The following are some of the papers I read last week (the good ones).

Meucci (entropy pooling) is really interesting, accessible, and right on the cutting edge (2009) of portfolio theory. Unfortunately I haven't been able to work out how one might make the implementation tractable/efficient and the paper only gives a general picture. Looking at Meucci's recent presentations, it looks like this is still imperfect. The use of confidence seems rather heuristic for a guy like Meucci who's published on Bayesian portfolio theory before.

This Bayesian Vector Autoregression paper is just a straightforward example from the economic prediction literature. It has very positive results, another algorithm to keep in mind even though it's from 1986.

These last two, on stacking, are really interesting if you've thought about the problem of combining multiple signals/systems, especially ones that are likely somewhat correlated. Stacking is sort of like crossvalidation, but for optimizing ensembles of models instead of a single model. The literature on ensembles/combining multiple learners has some really interesting unexplained results - especially the obvious one - why does it even improve the overall accuracy of the individual models? These papers on stacking shed some light on it. Tibshirani & LeBlanc 1993; Breiman 1996.

Finally, here's an excellent source for more research directly analyzing arbitrage opportunities. This is really a suprisingly good source.

Please share if you've read any interesting papers recently on machine learning or trading or anything else you think might be of interest.