Document Type : Original Article
Author
Department of Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Accurate forecasting of asset returns is essential for informed investment decisions and effective portfolio management. This paper explores a hybrid model that combines the Capital Asset Pricing Model (CAPM) with Long Short-Term Memory (LSTM) networks to enhance return predictions. While CAPM traditionally estimates expected returns based on market behavior, it has limitations due to its linear assumptions and reliance on uncertain market return forecasts. In contrast, LSTM models excel at capturing complex, nonlinear relationships and temporal dependencies in financial time series data. Our study integrates LSTM forecasts of market returns into the CAPM framework, hypothesizing that this combined approach will yield superior accuracy, particularly in volatile market conditions. Through empirical analysis using five major US equities spanning 2000-2024, we demonstrate that our hybrid model outperforms traditional CAPM predictions by 23-44% in mean squared error reduction. The findings provide valuable insights for future research and practical applications in financial forecasting, highlighting the potential of deep learning techniques in asset valuation while maintaining economic interpretability.
Keywords
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