Stock Price Prediction Based on Deep Learning

Xuejiao Chen

2024

Abstract

Financial time series forecasting stands as a cornerstone in investment decision-making and risk management. Nonetheless, traditional statistical models often grapple with capturing intricate nonlinear patterns and enduring dependencies within data. To enhance prediction accuracy, this study delves into the feasibility of employing deep learning technology in financial time series forecasting. 5 deep learning models have been constructed, containing deep multi-layer perceptron (DMLP), convolutional neural networks (CNN), long short-term memory networks (LSTM), recurrent neural networks (RNN), and auto-encoders (AE), leveraging real transaction market data to forecast log returns. Through empirical comparison, we ascertain that the CNN model excels in harnessing data features, outperforming other models in prediction accuracy. Nevertheless, AE models exhibit the poorest performance in this task, attributed to their deficiency in modeling time dependencies. Overall, this study validates the possible usefulness for predicting financial time series data and furnishes valuable insights for future research endeavors.

Paper Citation

in Harvard Style

Chen X. (2024). Stock Price Prediction Based on Deep Learning. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 151-159. DOI: 10.5220/0013004900004601

in Bibtex Style

@conference{iampa24,
author={Xuejiao Chen},
title={Stock Price Prediction Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={151-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013004900004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Stock Price Prediction Based on Deep Learning
SN - 978-989-758-722-1
AU - Chen X.
PY - 2024
SP - 151
EP - 159
DO - 10.5220/0013004900004601
PB - SciTePress