Momentum and Reversal: Does What Goes Up Always Come Down?*

Conrad, Jennifer; Yavuz, M. Deniz

doi:10.1093/rof/rfw006

Cited by 53 publications

(43 citation statements)

References 63 publications

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“…Blackburn and Cakici (2017) provide further evidence for the return reversal pattern in a broad international sample of 23 developed countries. Recently, Conrad and Yavuz (2017) invented a method that separated momentum stocks from stocks that gain reversal. They argue that when risk-based characteristics such as size and book-tomarket are taken into account by calculating winner and loser portfolios, a separation is possible.…”

Section: Momentum In the Literaturementioning

confidence: 99%

“…Jegadeesh and Titman (2011) contradict the risk-based explanations again by explaining that the magnitude and the persistence of the momentum anomaly over decades are too strong to be explained by risk and therefore favoring a behavioral approach. Recently, Conrad and Yavuz (2017) resume this debate again by arguing that stocks with momentum can be separated from stocks that gain reversal when risk-based characteristics are considered in the selection of winner and loser portfolios. In sum, the debate about whether returns are explained by risk factors or interpreted as a measure of mispricing is still ongoing.…”

Section: Momentum In the Literaturementioning

confidence: 99%

See 1 more Smart Citation

Overnight Returns: An International Sentiment Measure

Weißofner

Wessels

2019

Journal of Behavioral Finance

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Taking into account expected return characteristics like firm size and book-tomarket in the selection of winners and losers helps to ex ante separate stocks with momentum from those that exhibit reversal in international equity markets. A strategy that buys small value winners and sells large growth losers generates significantly larger momentum profits than a standard momentum strategy, is robust to common return controls, and does not suffer from return reversals for holding periods up to three years. The superior performance of the strategy is attributable to a rather systematic exploitation of cross-sectional mispricing among momentum stocks.

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Section: Momentum In the Literaturementioning

confidence: 99%

Section: Momentum In the Literaturementioning

confidence: 99%

Overnight Returns: An International Sentiment Measure

Weißofner

Wessels

2019

Journal of Behavioral Finance

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“…On top of these observations, various studies [8][9][10][11][12] have been trying to explain the mechanisms behind the effects. For instance, Reference [8] showed that the momentum effect may be correlated to the past trading volume.…”

Section: Introductionmentioning

confidence: 99%

“…Reference [5] argued that the market state has a strong relationship with the momentum effect on the Indian equity market. In addition, some researchers have sought to explain the phenomena via behavioral finance models, such as [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning View on Momentum and Reversal Trading

Li¹,

Tam²

2018

Algorithms

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Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

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“…These phenomena of short-run momentum and long-run reversal of stock price can still be seen in later empirical studies. For example, see Jagadeesh & Titman (2001) and Conrad & Yavuz (2016). They are also commonly seen in international financial markets.…”

Section: Introduce the Problemmentioning

confidence: 99%

Momentum Trading, Contrarian Trading and Smart Money Manipulation

Liao¹

2016

IBR

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There are two commonly recognized anomalies in the stock market, namely short-run momentum and long-run reversals. Under these two financial market anomalies, there are two trading strategies -momentum trading and contrarian trading -that can be adopted for the purpose of making profits. We model an asset market in which momentum traders, contrarian traders and informed rational speculators make transactions. We discovered that under certain conditions, the self-profiting motive of informed speculators will lead to their price manipulation behaviors, and result in momentum and reversals phenomenon on the asset price. We also found that the scenario of the relative quantity of the two types of behavioral traders and their profit margins is similar to that of a minority game.

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Momentum and Reversal: Does What Goes Up Always Come Down?*

Cited by 53 publications

References 63 publications

Overnight Returns: An International Sentiment Measure

Overnight Returns: An International Sentiment Measure

A Machine Learning View on Momentum and Reversal Trading

Momentum Trading, Contrarian Trading and Smart Money Manipulation

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