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BDCC
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Computational Finance and Big Data Analytics
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Computational Finance and Big Data Analytics

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 2067

Special Issue Editor

Dr. Chuan-Ju Wang

E-Mail Website
Guest Editor
Research Center for Information Technology Innovation, Academia Sinica, Taipei 106, Taiwan
Interests: computational finance; data analytics

Special Issue Information

Dear Colleagues,

The vast amount of data in today’s environment makes it increasingly important to determine how to discover useful insights for improved decision making. These insights can result in the ability to take advantage of opportunities, minimize risks, and control costs. Big data analytics refers to techniques for exploring, discovering, and making data-driven decisions in the context of abundant data, which have been widely employed to analyze business, financial, economic, and e-commerce data, with recently developed machine learning techniques. This Special Issue in the International Journal of Big Data and Cognitive Computing aims for publishing high-quality research and innovation results in all areas related to big data analytics in economics and finance.

The purpose of this Special Issue is to report and promote the latest progress in advancing specific techniques and methodologies and/or making relevant case studies.

This issue includes, but is not limited to, the following topics:

  • Financial data analytics;
  • Econometric analysis;
  • Risk management;
  • Computation and simulation;
  • E-commerce;
  • Risk and regulation;
  • Financial engineering;
  • Insurance;
  • Multivariate analysis;
  • Time series analysis;
  • Statistical modelling and inference.

Dr. Chuan-Ju Wang
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. Big Data and Cognitive Computing 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 1800 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

  • financial data analytics
  • computation
  • simulation
  • statistical modelling

Published Papers (1 paper)

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Research

22 pages, 727 KiB  
Article
Semi-Supervised Classification with A*: A Case Study on Electronic Invoicing
by Bernardo Panichi and Alessandro Lazzeri
Big Data Cogn. Comput. 2023, 7(3), 155; https://doi.org/10.3390/bdcc7030155 - 20 Sep 2023
Viewed by 1495
Abstract
This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine [...] Read more.
This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine learning holds promise for such repetitive tasks, the presence of low-quality training data often poses a hurdle. Frequently, labels pertain to invoice rows at a group level rather than an individual level, leading to the exclusion of numerous records during preprocessing. To enhance the efficiency of an invoice entry classifier within a semi-supervised context, this study proposes an innovative approach that combines the classifier with the A* graph search algorithm. Through experimentation across various classifiers, the results consistently demonstrated a noteworthy increase in accuracy, ranging between 1% and 4%. This improvement is primarily attributed to a marked reduction in the discard rate of data, which decreased from 39% to 14%. This paper contributes to the literature by presenting a method that leverages the synergy of a classifier and A* graph search to overcome challenges posed by limited and group-level label information in the realm of electronic invoicing classification. Full article
(This article belongs to the Special Issue Computational Finance and Big Data Analytics)
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