We propose a novel model, the Hyped Log-Periodic Power Law Model (HLPPL), to the problem of quantifying and detecting financial bubbles, an ever-fascinating one for academics and practitioners alike. Bubble labels are generated using a Log-Periodic Power Law (LPPL) model, sentiment scores, and a hype index we introduced in previous research on NLP forecasting of stock return volatility. Using these tools, a dual-stream transformer model is trained with market data and machine learning methods, resulting in a time series of confidence scores as a Bubble Score. A distinctive feature of our framework is that it captures phases of extreme overpricing and underpricing within a unified structure. We achieve an average yield of 34.13 percentage annualized return when backtesting U.S. equities during the period 2018 to 2024, while the approach exhibits a remarkable generalization ability across industry sectors. Its conservative bias in predicting bubble periods minimizes false positives, a feature which is especially beneficial for market signaling and decision-making. Overall, this approach utilizes both theoretical and empirical advances for real-time positive and negative bubble identification and measurement with HLPPL signals.

翻译：我们提出了一种新颖的模型——炒作对数周期幂律模型（HLPPL），用于解决金融泡沫的量化与检测问题，这一课题长期以来持续吸引着学术界与实务界的关注。泡沫标签的生成综合运用了对数周期幂律（LPPL）模型、情绪评分以及我们在先前关于股票收益波动率的自然语言处理预测研究中引入的炒作指数。借助这些工具，我们采用市场数据和机器学习方法训练了一个双流Transformer模型，从而生成一系列作为泡沫评分的置信度得分时间序列。我们框架的一个显著特点是，它能够在统一结构内捕捉极端高估与低估阶段。在2018年至2024年间对美国股票进行回测时，该方法实现了34.13%的年化平均收益率，同时在不同行业板块间展现出卓越的泛化能力。其在预测泡沫时期所持有的保守倾向最大限度地减少了误报，这一特性对于市场信号传递与决策制定尤为有益。总体而言，该方法综合运用理论与实证进展，借助HLPPL信号实现了对正向与负向泡沫的实时识别与度量。