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Title: Enhancing Stock Return Predictions: Comparing Machine Learning Methods with Traditional Financial Models
Authors: Arav Agarwalla
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Arav Agarwalla The British School, New Delhi, India
MLA 8 Agarwalla, Arav. "Enhancing Stock Return Predictions: Comparing Machine Learning Methods with Traditional Financial Models." Int. j. of Social Science and Economic Research, vol. 9, no. 11, Nov. 2024, pp. 5215-5228, doi.org/10.46609/IJSSER.2024.v09i11.016. Accessed Nov. 2024. APA 6 Agarwalla, A. (2024, November). Enhancing Stock Return Predictions: Comparing Machine Learning Methods with Traditional Financial Models. Int. j. of Social Science and Economic Research, 9(11), 5215-5228. Retrieved from https://doi.org/10.46609/IJSSER.2024.v09i11.016 Chicago Agarwalla, Arav. "Enhancing Stock Return Predictions: Comparing Machine Learning Methods with Traditional Financial Models." Int. j. of Social Science and Economic Research 9, no. 11 (November 2024), 5215-5228. Accessed November, 2024. https://doi.org/10.46609/IJSSER.2024.v09i11.016.
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ABSTRACT: This paper aims to understand if machine learning models can enhance stock price predictions compared to that of traditional financial models. The paper covers traditional financial models such as Stochastic Discount Factor models, factor-based models, option pricing models, and behavioural models, and machine learning techniques like supervised learning, deep learning, and hybrid models. By summarizing the results of various papers, this review compares the predictive accuracy of these models. The review found that machine learning methods, deep learning and hybrid models, outperformed traditional models as they captured nonlinear relationships between factors and stock price however found that some machine learning models were prone to overlearning. Through this review, financial analyst can understand if machine learning models should be used, which models to use specifically and can lead to enhanced stock price prediction.

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