Recent Methods in Stock Price Prediction: A Review

Yuxuan Li

2024

Abstract

This paper evaluates various methodologies for predicting stock prices, from traditional models such as the Autoregressive Integrated Moving Average (ARIMA) to more advanced neural networks. It addresses the inadequacies of the ARIMA model, particularly its limitations in predicting the future, which are commonplace in dynamic financial markets. Then, it introduces a hybrid model, the Bidirectional Cuda Deep Neural Network Long Short-Term Memory combined with a one-dimensional Convolutional Neural Network (BiCuDNNLSTM-1dCNN). This model excels at capturing both the long-term trends and short-term fluctuations essential for accurate financial forecasting. Through extensive preprocessing, the model ensures the highest quality of input data, leading to more reliable predictions. Comparative results demonstrate that the BiCuDNNLSTM-1dCNN model significantly surpasses both ARIMA and simpler neural networks in accuracy and reliability. The paper concludes with a call for continued advancement of hybrid modeling techniques to enhance the precision of forecasts and empower data-driven investment strategies in volatile markets.

Paper Citation

in Harvard Style

Li Y. (2024). Recent Methods in Stock Price Prediction: A Review. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 552-556. DOI: 10.5220/0013528200004619

in Bibtex Style

@conference{daml24,
author={Yuxuan Li},
title={Recent Methods in Stock Price Prediction: A Review},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={552-556},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013528200004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Recent Methods in Stock Price Prediction: A Review
SN - 978-989-758-754-2
AU - Li Y.
PY - 2024
SP - 552
EP - 556
DO - 10.5220/0013528200004619
PB - SciTePress