MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET
| dc.contributor.author | Saveliev, Myron | |
| dc.date.accessioned | 2025-09-24T07:12:20Z | |
| dc.date.available | 2025-09-24T07:12:20Z | |
| dc.date.issued | 2025-06-09 | |
| dc.description.abstract | This capstone project focuses on forecasting cyclical economic trends in the stock market, using Apple Inc. (AAPL) stock data as a case study. Key research questions include: (1) How effective are machine learning models in forecasting market cycles? (2) Which models provide the most accurate predictions? Historical stock price data from 2010 to 2023 was collected from Yahoo Finance, and seven forecasting models were implemented: ARIMA, SARIMA, Gradient Boosting, Random Forest, LSTM, GRU, and Prophet. The research involved preprocessing the data, performing exploratory analysis, and evaluating the performance of each model using key metrics, including RMSE, MAPE, and R². Results demonstrated that ARIMA and SARIMA provided the most accurate forecasts, achieving an RMSE of 3.11, MAPE of 0.01, and R² of 0.98. Among deep learning models, LSTM outperformed GRU with an RMSE of 4.35 and R² of 0.96, effectively capturing non-linear dependencies. Gradient Boosting, Random Forest, and Prophet models underperformed, with higher RMSE and negative R² values, making them less suitable for this task. The study concludes that ARIMA and SARIMA are optimal for efficient and accurate forecasting, while LSTM is well-suited for complex, non-linear patterns. Future work could explore the incorporation of macroeconomic indicators, hybrid models, or alternative data sources, such as sentiment analysis, to enhance predictive accuracy. This research offers practical tips for utilizing machine learning in financial forecasting. | |
| dc.identifier.citation | Saveliev, Myron. (2025). MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET. Kyiv: American University Kyiv. | |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/159 | |
| dc.language.iso | en_US | |
| dc.publisher | Manuscript | |
| dc.subject | stock market forecasting | |
| dc.subject | machine learning | |
| dc.subject | LSTM | |
| dc.subject | time series analysis | |
| dc.subject | App | |
| dc.title | MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET | |
| dc.type | Thesis |