Progression of Optimizational Principles in Portfolio Analysis through Isolated Entropic Models

Poonam Kumari¹

1. Dept. of Mathematics, Magadh Mahila College, Patna University, Patna, India.

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.6 , pp.1-8, Dec-2024

Online published on Dec 31, 2024

Copyright © Poonam Kumari . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Even if there is a vast array of parametric and non-parametric information models, predictability still emerges to expand further parametric models to strengthen plasticity in the structure under study. Additionally, there looks to be a well-built association sandwiched between information entropy and the theory of “Portfolio Analysis”. Moreover, innumerable methodologies of risk assessment, comprising entropy technique, divergence procedure, unified approach, etc. are accessible within the existing corpus of portfolio analysis literature. The present paper is a step towards making progress on some well-known optimizational principles by using new discrete entropic models and then showing how they can be used in portfolio analysis. In addition, the entrenched principle has been elucidated through the support of a well-managed numerical example.

Key-Words / Index Term :
Portfolio analysis, Modern portfolio theory, Entropy, Variance, Covariance matrix, Uncertainty

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