Review on Stochastic Hybridisation of Feedforward Neural Network in Stock Market

The stock market is an example of a stochastic environment in the real world. So, obtaining accurate forecasting models of the stock market can be challenging due to its complex characteristics (noisy environment), which result in uncertainty. Although machine learning models have been widely applied to forecast the market, it fails to capture the presence of stochasticity in it. As a result, a few studies had proposed a hybridization of Multilayer Perceptron and stochastic processes. Hence, this review paper aims to provide a systematic review of these hybridized models, which have been obtained from the scientific databases Scopus and Web of Science. Finally, it was found out that only eight studies had been conducted to forecast the stock market with Stochastic Neural Network (SNN), and all of them concluded that it has better accuracy than the deterministic model. Thus, the development of SNN is worth exploring in the future as there are rooms to explore cross-disciplinary between neural networks and stochastic processes to improve forecasting accuracy.