Application of Information Theoretic Divergence Measures- Forecasting Profit Maximation in Share Market

  • Ruchi Nager
  • R.P Singh

Abstract


In the present communication, we have discussed Weighted Non- Symmetric JS-AG Divergence Measures of type s,∀s ∈ R . Some bounds have been obtained for this Weighted Non- Symmetric JS-AG Divergence Measures and its particular cases for s=-1, 0, 1/2, 1, 2.Weighted Non-Symmetric Relative J-Divergence of type s, ∀s ∈ R has also been considered for its particular cases for s=0, 1, 2 in section-2. Some mixed bounds- Non-Symmetric and Symmetric have also been presented. “Application in business – Share Market,” to switch over from one share to another share utilizing Weighted Divergence Measure in decision making process, has been exemplified in section-3 via weighted χ 2 -divergence and its adjoint. Analysis for profit maximization has been presented numerically and graphically. Section-4 presents the study of profit maximization through weighted Kullback-Leibler divergence, K(P//Q;W) and its adjoint.



 

Published
2018-05-11
How to Cite
NAGER, Ruchi; SINGH, R.P. Application of Information Theoretic Divergence Measures- Forecasting Profit Maximation in Share Market. GSTF Journal of Mathematics, Statistics and Operations Research (JMSOR), [S.l.], v. 2, n. 1, may 2018. ISSN 2251-3396. Available at: <http://dl6.globalstf.org/index.php/jmsor/article/view/1524>. Date accessed: 19 dec. 2018.