A Survey on Stock Market Price Prediction System using Machine Learning Techniques

Abstract

Abstract: Prediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. In the finance world stock trading is one of the most important activities. Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by the most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. The paper focuses on the use of Linear Regression, Moving Average, K-Nearest Neighbours, Auto ARIMA, Prophet, and LSTM based Machine learning techniques to predict stock values. Factors considered are open, close, low, high and volume. The models are evaluated using standard strategic indicators: RMSE and MAPE. The low values of these two indicators show that the models are efficient in predicting stock closing price. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market price prediction. This work contributes to the stock analysis research community both in the financial and technical domains. Keywords: Stock Market, Machine Learning, Prediction, LSTM, Python, Analysis, Linear Regression, Feature Engineering.