Comparison of LSTM and ARIMA in Price Forecasting: Evidence from Five Indexes

DOI

10.2991/978-94-6463-268-2_6How to use a DOI?

Keywords

Stock price prediction; LSTM neural network model; ARIMA model; deep learning

Abstract

The financial industry has been increasingly researching and applying artificial intelligence in both academia and industry. The classical deep learning model, I.E., long-short term memory (LSTM) neural network model, has great advantages in predicting financial time series. This study uses data such as daily opening, closing, high and low prices of five representative global stock indices from 2015 to 2022 to predict stock prices using the LSTM neural network model and the linear autoregressive moving average model (ARIMA). The predicted results are compared with the actual stock prices, and the study findings demonstrate that the LSTM model outperforms the ARIMA in predicting stock index prices. Thus, incorporating deep learning models in a reasonable way can not only improve the accuracy of investment decision-making, but also enrich the methods for processing and analyzing financial time series data, so as to enhance the ability to monitor and warn of financial market risks.

Copyright

© 2024 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  - Zizhe Zhang
PY  - 2023
DA  - 2023/10/10
TI  - Comparison of LSTM and ARIMA in Price Forecasting: Evidence from Five Indexes
BT  - Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023)
PB  - Atlantis Press
SP  - 40
EP  - 46
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-268-2_6
DO  - 10.2991/978-94-6463-268-2_6
ID  - Zhang2023
ER  -