A hybrid stock trading system using genetic network programming and mean conditional value-at-risk

Chen, Yan; Wang, Xuancheng

doi:10.1016/j.ejor.2014.07.034

Cited by 35 publications

(20 citation statements)

References 45 publications

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“…Based on a series of analysis, it was found that the distribution of CVaR is uniformed, thus, in this paper, a is the considered equal to 0.95 [34,35]. In the model, k is a nonnegative trade-off coefficient representing the exchange rate of mean cost for risk, which is specified by decision makers according to their risk preferences, and it can be chosen as any real numbers.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

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Development of an inexact risk-aversion optimization model for regional carbon constrained electricity system planning under uncertainty

Zhang

Huang

et al. 2015

Energy Conversion and Management

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Section: Results Analysis and Discussionmentioning

confidence: 99%

“…where k is the weighting factor presenting the tradeoff between the expected cost and CVaR; n ± denotes the value of VaR; a is confidence level, usually it could be set as 0.90, 0.95 and 0.99 [34,35]. subject to:…”

Section: Model Formulationmentioning

confidence: 99%

Development of an inexact risk-aversion optimization model for regional carbon constrained electricity system planning under uncertainty

Zhang

Huang

et al. 2015

Energy Conversion and Management

View full text Add to dashboard Cite

“…Furthermore, a fuzzy rule based expert system has been developed for this problem [13]. The models proposed by [10,12,13] support portfolio managers in their middle term stock evaluation and portfolio construction decisions. They used two distinct models for single stock price forecasting and evaluation and portfolio management.…”

Section: Introductionmentioning

confidence: 99%

“…The GNP model is extended to consider the risk of trading in terms of conditional value at risk [9,10]. Also, a fuzzy rule based system has been developed using an artificial evolutionary process for portfolio trading considering transaction cost and risk [11].…”

Section: Introductionmentioning

confidence: 99%

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi

Esfahanipour

Zarandi

2015

Applied Soft Computing

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“…Their proposed method is shown to outperform a buy-and-hold strategy, and it is found that the constructed forecasts predict better the prices of small rather than large stocks. Chen and Wang [2015] combine a GA with a risk model that aims at building up portfolios. The effective risk attitude is computed taking into account not only the investor risk aversion but also the fluctuating market conditions (bull market vs. bear market).…”

Section: Asset Selection and Market Timingmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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A hybrid stock trading system using genetic network programming and mean conditional value-at-risk

Cited by 35 publications

References 45 publications

Development of an inexact risk-aversion optimization model for regional carbon constrained electricity system planning under uncertainty

Development of an inexact risk-aversion optimization model for regional carbon constrained electricity system planning under uncertainty

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

A Survey on Financial Applications of Metaheuristics

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