Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting

Kyong Joo Oh, Tae Yoon Kim, Chiho Kim, Suk Jun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Recently, Oh et al. [11, 12] developed a daily financial condition indicator (DFCI) which issues an early warning signal based on the daily monitoring of financial market volatility. The major strength of DFCI is that it is expected to serve as a quite useful early warning system (EWS) for the new type of crisis which starts as an instability of the financial markets and then develops into a major crisis (e.g., 1997 Asian crises). One of the problems with DFCI is that it may show a high degree of fluctuation because it handles daily variable, and this may harm its reliability as an EWS. The main purpose of this article is to propose and discuss a way of smoothing DFCI, i.e., it will be tuned using long-term (monthly or quarterly) fundamental economic variables. It turns out that such a tuning procedure could reveal influential macroeconomic variables on financial markets. Since tuning DFCI is done by the method of fitting various types of data simultaneously, neural networks are employed. Tuning the DFCI for the Korean financial market is given as an empirical example.

Original languageEnglish
Title of host publicationAI 2006
Subtitle of host publicationAdvances in Artificial Intelligence - 19th Australian Joint Conference on Artificial Intelligence, Proceedings
Pages607-616
Number of pages10
DOIs
Publication statusPublished - 2006 Dec 1
Event19th Australian Joint Conference onArtificial Intelligence, AI 2006 - Hobart, TAS, Australia
Duration: 2006 Dec 42006 Dec 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4304 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th Australian Joint Conference onArtificial Intelligence, AI 2006
CountryAustralia
CityHobart, TAS
Period06/12/406/12/8

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Oh, K. J., Kim, T. Y., Kim, C., & Lee, S. J. (2006). Using neural networks to tune the fluctuation of daily financial condition indicator for financial crisis forecasting. In AI 2006: Advances in Artificial Intelligence - 19th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 607-616). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4304 LNAI). https://doi.org/10.1007/11941439-65