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.