Algorithms for portfolio management based on the Newton method

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 9-16

  Agarwal, Amit ^a   Hazan, Elad ^a   Kale, Satyen ^a   Schapire, Robert E ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FINANCIAL DATA; OPTIMAL LOGARITHMIC; PORTFOLIO MANAGEMENT; POTENTIAL FUNCTION;

ALGORITHMS; DATA ACQUISITION; LEARNING SYSTEMS; NEWTON-RAPHSON METHOD; OPTIMIZATION;

ELECTRONIC COMMERCE;

EID: 34250718145     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143846     Document Type: Conference Paper

References (21)

1. Agarwal, A., & Hazan, E. (2005). New algorithms for repeated play and universal portfolio management. Princeton University Technical Report TR-740-05.
2. Algoet, P., & Cover, T. (1988). Asymptotic optimality and asymptotic equipartition properties of logoptimum investment. Annals of Probability, 2, 876-898.
3. Bell, R., & Cover, T. (1980). Competitive optimally of logarithmic investment. Mathematics of Operations Research, 2, 161-166.
4. Bell, R., & Cover, T. (1988). Game-theoretic optimal portfolios. Management Science, 6, 724-733.
5. Blum, A., & Kalai, A. (1999). Universal portfolios with and without transaction costs. Machine Learning, 35, 193-205.
6. Borodin, A., El-Yaniv, R., & Gogan, V. (2004). Can we learn to beat the best stock. Journal of Artificial Intelligence Research, 21, 579-594.
7. Brookes, M. (2005). The matrix reference manual. [online] www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html.
8. Cover, T. (1991). Universal portfolios. Mathematical Finance, 1, 1-19.
9. Hannan, J. (1957). Approximation to bayes risk in repeated play. In M. Dresher, A. W. Tucker and P. Wolfe, editors, Contributions to the Theory of Games, III, 97-139.
10. Hazan, E., Kalai, A., Kale, S., & Agarwal, A. (2006). Logarithmic regret algorithms for online convex optimization. To appear in the 19th Annual Conference on Learning Theory (COLT).
11. Helmbold, D., Schapire, R., Singer, Y., & Warmuth., M. (1998). On-line portfolio selection using multiplicative updates. Mathematical Finance, 8, 325-347.
12. Kalai, A., & Vempala, S. (2003). Efficient algorithms for universal portfolios. Journal Machine Learning Research, 3, 423-440.
13. Kalai, A., & Vempala, S. (2005). Efficient algorithms for on-line optimization. Journal of Computer and System Sciences, 71(3), 291-307.
14. Kelly, J. (1956). A new interpretation of information rate. Bell Systems Technical Journal, 917-926.
15. Larson, D. C. (1986). Growth optimal trading strategies. Ph.D. dissertation, Stanford Univ., Stanford, CA.
16. 3) sampling algorithm (Technical Report MSR-TR-2003-04). Microsoft Research.
17. 4) volume algorithm. Proceedings of the 44th Symposium on Foundations of Computer Science (FOCS) (pp. 650-659).
18. Luenberger, D. G. (1998). Investment science. Oxford: Oxford University Press.
19. Merhav, N., & Feder, M. (1992). Universal sequential learning and decision from individual data sequences. 5th COLT (pp. 413-427). Pittsburgh, Pennsylvania, United States.
20. Ordentlich, E., & Cover, T. M. (1996). On-line portfolio selection. 9th COLT (pp. 310-313). Desenzano del Garda, Italy.
21. Stoltz, G., & Lugosi, G. (2005). Internal regret in on-line portfolio selection. Machine Learning, 59, 125-159.