BIBLIOGRAPHY 197
E. Fagiuoli, F. Stella, and A. Ventura. Constant rebalanced portfolios and side-
information. Quantitative Finance, 7(2):161–173, 2007.
E. F. Fama and K. R. French. The cross-section of expected stock returns. The Journal
of Finance, 47(2):427–465, 1992.
M. Feder, N. Merhav, and M. Gutman. Universal prediction of individual sequences.
IEEE Transactions on Information Theory, 38(4):1258–1270, 1992.
M. Finkelstein and R. Whitley. Optimal strategies for repeated games. Advances in
Applied Probability, 13(2):415–428, 1981.
T. Foucault, O. Kadan, and E. Kandel. Limit order book as a market for liquidity.
Review of Financial Studies, 18(4):1171–1217, 2005.
W. J. Fu. Penalized regressions: The bridge versus the lasso. Journal of Computa-
tional and Graphical Statistics, 7(3):397–416, 1998.
A. A. Gaivoronski and F. Stella. Stochastic nonstationary optimization for finding
universal portfolios. Annals of Operations Research, 100:165–188, 2000.
A. A. Gaivoronski and F. Stella. On-line portfolio selection using stochastic program-
ming. Journal of Economic Dynamics and Control, 27(6):1013–1043, 2003.
K. Ganchev, Y. Nevmyvaka, M. Kearns, and J. W. Vaughan. Censored explo-
ration and the dark pool problem. Communications of the ACM, 53(5):99–107,
2010.
M. Gilli and E. Këllezi. The threshold accepting heuristic for index tracking. In
Financial Engineering, E-Commerce, and Supply Chain, P. M. Pardalos and
V. Tsitsiringos (eds.), Boston: Kluwer Academic, 1–18, 2002.
G. H. Golub and C. F. Van Loan. Matrix Computations. Baltimore, MD: Johns
Hopkins University Press, 1996.
T. F. Gosnell, A. J. Keown, and J. M. Pinkerton. The intraday speed of stock
price adjustment to major dividend changes: Bid-ask bounce and order flow
imbalances. Journal of Banking & Finance, 20(2):247–266, 1996.
R. Grinold and R. Kahn. Active Portfolio Management: A Quantitative Approach for
Producing Superior Returns and Controlling Risk. New York: McGraw-Hill,
1999.
L. Györfi, G. Lugosi, and F. Udina. Nonparametric kernel-based sequential invest-
ment strategies. Mathematical Finance, 16(2):337–357, 2006.
L. Györfi, G. Ottucsák, and H. Walk. Machine Learning for Financial Engineering.
Singapore: World Scientific, 2012.
L. Györfi, A. Urbán, and I. Vajda. Kernel-based semi-log-optimal empirical portfolio
selection strategies. International Journal of Theoretical and Applied Finance,
10(3):505–516, 2007.
L. Györfi, F. Udina, and H. Walk. Nonparametric nearest neighbor based empirical
portfolio selection strategies. Statistics and Decisions, 26(2):145–157, 2008.
L. Györfi and D. Schäfer. Nonparametric prediction. In Advances in Learning Theory:
Methods, Models and Applications, J. Suykens, G. Horvath, and S. Basu (eds.),
The Netherlands: IOS Press, Amsterdam, 339–354, 2003.
L. Györfi and I. Vajda. Growth optimal investment with transaction costs. In Proceed-
ings of the International Conference on Algorithmic Learning Theory, Budapest,
Hungary, 108–122, 2008.
T&F Cat #K23731 — K23731_A004 — page 197 — 9/26/2015 — 8:06