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An Exploratory Comparison of Stock Prices Prediction using Multiple Machine Learning Approaches based on Hong Kong Share Market

Resource type
Authors/contributors
Title
An Exploratory Comparison of Stock Prices Prediction using Multiple Machine Learning Approaches based on Hong Kong Share Market
Abstract
Stock price prediction has always been challenging due to its volatility and unpredictability. This paper performs a preliminary exploratory comparison that utilizes Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms to forecast the stock market in Hong Kong. It considers a public dataset publicly available and uses feature engineering to extract relevant features. Then, LSTM and SVM algorithms are applied to predict stock prices. Our results show that the proposed machine learning techniques can predict stock prices in Hong Kong's share market with the error metrics presented, and, for this purpose, LSTM achieved better results than SVM, with MSE = 0.0026, RMSE = 0.0508, MAE = 0.0406, and MAPE = 1.325.
Date
December 15, 2023
Proceedings Title
Proceedings of the 2023 14th International Conference on E-business, Management and Economics
Place
New York, NY, USA
Publisher
Association for Computing Machinery
Pages
311–316
Series
ICEME '23
ISBN
9798400708022
Accessed
2024-01-14
Library Catalog
ACM Digital Library
Citation
Lin, C., & Lobo Marques, J. A. (2023). An Exploratory Comparison of Stock Prices Prediction using Multiple Machine Learning Approaches based on Hong Kong Share Market. Proceedings of the 2023 14th International Conference on E-Business, Management and Economics, 311–316. https://doi.org/10.1145/3616712.3616762