Stock Price Prediction Based on Linear Regression

Abstract

Stock market price prediction is one of the core subjects in the financial field. With the increase in market complexity and volatility, accurate stock price prediction has important decision-making significance for investors, financial analysts, and related institutions. This paper aims to use a linear regression model to analyze the historical data of Samsung Electronics stock, and then predict its future stock price trend. The research begins with a pre-processing of the original data set, including data cleaning and feature engineering, to remove noise data and select valuable feature variables. Then, a linear regression model was used to build a stock price prediction model, and the prediction effect of the model was evaluated using mean square error (MSE) to measure the accuracy of the forecast results. To further verify the validity of the model, this paper also makes a comparative analysis of the actual stock price and the predicted stock price and reveals the performance of the model in practical application through error analysis. The results show that the linear regression model can reflect the trend change in stock price to a certain extent. However, its prediction accuracy is still affected by many factors, suggesting that more complex models or technologies should be introduced for further optimization in the future.