Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.7
Journals
Entropy
Special Issues
Applications of Information Theory in Economics
entropy-logo
Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Applications of Information Theory in Economics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 5423

Special Issue Editor

Prof. Dr. Joanna Tyrcha

E-Mail Website
Guest Editor
Department of Mathematics, Stockholm University, Kraftriket, 106 91 Stockholm, Sweden
Interests: statistical inference; econometrics; neuroscience; biological information processing and causality problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The origins of information theory date back to Claude E. Shannon’s publication “A Mathematical Theory of Communication” in the Bell System Technical Journal in 1948. In terms of the colloquial meaning of information, Shannon’s paper focuses the carriers of information and not with information itself. However, the significance and flexibility of Shannon's work was quickly recognized, and many attempts have been enacted to apply his theory in various fields outside its original scope in communication. One such area is economics, particularly econometrics. Many scientists have defined the measures of causality through the combination of Granger causality (a well-known concept established in the econometrics field in 1969) with concepts in information theory such as, for example, transfer entropy.

This Special Issue aims to act as a forum for the presentation of novel approaches in economics using information theory and seeks to aid in the development of new information theoretic research inspired by challenges in economical time series.

Prof. Dr. Joanna Tyrcha
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • information flow
  • transfer entropy
  • causality measures
  • multivariate statistics
  • time series
  • statistical inference
  • economics

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 6777 KiB  
Article
A New Chaotic Image Encryption Algorithm Based on Transversals in a Latin Square
by Honglian Shen, Xiuling Shan, Ming Xu and Zihong Tian
Entropy 2022, 24(11), 1574; https://0-doi-org.brum.beds.ac.uk/10.3390/e24111574 - 31 Oct 2022
Cited by 9 | Viewed by 1355
Abstract
In this paper, a new combinatorial structure is introduced for image encryption, which has an excellent encryption effect on security and efficiency. An n-transversal in a Latin square has the function of classifying all the matrix’s positions, and it can provide a [...] Read more.
In this paper, a new combinatorial structure is introduced for image encryption, which has an excellent encryption effect on security and efficiency. An n-transversal in a Latin square has the function of classifying all the matrix’s positions, and it can provide a pair of orthogonal Latin squares. Employing an n-transversal of a Latin square, we can permutate all the pixels of an image group by group for the first time, then use two Latin squares for auxiliary diffusion based on a chaotic sequence, and finally, make use of a pair of orthogonal Latin squares to perform the second scrambling. The whole encryption process is “scrambling–diffusion–scrambling”. The experimental results indicated that this algorithm passed various tests and achieved a secure and fast encryption effect, which outperformed many of the latest papers. The final information entropy was very close to 8, and the correlation coefficient was approximately 0. All these tests verified the robustness and practicability of the proposed algorithm. Full article
(This article belongs to the Special Issue Applications of Information Theory in Economics)
►▼ Show Figures

Figure 1

13 pages, 371 KiB  
Article
Synergistic Information Transfer in the Global System of Financial Markets
by Tomas Scagliarini, Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia and Rosario N. Mantegna
Entropy 2020, 22(9), 1000; https://0-doi-org.brum.beds.ac.uk/10.3390/e22091000 - 08 Sep 2020
Cited by 12 | Viewed by 3358
Abstract
Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information [...] Read more.
Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system. Full article
(This article belongs to the Special Issue Applications of Information Theory in Economics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop