MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET
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Date
2025-06-09
Authors
Saveliev, Myron
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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.
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Keywords
stock market forecasting, machine learning, LSTM, time series analysis, App
Citation
Saveliev, Myron. (2025). MACHINE LEARNING METHODS AND SOFTWARE TOOLS FOR PREDICTING CYCLICAL ECONOMIC PROCESSES IN A STOCK MARKET. Kyiv: American University Kyiv.