Abstract
Stock market prediction is crucial for financial analysis, and numerous machine-learning algorithms have been utilized to forecast trends and movements. This study examines the efficacy of various algorithms, such as ARIMA, TBATS, Holt-Winters, Random Forest, ANN, RNN, LSTM, and others, in predicting stock market behavior. A range of leading and lagging technical indicators was incorporated and extensive EDA to identify and manage outliers was conducted. The indicators were further assessed to minimize feature collinearity, thereby enhancing prediction quality and performance. This research compares the outcomes of these algorithms to determine a consistently accurate forecasting method. The experimental results reveal a peak prediction accuracy of approximately 91% across different algorithms. Interestingly, while complex deep learning models show strong performance, simpler models like Linear Regression, MLP, and the Theta Model yield impressive results, with a Mean Absolute Percentage Error (MAPE) of 1. This study provides a comprehensive evaluation of various machine learning algorithms applied to stock market prediction, emphasizing the effectiveness of simpler models in generating precise and dependable forecasts.
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Kumar, B.R.A. et al. (2023). Evaluating the Performance of Diverse Machine Learning Approaches in Stock Market Forecasting. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_23
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