The Experts below are selected from a list of 28308 Experts worldwide ranked by ideXlab platform
Shouyang Wang - One of the best experts on this subject based on the ideXlab platform.
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artificial bee colony algorithm based on novel mechanism for fuzzy Portfolio Selection
IEEE Transactions on Fuzzy Systems, 2019Co-Authors: Weifeng Gao, Hailong Sheng, Jue Wang, Shouyang WangAbstract:Although the introduction of fuzzy theory into a Portfolio Selection model can help improve the model's practicality, it would increase the difficulty of solving the model. To tackle the issue, this paper proposes a novel mechanism based artificial bee colony algorithm (ABC) consisting of two new proposed learning strategies—direction learning and elite learning. The direction learning strategy has a great potential to guide the search toward the promising areas. The elite learning strategy can gradually pick up the convergence rate without loss of the population diversity. The cooperation of the two approaches forms a mechanism, complementing each other to improve the performance of the algorithms. The proposed mechanism, named LL-mechanism, is introduced into three ABC variants-ABC, gbest-guided ABC (GABC), and CABC, generating LL-ABC, LL-GABC, and LL-CABC, respectively. The experimental results demonstrate the superior performance of the LL-mechanism and LL-CABC outperforms other methods in terms of solution quality, convergence rate, robustness, and numerical stability. Finally, the proposed LL-CABC approach is employed to solve the Portfolio Selection with fuzzy security return. The experiments on two Portfolio Selection models illustrate that LL-CABC is effective and promising for a fuzzy Portfolio Selection.
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time cardinality constrained mean variance dynamic Portfolio Selection and market timing
Automatica, 2015Co-Authors: Jianjun Gao, Xiangyu Cui, Shouyang WangAbstract:An investor does not always invest in risky assets in all the time periods, often due to a market timing consideration and various forms of market friction, including the management fee. Motivated by this observed common phenomenon, this paper considers the time cardinality constrained mean-variance dynamic Portfolio Selection problem (TCCMV) in markets with correlated returns and in which the number of time periods to invest in risky assets is limited. Both the analytical optimal Portfolio policy and the analytical expression of the efficient mean-variance (MV) frontier are derived for TCCMV. It is interesting to note whether investing in risky assets or not in a given time period depends entirely on the realization of the two adaptive processes which are closely related to the local optimizer of the conditional Sharpe ratio. By implementing such a solution procedure for different cardinalities, the MV dynamic Portfolio Selection problem with management fees can be efficiently solved for a purpose of developing the best market timing strategy. The final product of our solution framework is to provide investors advice on the best market timing strategy including the best time cardinality and its distribution, as well as the corresponding investment policy, when balancing the consideration of market opportunity and market frictions.
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genetic algorithm based multi criteria project Portfolio Selection
Annals of Operations Research, 2012Co-Authors: Shouyang Wang, Fenghua Wen, Kin Keung LaiAbstract:Project Portfolio Selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project Portfolio decisions are very complicated when project interactions in terms of multiple Selection criteria and preference information of decision makers (DMs) in terms of the criteria importance are taken into consideration simultaneously. In order to solve this complex decision-making problem, a multi-criteria project Portfolio Selection problem considering project interactions in terms of multiple Selection criteria and DMs’ preferences is first formulated. Then a genetic algorithm (GA)-based nonlinear integer programming (NIP) approach is used to solve the multi-criteria project Portfolio Selection problem. Finally, two illustrative examples are presented for demonstration and verification purposes. Experimental results obtained indicate that the GA-based NIP approach can be used as a feasible and effective solution to multi-criteria project Portfolio Selection problems.
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a cutting plane algorithm for mv Portfolio Selection model
Applied Mathematics and Computation, 2009Co-Authors: Guohua Chen, Xiaolian Liao, Shouyang WangAbstract:This paper deals with a Portfolio Selection problem with fuzzy return rates. A possibilistic mean variance (FMVC) Portfolio Selection model was proposed. The possibilistic programming problem can be transformed into a linear optimal problem with an additional quadratic constraint by possibilistic theory. For such problems there are no special standard algorithms. We propose a cutting plane algorithm to solve (FMVC). The nonlinear programming problem can be solved by sequence linear programming problem. A numerical example is given to illustrate the behavior of the proposed model and algorithm.
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an interval semi absolute deviation model for Portfolio Selection
Fuzzy Systems and Knowledge Discovery, 2006Co-Authors: Yong Fang, Shouyang WangAbstract:Interval number is a kind of special fuzzy number and the interval approach is a good method to deal with some uncertainty. The semi-absolute deviation risk function is extended to an interval case. Based on the extended semi-absolute deviation risk function, an interval semi-absolute deviation model for Portfolio Selection is proposed. By introducing the concepts of pessimistic satisfactory index and optimistic satisfactory index of interval inequality relation, an approach to compare interval numbers is given. The interval Portfolio Selection problem is converted to two parametric linear programming problems. A numerical example is given to illustrate the behavior of the proposed Portfolio Selection model.
Bin Li - One of the best experts on this subject based on the ideXlab platform.
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moving average reversion strategy for on line Portfolio Selection
Artificial Intelligence, 2015Co-Authors: Bin Li, Doyen SahooAbstract:On-line Portfolio Selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line Portfolio Selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.
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online Portfolio Selection a survey
ACM Computing Surveys, 2014Co-Authors: Bin LiAbstract: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.
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confidence weighted mean reversion strategy for online Portfolio Selection
ACM Transactions on Knowledge Discovery From Data, 2013Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:Online Portfolio Selection has been attracting increasing attention from the data mining and machine learning communities. All existing online Portfolio Selection strategies focus on the first order information of a Portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online Portfolio Selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR models the Portfolio vector as a Gaussian distribution, and sequentially updates the distribution by following the mean reversion trading principle. CWMR’s closed-form updates clearly reflect the mean reversion trading idea. We also present several variants of CWMR algorithms, including a CWMR mixture algorithm that is theoretical universal. Empirically, CWMR strategy is able to effectively exploit the power of mean reversion for online Portfolio Selection. Extensive experiments on various real markets show that the proposed strategy is superior to the state-of-the-art techniques. The experimental testbed including source codes and data sets is available online.
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online Portfolio Selection a survey
arXiv: Computational Finance, 2012Co-Authors: Bin LiAbstract: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, etc. This article aims to provide a comprehensive survey and a structural understanding of published online Portfolio Selection techniques. From an online machine learning perspective, we first formulate online Portfolio Selection as a sequential decision problem, and then 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 in order 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 directions.
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pamr passive aggressive mean reversion strategy for Portfolio Selection
Machine Learning, 2012Co-Authors: Bin Li, Peilin Zhao, Vivekanand GopalkrishnanAbstract:This article proposes a novel online Portfolio Selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed Portfolio Selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between Portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art Portfolio Selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/ .
Weiguo Zhang - One of the best experts on this subject based on the ideXlab platform.
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multiperiod mean absolute deviation fuzzy Portfolio Selection model with risk control and cardinality constraints
Fuzzy Sets and Systems, 2014Co-Authors: Peng Zhang, Weiguo ZhangAbstract:Abstract This paper considers a multiperiod fuzzy Portfolio Selection problem maximizing the terminal wealth imposed by risk control, in which the returns of assets are characterized by possibilistic mean values. A possibilistic absolute deviation is defined as the risk control of Portfolio. A new multiperiod mean absolute deviation fuzzy Portfolio Selection model with transaction cost, borrowing constraints, threshold constraints and cardinality constraints is proposed. Based on the theory of possibility measure, the proposed model is transformed into a crisp nonlinear programming problem. Because of the transaction cost, the multiperiod Portfolio Selection is a dynamic optimization problem with path dependence. The discrete approximate iteration method is designed to obtain the optimal Portfolio strategy, and is proved convergent. Finally, an example is given to illustrate the behavior of the proposed model and the designed algorithm using real data from the Shanghai Stock Exchange.
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Portfolio Selection under possibilistic mean variance utility and a smo algorithm
European Journal of Operational Research, 2009Co-Authors: Weiguo Zhang, Xili Zhang, Weilin XiaoAbstract:In this paper, we propose a new Portfolio Selection model with the maximum utility based on the interval-valued possibilistic mean and possibilistic variance, which is a two-parameter quadratic programming problem. We also present a sequential minimal optimization (SMO) algorithm to obtain the optimal Portfolio. The remarkable feature of the algorithm is that it is extremely easy to implement, and it can be extended to any size of Portfolio Selection problems for finding an exact optimal solution.
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possibilistic mean variance models and efficient frontiers for Portfolio Selection problem
Information Sciences, 2007Co-Authors: Weiguo Zhang, Ying-luo Wang, Zhiping Chen, Zankan NieAbstract:In this paper, it is assumed that the rates of return on assets can be expressed by possibility distributions rather than probability distributions. We propose two kinds of Portfolio Selection models based on lower and upper possibilistic means and possibilistic variances, respectively, and introduce the notions of lower and upper possibilistic efficient Portfolios. We also present an algorithm which can derive the explicit expression of the possibilistic efficient frontier for the possibilistic mean-variance Portfolio Selection problem dealing with lower bounds on asset holdings.
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on admissible efficient Portfolio Selection policy
Applied Mathematics and Computation, 2005Co-Authors: Weiguo Zhang, Zankan NieAbstract:The expected return and risk of asset cannot be predicted accurately because of uncertain factors that affect the finical markets. In this paper, the admissible efficient Portfolio model is proposed under the assumption that the expected return and risk of asset have admissible errors with general investment constraints. The upper and lower admissible efficient Portfolios can be defined by the spreads of the Portfolio expected returns and risks from the upper and lower bounds of admissible errors. The admissible efficient Portfolio frontiers are derived explicitly when short sales are not allowed. A numerical example of a Portfolio Selection problem is given to illustrate our proposed effective means and approaches. s and approaches.
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Portfolio Selection possibilistic mean variance model and possibilistic efficient frontier
Algorithmic Applications in Management, 2005Co-Authors: Weiguo Zhang, Ying-luo WangAbstract:There are many non-probabilistic factors that affect the financial markets. In this paper, the possibilistic mean-variance model of Portfolio Selection is presented under the assumption that the returns of assets are fuzzy numbers, which can better integrate the experts' knowledge and the managers' subjective opinions to compare with conventional probabilistic mean-variance methodology. The possibilistic efficient frontier is derived explicitly when short sales are not allowed on all risky assets and a risk-free asset.
Doyen Sahoo - One of the best experts on this subject based on the ideXlab platform.
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moving average reversion strategy for on line Portfolio Selection
Artificial Intelligence, 2015Co-Authors: Bin Li, Doyen SahooAbstract:On-line Portfolio Selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line Portfolio Selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.
Kin Keung Lai - One of the best experts on this subject based on the ideXlab platform.
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genetic algorithm based multi criteria project Portfolio Selection
Annals of Operations Research, 2012Co-Authors: Shouyang Wang, Fenghua Wen, Kin Keung LaiAbstract:Project Portfolio Selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project Portfolio decisions are very complicated when project interactions in terms of multiple Selection criteria and preference information of decision makers (DMs) in terms of the criteria importance are taken into consideration simultaneously. In order to solve this complex decision-making problem, a multi-criteria project Portfolio Selection problem considering project interactions in terms of multiple Selection criteria and DMs’ preferences is first formulated. Then a genetic algorithm (GA)-based nonlinear integer programming (NIP) approach is used to solve the multi-criteria project Portfolio Selection problem. Finally, two illustrative examples are presented for demonstration and verification purposes. Experimental results obtained indicate that the GA-based NIP approach can be used as a feasible and effective solution to multi-criteria project Portfolio Selection problems.
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a class of linear interval programming problems and its application to Portfolio Selection
IEEE Transactions on Fuzzy Systems, 2002Co-Authors: Kin Keung Lai, Shouyang Wang, Shushang Zhu, Yong FangAbstract:This paper discusses a class of linear programming problems with interval coefficients in both the objective functions and constraints. The noninferior solutions to such problems are defined based on two order relations between intervals, and can be found by solving a parametric linear programming problem. Considering the uncertain returns of assets in capital markets as intervals, we propose a model for Portfolio Selection based on the semiabsolute deviation measure of risk, which can be transformed to a linear interval programming model studied in the paper. The method is illustrated by solving a simplified Portfolio Selection problem.
