Predictive Modeling for Asset Bubble Detection in Financial Markets

In response to the volatile nature of financial markets, our project, titled ‘A Comprehensive Approach to Stock Market Crash Prediction using Time Series Forecasting, Machine Learning, and Deep Learning Techniques,’ endeavors to create a robust prediction model for anticipating stock market crashes. Leveraging a diverse dataset gathered from various open-source links, with a primary source being YahooFinance, we employ an extensive feature set including time stamps, economic indicators such as unemployment, consumer price index (CPI), price-to-earning ratio (P/E), and market yields. Our innovative approach involves labeling major crashes on the S&P 500 chart and correlating them with diverse economic data. By filtering and marking significant crash phases, we create a binary classifier. Forecasting S&P 500 values for 24 months using ARIMA and SARIMAX models allows for better evaluation and economic analysis. The project integrates various machine learning algorithms, with random forests and decision trees emerging as top performers. Deep learning techniques using Keras, such as Convolutional Neural Networks (CNNs) and Dense Neural Networks (DNNs), achieve notable accuracy. Additionally, real-time stock-related tweets obtained through the Twitter API and market sentiments from the Alpha Vantage API enhance the predictive capabilities. The model employs ARIMA and SARIMAX for forecasting, alongside a CNN architecture for crash classification. The UI/UX showcases data visualizations, sentiment analysis, and predictive models. The technology stack includes REACT, NODE JS, TENSORFLOW, A WS, and more. This work presents a holistic strategy to equip investors with valuable insights and aid in making informed decisions during potential market downturns.