Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection

This paper aims to develop a risk-free protection index model for portfolio selection based on the uncertain theory. First, the returns of risk assets are assumed as uncertain variables and subject to reputable experts' evaluations. Second, under this assumption, combining with the risk-free interest rate we define a risk-free protection index (RFPI), which can measure the protection degree when the loss of risk assets happens. Third, note that the proportion entropy serves as a complementary means to reduce the risk by the preset diversification requirement. We put forward a risk-free protection index model with an entropy constraint under an uncertainty framework by applying the RFPI, Huang's risk index model (RIM), and mean-variance-entropy model (MVEM). Furthermore, to solve our portfolio model, an algorithm is given to estimate the uncertain expected return and standard deviation of different risk assets by applying the Delphi method. Finally, an example is provided to show that the risk-free protection index model performs better than the traditional MVEM and RIM.

Section: Entropy Constraintmentioning

confidence: 99%

Section: Entropy Constraintmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Risk-Free Protection Index Model for Portfolio Selection with Entropy Constraint under an Uncertainty Framework

Gao

Huicheng

2017

“…For instance, Xu et al [7] investigated portfolio selection problems by utilizing the hybrid entropy to estimate the asset risk caused by both randomness and fuzziness. Usta and Kantar [8] tested the mean-variance-skewness-entropy model with the entropy element, which performed better than traditional portfolio selection models in out-of-sample tests. After proposing a mean-variance-skewness model for portfolio selection, Jana et al [9] added the entropy objective function to generate a well-diversified asset portfolio within optimal asset allocation.…”

Section: Introductionmentioning

confidence: 99%

Applications of Entropy in Finance: A Review

Zhou

Cai

Tong

2013

205

129

Although the concept of entropy is originated from thermodynamics, its concepts and relevant principles, especially the principles of maximum entropy and minimum cross-entropy, have been extensively applied in finance. In this paper, we review the concepts and principles of entropy, as well as their applications in the field of finance, especially in portfolio selection and asset pricing. Furthermore, we review the effects of the applications of entropy and compare them with other traditional and new methods.

“…Then, the neighbor diversity of an account is calculated using the class distribution of his/her neighbors in the social network. Because Shannon entropy [14] is often used to quantify the diversity of a group [15][16][17], it is adopted in this paper to calculate the neighbor diversity.…”

Section: Introductionmentioning

confidence: 99%

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Lin

Khomnotai

2014

Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight.