Enhancing stock market anomalies with machine learning

Abstract

We examine the predictability of 299 capital market anomalies enhanced by 30 machine learning approaches and over 250 models in a dataset with more than 500 million firm-month anomaly observations. We find significant monthly (out-of-sample) returns of around 1.8–2.0%, and over 80% of the models yield returns equal to or larger than our linearly constructed baseline factor. For the best performing models, the risk-adjusted returns are significant across alternative asset pricing models, considering transaction costs with round-trip costs of up to 2% and including only anomalies after publication. Our results indicate that non-linear models can reveal market inefficiencies (mispricing) that are hard to conciliate with risk-based explanations.

Investigation of winner-loser portfolio anomalies and size effect anomalies in LQ45 index, Indonesia stock exchange

“…Some market anomalies occur only once and then disappear, while others sometimes occur continuously or frequently (Tversky & Kahneman, 1986). Market anomalies can also happen in a non-standardized market environment (Azevedo & Hoegner, 2023). Previous studies have shown anomalies exist in the U.S. and other countries (Salur & Ekinci, 2023).…”

“…Inefficiency is related to human behavior that leads to irrationally (Azevedo & Hoegner, 2023;Musnadi & Majid, 2018). Therefore, financial behavior can explain financial inefficiencies, such as market anomalies (Hudson & Muradoglu, 2020).…”

“…Behavioral finance examines investors' reasoning patterns for information, including the emotional processes involved and how these processes influence investors in determining investment strategies (Jiménez & Calisto, 2020;Ricciardi & Simon, 2000). In investor behavior, many behavioral biases can be related to irrational pricing (Azevedo & Hoegner, 2023;Traut, 2023). Behavioral finance occurs not only in investment decisions but also in funding decisions.…”

Emerging Trends in AI-Based Stock Market Prediction: A Comprehensive and Systematic Review

“…Identifying complex stock market patterns requires intricate techniques, with feature selection being pivotal for robust predictions, as features unveil crucial insights from intricate data [3]. In their 2023 research, Vitor Azevedo et al investigated the prediction of capital market anomalies, using diverse machine learning techniques and models on a massive dataset, with top models showing significant monthly returns of 1.8% to 2.0% [4]. The authors investigate ANN, SVM, and LSTM neural networks, highlighting their distinct characteristics and practical applications, underscoring the transformative role of machine learning in shaping investment strategies [5].…”

Forecasting Stock Prices Changes Using Long-Short Term Memory Neural Network with Symbolic Genetic Algorithm

“…Due to such inconsistency, Scholars started to apply non-linear techniques such as machine learning technologies to enhance anomalies. Empirical results show those results have further been enhanced by applying a diverse set of machine learning approaches 9,10 .…”