BIBLIOGRAPHY 203
Y. Singer. Switching portfolios. International Journal of Neural Systems, 8(4):
488–495, 1997.
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and perfor-
mance improvements. In Proceedings of the 5th International Conference of
Extending Database Technology, Avignon, France, 1–17, 1996.
G. Stoltz and G. Lugosi. Internal regret in on-line portfolio selection. Machine
Learning, 59(1–2):125–159, 2005.
Y. Takano and J.-y. Gotoh. Constant rebalanced portfolio optimization under
nonlinear transaction costs. Asia-Pacific Financial Markets, 18:191–211, 2011.
N. Taleb. Fooled by Randomness: The Hidden Role of Chance in Life and in the
Markets. New York: Random House, 2008.
F. E. H. Tay and L. Cao. Application of support vector machines in financial time
series forecasting. Omega, 29(4):309–317, 2001.
E. O. Thorp. Optimal gambling systems for favorable games. Review of the
International Statistical Institute, 37(3):273–293, 1969.
E. O. Thorp. Portfolio choice and the Kelly criterion. In Proceedings of the Business
and Economics Section of the American Statistical Association, Fort Collins,
Colorado, 599–619, 1971.
E. O. Thorp. The Kelly criterion in blackjack, sports betting, and the stock market.
In Proceedings of the International Conference on Gambling and Risk Taking,
Montreal, 1997.
R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal
Statistical Society (Series B), 58:267–288, 1996.
J. Ting, T. Fu, and F. Chung. Mining of stock data: Intra- and inter-stock pattern
associative classification. Threshold, 5(100):5–99, 2006.
E. Tsang, P. Yung, and J. Li. Eddie-automation, a decision support tool for financial
forecasting. Decision Support Systems, 37:559–565, 2004.
R. S. Tsay. Analysis of Financial Time Series. New York: Wiley, 2002.
I. Vajda. Analysis of semi-log-optimal investment strategies. In Proceedings of
Prague Stochastic, Prague, 2006.
V. Vovk. Derandomizing stochastic prediction strategies. In Proceedings of Annual
Conference on Computational Learning Theory, Nashville, Tennessee, 32–44,
1997.
V. Vovk. Derandomizing stochastic prediction strategies. Machine Learning, 35:
247–282, 1999.
V. Vovk. Competitive on-line statistics. International Statistical Review/Revue
Internationale de Statistique, 69(2):213–248, 2001.
V. G. Vovk. Aggregating strategies. In Proceedings of the Annual Conference on
Learning Theory, Rochester, NY, 371–383, 1990.
V. G. Vovk and C. Watkins. Universal portfolio selection. In Proceedings of the
Annual Conference on Learning Theory, Madison, WI, 12–23, 1998.
X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh.
Experimental comparison of representation methods and distance measures
for time series data. Data Mining and Knowledge Discovery, 26(2):275–309,
2013.
T&F Cat #K23731 — K23731_A004 — page 203 — 9/26/2015 — 8:06
204 BIBLIOGRAPHY
R. J. Yan and C. X. Ling. Machine learning for stock selection. In Proceedings of
the ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining, San Jose, CA, 1038–1042, 2007.
H. Yang, Z. Xu, I. King, and M. R. Lyu. Online learning for group lasso. In
Proceedings of the International Conference on Machine Learning, Haifa,
Israel, 1191–1198, 2010.
B.-K. Yi, H. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences
under time warping. In Proceedings of the International Conference on Data
Engineering, Orlando, FL, 201–208, 1998.
W. Young. Calmar ratio: A smoother tool. Futures, 20(1):40, 1991.
W. Zhang and S. Skiena. Financial Analysis Using News Data. Technical report,
State University of New York at Stony Brook, 2008.
W. Zhang and S. Skiena. Trading strategies to exploit blog and news sentiment.
In Proceedings of the International AAAI Conference on Weblogs and Social
Media, Atlanta, 375–378, 2010.
Y. Zhou, R. Jin, and S. C. Hoi. Exclusive lasso for multi-task feature selection.
In Proceedings of the International Conference on Artificial Intelligence and
Statistics, Chia Laguna Resort, Sardinia, Italy, 988–995, 2010.
M. Zinkevich. Online convex programming and generalized infinitesimal gradient
ascent. In Proceedings of the International Conference on Machine Learning,
Washington, DC, 928–936, 2003.
H. Zou. The adaptive lasso and its oracle properties. Journal of the American
Statistical Association, 101:1418–1429, 2006.
T&F Cat #K23731 — K23731_A004 — page 204 — 9/26/2015 — 8:06
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
3.142.35.75