Impact of Technical Parameters for Short- and Long-term Analysis of Stock Behavior

Abstract

Stock price forecasting is a type of time series problem that forecasts the future price or status of a company on the basis of analysis of time respective values. As the price of stock or company varies with respect to time, its behavior can be analyzed by different machine learning approaches. In this work, methodology is proposed to evaluate the stock position with variation in time using deep learning approach such as recurrent neural network (RNN). This methodology used the technical parameters to evaluate the long term and short-term analysis of any stock or share. This approach also evaluates and gives suggestions to investors either to buy or sell any stock for long term and gives return at very low risk. In this paper work, hybridization of co-relation analysis and deep learning approach for stock price and long-term behavior analysis. The proposed work is termed as time lagged weight optimized RNN (TL-WO-RNN) that is adopted in this work and effectively predict the technical parameters and on the basis of that stock behavior is also predicted. The result analysis was performed on data from different sectors and such as Telecom, Powers, Manufacturing, Finance, Software sectors, etc. The result analysis shows the effectiveness of the TL-WO-RNN algorithm as compared to existing work.