用新闻情绪+经济指标，三步预测美股泡沫。
A three-step machine learning approach to predict market bubbles with financial news
- 三步流程：识别泡沫期、提取新闻情绪、融合预测
- 模型准确率显著高于基准算法，具备实时预警能力
- 适合投资者与监管者提前防范金融风险
本研究提出一种三步机器学习框架，通过融合金融新闻情感与宏观经济指标，预测标普500指数的市场泡沫。第一步采用右尾单位根检验识别泡沫期，该方法被广泛用于实时泡沫检测；第二步利用自然语言处理技术从大规模金融新闻中提取情感特征，捕捉投资者预期与行为模式；第三步采用集成学习方法，基于高维度的情感与宏观经济预测因子进行泡沫发生预测。模型通过k折交叉验证评估，并与基准机器学习算法对比。实证结果表明，所提出的三步集成方法显著提升预测准确率与鲁棒性，为投资者、监管机构和政策制定者提供有价值的早期预警信息，有助于缓解系统性金融风险。
原文摘要 · Abstract (English)
This study presents a three-step machine learning framework to predict bubbles in the S&P 500 stock market by combining financial news sentiment with macroeconomic indicators. Building on traditional econometric approaches, the proposed approach predicts bubble formation by integrating textual and quantitative data sources. In the first step, bubble periods in the S&P 500 index are identified using a right-tailed unit root test, a widely recognized real-time bubble detection method. The second step extracts sentiment features from large-scale financial news articles using natural language processing (NLP) techniques, which capture investors' expectations and behavioral patterns. In the final step, ensemble learning methods are applied to predict bubble occurrences based on high sentiment-based and macroeconomic predictors. Model performance is evaluated through k-fold cross-validation and compared against benchmark machine learning algorithms. Empirical results indicate that the proposed three-step ensemble approach significantly improves predictive accuracy and robustness, providing valuable early warning insights for investors, regulators, and policymakers in mitigating systemic financial risks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。