Advanced search ?

Object

Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data
Download

Title: Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data

Title in english:

Syntetyczne dane finansowe: studium przypadku dla danych polskich spółek z ograniczoną odpowiedzialnością

Creator:

Szymura, Aleksandra

Description:

Econometrics = Ekonometria, 2024, Vol. 28, No. 2, s. 1-17

Abstrakt:

Aim: The aim of this article was to present and evaluate the concept of synthetic data. They are completely new, artificially generated data, but keep the statistical properties of real data. Due to the statistical similarity with real data, they can be used instead of them. This action allows data to be shared externally while guaranteeing their privacy. Methodology: New datasets were generated based on financial information about Polish limited liability companies, which come from the Orbis database and refer to 2020. To create synthetic data, it was decided to use generative models: CTGAN (based on GAN architecture) and TVAE (based on autoencoders). Lastly, the synthetic data were compared with the real ones in terms of statistical properties (e.g. shape of distributions, correlations etc.) and their applicability in data analysis (the PCA method). Results: The Overall Quality Score was higher for the data generated by TVAE, but after examining the results in more detail, it was seen that the data generated by CTGAN had a better quality in terms of keeping the statistical properties of the real data. Comparing the results of the PCA method, TVAE was better than CTGAN. In addition, the TVAE method was less time-consuming than CTGAN. Implications and recommendations: Before publishing the synthetic data externally, it is recommended that the data are generated using several algorithms, evaluating their final results and finally selecting the best option. This action enables the resulting dataset to be of the highest quality. In further research, it is proposed that other algorithms are tested (e.g. CopulaGAN or TableGAN), in an attempt to deal with some of the realistic data problems that were missed in this analysis, such as missing values (the work was carried out with a complete dataset). Data generated in this study may be used to build financial indicators, which in turn could be used to construct company assessment models. Originality/value: Synthetic data help to deal with some of the data limitations, such as data privacy or scarcity. Due to their statistical similarity with real data, it is possible to use them in advanced machine learning methods instead of real datasets. Analysis on high quality synthetic data allows conclusions similar to analysis on real data to be achieved, while retaining privacy and without publishing sensitive data to third parties.

Publisher:

Publishing House of Wroclaw University of Economics and Business

Place of publication:

Wroclaw

Date:

2024

Resource Type:

artykuł

Resource Identifier:

doi:10.15611/eada.2024.2.01 ; oai:dbc.wroc.pl:127165

Language:

eng

Relation:

Econometrics = Ekonometria, 2024, Vol. 28, No. 2

Rights:

Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy

Access Rights:

Dla wszystkich zgodnie z licencją

License:

CC BY-SA 4.0

Location:

Uniwersytet Ekonomiczny we Wrocławiu

Group publication title:

Ekonometria = Econometrics

Subject and keywords:

synthetic data generative models financial data CTGAN TVAE dane syntetyczne modele generatywne dane finansowe

Object collections:

Last modified:

Jul 24, 2024

In our library since:

Jul 24, 2024

Number of object content hits:

39

All available object's versions:

https://dbc.wroc.pl/publication/165811

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Edition name Date
Synthetic Financial Data: A Case Study Regarding Polish Limited Liability Companies Data Jul 24, 2024

Similar

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

A clinical efficiency index for the measurement of hospital performance: development and validation

Cygańska, Małgorzata

2019
artykuł
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

Jakość danych w systemach statystycznych banków centralnych (na przykładzie NBP)

Gatnar, Eugeniusz

2012
artykuł
Informatyka Ekonomiczna = Business Informatics
Informatyka Ekonomiczna = Business Informatics

Inteligentny Kokpit Menedżerski jako innowacyjny system wspomagający zarządzaniew MŚP

Korczak, Jerzy Dudycz, Helena Dyczkowski, Mirosław

2014
artykuł
Informatyka Ekonomiczna = Business Informatics
Informatyka Ekonomiczna = Business Informatics

Intelligent decision support for SME managers - project InKoM

Korczak, Jerzy Dudycz, Helena Dyczkowski, Mirosław

2012
artykuł
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

Weryfikacja oceny sytuacji finansowej przedsiębiorstwa po zmianie polityki rachunkowości

Buk, Halina

2016
artykuł
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

Gromadzenie danych z wykorzystaniem wymiarów finansowych

Chojnacka-Komorowska, Anna

2018
artykuł
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

Metody szacowania wewnątrzdziennej sezonowości w analizie danych finansowych pochodzących z pojedynczych transakcji

Huptas, Roman

2009
artykuł
Mathematical Economics
Mathematical Economics

The normality of financial data after an extraction of jumps in the jump-diffusion model

Gardoń, Albert

2011
artykuł
More
×

Citation

Citation style:

Contact details

Address

Wrocław University of Science and Technology (Coordinator)
Library of Wrocław University of Science and Technology
Wybrzeże Wyspiańskiego 27
50-370 Wrocław, Poland

Phone

(+48) 71-320-38-30
(+48) 71-320-47-50

E-Mail

bc@pwr.edu.pl

Visit us!

https://biblioteka.pwr.edu.pl/

This page uses 'cookies'. More information