Gangless cross-evaluation in DEA: an application to stock selection

Amin, Gholam R.; Oukil, Amar

doi:10.1051/ro/2018060

Cited by 26 publications

(4 citation statements)

References 28 publications

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“…Finally, the relationship between these coefficients and stock price from the perspective of reporting risk was correlated, thus proving the model's validity. DEA was used in [10][11][12]. The DEA model was used to determine the efficiency score of stock companies and then selected the stocks with high efficiency.…”

Section: Multiattribute Decision-making (Madm)mentioning

confidence: 99%

A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

Jin

2022

Wireless Communications and Mobile Computing

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Due to the computer’s powerful data computing ability, more and more traditional investors pay attention to computer-based quantitative investment. Moreover, computer-based quantitative investment has gradually replaced some traditional investment methods which rely on subjective judgments. As a typical quantitative trading strategy, stock selection has attracted a lot of attention. And many researchers have put forward various methods and published a large number of research results. In particular, with the development of artificial intelligence technology, an increasing number of researchers try to apply different machine learning and deep learning methods to this field to obtain more stable and efficient stock selection models. Even though there is a growing interest in developing methods for stock selection, there is a lack of review papers that are solely focused on different types of methods for stock selection. Hence, our motivation in this paper is to provide a comprehensive literature review on different types of methods for stock selection in the field of quantitative investment. Firstly, we introduce the basic concept of stock selection. Secondly, according to the classification of traditional methods and machine learning methods, we introduce the widely used stock selection methods in detail. Then, we give a statistical analysis about the relevant literatures in this field. Finally, the stock selection methods are summarized. The main contribution of this paper is we analyse various quantitative analysis methods from the perspective of stock selection for the first time. And it has some guidance for researchers who engaged in quantitative trading or interested in quantitative investment, and they will benefit from it.

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Section: Multiattribute Decision-making (Madm)mentioning

confidence: 99%

A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

Jin

2022

Wireless Communications and Mobile Computing

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“…Deng and Fang [6] incorporated a DEA prospect cross-efficiency evaluation into an innovative MVM (mean-variance-maverick) framework for fuzzy portfolio selection. Amin and Oukil [2] and Sanei and Banihashemi [19] effectively applied the cross-efficiency evaluation for portfolio section.…”

Section: Introductionmentioning

confidence: 99%

Stock assessment using Cumulative Prospect Theory in DEA cross-efficiency model

Kumari,

Gupta,

Aggarwal

2024

Croat. oper. res. rev. (Online)

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Market volatility is becoming increasingly common as numerous factors are implemented in the financial system. As a result, portfolio managers and individual investors require reliable methods to assess stock performance. This study examines stock assessments using cross-efficiency evaluations in cases where negative data is present. An alternative approach to achieve this goal is to use an RDM DDF-based cross-efficiency model which oversees the negative data. We expand the RDM-based cross-efficiency analysis, which uses row and column average values to select portfolios and identify different groups for stock management. To explore the psychological factors that influence the choices made by stock market investors, we incorporate the cumulative prospect theory value for each stock as an output and the variance as an input to evaluate the overall efficiency of the assets. For the empirical analysis, our study focuses on a sample of 30 stocks listed on the Nifty-50 on India's National Stock Exchange. The results of our empirical study verify that the proposed method can serve as an effective tool for stock selection. This demonstrates how the chosen portfolio gives companies a more diversified and well-balanced approach for selecting stocks, thus improving logical decision-making.

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“…Tavana et al () utilize DEA to screen the efficient PPs, which are isolated and ranked separately using TOPSIS. Nonetheless, by discarding inefficient PPs, the authors disregard the ganging phenomenon (Amin & Oukil, ) that often affects the ranking.…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

Oukil

Govindaluri

2019

Manage Decis Econ

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This paper develops a hybrid multiattribute decision‐making methodology for ranking project proposals (PPs) through a judicious usage of historical data of completed projects to determine attribute weights, enabling elimination of problems associated with projected data such as cost and schedule overruns of real‐world projects. The weights generated from data envelopment analysis are explicitly utilized for ranking PPs while allowing subjectivity to be ingeniously incorporated into the decision process. The new approach is implemented for ranking 25 PPs, and the rankings it yields are found indifferent to the decision maker's attitude, which ascertains the robustness of the ranking methodology.

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Gangless cross-evaluation in DEA: an application to stock selection

Cited by 26 publications

References 28 publications

A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

A Study of Different Existing Methods for the Stock Selection in the Field of Quantitative Investment

Stock assessment using Cumulative Prospect Theory in DEA cross-efficiency model

A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective

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