Research of RNN Models Performance on New York Stock
- 10.2991/assehr.k.220401.121How to use a DOI?
- Artificial intelligence; Recurrent neural networks; Stock market prediction
Stock market forecasts have become a popular topic for researchers and investors. Stock market forecasting methods range from traditional analysis based on statistics to machine learning models such as decision trees, SVM, and neural networks. In this project, we decided to use Recurrent Neural Network (RNN) models to make stock price forecasts due to the time series nature of stock prices. From simple RNN models to more complex models such as the GRU and LSTM, three different RNN models have been used to compare error values and the performance of each. Based on the results, we found that the LSTM was taking a longer time to train but better performance compared to the other two simple models. This RNN stock forecast study lays the foundation for the future use of RNN models in economic markets.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license.
Cite this article
TY - CONF AU - Zhentong Fan AU - Yang Wang AU - Cheng Ma PY - 2022 DA - 2022/04/08 TI - Research of RNN Models Performance on New York Stock BT - Proceedings of the 2022 International Conference on Social Sciences and Humanities and Arts (SSHA 2022) PB - Atlantis Press SP - 637 EP - 642 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220401.121 DO - 10.2991/assehr.k.220401.121 ID - Fan2022 ER -