Multimedia Library

Hear our expert staff and Members speak on topics ranging from practical strategies in hedge fund investing to the importance of education in the alternatives space.

Thomas Meyer discussed his new book and, together with Jacob Perner, explained how the concepts presented can be applied to the management of private asset portfolios.

This Shaping the Future of Investing: The Transformative Influence of the CAIA Charter session explored what's next for our industry and how CAIA is at the forefront of redefining investment professionalism.

A lot has happened in digital assets over the past few months. While broader prices have rebounded after 2022's drawdown, regulatory bodies still maintain disparate attitudes on how this technology should be regulated. Similarly, the ways in which clients can access digital assets continues to evolve, yet differ, around the world. How do these challenges impact the future of the technology, and what are the implications for portfolios? We discussed some of these relevant topics that remain top of mind for investors, discussed some frameworks for adoption in client portfolios, and finished with some vision-casting of where the industry is expected to go over the coming years.

With a total outstanding balance of more than $8 trillion, 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.