撞车
计算机科学
文件夹
机器学习
库存(枪支)
人工智能
股市崩盘
情绪分析
股票价格
股票市场
预测建模
计量经济学
财务
工程类
经济
机械工程
古生物学
系列(地层学)
程序设计语言
马
生物
作者
Yankai Sheng,Yuanyu Qu,Ding Ma
标识
DOI:10.1016/j.frl.2024.105195
摘要
This study introduces multimodal data machine learning framework to predict stock crashes. It encapsulates market data, graph data cultivated from industry affiliations through node2vec, and text data derived from sentiment analysis. The LightGBM is utilized, marking an improvement by 7.13% over preceding studies, achieving 75.85% balanced accuracy. An innovative long-short portfolio construction approach is articulated, demonstrating the practical significance of the predictions, with a 4.75% portfolio return in 2022 — a 27.26% advancement over the CSI 300. This endeavour in leveraging multimodal data machine learning for stock crash prediction offers a promising performance, serving as a valuable reference for investors.
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