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Portfolio Optimization Strategy Based on Fama-French and LightGBM
- 1 Fisher College of Business, The Ohio State University, Columbus, Ohio, 43210, The United States of America
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Abstract
Financial portfolio optimization is the process of selecting the optimal combination of assets to generate returns within a specified risk threshold. Investors and financial institutions put a lot of time into this process, trying to find the optimal weights. This study selects eight top performers from different industry sectors. The study first uses the Fama-French model to process the first 83.33% of data and make predictions for the remaining 16.67% directly at once. Next, the LightGBM model is applied to forecast returns. This approach enables researchers to identify the maximum return model and the minimum risk model on the efficient frontier using the mean variance model and Monte Carlo simulations. By comparing the performance of the models with the benchmark S&P 500 over the same period, the study concludes that all models in the study delivered superior results. This research highlights a novel portfolio method that applies the Fama-French model or LightGBM model for prediction. The results underscore the strength of integrating these two models in portfolio optimization and showcase the useful applications of CPAM and machine learning in the financial field.
Keywords
Portfolio Optimization, Mean-Variance Model, Mento Carlo Simulation, Fama-French Model, LightGBM Model
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Cite this article
Xiao,M. (2024). Portfolio Optimization Strategy Based on Fama-French and LightGBM. Advances in Economics, Management and Political Sciences,90,169-179.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
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