The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman,ĬSO-ARMA and GA-LSTM to prove its superiority. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data.
Optimized ENN is utilized to predict the future price of stock data in 1 day advance. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN.
Trial and error-based method is widely used to determine the parameters of ENN. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. Thus, an accurate prediction of stock price can be considered as a tough task. Stock price always fluctuates due to many variables.
Over the past two decades, assessing future price of stock market has been a very active area of research in financial world.
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