Study and Analysis of Stock Market Prediction Techniques

Study and Analysis of  Stock Market Prediction Techniques

Rashi Chouksey (0225CS20MT15, kushichouksey148@gmail.com)

(Department of CSE, Global Nature Care Sangathan's Group of Institutions, Jabalpur)

Abstract. The stock marketplace is a complex and demanding system in which people make more money or lose their entire savings. High-accuracy stock market forecasts offer higher returns for stock investors. Stock market data is generated in very large quantities and it changes rapidly every second. Making decisions in the stock market is a very challenging and difficult task of the financial stock market. The development of efficient models for forecasting decisions is very difficult due to the integration of stock market financial data and should have high accuracy. This study attempts to compare existing models for the stock market. Various machine learning methods such as Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Convolution Neural Networks - Long Term Short Memory (CNN-LSTM) have been used for comparison. Models are estimated using conventional strategic measurements: MAE (Mean Absolute Error).

Keywords: Stock Market Prediction, LSTM, CNN, CNN-LSTM.