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 Neural Network Autoregressive (NNAR) models to enhance return predictions. While CAPM traditionally estimates expected returns based on market behavior, it has limitations due to its linear assumptions. In contrast, NNAR models excel at capturing complex, nonlinear relationships in financial time series data. Our study integrates NNAR 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, we demonstrate that our hybrid model outperforms traditional CAPM predictions, highlighting the potential of machine learning techniques in asset valuation. The findings provide valuable insights for future research and practical applications in financial forecasting.
Mohammad Zare (Wed,) studied this question.