Predictive Modeling for Asset Bubble Detection in Financial Markets

Abstract

This paper presents a comprehensive approach to predicting and detecting asset bubbles in financial markets, utilizing advanced analytics, machine learning models, and deep learning techniques. The study focuses on the S&P 500 as a representative indicator of the U.S. stock market, aiming to develop a robust methodology for identifying early signs of asset bubbles and providing actionable insights for investors, financial institutions, and policymakers.