forecasting returns with a hybrid model: neural network autoregressive market predictions and capm for asset valuation

‎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.