From NLP to Hype and Financial Bubbles: Integrating News Attention with Bubble Detection Models

Helyette Geman

No 2617, Policy briefs on Economic Trends and Policies from Policy Center for the New South

Abstract: In 2017, eight scientists from the Google research team published in the journal Advances in Neural Info Processing Systems the remarkable article “Attention is all you need,” which introduced a Transformer neural network architecture. The paper has been cited over 173,000 times and ranks among the top 100 most cited papers of the 21st century. It builds on the attention principle introduced in 2014 by Bahdanau, Cho and Turing Award winner Bengio, who proposed neural machine translation by jointly learning to align and translate. This transformer approach has become the main architecture for a wide variety of AI tasks, including large language models. In machine learning, “attention” refers to a mechanism that allows models to focus on specific parts of the input data during the learning process and to determine the relative importance of each component within a sequence. Turning to financial economics, financial news—whether in terms of volume, unusual frequency, or sentiment (positive versus negative tone)—has long attracted the attention of researchers seeking to forecast market dynamics—“buy on rumors, sell on news.” Financial bubbles, however, remain among the most challenging phenomena to model and trade. Traditional models relying solely on price dynamics often fail to detect bubbles in real time, a key objective for stock picking and portfolio selection. Advances in natural language processing (NLP) now enable researchers to quantify market attention and sentiment using financial news and social media activity. This paper builds on recent research on sentiment in financial markets and integrates these insights into quantitative bubble detection models derived from the Log-Periodic Power Law (LPPL) literature, while incorporating a Hype Index that measures disproportionate news attention at a given moment, in order to obtain a hype-adjusted view of speculative dynamics. Within this framework, sentiment and news intensity modify bubble scores derived from price dynamics. The resulting Hyped Log-Periodic Power Law (HLPPL) model improves the identification of emerging bubbles and enables the detection of negative bubbles, corresponding to temporarily overvalued assets. The approach further highlights the importance of the choice of numéraire with respect to which prices are expressed (e.g., gold versus the dollar), emphasizing that bubbles must be assessed relative to a chosen reference asset. Empirical illustrations across equities and cryptocurrencies show how media attention and narrative amplification interact with price dynamics during speculative episodes. Taken together, these results suggest that incorporating information flows and market narratives can significantly improve the early detection and interpretation of financial bubbles.

Date: 2026-06
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