我也来贴几个吧。 Kelly, J., J. A New Interpretation of Information Rate Bell Systems Technical Journal, 1956, 35, 917-926 Latané, H. A. Criteria for Choice Among Risky Ventures The Journal of Political Economy, 1959, 67, 144-155 Breiman, L. Optimal Gambling Systems For Favorable Games Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, 1961, 1, 65-78 Latané, H. A. & Tuttle, D. L. Criteria for Portfolio Building The Journal of Finance, 1967, 22, 359-373 Thorp, E. & Kassouf, S. Beat the Market Random House: New York, 1967 Thorp, E. O. Optimal Gambling Systems for Favorable Games Revue de l'Institut International de Statistique / Review of the International Statistical Institute, 1969, 37, 273-293 Thorp, E. Portfolio Choice and the Kelly Criterion Business and Economics Section of the American Statistical Association, 1971, 215-224 Cover, T. M. Universal Portfolios Mathematical Finance, 1991, 1, 1-29 Ordentlich, E. & Cover, T. M. On-Line Portfolio Selection COLT, 1996, 310-313 Helmbold, D. P.; Schapire, R. E.; Singer, Y. & Warmuth, M. K. On-Line Portfolio Selection Using Multiplicative Updates Mathematical Finance, 1998, 8, 325-347 Kalai, A. & Vempala, S. Efficient Algorithms for Universal Portfolios The Journal of Machine Learning Research, 2002, 3, 423-440 Borodin, A.; El-Yaniv, R. & Gogan, V. Can We Learn to Beat the Best Stock Journal of Artificial Intelligence Research, 2004, 21, 579-594 Agarwal, A.; Hazan, E.; Kale, S. & Schapire, R. E. Algorithms for portfolio management based on the Newton method ICML, 2006, 9-16 Györfi, L.; Lugosi, G. & Udina, F. Nonparametric Kernel-Based Sequential Investment Strategies Mathematical Finance, 2006, 16, 337 - 357 Györfi, L.; Urbán, A. & Vajda, I. Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies International Journal of Theoretical and Applied Finance, 2007, 10, 505-516 Levina, T. & Shafer, G. Portfolio Selection and Online Learning International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2008, 16, 437-473 Györfi, L.; Udina, F. & Walk, H. Nonparametric Nearest Neighbor Based Empirical Portfolio Selection Strategies Statistics and Decisions, 2008, 26, 145 - 157 Hazan, E. & Seshadhri, C. Efficient learning algorithms for changing environments ICML, 2009, 50 Hazan, E. & Kale, S. On Stochastic and Worst-case Models for Investing NIPS, 2009, 709-717 Li, B.; Hoi, S. C. & Gopalkrishnan., V. CORN : Correlation-driven Nonparametric Learning Approach for Portfolio Selection ACM Transactions on Intelligent Systems and Technology Special Issue on Machine Learning for Business Applications, 2010
作为科研方向的话，熵和集智都是比较能够出成果的方向。比如说你可以把利用其他方法估计的模型用最大熵原理重新估计一下（基于熵理论的投资组合或者套利模型属于这一类的）；而对于集智你可以用Agent-based modelling实验各种对市场的想法，集智方法已成为是进化经济学的一个重要领域。但是在实际交易中他们是否有用就是另外一个问题了。