198 BIBLIOGRAPHY
L. Gyorfi and H.Walk. Empirical portfolio selection strategieswithproportionaltrans-
action costs. IEEE Transactions on Information Theory, 58(10):6320–6331,
2012.
N. H. Hakansson. Optimal investment and consumption strategies under risk for
a class of utility functions. Econometrica, 38(5):587–607, 1970.
N. H. Hakansson. Capital growth and the mean-variance approach to portfolio
selection. The Journal of Financial and Quantitative Analysis, 6(1):517–557,
1971.
J. D. Hamilton. Time Series Analysis. Princeton, NJ: Princeton University Press,
1994.
J. D.Hamilton. Regime-switching models. InNewPalgrave Dictionary of Economics,
S. N. Durlauf and L. E. Blume (eds.), New York: Palgrave McMillan, 53–57,
2008.
M. R. Hardy. A regime-switching model of long-term stock returns. North American
Actuarial Journal Society of Acutaries, 5(2):41–53, 2001.
L. Harris. Trading and Exchanges: Market Microstructure for Practitioners. New
York: Oxford University Press, 2003.
C. R. Harvey, J. C. Liechty, M. W. Liechty, and P. Müller. Portfolio selection with
higher moments. Quantitative Finance, 10(5):469–485, 2010.
R. A. Haugen and J. Lakonishok. The Incredible January Effect: The Stock Market’s
Unsolved Mystery. Homewood, IL: Dow Jones-Irwin, 1987.
E. Hazan. Efficient Algorithms for Online Convex Optimization and Their Applica-
tions. PhD thesis, Princeton University, 2006.
E. Hazan, A. Agarwal, and S. Kale. Logarithmic regret algorithms for online convex
optimization. Machine Learning, 69(2–3):169–192, 2007.
E. Hazan, A. Kalai, S. Kale, and A. Agarwal. Logarithmic regret algorithms for
online convex optimization. In Proceedings of the Annual Conference on
Learning Theory, 2006.
E. Hazan and S. Kale. On stochastic and worst-case models for investing. In Pro-
ceedings of Annual Conference on Neural Information Processing Systems,
Vancouver, 709–717, 2009.
E. Hazan and S. Kale. An online portfolio selection algorithm with regret logarith-
mic in price variation. Mathematical Finance, 25(2):288–310, 2015.
E. Hazan and C. Seshadhri. Efficient learning algorithms for changing environments.
In Proceedings of the International Conference on Machine Learning, Montreal,
393–400, 2009.
D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. On-line portfo-
lio selection using multiplicative updates. In Proceedings of the International
Conference on Machine Learning, Bari, Italy, 243–251, 1996.
D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. A comparison of new
and old algorithms for a mixture estimation problem. Machine Learning, 27(1):
97–119, 1997.
D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. On-line portfolio
selection using multiplicative updates. Mathematical Finance, 8(4):325–347,
1998.
T&F Cat #K23731 — K23731_A004 — page 198 — 9/26/2015 — 8:06
BIBLIOGRAPHY 199
M. Herbster and M. K. Warmuth. Tracking the best expert. Machine Learning, 32(2):
151–178, 1998.
E. Hillebrand. Mean Reversion Models of Financial Markets. PhD thesis, University
of Bremen, 2003.
D. Huang, J. Zhou, B. Li, S. C. Hoi, and S. Zhou. Robust median reversion strategy
for on-line portfolio selection. In Proceedings of the Twenty-Third International
Joint Conference on Artificial Intelligence, 2006–2012, AAAI Press, Beijing,
China, 2013.
S.-H. Huang, S.-H. Lai, and S.-H. Tai. Alearning-based contrarian trading strategy via
a dual-classifier model. ACM Transactions on Interactive Intelligent Systems,
2:20:1–20:20, 2011.
J. C. Hull. Options, Futures, and Other Derivatives. Upper Saddle River, NJ: Prentice
Hall, 1997.
G. Iyengar. Universal investment in markets with transaction costs. Mathematical
Finance, 15(2):359–371, 2005.
F. Jamshidian. Asymptotically optimal portfolios. Mathematical Finance, 2(2):131–
150, 1992.
N. Jegadeesh. Evidence of predictable behavior of security returns. Journal of
Finance, 45(3):881–898, 1990.
A. Kalai and S. Vempala. Efficient algorithms for universal portfolios. Journal of
Machine Learning Research, 3:423–440, 2002.
J. O. Katz and D. L. McCormick. The Encyclopedia of Trading Strategies. New York:
McGraw-Hill, 2000.
M. Kearns, A. Kulesza, and Y. Nevmyvaka. Empirical limitations on high frequency
trading profitability. Journal of Trading, 5(4):50–62, 2010.
D. B. Keim and A. Madhavan. Anatomy of the trading process empirical evidence
on the behavior of institutional traders. Journal of Financial Economics, 37(3):
371–398, 1995.
J. Kelly. A new interpretation of information rate. Bell Systems Technical Journal,
35:917–926, 1956.
E. Keogh. Exact indexing of dynamic time warping. In Proceedings of the 28th
International Conference on Very Large Data Bases, 406–417, 2002.
E. J. Keogh and M. J. Pazzani. Scaling up dynamic time warping for datamining
applications. In Proceedings of the Sixth ACM SIGKDD International Con-
ference on Knowledge Discovery and Data Mining, Boston, MA, 285–289,
2000.
T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka. Stock market prediction system
with modular neural networks. Neural Networks in Finance and Investing, 343–
357, 1993.
R. Kissell, M. Glantz, and R. Malamut. Optimal Trading Strategies: Quantita-
tive Approaches for Managing Market Impact and Trading Risk. New York:
AMACOM, 2003.
W. M. Koolen and V. Vovk. Buy low, sell high. In Proceedings of Interna-
tional Conference on Algorithmic Learning Theory, Lyon, France, 335–349,
2012.
T&F Cat #K23731 — K23731_A004 — page 199 — 9/26/2015 — 8:06
200 BIBLIOGRAPHY
S. S. Kozat and A. C. Singer. Universal constant rebalanced portfolios with switch-
ing. In Proceedings of the International Conference on Acoustics, Speech, and
Signal Processing, Honolulu, 1129–1132, 2007.
S. S. Kozat and A. C. Singer. Universal switching portfolios under transaction costs.
In Proceedings of the International ConferenceonAcoustics, Speech, and Signal
Processing, Las Vegas, NV, 5404–5407, 2008.
S. S. Kozat and A. C. Singer. Switching strategies for sequential decision problems
with multiplicative loss with application to portfolios. IEEE Transactions on
Signal Processing, 57(6):2192–2208, 2009.
S. S. Kozat and A. C. Singer. Universal randomized switching. IEEE Transactions
on Signal Processing, 58:3, 2010.
S. S. Kozat and A. C. Singer. Universal semiconstant rebalanced portfolios.
Mathematical Finance, 21(2):293–311, 2011.
S. S. Kozat, A. C. Singer, and A. J. Bean. Universal portfolios via context trees. In
Proceedings of the International Conference on Acoustics, Speech, and Signal
Processing, Las Vegas, NV, 2093–2096, 2008.
S. S. Kozat, A. C. Singer, and A. J. Bean. A tree-weighting approach to sequential
decision problems with multiplicative loss. Signal Processing, 91(4):890–905,
2011.
S. Kullback and R. Leibler. On information and sufficiency. Annals of Mathematical
Statistics, 22:79–86, 1951.
H. A. Latané. Criteria for choice among risky ventures. The Journal of Political
Economy, 67(2):144–155, 1959.
T. Levina and G. Shafer. Portfolio selection and online learning. International Jour-
nal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16(4):437–473,
2008.
B. Li and S. C. Hoi. Online portfolio selection: A survey. ACM Computing Surveys,
36:35:1–35:36, 2014.
B. Li, S. C. Hoi, and V. Gopalkrishnan. CORN: Correlation-driven nonparamet-
ric learning approach for portfolio selection. ACM Transactions on Intelligent
Systems and Technology, 2(3):21:1–21:29, 2011a.
B. Li, S. C. Hoi, D. Sahoo, and Z. Liu. Moving average reversion strategy for on-line
portfolio selection. Artificial Intelligence, 222:104–123, 2015.
B. Li, S. C. Hoi, P. Zhao, and V. Gopalkrishnan. Confidence weighted mean reversion
strategy for on-line portfolio selection. In Proceedings of the International Con-
ference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, 434–442,
2011b.
B. Li, S. C. Hoi, P. Zhao, and V. Gopalkrishnan. Confidence weighted mean
reversion strategy for on-line portfolio selection. In ACM Transactions on
Knowledge Discovery from Data, 2013.
B. Li and S. C. H. Hoi. On-line portfolio selection with moving average reversion.
In Proceedings of the International Conference on Machine Learning,
Edinburgh, 273–280, 2012.
B. Li, P. Zhao, S. Hoi, and V. Gopalkrishnan. PAMR: Passive–aggressive mean
reversion strategy for portfolio selection. Machine Learning, 87(2):221–258,
2012.
T&F Cat #K23731 — K23731_A004 — page 200 — 9/26/2015 — 8:06
BIBLIOGRAPHY 201
A. W. Lo. Where do alphas come from? A measure of the value of active investment
management. Journal of Investment Management, 6:1–29, 2008.
A. W. Lo and A. C. MacKinlay. When are contrarian profits due to stock market
overreaction? Review of Financial Studies, 3(2):175–205, 1990.
M. S. Lobo, M. Fazel, and S. Boyd. Portfolio optimization with linear and fixed
transaction costs. Annals of Operations Research, 152(1):341–365, 2007.
J. Loveless, S. Stoikov, and R. Waeber. Online algorithms in high-frequency trading.
Communication of the ACM, 56(10):50–56, 2013.
C.-J. Lu, T.-S. Lee, and C.-C. Chiu. Financial time series forecasting using inde-
pendent component analysis and support vector regression. Decision Support
Systems, 47:115–125, 2009.
D. G. Luenberger. Investment Science. New York: Oxford University Press, 1998.
L. C. MacLean, E. O. Thorp, and W. T. Ziemba. The Kelly Capital Growth Invest-
ment Criterion: Theory and Practice. Volume 3. Singapore: World Scientific,
2011.
M. Magdon-Ismail and A. Atiya. Maximum drawdown. Risk Magazine, 10:99–102,
2004.
C. D. Manning and H. Schütze. Foundations of Statistical Natural Language
Processing. Cambridge, MA: MIT Press, 1999.
D. Maringer. Constrained index tracking under loss aversion using differential evo-
lutionary, natural computing in computational finance. In Natural Computing
in Computational Finance, A. Brabazon and M. O’Neill (eds.), 7–24. Berlin:
Springer, 2008.
H. Markowitz. Portfolio selection. The Journal of Finance, 7(1):77–91, 1952.
H. Markowitz. Portfolio Selection: Efficient Diversification of Investments. New
York: Wiley, 1959.
T. H. Mcinish and R. A. Wood. An analysis of intraday patterns in bid/ask spreads for
NYSE stocks. The Journal of Finance, 47(2):753–764, 1992.
B. McWilliams and G. Montana. Sparse partial least squares regression for on-line
variable selection with multivariate data streams. Statistical Analysis and Data
Mining, 3(3):170–193, 2010.
N. Meade and G. R. Salkin. Index funds-construction and performance measurement.
The Journal of the Operational Research Society, 40(10):871–879, 1989.
N. Meade and G. R. Salkin. Developing and maintaining an equity index fund.
The Journal of the Operational Research Society, 41(7):599–607, 1990.
M. H. Miller. Financial innovation: The last twenty years and the next. The Journal
of Financial and Quantitative Analysis, 21(4):459–471, 1986.
T. Mitchell. Machine Learning. Burr Ridge, IL: McGraw-Hill, 1997.
J. Moody and M. Saffell. Learning to trade via direct reinforcement. IEEE
Transactions on Neural Networks, 12(4):875–889, 2001.
J. Moody, L. Wu, Y. Liao, and M. Saffell. Performance functions and reinforcement
learning for trading systems and portfolios. Journal of Forecasting, 17:441–471,
1998.
Y. Nevmyvaka, Y. Feng, and M. S. Kearns. Reinforcement learning for optimized
trade execution. In Proceedings of the International Conference on Machine
Learning, 673–680, 2006.
T&F Cat #K23731 — K23731_A004 — page 201 — 9/26/2015 — 8:06
202 BIBLIOGRAPHY
E. Ordentlich. Universal Investment and Universal Data Compression. PhD thesis,
Stanford University, 1996.
E. Ordentlich. Encyclopedia of Quantitative Finance, Universal Portfolios. Sussex:
Wiley, 2010.
E. Ordentlich and T. M. Cover. On-line portfolio selection. In Proceedings of the
Annual Conference on Learning Theory, Desenzano del Garda, Italy, 310–313,
1996.
E. Ordentlich and T. M. Cover. The cost of achieving the best portfolio in hindsight.
Mathematics of Operations Research, 23(4):960–982, 1998.
M. F. M. Osborne. Brownian motion in the stock market. Operations Research, 7(2):
145–173, 1959.
G. Ottucsák and I. Vajda. An asymptotic analysis of the mean-variance portfolio
selection. Statistics and Decisions, 25:63–88, 2007.
D. C. Porter. The probability of a trade at the ask: An examination of interday and
intraday behavior. The Journal of Financial and Quantitative Analysis, 27(2):
209–227, 1992.
J. M. Poterba and L. H. Summers. Mean reversion in stock prices: Evidence and
implications. Journal of Financial Economics, 22(1):27–59, 1988.
E. Qian, R. Hua, and E. Sorensen. Quantitative Equity Portfolio Management: Mod-
ern Techniques and Applications. Boca Raton: Chapman & Hall/CRC, 2007.
L. Rabiner and S. Levinson. Isolated and connected word recognition—theory and
selected applications. IEEE Transactions on Communications, 29(5):621–659,
1981.
T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu,
J. Zakaria, and E. Keogh. Searching and mining trillions of time series
subsequences under dynamic time warping. In Proceedings of the 18th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining,
Beijing, China, 262–270, 2012.
M. R. Reinganum. The anomalous stock market behavior of small firms in January:
Empirical tests for tax-loss selling effects. Journal of Financial Economics,
12(1):89–104, 1983.
J. Rissanen. A universal data compression system. IEEE Transactions on
Information Theory, 29(5):656–663, 1983.
H. Sakoe and S. Chiba. Dynamic programming algorithm optimization for spoken
word recognition. In Readings in Speech Recognition, A. Waibel and K. Lee
(eds.), Morgan Kaufmann, San Mateo, 159–165. 1990.
S. Shalev-Shwartz. Online learning and online convex optimization. Foundations
and Trends in Machine Learning, 4(2):107–194, 2012.
W. F. Sharpe. A simplified model for portfolio analysis. Management Science,9:
277–293, 1963.
W. F. Sharpe. Capital asset prices: A theory of market equilibrium under conditions
of risk. The Journal of Finance, 19(3):425–442, 1964.
W. F. Sharpe. Mutual fund performance. The Journal of Business, 39(1):119, 1966.
W. F. Sharpe. The sharpe ratio. Journal of Portfolio Management, 21(1):49–58,
1994.
T&F Cat #K23731 — K23731_A004 — page 202 — 9/26/2015 — 8:06
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