I am Jonathan Sandberg and I run the Princeton Bank Consortium. I came to Wall Street from Princeton University in the 90s. I have worked on the trading floor in Morgan Stanley DPG, Lehman Brothers Credit, Citi FX, and Goldman PB High Frequency Equities. I am a floating point inner-loop hacker  for exotic derivatives, large portfolios, and low-latency trade execution (see Black Scholes in 36 nanoseconds, Totally Serial, or the 2012 Due Diligence series on Maxeler/JPM Credit Derivative Supercomputer).

The Princeton Bank Consortium (PBC) will be a data publication outlet for a new type of Big Data stochastic programming problem focussed on the optimization of U.S. Bank Net Interest Margin. Stochastic Programming is a well researched industrial and academic topic see Shapiro, Shapiro et.al., or Nemirovski. There is even solid practical experience in Stochastic Programming applied directly to Bank Asset and Liability Management see Birge, QRM, Oracle Financial, and IBM Algo. What has changed within the past couple years is that the Banking industry efforts in automation, centralization, and regulatory reporting have made Net Interest Margin Optimization feasible on a massive scale. New Technology including: inexpensive vectorized x86 microprocessors, fast LP solvers, and solid optimizing compilers make it so PBC can run Net Interest Margin Optimization for the aggregated accrual portfolio of JPM, Citi, Wells Fargo, BofA, and the remaining top 50 Bank Holding Companies (BHCs) tracked by the U.S. Federal Reserve on an iMac with open source software (of course PBC does not know the exact accrual portfolio contents of each of these BHCs, but there is aggregated Fed data to work with). This is a very exciting, and lucrative, new bank automation quant-problem that the Princeton Bank Consortium will assist in promoting, finding, and optimizing industrial solutions.

This PBC blog will be used to provide the narrative for the development of Net Interest Margin Optimization solutions. The blog will present the people and the research that makes solutions possible and trace the evolution of the algorithms, programs, and systems that make progress in NIM Optimization possible.