Stock Market Prediction Using Deep Learning Based on Modified Long Short-Term Memory

DOI

10.2991/978-94-6463-005-3_52How to use a DOI?

Keywords

Stock Market Prediction; Deep Learning; Data Processing; Long Short Term Memory

Abstract

The stock market is a key factor in financial field. It is affected by the current trends and other market factors. The stock market prediction can provide precise information for investment and maximize investors interests. Nowadays, with the development of artificial intelligence, there is an increasing trend of using intelligent technique to predict stocks’ tendency, which is the main part of quantitative investment. The techniques applied to stock prediction include convolutional neural network (CNN), support vector machine (SVM) and other techniques. However, the performance of these methods are poor when dealing with the time series data. Therefore, we proposed a framework based on modified long short term memory (LSTM). In order to evaluate the effectiveness of proposed method, other mainstream methods are applied in comparative experiments. The results of the experiments reveal that the proposed method has higher prediction accuracy.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Wenxuan Li
AU  - Meiying Huang
AU  - Yangqiu Pi
PY  - 2022
DA  - 2022/11/10
TI  - Stock Market Prediction Using Deep Learning Based on Modified Long Short-Term Memory
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 522
EP  - 530
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_52
DO  - 10.2991/978-94-6463-005-3_52
ID  - Li2022
ER  -