Affiliation:
1. University of Tunis El Manar, Department of Mathematics, Faculty of Sciences of Tunis, Tunisia
2. Telecommunications High School of Tunis, Technological City of Communications, Tunisia
Abstract
In uncertainty theory, entropy is a measure to capture the level of unpredictability associated with an uncertain variable. In this paper, we introduce an alternative type of entropy named Tsallis entropy as a generalized form of logarithmic entropy in an uncertain environment. Then, we provide a formula to calculate the Tsallis entropy via inverse uncertainty distribution. In addition, we study some of its mathematical properties such as translation invariance and positive linearity. Furthermore, we establish a mean-CVaR-Tsallis entropy portfolio selection model, in which security returns are regarded as uncertain variables and we derive its equivalent form. Finally, a numerical example is given and the effect of Tsallis entropy and uncertainty distribution on the proposed model is discussed, including a comparative study.
Publisher
World Scientific Pub Co Pte Ltd
Subject
Artificial Intelligence,Control and Optimization,Computer Vision and Pattern Recognition
Cited by
1 articles.
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