News Processing during Speculative Bubbles: Evidence from the Oil Market

Speculative bubbles are commonly referred to situations where stock prices considerably deviate from their fundamentals until the bubbles bust. Bursting of bubbles such as the dot-com or U.S. housing bubble is very costly, so there is a need for mechanisms to detect them. In this paper, we attempt to predict when bubbles may bust using the sentiment of news announcements. Accordingly, we first try to understand how news reception evolves depending on the market phase (boom or bust). The probability of bubble bursts are calculated on the basis of a Markov-regime switching model. The approach is applied and validated using the oil market which appears to be one of the most important markets in the globalized world. Our methodology can be similarly extended to other markets such as gold or wheat.