Conferences

Infima Presented at the Bootcamp on Machine Learning for Quantitative Finance at the Fields Institute

Kay Giesecke, CEO and Founder of Infima Technologies, spoke at the Fields-CFI Bootcamp on Machine Learning for Quantitative Finance, which was held at the renowned Fields Institute on October 1, 2021. Infima’s transformative Deep Learning technologies deliver prepayment predictions at borrower, security, and cohort levels that set new accuracy standards in the Agency MBS market, empowering investors to make superior security selection and portfolio construction decisions. Innovative analytical tools support a range of workflows, from powerful querying to in-depth evaluation of securities.

The presentation entitled "Deep Learning for MBS Prepayments" was based on the material discussed in this Infima White Paper. Here's the abstract of the presentation:

Predictions of prepayment speeds are mission-critical for investors, dealers, originators and other participants in the $10T Agency MBS market. Legacy prediction technologies produce flawed and stale data wrecking returns and profits. We develop deep learning systems that set new accuracy and latency standards, delivering performance-boosting edges to market participants. Our systems harness data of unprecedented size and granularity, covering monthly records for tens of millions of borrowers across the US over two decades. By uncovering hidden nonlinear patterns in borrower behavior at the individual loan level, they improve prediction accuracy for MBS pool CPRs by a full order of magnitude relative to the market’s current “gold standard.” Our predictions are robust in all market environments including the pandemic. Rigorous significance tests offer deep insights into the variables influencing predictions.

The event was a follow-up to three very successful events previously held at the Fields Institute in May 2015 (Workshop on Big Data in Commercial and Retail Banking), May 2017 (Big Data for Quants Boot Camp), and September 2019 (Bootcamp on Machine Learning for Finance), focusing on presenting state-of-the-art data analytics techniques to graduate students, academic researchers, and financial practitioners. This edition of the event focused on advanced machine learning techniques currently being used in the financial industry, as well as novel techniques at the forefront of academic research. Topics included applications of deep learning to stochastic control and to prepayment of mortgage-backed securities, machine learning using alternative data for finance, and applying graph machine learning to mutual fund data.