Exploring LSTM Networks for Stock Price Prediction in the Chinese Baijiu Industry

Yixiang Kong

2024

Abstract

This comprehensive essay explores the use of Long Short-Term Memory (LSTM) networks for stock price prediction, focusing on China’s Baijiu industry. It addresses the challenges in stock market prediction and the emergence of LSTM as a solution. The study elaborates on LSTM’s architecture, its core components, and its application in predicting stock prices. It details parameterization strategies for LSTM models, including time step, batch size, epochs, optimizer, loss function, and feature incorporation. The essay examines model performance through various metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), and provides insights into the model’s efficiency in handling time-series data for stock prediction. The research aims to demonstrate the practicality and reliability of LSTM models in financial market analysis, underlining the potential of machine learning in revolutionizing stock market predictions. The essay also discusses the real-world implications of LSTM-based models in the finance sector, emphasizing their role in informed decision-making and investment strategies.

Paper Citation

in Harvard Style

Kong Y. (2024). Exploring LSTM Networks for Stock Price Prediction in the Chinese Baijiu Industry. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 110-115. DOI: 10.5220/0012827500004547

in Bibtex Style

@conference{icdse24,
author={Yixiang Kong},
title={Exploring LSTM Networks for Stock Price Prediction in the Chinese Baijiu Industry},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={110-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012827500004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Exploring LSTM Networks for Stock Price Prediction in the Chinese Baijiu Industry
SN - 978-989-758-690-3
AU - Kong Y.
PY - 2024
SP - 110
EP - 115
DO - 10.5220/0012827500004547
PB - SciTePress