Long Short-Term Memory optimized with Improved Artificial Rabbits optimization Algorithm for Stock Market Price Prediction

The stock market is financial market where stocks of openly listed corporations are bought and sold. It is needle of economic health of country, reflects the company performance and entire environment of business. The stock prices are determined through demand and supply. Investing in stock market may be risk, but that offered potential fir important returns over high term. In this research, the Long Short-term Memory (LSTM) with Improved Artificial Rabbits optimization (IARO) algorithm for predicting the stock market price. The IARO algorithm is utilized to optimize hyperparameters of LSTM method to maximize accuracy of stock market price prediction. The dataset used for predicting stock market price is collected dataset. The performance of proposed method is estimated with error rates of Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The proposed method less MSE of 0.43, MAE of 0.37, MAPE of 0.21 and R2 of 0.17 which is superior than other existing methods.