Online Portfolio Selection Strategy Based on Combining Experts’ Advice

Zhang, Yong; Yang, Xing-guo

doi:10.1007/s10614-016-9585-0

Cited by 29 publications

(10 citation statements)

References 22 publications

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“…O'Sullivan and Edelman (2015) proposed the adaptive universal portfolio (AUP), which retains much of the qualitative nature of Cover's UP while enhancing early performance, so it do not need to take a long time to produce significant growth. Zhang and Yang (2017) proposed that one WAAS strategy aggregate experts' strategies that only invest in a single stock and the other WAAC strategy aggregates a series of experts' CRP, and proved that the latter is a universal strategy. Moreover, Hazan and Kale (2015) combined the worst-case model with the geometric Brownian model to create a universal strategy whose regret bound has been greatly improved by probing into the information in the Brownian motion's assumptions.…”

Section: Existing Workmentioning

confidence: 99%

“…Thus, it is a dynamic process. In recent years, some research methods from computer sciences have been borrowed by online portfolio theory, which designed a number of well-performing strategies (Huang et al, 2016;Lin et al, 2017;Zhang and Yang, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…6 WAAC: Online portfolio selection strategy based on combining experts' advice by Zhang and Yang (2017).…”

mentioning

confidence: 99%

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Adaptive online successive constant rebalanced portfolio based on moving window

Jin'

Yang

et al. 2020

IJISE

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In the non-stationary financial market, considering that earlier observations may have little or no relevance to the current investment decision making, we design two kinds of adaptive online portfolio strategies only based on recent historical data. Firstly, we design an adaptive online portfolio strategy by linearly combining the last portfolio and the best constant rebalanced portfolio corresponding to the recent historical data, which we call moving window. We determine the length of the moving window by adaptive learning. More precisely, we consider the strategies that always adopt the best constant rebalanced portfolio corresponding to the moving window of different fixed lengths as different experts, and at the beginning of the current period, we choose the length of moving window the same as the expert achieving maximum current cumulative return. Furthermore, we determine the length of moving window by only using the recent historical data to adaptively learn, and design another adaptive online portfolio strategy. We present numerical analysis by using real stock data from the US and Chinese markets, and the results illustrate that our strategies perform well, compared with some benchmark strategies and existing online portfolio strategies.

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Section: Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive online successive constant rebalanced portfolio based on moving window

Jin'

Yang

et al. 2020

IJISE

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“…Based on a machine learning algorithm, Li et al (2016) designed a method that adaptively switches between two distinct asset allocation strategies. Zhang and Yang (2017) considered CRPs as expert advice and constructed a universal portfolio strategy WAAC by using weak aggregating algorithm (WAA). One underlying assumption for the optimality of BCRP is that stock returns are i.i.d., which may not be satisfied.…”

Section: Yang Et Almentioning

confidence: 99%

Reversion Strategy for Online Portfolio Selection with Transaction Costs

Yang

Zhang

et al. 2018

IJADS

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This paper is about the online portfolio selection problem with transaction costs, which is an unavoidable factor in real financial trading. By exploiting the mean reversion property of stock prices, we propose a portfolio selection strategy named 'mean reversion strategy with transaction costs (MRTC)'. To avoid overmuch transaction costs, the strategy adaptively transfers a proper amount of capital between stocks to adjust the turnover. Furthermore, we conduct numerical experiments on several real market datasets, and show that our proposed algorithm outperforms the existing state-of-the-art ones when taking transaction costs into account.

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“…Following the Kelly investment model [4], several stateof-the-art online portfolio selection strategies with different intuitions [5][6][7][8] adopt the same trend-following approach. In other words, they assume that the current well-performing securities would not perform poor in the following trading days.…”

Section: Introductionmentioning

confidence: 99%

A Novel Online Portfolio Selection Strategy with Multiperiodical Asymmetric Mean Reversion

Peng

2020

Discrete Dynamics in Nature and Society

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Mean reversion is an important property when constructing efficient contrarian strategies. Researchers observe that mean reversion has multiperiodical and asymmetric nature simultaneously in real market. To better utilize mean reversion and improve the existing online portfolio selection strategies, we propose a new online strategy named multiperiodical asymmetric mean reversion (MAMR). The MAMR strategy incorporates a multipiecewise loss function with the moving average method and then imitates the passive-aggressive algorithm. We further provide a solution via convex optimization. This strategy runs in linear time and thus is suitable for large-scale trading applications. Our empirical results testing six real market datasets show that this strategy can achieve better results in bearing higher transaction cost.

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Online Portfolio Selection Strategy Based on Combining Experts’ Advice

Cited by 29 publications

References 22 publications

Adaptive online successive constant rebalanced portfolio based on moving window

Adaptive online successive constant rebalanced portfolio based on moving window

Reversion Strategy for Online Portfolio Selection with Transaction Costs

A Novel Online Portfolio Selection Strategy with Multiperiodical Asymmetric Mean Reversion

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