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The impact of machine learning on stock momentum strategies
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 1.27 MB | Adobe PDF |
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Resumo(s)
The individual project investigates the impact of machine learning, specifically the
Random Forest model, on momentum-based stock selection strategies. It evaluates the model's
predictive accuracy and compares the performance of strategies based on actual versus
predicted returns. Findings reveal that machine learning enhances prediction accuracy and,
when applied to momentum strategies, demonstrates reduced volatility, lower drawdowns, and
improved risk-adjusted returns. The study highlights the model's resilience, particularly in
volatile market conditions, while acknowledging limitations related to data and computational
resources. This research offers insights into the integration of machine learning in financial
strategies to navigate complex market dynamics.
Descrição
Palavras-chave
Asset management Trading Machine learning Stock prediction Random forest Momentum strategy