Can machine learning surpass human investors? Evidence from adaptive herding behaviour in US, China and India
This study examined the effectiveness of machine learning models in capturing adaptive herding behaviour in the US, China, and India. Utilising data from January 2010 to December 2023, this study incorporates market sentiment (Thomson Reuters MarketPsych Indices), news sentiment (Bloomberg Sentiment...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Journal of Applied Economics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15140326.2024.2435796 |
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| Summary: | This study examined the effectiveness of machine learning models in capturing adaptive herding behaviour in the US, China, and India. Utilising data from January 2010 to December 2023, this study incorporates market sentiment (Thomson Reuters MarketPsych Indices), news sentiment (Bloomberg Sentiment Analysis), and investor happiness measures (Hedonometer). The methodology employs both static and adaptive herding analyses using the CSAD approach, enhanced by real-time sentiment analysis and various machine learning models, including single- and multi-layer neural networks. The results indicate significant differences in herding behaviour across the three markets, with machine learning models demonstrating superior performance in capturing herding behaviour and faster normalisation after major macroeconomic events than traditional methods. These findings highlight the potential of machine learning models to challenge the static assumptions of the Efficient Market Hypothesis and provide insights for designing better trading algorithms by considering the impact of market sentiment, news sentiment, and investor happiness. |
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| ISSN: | 1514-0326 1667-6726 |