There is a brand new journal combining Computer Science and Finance, Journal of Algorithmic Finance. Both the advisory board and editorial team are ensemble casts with some of the biggest names in computer science and finance. They just published their inaugural issue in December 2011. The top paper (as indicated by number of SSRN downloads) is one on Markets are Efficient if and Only if P = NP, which perhaps is very reassuring to all the traders out there, at least at a theoretical level. For those unfamiliar with the terminology, P means solvable in polynomial time (i.e., we have reasonably smart, efficient algorithms to solve them) and NP means verifiable in polynomial time (i.e., given a solution, we can verify that it works efficiently). NP-complete is a class of problems such that all problems in NP can be (efficiently) reframed in terms of any NP-complete problem. For problems in NP but not in P, we generally do not have efficient algorithms for exact solutions. Instead, we pretty much have to fall back on exhaustive search (i.e., try all the possible answers and see what works). If one can solve an NP-complete problem efficiently in the general case, then one can solve any problem in NP efficiently, which potentially saves some astounding sum of money and enables a lot of previously impractical technologies. The more interesting implication of the paper is that the market can serve as an oracle to solve NP-complete problems. Recently DARPA has decided to invest in crowd-sourcing and gamification to solve difficult formal verification problems. Instead of potentially awkward game embeddings of difficult computational problems, I wonder if market strategies may be more effective and direct.

All 6 articles and 1 interview (with Emanuel Derman) in this issue are penned by finance and math researchers, and none by computer scientists. Even the papers that would have seemed to involve computer scientists such as one on Tweets and Peers: Defining Industry Groups and Strategic Peers based on Investor Perceptions of Stocks on Twitter and Markets are Efficient if and Only if P = NP had finance folks as authors. Perhaps this will change in the future. I think there are a lot of potential synergies and cross-pollination between computer science and finance, but apparently computer scientists have not been involved as much.

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