Elwyn Berlekamp, UC Berkeley

Elwyn Berlekamp is a professor emeritus at Berkeley. His 1984 revision of the  book Algebraic Coding Theory rests on my shelf at arm’s distance from where I write as do the 2 volumes of Winning Ways  written with Conway and Guy and published in 1982. Twophoto things I did not remember about Berlekamp are he was Ken Thompson’s thesis advisor back in the day and he managed all the trading for the original 1986 Medallion Fund later to be Jim Simons’ flagship fund at Renaissance Technologies. But the reason Berlekamp’s name came up here is for a book review he wrote in 2005, titled Bettor Math, for William Poundstone’s book Fortune’s Formula. I was compelled to read the book immediately after reading the review. It is a vivid fast read recounting among other things Claude Shannon’s and Edward Thorp’s early careers at Bell Labs and MIT. Ultimately the book is about the tradeoffs between two views of optimal money management the “Kelly Criterion” and the Efficient Market Hypothesis. Berlekamp writes in his review (recall 2005 is after the LTCM event) :

No one who has made a legitimate fortune in the markets believes the efficient-market hypothesis. And conversely, no one who believes the efficient-market hypothesis has ever made a large fortune investing the the financial markets, unless she began with a moderately large fortune.

From the Net Interest Margin Optimization perspective we are somewhat indifferent to the trade-offs and arguments about Kelly and EMH since the optimization we refer to is in the selection, timing, and placement of buy and hold positions in a banking book.  This is not a trading book or a hedge fund portfolio, we are looking at the $15T in the aggregate US Bank accrual portfolios. We are simply using trading book analytics to automate and numerically  optimize the execution of the capital allocation plan in the banking book for the current market and its implied expectations. We are not suggesting any optimization relative to forward market expectations, those are unknowns to our simulations. Forward simulation with NIM Optimization  automates the implementation of the capital allocation plan and provides a most efficient implementation (of an exogenously given plan) that meets regulatory targets as well as maximizing the Net Interest Margin. Backward simulation of NIM Optimization explaining the actual market moves relative to the actual capital allocation plan implementation provides a detailed measure of the overall Treasury/Transfer Pricing system performance.

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