Forecasting Stock Price Movement using Long Short Term Memory(FSPMLSTM)

Abstract

the stock market forecasts are fraught with risk, they are crucial in both financial and commercial terms. Stock forecasting is influenced by a variety of factors, including rational and irrational behavior, investor emotion, market rumors, and so on. All of these variables contribute to stock price volatility and make accurate forecasting extremely difficult. Every second, a vast quantity of data is created from many sources, which has an influence on the stock market. The idea is to create a stock market forecast model utilizing publicly available financial data. Because of its great reading capacity on a vast data collection, we apply the machine learning process (ML). The LSTM model was used to assess and forecast stock prices in this investigation. The primary goal of this thesis is to compare the performance of India's stock market (Nify50) from January 2, 2017 to January 1, 2021. Based on the study's findings, it's clear that adopting the LSTM model improves performance accuracy.