Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk

Wisnowan Hendy Saputra; Dedy Dwi Prastyo; Heri Kuswanto

doi:10.1016/j.procs.2024.03.020

Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk

Wisnowan Hendy Saputra, Dedy Dwi Prastyo^*, Heri Kuswanto

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic.

Original language	English
Pages (from-to)	397-403
Number of pages	7
Journal	Procedia Computer Science
Volume	234
DOIs	https://doi.org/10.1016/j.procs.2024.03.020
Publication status	Published - 2024
Event	7th Information Systems International Conference, ISICO 2023 - Washington, United States Duration: 26 Jul 2023 → 28 Jul 2023

Keywords

Covid-19 pandemic
Financial growth at risk
Indonesian economic growth
Mixed-data sampling
Quantile regression neural network

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.procs.2024.03.020

Cite this

@article{fb058f8460da4406b3c6585aef78344d,

title = "Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk",

abstract = "Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic.",

keywords = "Covid-19 pandemic, Financial growth at risk, Indonesian economic growth, Mixed-data sampling, Quantile regression neural network",

author = "Saputra, {Wisnowan Hendy} and Prastyo, {Dedy Dwi} and Heri Kuswanto",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors. Published by Elsevier B.V.; 7th Information Systems International Conference, ISICO 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

year = "2024",

doi = "10.1016/j.procs.2024.03.020",

language = "English",

volume = "234",

pages = "397--403",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk

AU - Saputra, Wisnowan Hendy

AU - Prastyo, Dedy Dwi

AU - Kuswanto, Heri

PY - 2024

Y1 - 2024

N2 - Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic.

AB - Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic.

KW - Covid-19 pandemic

KW - Financial growth at risk

KW - Indonesian economic growth

KW - Mixed-data sampling

KW - Quantile regression neural network

UR - http://www.scopus.com/inward/record.url?scp=85193201818&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2024.03.020

DO - 10.1016/j.procs.2024.03.020

M3 - Conference article

AN - SCOPUS:85193201818

SN - 1877-0509

VL - 234

SP - 397

EP - 403

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 7th Information Systems International Conference, ISICO 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Machine Learning Modeling on Mixed-frequency Data for Financial Growth at Risk

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this