Stock Price Prediction Based on SDE and LSTM: Evidence from BYD

Yaling Xu

2024

Abstract

With the increasing complexity and dynamism of the stock market, traditional forecasting methods make it difficult to accurately capture market fluctuations. In recent years, deep learning models have gradually been applied to the stock market, demonstrating superior research results. This article investigates a stock price prediction model based on the combination of Long Short-Term Memory (LSTM) and Stochastic Differential Equations (SDE). The model utilizes the excellent time series processing capability of LSTM and the advantage of SDE in describing stochastic processes, which can improve the prediction accuracy of stock prices. This article concludes that by introducing SDE, the model can better simulate the randomness and volatility of stock prices, while LSTM effectively captures long-term and short-term dependencies in historical data. With machine learning models can be explored to enhance the real-time performance and adaptability of the models. In addition, this study only considered the price prediction and simulation of a single stock, the experimental results show that the LSTM-SDE model has good predictive performance in stock price prediction and can provide investors with more reliable decision support.

Paper Citation

in Harvard Style

Xu Y. (2024). Stock Price Prediction Based on SDE and LSTM: Evidence from BYD. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 209-213. DOI: 10.5220/0013213100004568

in Bibtex Style

@conference{ecai24,
author={Yaling Xu},
title={Stock Price Prediction Based on SDE and LSTM: Evidence from BYD},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={209-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013213100004568},
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 - Stock Price Prediction Based on SDE and LSTM: Evidence from BYD
SN - 978-989-758-726-9
AU - Xu Y.
PY - 2024
SP - 209
EP - 213
DO - 10.5220/0013213100004568
PB - SciTePress