Abstract
Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.
- A. Agarwal, E. Hazan, S. Kale, and R. E. Schapire. 2006. Algorithms for Portfolio Management Based on the Newton Method. In Proceedings of International Conference on Machine Learning. 9--16. Google ScholarDigital Library
- K. Akcoglu, P. Drineas, and M. Kao. 2002. Fast universalization of investment strategies with provably good relative returns. In Proceedings of International Colloquium on Automata, Languages and Programming. 888--900. Google ScholarDigital Library
- K. Akcoglu, P. Drineas, and M. Kao. 2004. Fast Universalization of Investment Strategies. SIAM J. Comput. 34 (2004), 1--22. Google ScholarDigital Library
- M. Akian, A. Sulem, and M. I. Taksar. 2001. Dynamic Optimization of Long-Term Growth Rate for a Portfolio with Transaction Costs and Logarithmic Utility. Mathematical Finance 11, 2 (2001), 153--188.Google ScholarCross Ref
- P. Algoet. 1992. Universal Schemes for Prediction, Gambling and Portfolio Selection. Annals of Probability 20, 2 (1992), 901--941.Google ScholarCross Ref
- P. H. Algoet and T. M. Cover. 1988. Asymptotic Optimality and Asymptotic Equipartition Properties of Log-Optimum Investment. Annals of Probability 16, 2 (1988), 876--898.Google ScholarCross Ref
- F. Allen and R. Karjalainen. 1999. Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics 51 (1999), 245--271.Google ScholarCross Ref
- L. Bachelier. 1900. Théorie de la spéculation. Annales Scientifiques de l'École Normale Supérieure 3, 17 (1900), 21--86.Google Scholar
- A. R. Barron and T. M. Cover. 1988. A Bound on the Financial Value of Information. IEEE Transactions on Information Theory 34, 5 (1988), 1097--1100. Google ScholarDigital Library
- C. Y. Belentepe. 2005. A Statistical View of Universal Portfolios. Ph.D. Dissertation. University of Pennsylvania.Google Scholar
- R. M. Bell and T. M. Cover. 1980. Competitive Optimality of Logarithmic Investment. Mathematics of Operations Research 5, 2 (1980), 161--162. Google ScholarDigital Library
- G. Biau, K. Bleakley, L. Györfi, and G. Ottucsák. 2010. Nonparametric Sequential Prediction of Time Series. Journal of Nonparametric Statistics 22, 3 (2010), 297--317.Google ScholarCross Ref
- J. R. Birge and F. Louveaux. 1997. Introduction to Stochastic Programming. Springer, New York.Google Scholar
- A. Blum and A. Kalai. 1999. Universal Portfolios with and without Transaction Costs. Machine Learning 35, 3 (1999), 193--205. Google ScholarDigital Library
- A. Blum and Y. Mansour. 2007. From External to Internal Regret. Journal of Machine Learning Research 8 (2007), 1307--1324. Google ScholarDigital Library
- W. F. M. Bondt and R. Thaler. 1985. Does the Stock Market Overreact? The Journal of Finance 40, 3 (1985), 793--805.Google Scholar
- W. F. M. Bondt and R. Thaler. 1987. Further Evidence on Investor Overreaction and Stock Market Seasonality. Journal of Finance 42, 3 (1987), 557--581.Google ScholarCross Ref
- A. Borodin, R. El-Yaniv, and V. Gogan. 2000. On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract). In Proceedings of the Latin American Symposium on Theoretical Informatics. 173--196. Google ScholarDigital Library
- A. Borodin, R. El-Yaniv, and V. Gogan. 2003. Can We Learn to Beat the Best Stock. In Proceedings of the Annual Conference on Neural Information Processing Systems.Google Scholar
- A. Borodin, R. El-Yaniv, and V. Gogan. 2004. Can We Learn to Beat the Best Stock. Journal of Artificial Intelligence Research 21 (2004), 579--594. Google ScholarDigital Library
- S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press, New York. Google ScholarDigital Library
- L. Breiman. 1960. Investment Policies for Expanding Businesses Optimal in a Long-Run Sense. Naval Research Logistics Quarterly 7, 4 (1960), 647--651.Google ScholarCross Ref
- L. Breiman. 1961. Optimal Gambling Systems for Favorable Games. In Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability 1 (1961), 65--78.Google Scholar
- P. Brockman and D. Michayluk. 1998. The Persistent Holiday Effect: Additional Evidence. Applied Economics Letters 5 (1998), 205--209.Google ScholarCross Ref
- L. J. Cao and F. E. H. Tay. 2003. Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting. IEEE Transactions on Neural Networks 14, 6 (2003), 1506--1518. Google ScholarDigital Library
- N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press, New York. Google ScholarDigital Library
- L. K. C. Chan, N. Jegadeesh, and J. Lakonishok. 1996. Momentum Strategies. Journal of Finance 51, 5 (1996), 1681--1713.Google ScholarCross Ref
- K. Chaudhuri and Y. Wu. 2003. Mean Reversion in Stock Prices: Evidence from Emerging Markets. Managerial Finance 29 (2003), 22--37.Google ScholarCross Ref
- J. Conrad and G. Kaul. 1998. An Anatomy of Trading Strategies. Review of Financial Studies 11, 3 (1998), 489--519.Google ScholarCross Ref
- R. Cont. 2001. Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance 1, 2 (2001), 223--236.Google ScholarCross Ref
- M. J. Cooper, R. C. Gutierrez, and A. Hameed. 2004. Market States and Momentum. The Journal of Finance 59, 3 (2004), 1345--1365.Google ScholarCross Ref
- P. H. Cootner. 1964. The Random Character of Stock Market Prices. MIT Press.Google Scholar
- T. M. Cover. 1991. Universal Portfolios. Mathematical Finance 1, 1 (1991), 1--29.Google ScholarCross Ref
- T. M. Cover. 1996. Universal Data Compression and Portfolio Selection. In Proceedings of the Annual IEEE Symposium on Foundations of Computer Science. 534--538. Google ScholarDigital Library
- T. M. Cover and D. H. Gluss. 1986. Empirical Bayes Stock Market Portfolios. Advances in Applied Mathematics 7, 2 (1986), 170--181. Google ScholarDigital Library
- T. M. Cover and E. Ordentlich. 1996. Universal Portfolios with Side Information. IEEE Transactions on Information Theory 42, 2 (1996), 348--363. Google ScholarDigital Library
- T. M. Cover and J. A. Thomas. 1991. Elements of Information Theory. Wiley-Interscience, New York. Google ScholarDigital Library
- K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. 2006. Online Passive-Aggressive Algorithms. Journal of Machine Learning Research 7 (2006), 551--585. Google ScholarDigital Library
- K. Crammer, M. Dredze, and A. Kulesza. 2009. Multi-Class Confidence Weighted Algorithms. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 496--504. Google ScholarDigital Library
- K. Crammer, M. Dredze, and F. Pereira. 2008. Exact Convex Confidence-Weighted Learning. In Proceedings of the Annual Conference on Neural Information Processing Systems. 345--352.Google Scholar
- G. Creamer. 2007. Using Boosting for Automated Planning And Trading Systems. Ph.D. Dissertation. Columbia University.Google Scholar
- G. Creamer. 2012. Model Calibration and Automated Trading Agent for Euro Futures. Quantitative Finance 12, 4 (2012), 531--545.Google ScholarCross Ref
- G. Creamer and Y. Freund. 2007. A Boosting Approach for Automated Trading. Journal of Trading 2, 3 (2007), 84--96.Google ScholarCross Ref
- G. Creamer and Y. Freund. 2010. Automated Trading with Boosting and Expert Weighting. Quantitative Finance 4, 10 (2010).Google Scholar
- J. E. Cross and A. R. Barron. 2003. Efficient Universal Portfolios for Past-Dependent Target Classes. Mathematical Finance 13, 2 (2003), 245--276.Google ScholarCross Ref
- M. Dai, Z. Q. Xu, and X. Y. Zhou. 2010. Continuout-Time Mean-Variance Portfolio Selection with Proportional Transaction Costs. SIAM Journal on Financial Mathematics 1, 1 (2010), 96--125. Google ScholarDigital Library
- P. Das and A. Banerjee. 2011. Meta Optimization and Its Application to Portfolio Selection. In Proceedings of International Conference on Knowledge Discovery and Data Mining. Google ScholarDigital Library
- P. Das, N. Johnson, and A. Banerjee. 2013. Online Lazy Updates for Portfolio Selection with Transaction Costs. In Proceedings of the 27th Conference on Artificial Intelligence.Google Scholar
- M. H. A. Davis and A. R. Norman. 1990. Portfolio Selection with Transaction Costs. Mathematics of Operations Research 15, 4 (1990), 676--713. Google ScholarDigital Library
- M. A. H. Dempster, T. W. Payne, Y. Romahi, and G. W. P. Thompson. 2001. Computational Learning Techniques for Intraday FX Trading Using Popular Technical Indicators. IEEE Transactions on Neural Networks 12, 4 (2001), 744--754. Google ScholarDigital Library
- V. Dhar. 2011. Prediction in Financial Markets: The Case for Small Disjuncts. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 19:1--19:22. Google ScholarDigital Library
- M. Dredze, K. Crammer, and F. Pereira. 2008. Confidence-Weighted Linear Classification. In Proceedings of the International Conference on Machine Learning. 264--271. Google ScholarDigital Library
- M. Dredze, A. Kulesza, and K. Crammer. 2010. Multi-Domain Learning by Confidence-Weighted Parameter Combination. Machine Learning 79, 1--2 (2010), 123--149. Google ScholarDigital Library
- J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra. 2008. Efficient Projections onto the l1-ball for Learning in High Dimensions. In Proceedings of the International Conference on Machine Learning. 272--279. Google ScholarDigital Library
- C. Dzhabarov and W. T. Ziemba. 2010. Do Seasonal Anomalies Still Work? Journal of Portfolio Management 36, 3 (2010), 93--104.Google Scholar
- R. El-Yaniv. 1998. Competitive Solutions for Online Financial Problems. ACM Computing Survey 30, 1 (1998), 28--69. Google ScholarDigital Library
- E. F. Fama. 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25, 2 (1970), 383--417.Google ScholarCross Ref
- M. Feder, N. Merhav, and M. Gutman. 1992. Universal Prediction of Individual Sequences. IEEE Transactions on Information Theory 38, 4 (1992), 1258--1270. Google ScholarDigital Library
- M. J. Fields. 1934. Security Prices and Stock Exchange Holidays in Relation to Short Selling. Journal of Business of the University of Chicago 7, 4 (1934), 328--338.Google ScholarCross Ref
- M. Finkelstein and R. Whitley. 1981. Optimal Strategies for Repeated Games. Advances in Applied Probability 13, 2 (1981), 415--428.Google ScholarCross Ref
- A. A. Gaivoronski and F. Stella. 2000. Stochastic Nonstationary Optimization for Finding Universal Portfolios. Annals of Operationas Research 100 (2000), 165--188.Google ScholarCross Ref
- A. A. Gaivoronski and F. Stella. 2003. On-line Portfolio Selection Using Stochastic Programming. Journal of Economic Dynamics and Control 27, 6 (2003), 1013--1043.Google ScholarCross Ref
- T. J. George and C.-Y. Hwang. 2004. The 52-Week High and Momentum Investing. Journal of Finance 59, 5 (2004), 2145--2176.Google ScholarCross Ref
- L. Györfi, G. Lugosi, and F. Udina. 2006. Nonparametric Kernel-Based Sequential Investment Strategies. Mathematical Finance 16, 2 (2006), 337--357.Google ScholarCross Ref
- L. Györfi, G. Ottucsák, and H. Walk. 2012. Machine Learning for Financial Engineering. Imperial College Press.Google Scholar
- L. Györfi and D. Schäfer. 2003. Nonparametric Prediction. In Advances in Learning Theory: Methods, Models and Applications, J. A. K. Suykens, G. Horváth, S. Basu, C. Micchelli, and J. Vandevalle (Eds.). IOS Press, Amsterdam, Netherlands, 339--354.Google Scholar
- L. Györfi, F. Udina, and H. Walk. 2008. Nonparametric Nearest Neighbor Based Empirical Portfolio Selection Strategies. Statistics and Decisions 26, 2 (2008), 145--157.Google ScholarCross Ref
- L. Györfi, A. Urbán, and I. Vajda. 2007. Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies. International Journal of Theoretical and Applied Finance 10, 3 (2007), 505--516.Google ScholarCross Ref
- L. Györfi and I. Vajda. 2008. Growth Optimal Investment with Transaction Costs. In Proceedings of the International Conference on Algorithmic Learning Theory. 108--122. Google ScholarDigital Library
- L. Györfi and H. Walk. 2012. Empirical Portfolio Selection Strategies with Proportional Transaction Costs. IEEE Transactions on Information Theory 58, 10 (2012), 6320--6331.Google ScholarDigital Library
- N. H Hakansson. 1970. Optimal Investment and Consumption Strategies under Risk for a Class of Utility Functions. Econometrica 38, 5 (1970), 587--607.Google ScholarCross Ref
- N. H. Hakansson. 1971. Capital Growth and the Mean-Variance Approach to Portfolio Selection. Journal of Financial and Quantitative Analysis 6, 1 (1971), 517--557.Google ScholarCross Ref
- N. H. Hakansson and W. T. Ziemba. 1995. Capital Growth Theory. In Handbooks in OR & MS. Elsevier Science.Google Scholar
- J. D. Hamilton. 1994. Time Series Analysis. Princeton University Press, Princeton, NJ.Google Scholar
- J. D. Hamilton. 2008. New Palgrave Dictionary of Economics. Palgrave McMillan Ltd.Google Scholar
- M. R. Hardy. 2001. A Regime-Switching Model of Long-Term Stock Returns. North American Actuarial Journal Society of Acutaries 5, 2 (2001), 41--53.Google ScholarCross Ref
- R. A. Haugen and J. Lakonishok. 1987. The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin, Homewood, IL.Google Scholar
- D. B. Hausch, W. T. Ziemba, and M. Rubinstein. 1981. Efficiency of the Market for Racetrack Betting. Management Science 27, 12 (1981), 1435--1452. Google ScholarDigital Library
- E. Hazan. 2006. Efficient Algorithms for Online Convex Optimization and Their Applications. Ph.D. Dissertation. Princeton University. Google ScholarDigital Library
- E. Hazan, A. Agarwal, and S. Kale. 2007. Logarithmic Regret Algorithms for Online Convex Optimization. Machine Learning 69, 2--3 (2007), 169--192. Google ScholarDigital Library
- E. Hazan, A. Kalai, S. Kale, and A. Agarwal. 2006. Logarithmic Regret Algorithms for Online Convex Optimization. In Proceedings of the Annual Conference on Learning Theory. 499--513. Google ScholarDigital Library
- E. Hazan and S. Kale. 2009. On Stochastic and Worst-case Models for Investing. In Proceedings of the Annual Conference on Neural Information Processing Systems. 709--717.Google Scholar
- E. Hazan and S. Kale. 2012. An Online Portfolio Selection Algorithm With Regret Logarithmic In Price Variation. Mathematical Finance (2012).Google Scholar
- E. Hazan and C. Seshadhri. 2009. Efficient Learning Algorithms for Changing Environments. In Proceedings of the International Conference on Machine Learning. 393--400. Google ScholarDigital Library
- D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. 1996. On-Line Portfolio Selection Using Multiplicative Updates. In Proceedings of the International Conference on Machine Learning. 243--251.Google Scholar
- D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. 1997. A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Machine Learning 27, 1 (1997), 97--119. Google ScholarDigital Library
- D. P. Helmbold, R. E. Schapire, Y. Singer, and M. K. Warmuth. 1998. On-Line Portfolio Selection Using Multiplicative Updates. Mathematical Finance 8, 4 (1998), 325--347.Google ScholarCross Ref
- M. Herbster and M. K. Warmuth. 1998. Tracking the Best Expert. Machine Learning 32, 2 (1998), 151--178. Google ScholarDigital Library
- D. Huang, J. Zhou, B. Li, S. C. H. Hoi, and S. Zhou. 2013. Robust Median Reversion Strategy for On-Line Portfolio Selection. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Google ScholarDigital Library
- S.-H. Huang, S.-H. Lai, and S.-H. Tai. 2011. A Learning-based Contrarian Trading Strategy via a Dual-Classifier Model. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 20:1--20:20. Google ScholarDigital Library
- J. C. Hull. 2008. Options, Futures, and Other Derivatives (7 ed.). Prentice Hall, Upper Saddle River, NJ.Google Scholar
- G. Iyengar. 2005. Universal Investment in Markets with Transaction Costs. Mathematical Finance 15, 2 (2005), 359--371.Google ScholarCross Ref
- G. N. Iyengar and T. M. Cover. 2000. Growth Optimal Investment in Horse Race Markets with Costs. IEEE Transactions on Information Theory 46, 7 (2000), 2675--2683. Google ScholarDigital Library
- B. I. Jacobs and K. N. Levy. 1993. Long/Short Equity Investing. Journal of Portfolio Management 20, 1 (1993), 52--63.Google ScholarCross Ref
- F. Jamshidian. 1992. Asymptotically optimal portfolios. Mathematical Finance 2, 2 (1992), 131--150.Google ScholarCross Ref
- N. Jegadeesh. 1990. Evidence of Predictable Behavior of Security Returns. Journal of Finance 45, 3 (1990), 881--898.Google ScholarCross Ref
- N. Jegadeesh. 1991. Seasonality in Stock Price Mean Reversion: Evidence from the U.S. and the U.K. Journal of Finance 46, 4 (1991), 1427--1444.Google ScholarCross Ref
- A. Kalai and S. Vempala. 2002. Efficient Algorithms for Universal Portfolios. Journal of Machine Learning Research 3 (2002), 423--440. Google ScholarDigital Library
- J. O. Katz and D. L. McCormick. 2000. The Encyclopedia of Trading Strategies. McGraw-Hill, New York.Google Scholar
- J. Kelly Jr. 1956. A New Interpretation of Information Rate. Bell Systems Technical Journal 35 (1956), 917--926.Google ScholarCross Ref
- E. Keogh. 2002. Exact Indexing of Dynamic Time Warping. In Proceedings of the 28th International Conference on Very Large Data Bases. 406--417. Google ScholarDigital Library
- T. Kimoto, K. Asakawa, M. Yoda, and M. Takeoka. 1993. Stock Market Prediction System with Modular Neural Networks. Neural Networks in Finance and Investing (1993), 343--357.Google Scholar
- W. M. Koolen and V. Vovk. 2012. Buy Low, Sell High. In Proceedings of International Conference on Algorithmic Learning Theory. 335--349. Google ScholarDigital Library
- S. S. Kozat and A. C. Singer. 2007. Universal Constant Rebalanced Portfolios with Switching. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 1129--1132.Google Scholar
- S. S. Kozat and A. C. Singer. 2008. Universal Switching Portfolios under Transaction Costs. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 5404--5407.Google Scholar
- S. S. Kozat and A. C. Singer. 2009. Switching Strategies for Sequential Decision Problems With Multiplicative Loss With Application to Portfolios. IEEE Transactions on Signal Processing 57, 6 (2009), 2192--2208. Google ScholarDigital Library
- S. S. Kozat and A. C. Singer. 2010. Universal Randomized Switching. IEEE Transactions on Signal Processing 58 (2010), 3. Google ScholarDigital Library
- S. S. Kozat and A. C. Singer. 2011. Universal Semiconstant Rebalanced Portfolios. Mathematical Finance 21, 2 (2011), 293--311.Google Scholar
- S. S. Kozat, A. C. Singer, and A. J. Bean. 2008. Universal Portfolios via Context Trees. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. 2093--2096.Google Scholar
- S. S. Kozat, A. C. Singer, and A. J. Bean. 2011. A Tree-Weighting Approach to Sequential Decision Problems with Multiplicative Loss. Signal Processing 91, 4 (2011), 890--905. Google ScholarDigital Library
- C. M. C. Lee and B. Swaminathan. 2000. Price Momentum and Trading Volume. Journal of Finance 55 (2000), 2017--2069.Google ScholarCross Ref
- T. Levina and G. Shafer. 2008. Portfolio Selection and Online Learning. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, 4 (2008), 437--473.Google ScholarCross Ref
- B. Li and S. C. H. Hoi. 2012. On-Line Portfolio Selection with Moving Average Reversion. In Proceedings of the International Conference on Machine Learning.Google Scholar
- B. Li, S. C. H. Hoi, and V. Gopalkrishnan. 2011a. CORN: Correlation-driven Nonparametric Learning Approach for Portfolio Selection. ACM Transactions on Intelligent Systems and Technology 2, 3 (2011), 21:1--21:29. Google ScholarDigital Library
- B. Li, S. C. H. Hoi, P. Zhao, and V. Gopalkrishnan. 2011b. Confidence Weighted Mean Reversion Strategy for On-Line Portfolio Selection. In Proceedings of the International Conference on Artificial Intelligence and Statistics.Google Scholar
- B. Li, S. C. H. Hoi, P. Zhao, and V. Gopalkrishnan. 2013. Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection. ACM Transactions on Knowledge Discovery from Data 7, 1 (2013), 4:1--4:38. Google ScholarDigital Library
- B. Li, P. Zhao, S. C. H. Hoi, and V. Gopalkrishnan. 2012. PAMR: Passive aggressive mean reversion strategy for portfolio selection. Machine Learning 87, 2 (2012), 221--258. Google ScholarDigital Library
- D. Li and W.-L. Ng. 2000. Optimal Dynamic Portfolio Selection: Multiperiod Mean-Variance Formulation. Mathematical Finance 10, 3 (2000), 387--406.Google ScholarCross Ref
- G. Llorente, R. Michaely, G. Saar, and J. Wang. 2002. Dynamic Volume-Return Relation of Individual Stocks. Review of Financial Studies 15, 4 (2002), 1005--1047.Google ScholarCross Ref
- A. W. Lo. 2008. Where Do Alphas Come From? A Measure of the Value of Active Investment Management. Journal of Investment Management 6 (2008), 1--29.Google Scholar
- A. W. Lo and A. C. MacKinlay. 1988. Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial Studies 1, 1 (1988), 41--66.Google ScholarCross Ref
- A. W. Lo and A. C. MacKinlay. 1990. When Are Contrarian Profits Due to Stock Market Overreaction? Review of Financial Studies 3, 2 (1990), 175--205.Google Scholar
- A. W. Lo and A. C. MacKinlay. 1999. A Non-Random Walk Down Wall Street. Princeton University Press, Princeton, NJ.Google Scholar
- C.-J. Lu, T.-S. Lee, and C.-C. Chiu. 2009. Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems 47 (2009), 115--125. Issue 2. Google ScholarDigital Library
- D. G. Luenberger. 1998. Investment Science. Oxford University Press.Google Scholar
- L. MacLean, E. Thorp, and W. Ziemba. 2010. Long-term Capital Growth: The Good and Bad Properties of the Kelly and Fractional Kelly Capital Growth Criteria. Quantitative Finance 10, 7 (2010), 681--687.Google ScholarCross Ref
- L. C. MacLean, E. O. Thorp, and W. T. Ziemba. 2011. The Kelly Capital Growth Investment Criterion: Theory and Practice. Vol. 3. World Scientific Publishing.Google Scholar
- L. C. MacLean and W. T. Ziemba. 1999. Growth versus Security Tradeoffs in Dynamic Investment Snalysis. Annals of Operations Research 85 (1999), 193--225.Google ScholarCross Ref
- L. C. MacLean and W. T. Ziemba. 2008. Capital Growth: Theory and Practice. In Handbook of Asset and Liability Management, S. A. Zenios and W. T. Ziemba (Eds.). North-Holland, 429--473.Google ScholarCross Ref
- L. C. MacLean, W. T. Ziemba, and G. Blazenko. 1992. Growth versus Security in Dynamic Investment Analysis. Management Science 38, 11 (1992), 1562--1585. Google ScholarDigital Library
- M. Magdon-Ismail and A. Atiya. 2004. Maximum Drawdown. Risk Magazine 10 (2004), 99--102.Google Scholar
- S. Mahfoud and G. Mani. 1996. Financial Forecasting Using Genetic Algorithms. Applied Artificial Intelligence 10 (1996), 543--565.Google ScholarCross Ref
- B. Mandelbrot. 1963. The Variation of Certain Speculative Prices. Journal of Business 36, 4 (1963), 394--419.Google ScholarCross Ref
- H. Markowitz. 1952. Portfolio Selection. Journal of Finance 7, 1 (1952), 77--91.Google Scholar
- H. Markowitz. 1959. Portfolio Selection: Efficient Diversification of Investments. Wiley, New York.Google Scholar
- H. Markowitz, G. P. Todd, and W. F. Sharpe. 2000. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley.Google Scholar
- J. Mandziuk and M. Jaruszewicz. 2011. Neuro-genetic System for Stock Index Prediction. Journal of Intelligent & Fuzzy Systems 22 (2011), 93--123. Google ScholarCross Ref
- N. Merhav and M. Feder. 1998. Universal Prediction. IEEE Transactions on Information Theory 44, 6 (1998), 2124--2147. Google ScholarDigital Library
- H. Mlnařĺk, S. Ramamoorthy, and R. Savani. 2009. Multi-Strategy Trading Utilizing Market Regimes. In Advances in Machine Learning for Computational Finance Workshop.Google Scholar
- N. Moller and S. Zilca. 2008. The Evolution of the January Effect. Journal of Banking & Finance 32, 3 (2008), 447--457.Google ScholarCross Ref
- J. Moody and M. Saffell. 2001. Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks 12, 4 (2001), 875--889. Google ScholarDigital Library
- J. Moody, L. Wu, Y. Liao, and M. Saffell. 1998. Performance Functions and Reinforcement Learning For Trading Systems and Portfolios. Journal of Forecasting 17 (1998), 441--471.Google ScholarCross Ref
- T. J. Moskowitz and M. Grinblatt. 1999. Do Industries Explain Momentum? The Journal of Finance 54, 4 (1999), 1249--1290.Google Scholar
- J. O, J.-W. Lee, and B.-T. Zhang. 2002. Stock Trading System Using Reinforcement Learning with Cooperative Agents. In Proceedings of the 19th International Conference on Machine Learning. 451--458. Google ScholarDigital Library
- E. Ordentlich. 1996. Universal Investment and Universal Data Compression. Ph.D. Dissertation. Stanford University. Google ScholarDigital Library
- E. Ordentlich and T. M. Cover. 1998. The Cost of Achieving the Best Portfolio in Hindsight. Mathematics of Operations Research 23, 4 (1998), 960--982. Google ScholarDigital Library
- M. Ormos and A. Urbán. 2011. Performance Analysis of Log-Optimal Portfolio Strategies with Transaction Costs. Quantitative Finance (2011), 1--11.Google Scholar
- M. F. M. Osborne. 1959. Brownian Motion in the Stock Market. Operations Research 7, 2 (1959), 145--173.Google ScholarCross Ref
- G. Ottucsák and I. Vajda. 2007. An Asymptotic Analysis of the Mean-Variance portfolio selection. Statistics and Decisions 25 (2007), 63--88.Google ScholarCross Ref
- J. M. Poterba and L. H. Summers. 1988. Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics 22, 1 (1988), 27--59.Google ScholarCross Ref
- W. Poundstone. 2005. Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street. Hill and Wang, New York.Google Scholar
- Michael S. Rozeff and William R. Kinney Jr. 1976. Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics 3, 4 (1976), 379--402.Google ScholarCross Ref
- L. Rabiner and S. E. Levinson. 1981. Isolated and Connected Word Recognition--Theory and Selected Applications. IEEE Transactions on Communications 29, 5 (1981), 621--659.Google ScholarCross Ref
- J. Rissanen. 1983. A Universal Data Compression System. IEEE Transactions on Information Theory 29, 5 (1983), 656--663. Google ScholarDigital Library
- L. M. Rotando and E. O. Thorp. 1992. The Kelly Criterion and the Stock Market. Amer. Math. Monthly (1992), 922--931. Google ScholarDigital Library
- K. G. Rouwenhorst. 1998. International Momentum Strategies. Journal of Finance 53, 1 (1998), 267--284.Google ScholarCross Ref
- H. Sakoe and S. Chiba. 1990. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. In Readings in Speech Recognition. 159--165. Google ScholarDigital Library
- D. Schäfer. 2002. Nonparametric Estimation for Fnancial Investment under Log-Utility. Ph.D. Dissertation. Mathematical Institute, Universität Stuttgart.Google Scholar
- S. Shalev-Shwartz, K. Crammer, O. Dekel, and Y. Singer. 2003. Online Passive-Aggressive Algorithms. In Proceedings of the Annual Conference on Neural Information Processing Systems.Google Scholar
- Y. Singer. 1997. Switching Portfolios. International Journal of Neural Systems 8, 4 (1997), 488--495.Google ScholarCross Ref
- G. Stoltz and G. Lugosi. 2005. Internal Regret in On-Line Portfolio Selection. Machine Learning 59, 1--2 (2005), 125--159. Google ScholarDigital Library
- F. E. H. Tay and L. J. Cao. 2002. Modified Support Vector Machines in Financial Time Series Forecasting. Neurocomputing 48 (2002), 847--861.Google ScholarCross Ref
- S. Taylor. 2005. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, Princeton, NJ.Google Scholar
- E. O. Thorp. 1962. Beat the Dealer: A Winning Strategy for the Game of Twenty-One. Blaisdell Publishing, New York.Google Scholar
- E. O. Thorp. 1969. Optimal Gambling Systems for Favorable Games. Review of the International Statistical Institute 37, 3 (1969), 273--293.Google ScholarCross Ref
- E. O. Thorp. 1971. Portfolio Choice and the Kelly Criterion. In Business and Economics Section of the American Statistical Association. 215--224.Google Scholar
- E. O. Thorp. 1997. The Kelly Criterion In Blackjack, Sports Betting, and The Stock Market. In Proceedings of the International Conference on Gambling and Risk Taking.Google Scholar
- E. O. Thorp and S. T. Kassouf. 1967. Beat the Market: A Scientific Stock Market System. Random House, New York.Google Scholar
- E. Tsang, P. Yung, and J. Li. 2004. EDDIE-Automation, A Decision Support Tool for Financial Forecasting. Decision Support Systems 37 (2004), 559--565. Google ScholarDigital Library
- I. Vajda. 2006. Analysis of Semi-Log-Optimal Investment Strategies. In Proceedings of Prague Stochastic, M. Huskova and M. Janzura (Eds.). Matfyzpress, 719--727.Google Scholar
- R. Vince. 1990. Portfolio Management Formulas: Mathematical Trading Methods for the Futures, Options, and Stock Markets. Wiley, Hoboken, NJ.Google Scholar
- R. Vince. 1992. The Mathematics of Money Management: Risk Analysis Techniques for Traders. Wiley, Hoboken, NJ.Google Scholar
- R. Vince. 1995. The New Money Management: A Framework for Asset Allocation. Wiley, Hoboken, NJ.Google Scholar
- R. Vince. 2007. The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage. Wiley, Hoboken, NJ.Google Scholar
- R. Vince. 2009. The Leverage Space Trading Model: Reconciling Portfolio Management Strategies and Economic Theory. Wiley.Google Scholar
- V. Vovk. 1990. Aggregating Strategies. In Proceedings of the Annual Conference on Learning Theory. 371--386. Google ScholarDigital Library
- V. Vovk. 1997. Derandomizing Stochastic Prediction Strategies. In Proceedings of the 10th Annual Conference on Computational Learning Theory. 32--44. Google ScholarDigital Library
- V. Vovk. 1999. Derandomizing Stochastic Prediction Strategies. Machine Learning 35 (1999), 247--282. Google ScholarDigital Library
- V. Vovk. 2001. Competitive On-Line Statistics. International Statistical Review/Revue Internationale de Statistique 69, 2 (2001), 213--248.Google ScholarCross Ref
- V. Vovk and C. Watkins. 1998. Universal Portfolio Selection. In Proceedings of the Annual Conference on Learning Theory. 12--23. Google ScholarDigital Library
- A. Weber. 1929. Theory of the Location of Industries. The University of Chicago Press, Chicago.Google Scholar
- E. Weiszfeld. 1937. Sur le point pour lequel la somme des distances de n points donnes est minimum. Tohoku Mathematical Journal 43 (1937), 355--386.Google Scholar
- R. E. S. Ziemba and W. T. Ziemba. 2007. Scenarios for Risk Management and Global Investment Strategies. John Wiley & Sons.Google Scholar
- W. T. Ziemba. 2005. The Symmetric Downside-Risk Sharpe Ratio and the Evaluation of Great INvestors and Speculators. Journal of Portfolio Management 32, 1 (2005), 108--122.Google ScholarCross Ref
- W. T. Ziemba and D. B. Hausch. 1984. Beat the racetrack. Harcourt Brace Jovanovich.Google Scholar
- W. T. Ziemba and D. B. Hausch. 2008. The Dr. Z Betting System in England. In Efficiency of Racetrack Betting Markets. World Scientific.Google Scholar
- M. Zinkevich. 2003. Online Convex Programming and Generalized Infinitesimal Gradient Ascent. In Proceedings of the International Conference on Machine Learning.Google Scholar
Index Terms
- Online portfolio selection: A survey
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