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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Pasaribu, Asysta
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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A Study of Machine Learning Approach to Predict the Out performance Market of Japan’s Stock Price Amadea, Charissa; Karina, Karina; Addina, Kanisha; Adara, Keisha; Pasaribu, Asysta
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.12905

Abstract

This study employs machine learning algorithms to estimate the stock performance of Japanese companies in 2022, with a focus on examining the relationship between significant financial factors—Market Capitalization (Market Cap), Price-to-Earnings Ratio (PER), Price-to-Book Value (PBV), Return on Equity (ROE), and Debt-to-Equity Ratio (DER)—and stock performance, classified as either high-performing or low-performing. These factors are designated as independent variables. The dataset comprises 1,000 publicly listed companies in Japan and is analyzed using a logistic regression model. The dependent variable in this study is stock performance. The Akaike Information Criterion (AIC) guided model selection to optimize predictive accuracy and model complexity. The dataset was split into 70% for training and 30% for testing to ensure robust model validation. The best-performing model achieved a prediction accuracy of 62.67%, demonstrating strong sensitivity (88.83%) but weak specificity (18.75%). An AUC value of 0.6226 indicates moderate discriminatory power. The model shows good capability in detecting underperforming stocks, while its limitation lies in classifying well-performing stocks. The study suggests enhancing prediction accuracy by incorporating additional relevant variables such as macroeconomic indicators or market trends, as well as employing more complex machine learning algorithms like Random Forest or Gradient Boosting. These findings not only contribute to the literature on stock market prediction but also offer practical insights for investors in making investment decisions.
Co-Authors Adara, Keisha Addina, Kanisha Amadea, Charissa Karina Karina