research-article

Online portfolio selection: A survey

Authors:

Steven C. H. HoiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 46, Issue 3

Article No.: 35, Pages 1 - 36

https://doi.org/10.1145/2512962

Published: 01 January 2014 Publication History

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.

References

[1]

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.

Digital Library

[2]

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.

Digital Library

[3]

K. Akcoglu, P. Drineas, and M. Kao. 2004. Fast Universalization of Investment Strategies. SIAM J. Comput. 34 (2004), 1--22.

Digital Library

[4]

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.

[5]

P. Algoet. 1992. Universal Schemes for Prediction, Gambling and Portfolio Selection. Annals of Probability 20, 2 (1992), 901--941.

[6]

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.

[7]

F. Allen and R. Karjalainen. 1999. Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics 51 (1999), 245--271.

[8]

L. Bachelier. 1900. Théorie de la spéculation. Annales Scientifiques de l'École Normale Supérieure 3, 17 (1900), 21--86.

[9]

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.

Digital Library

[10]

C. Y. Belentepe. 2005. A Statistical View of Universal Portfolios. Ph.D. Dissertation. University of Pennsylvania.

[11]

R. M. Bell and T. M. Cover. 1980. Competitive Optimality of Logarithmic Investment. Mathematics of Operations Research 5, 2 (1980), 161--162.

Digital Library

[12]

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.

[13]

J. R. Birge and F. Louveaux. 1997. Introduction to Stochastic Programming. Springer, New York.

[14]

A. Blum and A. Kalai. 1999. Universal Portfolios with and without Transaction Costs. Machine Learning 35, 3 (1999), 193--205.

Digital Library

[15]

A. Blum and Y. Mansour. 2007. From External to Internal Regret. Journal of Machine Learning Research 8 (2007), 1307--1324.

Digital Library

[16]

W. F. M. Bondt and R. Thaler. 1985. Does the Stock Market Overreact&quest; The Journal of Finance 40, 3 (1985), 793--805.

[17]

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.

[18]

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.

Digital Library

[19]

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.

[20]

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.

Digital Library

[21]

S. Boyd and L. Vandenberghe. 2004. Convex Optimization. Cambridge University Press, New York.

Digital Library

[22]

L. Breiman. 1960. Investment Policies for Expanding Businesses Optimal in a Long-Run Sense. Naval Research Logistics Quarterly 7, 4 (1960), 647--651.

[23]

L. Breiman. 1961. Optimal Gambling Systems for Favorable Games. In Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability 1 (1961), 65--78.

[24]

P. Brockman and D. Michayluk. 1998. The Persistent Holiday Effect: Additional Evidence. Applied Economics Letters 5 (1998), 205--209.

[25]

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.

Digital Library

[26]

N. Cesa-Bianchi and G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press, New York.

Digital Library

[27]

L. K. C. Chan, N. Jegadeesh, and J. Lakonishok. 1996. Momentum Strategies. Journal of Finance 51, 5 (1996), 1681--1713.

[28]

K. Chaudhuri and Y. Wu. 2003. Mean Reversion in Stock Prices: Evidence from Emerging Markets. Managerial Finance 29 (2003), 22--37.

[29]

J. Conrad and G. Kaul. 1998. An Anatomy of Trading Strategies. Review of Financial Studies 11, 3 (1998), 489--519.

[30]

R. Cont. 2001. Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance 1, 2 (2001), 223--236.

[31]

M. J. Cooper, R. C. Gutierrez, and A. Hameed. 2004. Market States and Momentum. The Journal of Finance 59, 3 (2004), 1345--1365.

[32]

P. H. Cootner. 1964. The Random Character of Stock Market Prices. MIT Press.

[33]

T. M. Cover. 1991. Universal Portfolios. Mathematical Finance 1, 1 (1991), 1--29.

[34]

T. M. Cover. 1996. Universal Data Compression and Portfolio Selection. In Proceedings of the Annual IEEE Symposium on Foundations of Computer Science. 534--538.

Digital Library

[35]

T. M. Cover and D. H. Gluss. 1986. Empirical Bayes Stock Market Portfolios. Advances in Applied Mathematics 7, 2 (1986), 170--181.

Digital Library

[36]

T. M. Cover and E. Ordentlich. 1996. Universal Portfolios with Side Information. IEEE Transactions on Information Theory 42, 2 (1996), 348--363.

Digital Library

[37]

T. M. Cover and J. A. Thomas. 1991. Elements of Information Theory. Wiley-Interscience, New York.

Digital Library

[38]

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.

Digital Library

[39]

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.

Digital Library

[40]

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.

[41]

G. Creamer. 2007. Using Boosting for Automated Planning And Trading Systems. Ph.D. Dissertation. Columbia University.

[42]

G. Creamer. 2012. Model Calibration and Automated Trading Agent for Euro Futures. Quantitative Finance 12, 4 (2012), 531--545.

[43]

G. Creamer and Y. Freund. 2007. A Boosting Approach for Automated Trading. Journal of Trading 2, 3 (2007), 84--96.

[44]

G. Creamer and Y. Freund. 2010. Automated Trading with Boosting and Expert Weighting. Quantitative Finance 4, 10 (2010).

[45]

J. E. Cross and A. R. Barron. 2003. Efficient Universal Portfolios for Past-Dependent Target Classes. Mathematical Finance 13, 2 (2003), 245--276.

[46]

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.

Digital Library

[47]

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.

Digital Library

[48]

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.

[49]

M. H. A. Davis and A. R. Norman. 1990. Portfolio Selection with Transaction Costs. Mathematics of Operations Research 15, 4 (1990), 676--713.

Digital Library

[50]

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.

Digital Library

[51]

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.

Digital Library

[52]

M. Dredze, K. Crammer, and F. Pereira. 2008. Confidence-Weighted Linear Classification. In Proceedings of the International Conference on Machine Learning. 264--271.

Digital Library

[53]

M. Dredze, A. Kulesza, and K. Crammer. 2010. Multi-Domain Learning by Confidence-Weighted Parameter Combination. Machine Learning 79, 1--2 (2010), 123--149.

Digital Library

[54]

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.

Digital Library

[55]

C. Dzhabarov and W. T. Ziemba. 2010. Do Seasonal Anomalies Still Work&quest; Journal of Portfolio Management 36, 3 (2010), 93--104.

[56]

R. El-Yaniv. 1998. Competitive Solutions for Online Financial Problems. ACM Computing Survey 30, 1 (1998), 28--69.

Digital Library

[57]

E. F. Fama. 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25, 2 (1970), 383--417.

[58]

M. Feder, N. Merhav, and M. Gutman. 1992. Universal Prediction of Individual Sequences. IEEE Transactions on Information Theory 38, 4 (1992), 1258--1270.

Digital Library

[59]

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.

[60]

M. Finkelstein and R. Whitley. 1981. Optimal Strategies for Repeated Games. Advances in Applied Probability 13, 2 (1981), 415--428.

[61]

A. A. Gaivoronski and F. Stella. 2000. Stochastic Nonstationary Optimization for Finding Universal Portfolios. Annals of Operationas Research 100 (2000), 165--188.

[62]

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.

[63]

T. J. George and C.-Y. Hwang. 2004. The 52-Week High and Momentum Investing. Journal of Finance 59, 5 (2004), 2145--2176.

[64]

L. Györfi, G. Lugosi, and F. Udina. 2006. Nonparametric Kernel-Based Sequential Investment Strategies. Mathematical Finance 16, 2 (2006), 337--357.

[65]

L. Györfi, G. Ottucsák, and H. Walk. 2012. Machine Learning for Financial Engineering. Imperial College Press.

[66]

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.

[67]

L. Györfi, F. Udina, and H. Walk. 2008. Nonparametric Nearest Neighbor Based Empirical Portfolio Selection Strategies. Statistics and Decisions 26, 2 (2008), 145--157.

[68]

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.

[69]

L. Györfi and I. Vajda. 2008. Growth Optimal Investment with Transaction Costs. In Proceedings of the International Conference on Algorithmic Learning Theory. 108--122.

Digital Library

[70]

L. Györfi and H. Walk. 2012. Empirical Portfolio Selection Strategies with Proportional Transaction Costs. IEEE Transactions on Information Theory 58, 10 (2012), 6320--6331.

Digital Library

[71]

N. H Hakansson. 1970. Optimal Investment and Consumption Strategies under Risk for a Class of Utility Functions. Econometrica 38, 5 (1970), 587--607.

[72]

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.

[73]

N. H. Hakansson and W. T. Ziemba. 1995. Capital Growth Theory. In Handbooks in OR & MS. Elsevier Science.

[74]

J. D. Hamilton. 1994. Time Series Analysis. Princeton University Press, Princeton, NJ.

[75]

J. D. Hamilton. 2008. New Palgrave Dictionary of Economics. Palgrave McMillan Ltd.

[76]

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.

[77]

R. A. Haugen and J. Lakonishok. 1987. The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin, Homewood, IL.

[78]

D. B. Hausch, W. T. Ziemba, and M. Rubinstein. 1981. Efficiency of the Market for Racetrack Betting. Management Science 27, 12 (1981), 1435--1452.

Digital Library

[79]

E. Hazan. 2006. Efficient Algorithms for Online Convex Optimization and Their Applications. Ph.D. Dissertation. Princeton University.

Digital Library

[80]

E. Hazan, A. Agarwal, and S. Kale. 2007. Logarithmic Regret Algorithms for Online Convex Optimization. Machine Learning 69, 2--3 (2007), 169--192.

Digital Library

[81]

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.

Digital Library

[82]

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.

[83]

E. Hazan and S. Kale. 2012. An Online Portfolio Selection Algorithm With Regret Logarithmic In Price Variation. Mathematical Finance (2012).

[84]

E. Hazan and C. Seshadhri. 2009. Efficient Learning Algorithms for Changing Environments. In Proceedings of the International Conference on Machine Learning. 393--400.

Digital Library

[85]

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.

[86]

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.

Digital Library

[87]

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.

[88]

M. Herbster and M. K. Warmuth. 1998. Tracking the Best Expert. Machine Learning 32, 2 (1998), 151--178.

Digital Library

[89]

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.

Digital Library

[90]

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.

Digital Library

[91]

J. C. Hull. 2008. Options, Futures, and Other Derivatives (7 ed.). Prentice Hall, Upper Saddle River, NJ.

[92]

G. Iyengar. 2005. Universal Investment in Markets with Transaction Costs. Mathematical Finance 15, 2 (2005), 359--371.

[93]

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.

Digital Library

[94]

B. I. Jacobs and K. N. Levy. 1993. Long/Short Equity Investing. Journal of Portfolio Management 20, 1 (1993), 52--63.

[95]

F. Jamshidian. 1992. Asymptotically optimal portfolios. Mathematical Finance 2, 2 (1992), 131--150.

[96]

N. Jegadeesh. 1990. Evidence of Predictable Behavior of Security Returns. Journal of Finance 45, 3 (1990), 881--898.

[97]

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.

[98]

A. Kalai and S. Vempala. 2002. Efficient Algorithms for Universal Portfolios. Journal of Machine Learning Research 3 (2002), 423--440.

Digital Library

[99]

J. O. Katz and D. L. McCormick. 2000. The Encyclopedia of Trading Strategies. McGraw-Hill, New York.

[100]

J. Kelly Jr. 1956. A New Interpretation of Information Rate. Bell Systems Technical Journal 35 (1956), 917--926.

[101]

E. Keogh. 2002. Exact Indexing of Dynamic Time Warping. In Proceedings of the 28th International Conference on Very Large Data Bases. 406--417.

Digital Library

[102]

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.

[103]

W. M. Koolen and V. Vovk. 2012. Buy Low, Sell High. In Proceedings of International Conference on Algorithmic Learning Theory. 335--349.

Digital Library

[104]

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.

[105]

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.

[106]

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.

Digital Library

[107]

S. S. Kozat and A. C. Singer. 2010. Universal Randomized Switching. IEEE Transactions on Signal Processing 58 (2010), 3.

Digital Library

[108]

S. S. Kozat and A. C. Singer. 2011. Universal Semiconstant Rebalanced Portfolios. Mathematical Finance 21, 2 (2011), 293--311.

[109]

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.

[110]

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.

Digital Library

[111]

C. M. C. Lee and B. Swaminathan. 2000. Price Momentum and Trading Volume. Journal of Finance 55 (2000), 2017--2069.

[112]

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.

[113]

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.

[114]

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.

Digital Library

[115]

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.

[116]

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.

Digital Library

[117]

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.

Digital Library

[118]

D. Li and W.-L. Ng. 2000. Optimal Dynamic Portfolio Selection: Multiperiod Mean-Variance Formulation. Mathematical Finance 10, 3 (2000), 387--406.

[119]

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.

[120]

A. W. Lo. 2008. Where Do Alphas Come From&quest; A Measure of the Value of Active Investment Management. Journal of Investment Management 6 (2008), 1--29.

[121]

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.

[122]

A. W. Lo and A. C. MacKinlay. 1990. When Are Contrarian Profits Due to Stock Market Overreaction&quest; Review of Financial Studies 3, 2 (1990), 175--205.

[123]

A. W. Lo and A. C. MacKinlay. 1999. A Non-Random Walk Down Wall Street. Princeton University Press, Princeton, NJ.

[124]

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.

Digital Library

[125]

D. G. Luenberger. 1998. Investment Science. Oxford University Press.

[126]

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.

[127]

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.

[128]

L. C. MacLean and W. T. Ziemba. 1999. Growth versus Security Tradeoffs in Dynamic Investment Snalysis. Annals of Operations Research 85 (1999), 193--225.

[129]

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.

[130]

L. C. MacLean, W. T. Ziemba, and G. Blazenko. 1992. Growth versus Security in Dynamic Investment Analysis. Management Science 38, 11 (1992), 1562--1585.

Digital Library

[131]

M. Magdon-Ismail and A. Atiya. 2004. Maximum Drawdown. Risk Magazine 10 (2004), 99--102.

[132]

S. Mahfoud and G. Mani. 1996. Financial Forecasting Using Genetic Algorithms. Applied Artificial Intelligence 10 (1996), 543--565.

[133]

B. Mandelbrot. 1963. The Variation of Certain Speculative Prices. Journal of Business 36, 4 (1963), 394--419.

[134]

H. Markowitz. 1952. Portfolio Selection. Journal of Finance 7, 1 (1952), 77--91.

[135]

H. Markowitz. 1959. Portfolio Selection: Efficient Diversification of Investments. Wiley, New York.

[136]

H. Markowitz, G. P. Todd, and W. F. Sharpe. 2000. Mean-Variance Analysis in Portfolio Choice and Capital Markets. Wiley.

[137]

J. Mandziuk and M. Jaruszewicz. 2011. Neuro-genetic System for Stock Index Prediction. Journal of Intelligent & Fuzzy Systems 22 (2011), 93--123.

[138]

N. Merhav and M. Feder. 1998. Universal Prediction. IEEE Transactions on Information Theory 44, 6 (1998), 2124--2147.

Digital Library

[139]

H. Mlnařĺk, S. Ramamoorthy, and R. Savani. 2009. Multi-Strategy Trading Utilizing Market Regimes. In Advances in Machine Learning for Computational Finance Workshop.

[140]

N. Moller and S. Zilca. 2008. The Evolution of the January Effect. Journal of Banking & Finance 32, 3 (2008), 447--457.

[141]

J. Moody and M. Saffell. 2001. Learning to Trade via Direct Reinforcement. IEEE Transactions on Neural Networks 12, 4 (2001), 875--889.

Digital Library

[142]

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.

[143]

T. J. Moskowitz and M. Grinblatt. 1999. Do Industries Explain Momentum&quest; The Journal of Finance 54, 4 (1999), 1249--1290.

[144]

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.

Digital Library

[145]

E. Ordentlich. 1996. Universal Investment and Universal Data Compression. Ph.D. Dissertation. Stanford University.

Digital Library

[146]

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.

Digital Library

[147]

M. Ormos and A. Urbán. 2011. Performance Analysis of Log-Optimal Portfolio Strategies with Transaction Costs. Quantitative Finance (2011), 1--11.

[148]

M. F. M. Osborne. 1959. Brownian Motion in the Stock Market. Operations Research 7, 2 (1959), 145--173.

[149]

G. Ottucsák and I. Vajda. 2007. An Asymptotic Analysis of the Mean-Variance portfolio selection. Statistics and Decisions 25 (2007), 63--88.

[150]

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.

[151]

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.

[152]

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.

[153]

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.

[154]

J. Rissanen. 1983. A Universal Data Compression System. IEEE Transactions on Information Theory 29, 5 (1983), 656--663.

Digital Library

[155]

L. M. Rotando and E. O. Thorp. 1992. The Kelly Criterion and the Stock Market. Amer. Math. Monthly (1992), 922--931.

Digital Library

[156]

K. G. Rouwenhorst. 1998. International Momentum Strategies. Journal of Finance 53, 1 (1998), 267--284.

[157]

H. Sakoe and S. Chiba. 1990. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. In Readings in Speech Recognition. 159--165.

Digital Library

[158]

D. Schäfer. 2002. Nonparametric Estimation for Fnancial Investment under Log-Utility. Ph.D. Dissertation. Mathematical Institute, Universität Stuttgart.

[159]

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.

[160]

Y. Singer. 1997. Switching Portfolios. International Journal of Neural Systems 8, 4 (1997), 488--495.

[161]

G. Stoltz and G. Lugosi. 2005. Internal Regret in On-Line Portfolio Selection. Machine Learning 59, 1--2 (2005), 125--159.

Digital Library

[162]

F. E. H. Tay and L. J. Cao. 2002. Modified Support Vector Machines in Financial Time Series Forecasting. Neurocomputing 48 (2002), 847--861.

[163]

S. Taylor. 2005. Asset Price Dynamics, Volatility, and Prediction. Princeton University Press, Princeton, NJ.

[164]

E. O. Thorp. 1962. Beat the Dealer: A Winning Strategy for the Game of Twenty-One. Blaisdell Publishing, New York.

[165]

E. O. Thorp. 1969. Optimal Gambling Systems for Favorable Games. Review of the International Statistical Institute 37, 3 (1969), 273--293.

[166]

E. O. Thorp. 1971. Portfolio Choice and the Kelly Criterion. In Business and Economics Section of the American Statistical Association. 215--224.

[167]

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.

[168]

E. O. Thorp and S. T. Kassouf. 1967. Beat the Market: A Scientific Stock Market System. Random House, New York.

[169]

E. Tsang, P. Yung, and J. Li. 2004. EDDIE-Automation, A Decision Support Tool for Financial Forecasting. Decision Support Systems 37 (2004), 559--565.

Digital Library

[170]

I. Vajda. 2006. Analysis of Semi-Log-Optimal Investment Strategies. In Proceedings of Prague Stochastic, M. Huskova and M. Janzura (Eds.). Matfyzpress, 719--727.

[171]

R. Vince. 1990. Portfolio Management Formulas: Mathematical Trading Methods for the Futures, Options, and Stock Markets. Wiley, Hoboken, NJ.

[172]

R. Vince. 1992. The Mathematics of Money Management: Risk Analysis Techniques for Traders. Wiley, Hoboken, NJ.

[173]

R. Vince. 1995. The New Money Management: A Framework for Asset Allocation. Wiley, Hoboken, NJ.

[174]

R. Vince. 2007. The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage. Wiley, Hoboken, NJ.

[175]

R. Vince. 2009. The Leverage Space Trading Model: Reconciling Portfolio Management Strategies and Economic Theory. Wiley.

[176]

V. Vovk. 1990. Aggregating Strategies. In Proceedings of the Annual Conference on Learning Theory. 371--386.

Digital Library

[177]

V. Vovk. 1997. Derandomizing Stochastic Prediction Strategies. In Proceedings of the 10th Annual Conference on Computational Learning Theory. 32--44.

Digital Library

[178]

V. Vovk. 1999. Derandomizing Stochastic Prediction Strategies. Machine Learning 35 (1999), 247--282.

Digital Library

[179]

V. Vovk. 2001. Competitive On-Line Statistics. International Statistical Review/Revue Internationale de Statistique 69, 2 (2001), 213--248.

[180]

V. Vovk and C. Watkins. 1998. Universal Portfolio Selection. In Proceedings of the Annual Conference on Learning Theory. 12--23.

Digital Library

[181]

A. Weber. 1929. Theory of the Location of Industries. The University of Chicago Press, Chicago.

[182]

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.

[183]

R. E. S. Ziemba and W. T. Ziemba. 2007. Scenarios for Risk Management and Global Investment Strategies. John Wiley & Sons.

[184]

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.

[185]

W. T. Ziemba and D. B. Hausch. 1984. Beat the racetrack. Harcourt Brace Jovanovich.

[186]

W. T. Ziemba and D. B. Hausch. 2008. The Dr. Z Betting System in England. In Efficiency of Racetrack Betting Markets. World Scientific.

[187]

M. Zinkevich. 2003. Online Convex Programming and Generalized Infinitesimal Gradient Ascent. In Proceedings of the International Conference on Machine Learning.

Cited By

Zeng ZXu WZhong Y(2024)Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text AnalysisInternational Journal of Fuzzy System Applications10.4018/IJFSA.35524613:1(1-20)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.4018/IJFSA.355246
Mose NFumey M(2024)Public and private investments and economic growth in Ghana and KenyaFinancial Internet Quarterly10.2478/fiqf-2024-001720:3(29-41)Online publication date: 12-Sep-2024
https://doi.org/10.2478/fiqf-2024-0017
Lai ZLi CWu XGuan QFang L(2024)Multitrend Conditional Value at Risk for Portfolio OptimizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318389135:2(1545-1558)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3183891
Show More Cited By

Index Terms

Online portfolio selection: A survey
1. Applied computing
  1. Enterprise computing
  2. Law, social and behavioral sciences
    1. Economics
2. Computing methodologies
  1. Machine learning

Recommendations

Systemic Risk-Driven Portfolio Selection
How can we construct portfolios that perform well in the face of systemic events? The global financial crisis of 2007–2008 and the coronavirus disease 2019 pandemic have highlighted the importance of accounting for extreme form of risks. In “Systemic Risk-...
We consider an investor who trades off tail risk and expected growth of the investment. We measure tail risk through the portfolio’s expected losses conditioned on the occurrence of a systemic event: financial market loss being exactly at, or at least at, ...
A Portfolio Selection using Safety First Model for US Blue Chip and Top 30 Companies under NYSE list during COVID-19
IEIM 2021: 2021 The 2nd International Conference on Industrial Engineering and Industrial Management

A stock is an investment wherein you purchase one of the company's stocks which by means is called a share. Most investors think that when they purchase a stock, it will go up the value. It will result also in to impact of an increase to the company's ...
The efficient frontier for a portfolio that includes one risk-free asset
ASM'11: Proceedings of the 5th international conference on Applied mathematics, simulation, modelling

This paper presents a description of the efficient frontier of a portfolio composed by three assets, including a risk-free asset. We use a data analysis method to obtain two classes of assets and then we estimate the risk of each asset corresponding to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 46, Issue 3

January 2014

507 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2578702

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2014

Accepted: 01 August 2013

Revised: 01 May 2013

Received: 01 December 2012

Published in CSUR Volume 46, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Ministry of Education - Singapore

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

150
Total Citations
View Citations
3,511
Total Downloads

Downloads (Last 12 months)351
Downloads (Last 6 weeks)40

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zeng ZXu WZhong Y(2024)Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text AnalysisInternational Journal of Fuzzy System Applications10.4018/IJFSA.35524613:1(1-20)Online publication date: 16-Oct-2024
https://dl.acm.org/doi/10.4018/IJFSA.355246
Mose NFumey M(2024)Public and private investments and economic growth in Ghana and KenyaFinancial Internet Quarterly10.2478/fiqf-2024-001720:3(29-41)Online publication date: 12-Sep-2024
https://doi.org/10.2478/fiqf-2024-0017
Lai ZLi CWu XGuan QFang L(2024)Multitrend Conditional Value at Risk for Portfolio OptimizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318389135:2(1545-1558)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3183891
Li NXia ZLi YKuruoğlu EJiang YXia S(2024)Portfolio Selection via Graph-Aware Gaussian Processes With Generalized Gaussian LikelihoodIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32624565:2(505-515)Online publication date: Feb-2024
https://doi.org/10.1109/TAI.2023.3262456
Li ZTam V(2024)Developing An Attention-Based Ensemble Learning Framework for Financial Portfolio Optimisation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650941(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650941
Zheng ZShao JDeng SZhu AShen HZhou X(2024)Cross-Insight Trader: A Trading Approach Integrating Policies with Diverse Investment Horizons for Portfolio Management2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00356(4685-4698)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00356
Guan QHe JLai ZZhou YJiang QChen Z(2024)Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD61410.2024.10580003(2357-2362)Online publication date: 8-May-2024
https://doi.org/10.1109/CSCWD61410.2024.10580003
Yadav RSharma AKumar SDe P(2024)Tracking and Managing of Cryptocurrency Wallet2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE)10.1109/AMATHE61652.2024.10582164(1-6)Online publication date: 16-May-2024
https://doi.org/10.1109/AMATHE61652.2024.10582164
Tu XLi B(2024)Online portfolio selection with parameterized characteristicsJournal of Accounting Literature10.1108/JAL-06-2024-0114Online publication date: 21-Oct-2024
https://doi.org/10.1108/JAL-06-2024-0114
Zhang YLin HLi JYang X(2024)Combined peak price tracking strategies for online portfolio selection based on the meta-algorithmJournal of the Operational Research Society10.1080/01605682.2023.229597575:10(2032-2051)Online publication date: 6-Mar-2024
https://doi.org/10.1080/01605682.2023.2295975
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents