Research on Baijiu Stocks: Integrating Entropy-Weighted TOPSIS and LSTM
Year: 2025
Type: article
Abstract: This study addresses the significant stock price volatility in China’s Baijiu industry and the inefficacy of traditional prediction models by proposing an intelligent forecasting framework integrating entropy-weighted TOPSIS and LSTM neural networks. A 17-dimensional evaluation system is constructed, encompassing price, trading, valuation, technical indicators, and financial data. The entropy-weighted TOPSIS method identifies Kweichow Moutai (600519.SH) and Shanxi Xinghuacun Fenjiu (600809.SH) as target stocks. Subsequently, an LSTM model is employed for multi-timescale price prediction, demonstrating superior accuracy compared to conventional models, particularly in short-term forecasting. Empirical results validate the framework’s effectiveness in capturing nonlinear trends and mitigating overfitting. The research offers actionable insights for investors and regulatory strategies, advancing intelligent decision-making in volatile financial markets.
DOI: 10.54691/972p9653
Author: Zirui Tan
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4408043145