The Relationship Between Equities and Structured Finance of FinTech Lending Firms

The emergence of FinTech lending companies can be attributed to the convergence of factors such as the widespread use of the internet, advancements in machine learning, and a decline in trust towards traditional banking systems following the financial crisis of 2006-08. These companies have disrupted the conventional banking system by offering alternative credit solutions to consumers in the United States. However, despite their innovative approach, they still rely on the good old structured finance market to transfer risk from their balance sheets. This research quantitatively examines the relationship between two distinct financial assets: asset-backed securities (ABS) and equities. These assets derive their value and performance from the same FinTech lender, making them integral to this investigation. FinTech firms actively compete for capital from institutional investors in the capital markets, providing these investors with risk-adjusted returns and exposure to the US consumer asset class. The primary objective of this study is to explore the hypothesis that the performance of ABS is quantitatively connected with the equity performance of these firms. The research seeks to determine whether this connection can be leveraged in developing a trading strategy. The fundamental basis for hypothesizing such a connection lies in information arbitrage, primarily driven by the lack of transparency and explainability surrounding ABS performance data. Before implementing a systematic strategy, the research has identified four economically significant features within the ABS data: Constant Prepayment Rate (CPR), Constant Default Rate (CDR), Annualized Net Loss, and Gross Coupon. By leveraging this understanding, this research discovered that a machine learning-based long-short strategy effectively exploits ABS data to generate superior risk-adjusted returns compared to the benchmark. These findings indicate that ABS holds valuable information for the equities market. However, this information is incorporated into equity prices with a significant delay, potentially due to the information arbitrage dynamics elucidated in this study