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Princeton Bank Consortium is up




Opportunity: The Federal Reserve’s Comprehensive Capital Analysis and Review (CCAR) program, has opened up a $100+ billion annual market in optimizing the Net Interest Margin at significant US banks. An extra basis point of NIM is worth $200mm to a bank the size of JPM. The computer floating point execution needed to run NIM optimization is inexpensive and the cost is expected to continue to drop for another 5-7 years, even considering Colwell’s 2013 comments regarding the End of Moore’s Law.These financial and technology developments premier on a stage where US Bank Net Interest Margin is at a 30 year low and dropping, in a interest rate environment where rates are expected to remain low (see Rieder).

Problem: Net Interest Margin has only been a quantitative focus at large US Bank Treasury departments for the last several years, so the level of quantitive modeling is elementary and non-standard. The prepayment and cashflow models for the standard accrual portfolio securities mortgages, loans, and credit cards are well established in the two-way secondary trading securitized markets but have not completely moved from the small Front Office Trading portfolios to the large Treasury accrual portfolios. The stochastic modeling for the econometric and market variables needed for Accrual Portfolio Monte Carlo simulation is new field without a mature publication history. Finally, Bank technology tends to lag and remains very conservative with respect to upgrades. Simulation of 1+bn-security aggregate USD portfolios with static CCAR scenarios strains their technology organization for a variety of reasons.

Solution: The Princeton Bank Consortium presents aggregate Net Interest Margin projections and optimizations based on data from the US Federal Reserve. This website presents the data from a novel large-portfolio (e.g., aggregate US Bank Assets and Liabilities) numerical optimization implementation of the Net Interest Margin Optimization (NIMo) algorithm. Broadly, the idea is to automate the US Banking System’s implicit aggregate discretionary capital allocation plan to optimize the new business revenue (e.g., funding loans with deposits) relative to dynamic operational and market constraints. We can then run explanatories to fit our standard model parameters on individual US Banks to complete NIM P&L attribution. We expect the results will be useful for both Banking System Monitoring and Capital Planning.


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