Extreme learning with chemical reaction optimization for stock volatility prediction

Abstract

Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden neurons adversely influence network usability. Further, choosing the optimal number of hidden nodes for a network usually requires intensive human intervention, which may lead to an ill-conditioned situation. In this context, chemical reaction optimization (CRO) is a meta-heuristic paradigm with increased success in a large number of application areas. It is characterized by faster convergence capability and requires fewer tunable parameters. This study develops a learning framework combining the advantages of ELM and CRO, called extreme learning with chemical reaction optimization (ELCRO). ELCRO simultaneously optimizes the weight and bias vector and number of hidden neurons of a single layer feed-forward neural network without compromising prediction accuracy. We evaluate its performance by predicting the daily volatility and closing prices of BSE indices. Additionally, its performance is compared with three other similarly developed models—ELM based on particle swarm optimization, genetic algorithm, and gradient descent—and find the performance of the proposed algorithm superior. Wilcoxon signed-rank and Diebold–Mariano tests are then conducted to verify the statistical significance of the proposed model. Hence, this model can be used as a promising tool for financial forecasting.