better monthly performance shown in a specific month, such as the January effect (i.e., better stock price performance in January as opposed to other months), which has been extensively studied, the goal of this study is to determine whether investors would obtain better subsequent performance as technical trading signals emitted in a specific month because, from the investment perspective, investors purchasing stocks now would not know their performance until later. We contend that our analysis emphasizes its critical role in steering investment decisions and enhancing profitability; nonetheless, this issue appears to be overlooked in the relevant literature. As such, utilizing big data to analyze the constituent stocks of the DJI 30 and NDX 100 indices from 2003 to 2022 (i.e., two-decade data), this study investigates whether trading these stocks as trading signals emitted via contrarian regulation of stochastic oscillator indicators (SOIs) and the relative strength index (RSI) in specific months would result in superior subsequent performance (hereafter referred to as “monthly effects”). This study discovers that the oversold signals generated by these two contrarian regulations in March were associated with higher subsequent performance for holding 100 to 250 trading days (roughly one year) than other months. These findings highlight the importance of the trading time and the superiority of the RSI over SOIs in generating profits. This study sheds light on the significance of oversold trading signals and suggests that the “monthly effect” is crucial for achieving higher returns.

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Up and down together? On the linkage of momentum and reversal

Cited by 2 publications

References 31 publications

Extending the Omega model with momentum and reversal strategies to intraday trading

Extending the Omega model with momentum and reversal strategies to intraday trading

Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period

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