A Hybrid Evolutionary model for Stock Price Prediction Using Grey Wolf Optimizer

Abstract

Stock forecasting is one of the most crucial paramount financial techniques which leads to the development of effective stock exchange strategies in the financial world. Stock is considered as the equity of which gives any one as the ownership of that particular corporation. Stock became the current trend for managing the wealth. Stock market plays a major role in economical growth of a developing country. In any country only about 10% of the population engage in stock market. In this work, certain frameworks like ARIMA (Auto Regressive-Integrated-Moving Average), FLANN (Functional Link Artificial Neural Network), ELM (Extreme Learning Machine) models and Grey Wolf optimizer for stock price prediction have been proposed to do the predictions as effectively as possible. The performance of short and long-term predictions of both these models are evaluated with test data and a comparison of minimized errors of both the short and long-term predictions has been presented. The autors have developed a hybrid model using the ELM model and Grey Wolf Optimizer which can be used to change the weights and the number of layers of the ELM model to increase it's accuracy significantly and provide optimum results which are far better when compared to the previous models.