Designing an Early Warning System for Stock Market Crashes by Using ANFIS

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Author(s): Murat Acar (ISE Settlement and Custody Bank Inc., Turkey), Dilek Karahoca (Bahcesehir University, Turkey)and Adem Karahoca (Bahcesehir University, Turkey)
Copyright: 2011
Pages: 19
Source title: Surveillance Technologies and Early Warning Systems: Data Mining Applications for Risk Detection
Source Author(s)/Editor(s): Ali Serhan Koyuncugil (Capital Markets Board of Turkey, Turkey, and Baskent University, Turkey)and Nermin Ozgulbas (Baskent University, Turkey)
DOI: 10.4018/978-1-61692-865-0.ch006

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

This chapter focuses on building a financial early warning system (EWS) to predict stock market crashes by using stock market volatility and rising stock prices. The relation of stock market volatility with stock market crashes is analyzed empirically. Also, Istanbul Stock Exchange (ISE) national 100 index data used to achieve better results from the view point of modeling purpose. A risk indicator of stock market crash is computed to predict crashes and to give an early warning signal. Various data mining classifiers are compared to obtain the best practical solution for the financial early warning system. Adaptive neuro fuzzy inference system (ANFIS) model was proposed to forecast stock market crashes efficiently. Also, ANFIS was explained in detail as a training tool for the EWS. The empirical results show that the fuzzy inference system has advantages to gain successful results for financial crashes.

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Designing an Early Warning System for Stock Market Crashes by Using ANFIS

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