Research Article
Comparison and Analysis of Prediction for High Volatility Stocks using Different Machine Learning Models
@INPROCEEDINGS{10.4108/eai.28-10-2022.2328453, author={Minglong Chen and Mohan Ren and Yujie Zhou}, title={Comparison and Analysis of Prediction for High Volatility Stocks using Different Machine Learning Models}, proceedings={Proceedings of the International Conference on Financial Innovation, FinTech and Information Technology, FFIT 2022, October 28-30, 2022, Shenzhen, China}, publisher={EAI}, proceedings_a={FFIT}, year={2023}, month={4}, keywords={high-volatility; machine learning; lstm; stock prediction}, doi={10.4108/eai.28-10-2022.2328453} }- Minglong Chen
Mohan Ren
Yujie Zhou
Year: 2023
Comparison and Analysis of Prediction for High Volatility Stocks using Different Machine Learning Models
FFIT
EAI
DOI: 10.4108/eai.28-10-2022.2328453
Abstract
As the global situation is facing many unpredictable risks, like the covid pandemic and the wars between some countries, a high level of volatility is disturbing the stock market in a world range. Besides, due to the development of machine learning techniques, more and more of these techniques have been used in predicting the price of the stock to get profits, and some of them like Long Short-Term Memory (LSTM) have also shown an extraordinary performance. However, these applications should be reevaluated because of nowadays market volatility while in the past, most experiments were built based on generic volatility. In this experiment, therefore, this study chose two most volatile and two least volatile stocks in the past two years and utilized some popular machine learning models, such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Long Short-Term Memory to predict their prices. The results have shown that all of them will suffer from a roughly 4 to 5 times performance reduction in terms of the prediction ability, but when ignoring how the volatility is, LSTM will always give the best performance.