Advancing sustainable investing: A deep learning model for multi-source stock prediction

Hongxuan Yu, City University of MacauFollow
Tingting Zhang, University of South FloridaFollow
Murat Kizildag, University of Central FloridaFollow

Abstract

The burgeoning role of the stock market within the national economy elevates the importance of precise stock price analysis and prediction, a field that has garnered substantial interest in academic research. Stock price fluctuations, influenced by many factors, including company fundamentals, market sentiment, capital flows, industry news, and macroeconomic policies, present a highly dynamic and complex challenge for predictive modeling. Addressing this challenge, our study introduces an innovative method that capitalizes on the synthesis of news text and stock price data for forecasting market movements. We employ GloVe embeddings to capture semantic nuances from news text and integrate them with quantitative stock data through a sophisticated attention mechanism. This multifaceted approach is operationalized within a predictive model underpinned by long short-term memory (LSTM) networks and an attention framework geared toward deciphering the intricate, non-linear patterns characteristic of stock market time series data. Our empirical evaluation, conducted on a comprehensive dataset, reveals that our model significantly outperforms traditional methods, marking a notable advancement in prediction accuracy. The results underscore the efficacy of combining textual and numerical data through deep learning techniques, providing a robust tool for investors and analysts to navigate the volatile terrain of stock markets with enhanced foresight.

Keywords

deep learning, multi-source stock prediction, sustainable investing

Chinese Abstract

推进可持续投资：基于深度学习的多源股票预测模型

随着股票市场在国民经济中日益重要的作用，对股票价格的精确分析与预测也愈发关键，这一领域已成为学术研究的热点。股票价格的波动受多种因素影响，包括公司基本要素、市场情绪、资本流动、行业新闻以及宏观经济政策，这使得预测建模面临高度动态且复杂的挑战。为应对这一挑战，本研究提出了一种创新方法，结合新闻文本和股票价格数据以预测市场走势。我们采用GloVe嵌入技术捕捉新闻文本中的语义细微差别，并通过复杂的注意力机制将其与量化的股票数据相结合。这种多维方法在预测模型中得以实现，该模型以长短期记忆（LSTM）网络和注意力框架为基础，旨在解读股票市场时间序列数据中复杂的非线性模式。通过对综合数据集的实证评估，我们的模型在预测精度上显著优于传统方法，标志着预测能力的显著提升。研究结果突显了通过深度学习技术结合文本与数值数据的有效性，为投资者和分析师在应对股票市场的波动性时提供了一种更具前瞻性的强大工具。

深度学习、多源股票预测、可持续投资

ORCID Identifiers

Hongxuan Yu: https://orcid.org/0000-0000-0000-0000

Tingting Zhang: https://orcid.org/0000-0001-7162-0918

Murat Kizildag: https://orcid.org/0000-0000-0000-0000

DOI

10.5038/2640-6489.9.2.1297

Recommended Citation

Yu, H., Zhang, T., & Kizildag, M. (2024). Advancing sustainable investing: A deep learning model for multi-source stock prediction. Journal of Global Business Insights, 9(2), 176-191. https://www.doi.org/10.5038/2640-6489.9.2.1297