Research Article
Exploring the Effectiveness of Dimensionality Reduction Techniques for Stock Price Prediction
@INPROCEEDINGS{10.4108/eai.1-9-2023.2338744, author={Dongyu Zhuo}, title={Exploring the Effectiveness of Dimensionality Reduction Techniques for Stock Price Prediction}, proceedings={Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology, ICPDI 2023, September 1--3, 2023, Chongqing, China}, publisher={EAI}, proceedings_a={ICPDI}, year={2023}, month={11}, keywords={stock price prediction dimension reduction machine learning}, doi={10.4108/eai.1-9-2023.2338744} }- Dongyu Zhuo
Year: 2023
Exploring the Effectiveness of Dimensionality Reduction Techniques for Stock Price Prediction
ICPDI
EAI
DOI: 10.4108/eai.1-9-2023.2338744
Abstract
The research's objective is to anticipate fluctuations in average stock prices for short-term forecasts. The investigation provides valuable insights for investment decision-making, risk management, and evaluating industry and company performance. The study employs a range of techniques, such as data visualization, data cleaning, and dimensional reduction, to accomplish these goals. The models are trained using six machine-learning approaches and evaluated using six metrics. The primary focus of this research is to identify the crucial factors in predicting stock prices and to find the most effective combination of dimensional reduction techniques and machine learning methods.
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