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FinTech
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Advanced Financial Technologies
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Advanced Financial Technologies

A special issue of FinTech (ISSN 2674-1032).

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 4937

Special Issue Editors

Prof. Dr. Junzo Watada

E-Mail Website
Guest Editor
Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
Interests: artificial intelligence; image processing; soft computing, including meta heuristics, fuzzy systems, rough set analysis; model building; optimization; data analytics; big data mining; management engineering; financial engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Bing Xu

E-Mail Website
Guest Editor
School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China
Interests: quantitative economics; mathematics; statistics
Dr. Wentao Gu

E-Mail Website
Guest Editor
Institute of Quantitative Economics, Zhejiang Gongshang University, Hangzhou 310018, China
Interests: corporate finance; management science; economic statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Tan Shing Chiang

E-Mail Website
Guest Editor
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia
Interests: computer science

Special Issue Information

Dear Colleagues,

At present, financial technologies play a pivotal role in advancing and accelerating the development of various business areas. FinTech is one of the most important activities in the field, influencing corporations’ survival.

We call for innovative and unpublished papers on the topics listed below, including private and government corporations and institutes. Some of the most active areas of FinTech innovation include or revolve around, but are not limited to, the following areas:

  • Blockchain in finance
  • Distributed ledger technology (DLT)
  • Robo-advisors
  • Open banking
  • InsurTech
  • RegTech
  • Cybersecurity and data protect-tion
  • Text mining
  • Artificial intelligence
  • Machine learning (algorithms)
  • Deep learning
  • Predictive behavioral analytics
  • Data-driven marketing
  • Robotic process automation (RPA)
  • FinTech models
  • Risk
  • Consumer protection
  • Firms’ governance and risk governance
  • Amendments to anti-money laundering requirements
  • Social & ethical implications of FinTech
  • Visualization of big data finan= cial systems
  • Sustainability in FinTech
  • New developments in FinTech
  • Applications of FinTech,
  • Mobile payments,
  • Avanced computing
  • Smart contracts
  • Regulation of FinTech
  • Bitcoin, cryptocurrency and digital cash
  • Unbanked/Underbanked
  • Crowdfunding platforms

Prof. Dr. Junzo Watada
Dr. Bing Xu
Dr. Wentao Gu
Dr. Tan Shing Chiang
Guest Editors

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. FinTech is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (2 papers)

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Research

18 pages, 12109 KiB  
Article
Comparison between Information Theoretic Measures to Assess Financial Markets
by Luckshay Batra and Harish Chander Taneja
FinTech 2022, 1(2), 137-154; https://doi.org/10.3390/fintech1020011 - 19 May 2022
Viewed by 1923
Abstract
Information theoretic measures were applied to the study of the randomness associations of different financial time series. We studied the level of similarities between information theoretic measures and the various tools of regression analysis, i.e., between Shannon entropy and the total sum of [...] Read more.
Information theoretic measures were applied to the study of the randomness associations of different financial time series. We studied the level of similarities between information theoretic measures and the various tools of regression analysis, i.e., between Shannon entropy and the total sum of squares of the dependent variable, relative mutual information and coefficients of correlation, conditional entropy and residual sum of squares, etc. We observed that mutual information and its dynamical extensions provide an alternative approach with some advantages to study the association between several international stock indices. Furthermore, mutual information and conditional entropy are relatively efficient compared to the measures of statistical dependence. Full article
(This article belongs to the Special Issue Advanced Financial Technologies)
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24 pages, 371 KiB  
Article
Hybrid Uncertainty-Goal Programming Model with Scaled Index for Production Planning Assessment
by Junzo Watada, Nureize Binti Arbaiy and Qiuhong Chen
FinTech 2022, 1(1), 1-24; https://doi.org/10.3390/fintech1010001 - 23 Nov 2021
Viewed by 2054
Abstract
Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target [...] Read more.
Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts’ judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution. Full article
(This article belongs to the Special Issue Advanced Financial Technologies)
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