Quantitative Analysis of Financial Crisis: Unraveling the 2008 GFC and its Implications for 2023 Market Dynamics
In this project, we tackled one of the most critical challenges in quantitative finance - discerning potential financial crises and optimizing investment portfolios under volatile conditions. The analysis was triggered by recent financial tremors following the collapse of Silicon Valley Bank and Signature Bank, evoking fears of another episode akin to the 2008 Global Financial Crisis.
Our objective was to draw parallels between the 2008 crisis and the current economic scenario (2023), devise an updated risk model and recommend investment strategies to mitigate potential losses. We initiated by data cleaning and exploratory data analysis, ensuring data integrity while addressing challenges like survivorship bias and high variance induced by micro-cap stocks. This formed the bedrock of our subsequent analyses. We then proceeded to feature engineering, creating a train-test set to align with Bloomberg's risk model. We derived critical financial ratios such as ROE, EBIT/TEV, E/P, S/P, B/P, CF/P, FCF/P, EBIT/P, and Accrual Ratio, amongst others, while also exploring additional measures like Market Leverage and Book Leverage.
Upon constructing the train-test set, we then calculated factor returns using a newly engineered factor model. We also explored statistical factors using Principal Component Analysis (PCA), identifying clusters of stocks which were most indicative of the 2008 GFC. Post this comparative study, we turned our attention to the present year - 2023. We assessed factor returns, identified clusters of stocks, and evaluated the current situation against the 2008 backdrop.
With this detailed understanding, we were equipped to offer investment strategies and portfolio optimization techniques that would have navigated the 2008 GFC with minimal losses. This involved creating alpha models, implementing optimization models, and comparing optimizer, Monte Carlo, and genetic algorithm outputs and performances. Additionally, we developed long-only, short-only, and market-neutral portfolios based on our insights. Lastly, we employed Markov switching dynamic regression models to understand if distinct economic regimes might be triggered during a crisis. This helped us not just pinpoint when a crisis ends according to the regime model, but also identify factors affected by the adverse regime.