问题描述：
英语翻译
Abstract
This study proposes an early warning system (EWS) for detection of financial crisis with a daily financial condition indicator (DFCI) designed to monitor the financial markets and provide warning signals.The proposed EWS differs from other commonly used EWSs in two aspects:(i) it is based on dynamic daily movements of the financial markets; and (ii) it is established as a pattern classifier,which identifies predefined unstable states in terms of financial market volatility.Indeed it issues warning signals on a daily basis by judging whether the financial market has entered a predefined unstable state or not.The major strength of a DFCI is that it can issue timely warning signals while other conventional EWSs must wait for the next round input of monthly or quarterly information.Construction of a DFCI consists of two steps where machine learning algorithms are expected to play a significant role.i.e.(i) establishing sub-DFCIs on various daily financial variables by an artificial neural network,and (ii) integrating the sub-DFCIs into an integrated DFCI by a genetic algorithm.The DFCI for the Korean financial market is built as an empirical case study.
Abstract
This study proposes an early warning system (EWS) for detection of financial crisis with a daily financial condition indicator (DFCI) designed to monitor the financial markets and provide warning signals.The proposed EWS differs from other commonly used EWSs in two aspects:(i) it is based on dynamic daily movements of the financial markets; and (ii) it is established as a pattern classifier,which identifies predefined unstable states in terms of financial market volatility.Indeed it issues warning signals on a daily basis by judging whether the financial market has entered a predefined unstable state or not.The major strength of a DFCI is that it can issue timely warning signals while other conventional EWSs must wait for the next round input of monthly or quarterly information.Construction of a DFCI consists of two steps where machine learning algorithms are expected to play a significant role.i.e.(i) establishing sub-DFCIs on various daily financial variables by an artificial neural network,and (ii) integrating the sub-DFCIs into an integrated DFCI by a genetic algorithm.The DFCI for the Korean financial market is built as an empirical case study.
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