Certain Investigations of prediction on Stock trend using various Optimization Techniques

Abstract

A stock price represents a company’s value at any given point, trends of the same will be very volatile because of different trading activities, supply and demand of stocks, and companies’ financial outcomes. Predicting the correlation between price, time, and various other variables in any stock trend is an essential need for portfolio optimization. The model of LSTM(Long Short Term Memory) recurrent neural networks (RNN) is the optimal prediction method, with LSTM used for understanding temporal dependencies, which is well known for processing and understanding continuous data points, The above model gives structural integrity to most of the time-series data analysis. The stock market produces a vast amount of data, there will be fluctuation of prices every second, so training Neural Networks for an enormous amount of data takes extensive time, We are performing certain investigations on boosting the accuracy and reducing the time taken to train by further enhancing the above-given model, with modified versions of Adam, RMSProp, and AdaGrad optimization methods.