Prediction of Tencent Share Price Based on ARIMA, SVR and LSTM

Xinyu Yang

2024

Abstract

As a matter of fact, the predicting stock price can not only be a hard chicken to pluck but also it is an exciting research area of financial markets for the researchers’ due high return profit that can be made upon successful prediction accuracy, which matters based on informed investment decision especially in contemporary high volatility market. With this in mind, in this article, the stock price of Tencent Holdings is predicted using 1,203 parameter sets by comparing three models, i.e., ARIMA (Autoregressive Integrated Moving Average), Support Vector Regression (SVR), and Long Short-Term Memory Networks (LSTM). According to the analysis, the results reveal that SVR is very well suited for short-term predictions due to its ability to capture market volatility. In contrast, LSTM can handle long term patterns. The ARIMA model, on the other hand, hamstrung to make sense of non-linear data by its linear foundation. While they differ in performance, it is a useful guidance to investors choosing between models for specific needs.

Paper Citation

in Harvard Style

Yang X. (2024). Prediction of Tencent Share Price Based on ARIMA, SVR and LSTM. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 229-233. DOI: 10.5220/0013213800004568

in Bibtex Style

@conference{ecai24,
author={Xinyu Yang},
title={Prediction of Tencent Share Price Based on ARIMA, SVR and LSTM},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={229-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013213800004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Prediction of Tencent Share Price Based on ARIMA, SVR and LSTM
SN - 978-989-758-726-9
AU - Yang X.
PY - 2024
SP - 229
EP - 233
DO - 10.5220/0013213800004568
PB - SciTePress