Future Stock Price Prediction Based on Bayesian LSTM in CRSP

DOI

10.2991/978-94-6463-270-5_23How to use a DOI?

Keywords

future stock prices; RNN; LSTM; Bayesian LSTM

Abstract

This paper mainly studies the problem of forecasting future stock prices based on historical data. Taking six companies in the CRSP Daily Stock database as research objects, three models of RNN, LSTM, and Bayesian LSTM are used to predict stock prices, and Gaussian noise is added to the data set to analyze the disturbance of the three models. Finally, the results prove that Bayesian LSTM has better predictive effect and robustness for processing time series data.

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  - ZhangQi Wang
AU  - ZiYi Qi
PY  - 2023
DA  - 2023/10/29
TI  - Future Stock Price Prediction Based on Bayesian LSTM in CRSP
BT  - Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023)
PB  - Atlantis Press
SP  - 219
EP  - 230
SN  - 2667-1271
UR  - https://doi.org/10.2991/978-94-6463-270-5_23
DO  - 10.2991/978-94-6463-270-5_23
ID  - Wang2023
ER  -