Stock Crash Risk Prediction With Implied Volatility Index: A Comparison of Tree-Based and Transformer-Based Models

Stock price crash prediction is important for both risk management and investment decisions. We build machine learning models that predict whether individual stocks will experience a maximum drawdown over 40% within 60 trading days. The dataset covers 2,812 Korean listed stocks from 2015 to 2024. We...

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Bibliografische Detailangaben

Hauptverfasser:	Heeseung Chung, Cheong Kim
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2026-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Market crash individual stock implied volatility index wavelet transform gradient boosting decision tree
Online-Zugang:	https://ieeexplore.ieee.org/document/11359683/
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