Machine Learning–Based Systematic Investing in Agency Mortgage-Backed Securities

Abstract

With a total outstanding balance of more than $8 trillion as of this writing, agency mortgage-backed securities (MBS) represent the second largest segment of the US bond market and the second most liquid fixed-income market after US Treasuries. Institutional investors have long participated in this market to take advantage of its attractive spread over US Treasuries, low credit risk, low transaction cost, and the ability to transact large quantities with ease. MBS are made of individual mortgages extended to US homeowners. The ability for a homeowner to refinance at any point introduces complexity in prepayment analysis and investing in the MBS sector. Traditional prepayment modeling has been able to capture many of the relationships between prepayments and related factors such as the level of interest rates and the value of the embedded prepayment option, yet the manual nature of variable construction and sheer amount of available data make it difficult to capture the dynamics of extremely complex systems. The long history and large amount of data available in MBS make it a prime candidate to leverage machine learning (ML) algorithms to better explain complex relationships between various macro- and microeconomic factors and MBS prepayments. The authors propose a systematic investment strategy using an ML-based mortgage prepayment model approach combined with a coupon allocation optimization model to create an optimal portfolio to capture alpha vs. a benchmark.