Reversal and Momentum Patterns in Weekly Stock Returns: European Evidence

Hühn, Hannah Lea; Scholz, Hendrik

doi:10.2139/ssrn.2552580

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…SVM can overcome overfitting problems [23]. Essentially, SVM uses the kernel function to project the inputs into high-dimensional feature spaces so that SVM can efficiently solve non-linear classification problems, as shown in (6). In this research, we selected the radial basis function (RBF) as the kernel function, as described in (7).…”

Section: Support Vector Machinementioning

confidence: 99%

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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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Section: Support Vector Machinementioning

confidence: 99%

“…Reference [6] pointed out short-term reversal and mid-term momentum effects in weekly stock returns in the European markets. Reference [7] presented profitable arbitrage strategies built on the short-term reversal effect on the Hong Kong stock market.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning View on Momentum and Reversal Trading

Li¹,

Tam²

2018

Algorithms

View full text Add to dashboard Cite

show abstract

Reversal and Momentum Patterns in Weekly Stock Returns: European Evidence

Cited by 1 publication

References 22 publications

A Machine Learning View on Momentum and Reversal Trading

A Machine Learning View on Momentum and Reversal Trading

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