Novel Neural Network State Switching Models for Returns Predicting with Regime Switching: A Monte Carlo’s Simulation Approach

Abstract

This study proposes new neural network state switching models that will improve the regime predictive performance of nonlinear time series returns, in small sample size contexts. In this study, we developed four new neural network state switching models namely Recurrent Neural Network State Switching Model (RNNSSM), Generalized Regression Neural Network State Switching Model (GRNNSSM), Radial Basis Function Network State Switching Model (RBFSSM) and Multilayer Perceptrons State Switching Model (MLPSSM). The study presents comparative results of the estimations of the novel models alongside the traditional Markov state switching model as well as the regime prediction performances of the models using the Akaike Information Criterion (AIC), Log-likelihood and prediction accuracy measures under a Monte Carlo simulation study. Evidence from the models’ estimation results particularly the AIC and Log-likelihood statistics established the novel Multilayer Perceptrons State Switching Model (MLPSSM) as the parsimonious model for the simulated returns at small sample sizes. Results from the models’ regime predictions evaluation, precisely the RMSE and MAE, evidently affirmed the novel MLPSSM model as superior in its ability to predict or forecast market returns, particularly the bull and bear regimes, at small sample sizes. Therefore, this study concludes that the novel MLPSSM is the best-fitted model for market returns at small sample sizes with excellent ability of market returns’ regimes/states (i.e., bull and bear) prediction. This study recommends adoption of the novel Multilayer Perceptrons State Switching Model in modelling and regime predictions of time series returns.