Exploring Predictive Variables Affecting the Sales of Companies Listed With Korean Stock Indices Through Machine Learning Analysis

Abstract

This study uses machine learning algorithms to explore predictor variables that determine whether the national statistical indices managed and announced by the Korean government influence the sales of companies listed on the Korea Composite Stock Price Index (KOSPI) and Korean Securities Dealers Automated Quotation (KOSDAQ). Further, it proposes a machine learning algorithm suitable for forecasting the sales of these companies. The sales of 1,470 companies listed on KOSPI and KOSDAQ with more than 20 years of history and 58 national statistical indices were analyzed. The predictor variables and performance were explored using the analysis data from 2000 to 2021 and the following machine learning algorithms: random forest, gradient boost, extreme gradient boosting, adaptive boosting, and categorical boosting. The analysis result confirmed that the national statistical indices contain different variables that affect the sales of listed companies by industry. The primary variable that showed the greatest influence in each industry was the industrial accident rate for manufacturing, finance and insurance, gold for construction, number of automobiles produced for wholesale and retail, and foreign exchange reserves for information and communication. The regression performance evaluation indicators—mean absolute error, mean squared error, and root mean squared error—were used to determine the optimal machine learning algorithm. The results showed that gradient boost achieved the best performance. Consequently, this study proposes using national statistical indices for companies to establish management strategies based on machine learning results.

Keywords

• Economic indices
• machine learning
• national statistical indices
• predictor variables
• sales