Aligning Asset Pricing Models and Neural Networks for Predicting Portfolio Returns in Frontier Markets

Abstract

Abstract Forecasting Portfolio returns is a challenging task, and conventional forecasting models have partially succeeded in dealing with the nonlinear and complex nature of Equity Markets. Artificial neural networks are a mathematical modelling approach that are resilient enough to forecast portfolio returns in volatile and nonvolatile markets and act like the human brain to simulate the behaviour of stock prices. This research documents the predictive ability of Artificial Neural Networks (ANN) by using the constructs of Fama and French three-factor and five-factor models. A comprehensive methodology of neural networks is applied to achieve the purpose of forecasting. The methodology includes the declaration of the three layers, the hidden layer neurons for processing, and varying parameters for an effective ANN system. The study employs 48-month rolling windows to calculate and compare forecasting errors among competing asset pricing models. The predictive performance of ANN is measured by mean squared, and the accuracy of ANNs under both the pricing models and the accuracy level is evaluated by the Diebold Mariano test. The significant findings of the study include the identification of the optimum architecture of the ANN under both asset pricing models, the nonappearance of the overfitting phenomenon of the networks, and the investor’s compensation for holding high-risk portfolios. JEL Classification: C45, D53, E37, G11, G17