Data Analysis and Price Prediction of Stock Market Using Machine Learning Regression Algorithms

Abstract:

Stock analysis and forecasting is a very challenging study due to the unpredictable and volatile database environment. However, their patterns are often unique as they are influenced by many uncertainties, such as financial results of companies (Earnings per share), risk transactions, market sentiment, government policies, and conditions such as epidemics. Even though they are challenging our goal is to predict the accurate values within a shorter span of a dataset. In this paper we have compared and analyzed the best ML model that predicts the exact closing amount of the next few days, using three to four months of nifty50 Indian stock from Yahoo Finance. Five regression models are involved in this analysis, Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), SARIMAX (Integrated Seasonal Integrated Season with EXogenous features), Gated Recurrent Unit (GRU – deep learning). The performance metrics like RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) are used. On the basis of our comparison, we would like to conclude that GRU provides a low error value in all three performance metrics and also gives accurate predictions compared to the other five regression models used.

• Periodical: Advances in Science and Technology (Volume 124)
• Pages: 409-417
• DOI: https://doi.org/10.4028/p-46y2r2
• Online since: February 2023
• Authors: Abishek L. Gavin*, P.K. Venkatesh Prasanna, C. Veena Vedha, A. Sinduja
• Keywords: Decision Tree, Gated Recurrent Unit, Integrated Seasonal Integrated Season with Exogenous Features, Linear Regression, Machine Learning, Price Prediction, Regression, Stock Market, Support Vector Machine

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