An Optimized Deep Neural Net Model for Forecasting Stock Movement Trends

An Optimized Deep Neural Net Model for Forecasting Stock Movement Trends

Roshan1, Prof. Devkinandan Nagar2

Abstract— Stock Market prediction is a category of time series prediction which extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. Moreover, small inaccuracies in stock market price predictions may result in huge losses to firms which use stock market price prediction results for financial analysis and investments.  Off late, artificial intelligence and machine learning based techniques are being used widely for stock market prediction due to relatively higher accuracy compared to conventional statistical techniques. The proposed work employs the steepest descent based algorithm along with the data pre-processing using the discrete wavelet transform (DWT) for stock market prediction. It has been shown that the proposed system attains lesser mean square percentage error compared to previously existing technique.

Keywords—Stock Market Forecasting, Artificial Neural Network (ANN), Back Propagation, Discrete Wavelet Transform (DWT), Mean Absolute Percentage Error (MAPE).