PREDICTING STOCK PRICE USING TECHNICAL ANALYSIS AND MACHINE LEARNING

Abstract

This paper presents the advanced method for stock price forecasting. this text provides a review of the arguments and evidence for stock return predictability. The evidence for weak-form predictability, supported the data in past stock prices, is more fragile and fewer compelling than the evidence for semi-strong form predictability, supported publicly available information more generally. The art of forecasting stock prices has been a difficult task for several researchers and analysts. In fact, investors are highly curious about the research area of stock price prediction. For an honest and successful investment, many investors are keen on knowing the longer term situation of the securities market. Good and effective prediction systems for the securities market help traders, investors, and analysts by providing supportive information just like the future direction of the exchange. during this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict securities market indices.