Theory and Application of Linear Regression Model in Financial Market Forecasting

Abstract

Linear regression model in the theoretical basis and practical application in the financial markets was discussed by this article, the article first introduces basic principle of linear regression models and assumptions, such as least square method, multiple linear regression was also covers the core concept, And then explores the model used to predict the strengths and weaknesses of the financial markets and focuses on how to choose suitable for the independent variable and dependent variable, and how to deal with the particularity of financial time series data, the article also detailed in this paper, the model of diagnosis and evaluation methods, such as the multicollinearity test, heteroscedasticity test, autocorrelation test, such as the application part, In the application section, this paper shows the specific application of linear regression model in stock return prediction, bond yield curve analysis, exchange rate prediction and other fields through actual case studies, and discusses how to combine other advanced statistical methods with machine learning algorithms to improve the predictive ability of linear regression model. Finally, This article summarizes the linear regression model in the financial market forecast, development trend and the future research direction of financial practitioners and researchers to provide valuable theoretical guidance and practical reference.