Online portfolio selection based on quadratic smooth-gray prediction

LIU Xiaoyu; HUANG Dingjiang

doi:10.3969/j.issn.1000-5641.201921020

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Nov. 2020

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LIU Xiaoyu, HUANG Dingjiang. Online portfolio selection based on quadratic smooth-gray prediction[J]. Journal of East China Normal University (Natural Sciences), 2020, (6): 115-128. doi: 10.3969/j.issn.1000-5641.201921020

Citation:

LIU Xiaoyu, HUANG Dingjiang. Online portfolio selection based on quadratic smooth-gray prediction[J]. Journal of East China Normal University (Natural Sciences), 2020, (6): 115-128. doi: 10.3969/j.issn.1000-5641.201921020

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Online portfolio selection based on quadratic smooth-gray prediction

doi: 10.3969/j.issn.1000-5641.201921020

LIU Xiaoyu¹,
HUANG Dingjiang^{1, 2
,
,}

1.
School of Science, East China University of Science and Technology, Shanghai　200237, China
2.
School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received Date: 2019-08-27
Publish Date: 2020-11-25

Abstract

Abstract

In recent years, online portfolios have been a popular topic of research in computational finance. The existing strategies used to forecast stock prices is not ideal, and accurate prediction of stock prices is important for evaluating investment portfolios. Considering the lag in stock prices and the complexity of their distribution, this paper makes use of second-order information in stock prices, for the first time, and proposes four strategies, namely DMAR(DMA(double moving average) reversion), DEAR(DEA(double exponential average) reversion), GMR(GM reversion), and DA-GMR(DA-GM reversion). The second-order moving average method, the second exponential sliding prediction method, and the gray prediction method are used to predict price data for the next period; integrated learning optimizes the results of second-order smoothing prediction and the gray prediction is used to obtain the predicted price. Next, we use PA(passive-aggressive) algorithms to update the portfolio, and we arrive at four portfolio strategies. We verify the effectiveness of these strategies using real data from the financial market. The results show that compared with existing algorithms, the four strategies proposed in this paper achieved higher cumulative returns on datasets for NYSE(O), NYSE(N), DJIA, and MSCI.
- online learning,
- portfolio selection,
- quadratic smooth prediction

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References(22)

References

[1]	STEINBACH M C. Markowitz revisited: Mean-variance models in financial portfolio analysis [J]. SIAM Review, 2001, 43(1): 31-85. doi: 10.1137/S0036144500376650
[2]	KELLYJ L. A new interpretation of information rate [J]. IRE Transactions on Information Theory, 1956, 2(3): 185-189.
[3]	COVER M, ORDENTLICH E. Universal portfolios with side information [J]. IEEE Transactions on Information Theory, 1996, 42(2): 348-363. doi: 10.1109/18.485708
[4]	SINGER Y. Switching portfolios [J]. International Journal of Neural Systems, 1997, 8(4): 445-455. doi: 10.1142/S0129065797000434
[5]	HELMBOLD D P, SCHAPIRE R E, SINGER Y, et al. On-line portfolio selection using multipli-cative updates [J]. Mathematical Finance, 1998, 8(4): 243-251.
[6]	GAIVORONSKI A A, STELLA F. Stochastic nonstationary optimization for finding universal portfolios [J]. Annals of Operations Research, 2000, 100: 165-188. doi: 10.1023/A:1019271201970
[7]	FAGIUOLI E, STELLA F, VENTURA A. Constant rebalanced portfolios and side-information [J]. Quantitative Finance, 2007, 7(2): 161-173. doi: 10.1080/14697680601157942
[8]	AGARWAL A, HAZAN E, KALE S, et al. Algorithms for portfolio management based on the Newton method [C]//Proceedings of the 23rd International Conference on Machine learning. ACM, 2006: 9-16.
[9]	BORODIN A, EL-YANIV R, GOGAN V. Can we learn to beat the best stock [J]. Journal of Artificial Intelligence Research, 2004, 21: 579-594. doi: 10.1613/jair.1336
[10]	LI B, ZHAO P L, HOI S C H, et al. PAMR: Passive aggressive mean reversion strategy for portfolio selection [J]. Machine Learning, 2012, 87(2): 221-258. doi: 10.1007/s10994-012-5281-z
[11]	LI B, HOI S C H, ZHAO P L, et al. Confidence weighted mean reversion strategy for online portfolio selection [J]. ACM Transactions on Knowledge Discovery from Data, 2011, 15(1): 434-442.
[12]	LI B, HOI S C H. On-line portfolio selection with moving average reversion [C]// Proceedings of the 29th International Conference on Machine Learning. [S.l]: [s.n.], 2012: 563-570.
[13]	HUANG D J, ZHOU J L, LI B, et al. Robust median reversion strategy for online portfolio selection [J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(9): 2480-2493. doi: 10.1109/TKDE.2016.2563433
[14]	LI B, WANG J L, HUANG D J, et al. Transaction cost optimization for online portfolio Selection [J]. Quantitative Finance, 2017, 18(8): 1411-1424.
[15]	GYÖRFI L, UDINA F, WALK H. Nonparametric nearest neighbor based empirical portfolio selection strategies [J]. Statistics and Decisions, 2008, 26(2): 145-157.
[16]	GYÖRFI L, LUGOSI G, UDINA F. Nonparametric kernel-based sequential investment strategies [J]. Mathematical Finance, 2006, 16(2): 337-357. doi: 10.1111/j.1467-9965.2006.00274.x
[17]	LI B, HOI S C H, GOPALKRISHNAN V. CORN: Correlation-driven nonparametric learning approach for portfolio selection [J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article number 21. doi: 10.1145/1961189.1961193
[18]	DUFFY N, HELMBOLD D. Boosting methods for regression [J]. Machine Learning, 2002, 47(2/3): 153-200. doi: 10.1023/A:1013685603443
[19]	CLAESKENS G, MAGNUS J R, VASNEV A L, et al. The forecast combination puzzle: A simple theoretical explanation [J]. International Journal of Forecasting, 2016, 32(3): 754-762. doi: 10.1016/j.ijforecast.2015.12.005
[20]	CRAMMER K, DEKEL O, KESHET J, et al. Online passive-aggressive algorithms [J]. Journal of Machine Learning Research, 2006, 7(3): 551-585.
[21]	POTERBA J M, SUMMERS L H. Mean reversion in stock prices: Evidence and implications [J]. Social Science Electronic Publishing, 1987, 22(1): 27-59.
[22]	GRINOLD R C, KAHN R N. Active portfolio management: A quantitative approach for providing superior returns and controlling risk [M]. 2nd ed. New York: McGraw-Hill, 1999