Machine Learning and Fama-French Three-Factor Model Applications for Analyzing Stock Price in Technology Enterprises

Jiaxiang He

doi:10.54097/vbn69621

Authors

Jiaxiang He

DOI:

https://doi.org/10.54097/vbn69621

Keywords:

Technology Stock, Machine Learning, Fama-French Three-Factor Model.

Abstract

This study endeavors to forecast the stock prices of the leading U.S. technology entities - Google, Microsoft, Amazon, Meta, and Apple - through the application of diverse machine learning models, complemented by the traditional Fama-French three-factor model. The employed models encompass Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Support Vector Machines (SVM), and Decision Tree models. Initially, historical stock price data is utilized to train these machine learning models, enabling the identification of potential price trends. Subsequently, the integration of the Fama-French three-factor model enhances the analysis by scrutinizing the impacts of market risk, company size, and book-to-market value on stock prices. The outcomes illuminate both the effectiveness and limitations of various models in stock price prediction, highlighting the advantages of machine learning methodologies over traditional financial theories. This research provides financial market analysts and investors with a fresh perspective on the amalgamation of machine learning and traditional financial theories for enhanced stock price prediction.

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