Abstract
Increased access to the stock market leads to a growing interest in investing and an increase in the number of new investors. However, investing in the stock market involves risks and requires good preparation and analysis. To protect and grow their investments, investors should carefully analyze the company they plan to invest in. Our paper aims to develop a model for predicting financial indicators from companies’ business reports based on data analysis and machine learning using Python programming language.
Tasks of our research contain the collection of historical data from business reports or financial databases, data cleaning, and feature selection of relevant explanatory variables (such as net income, price-to-earnings ratio, total equity, operating margin, and gross margin) to predict the dependent variable (market price per share). Random Forest outperformed Multiple Linear Regression in predicting stock prices, displaying lower RMSE (9.53), Deviation Percentage (34.61%), and higher R-squared (97.22%).
The Deviation Percentage of 34.61% may seem relatively high, suggesting that there is still room for improvement in the model’s precision in predicting stock prices accurately. It is essential to consider other factors that might affect stock prices beyond the selected financial indicators.
The conclusion emphasizes the importance of considering additional market factors, such as competition, economic conditions, and industry changes when making investment decisions. Although the random forest model is an effective tool for analyzing the dependence of stock prices on various variables, it does not account for all factors affecting stock prices. Therefore, investors should use market analysis to make the correct investment decisions.
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Ivanov, O., Kobets, V. (2023). Data Analysis for Predicting Stock Prices Using Financial Indicators Based on Business Reports. In: Antoniou, G., et al. Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2023. Communications in Computer and Information Science, vol 1980. Springer, Cham. https://doi.org/10.1007/978-3-031-48325-7_17
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