Unveiling the Dynamics of Stock Market Crashes: From Price Bubbles to Machine Learning Predictions

This paper examines stock market crashes, marked by sudden and substantial declines in market value, such as 1987s Black Monday and 2008s Bubble House. The crashes are attributed to bursting price bubbles, revealing market inefficiencies where prices deviate from fundamental asset values due to traders’ expectations. George Soros’ concept of reflexivity emphasizes the feedback loop between traders’ expectations and market prices. The paper explores leveraging log-periodic power laws (LPPLs), a mathematical framework by physicist Professor Didier Sornette, to model market bubble dynamics. The central proposition is whether machine learning algorithms can autonomously identify recurring price structures associated with market crashes, providing a novel approach to anticipate and mitigate their impact.