Classification of Financial Events and Its Effects on Other Financial Data

Abstract

This research classifies financial events, i.e., the collapse of the Lehman Brothers (2008) and the flash crash (2010), and their effects on two different stocks corresponding to Citigroup Inc. (2009) and Iamgold Corporation (2011) to verify if the market data of these years were affected more by the crashes of 2008 or 2010. Applying the four techniques, dynamic Fourier methodology, wavelet analysis, discriminant analysis, and clustering analysis, the empirical evidence suggests that the Lehman Brothers’ event is predictable since the dynamics of the dataset can be likened to that of a natural earthquake. On the other hand, the flash crash event is associated with unpredictable explosions. In addition, the dynamics of the stocks from Citigroup (2009) and Iamgold Corporation (2011) are similar to that of the Lehman Brothers collapse. Hence, they are predictable. The accurate classification of the two financial events might help mitigate some of the potential effects of the events. In addition, the methodologies used in this study can help identify the strength of crashes and help practitioners and researchers make informed decisions in the financial market.

Keywords

• discrete Fourier transform
• wavelets methodology
• discriminant analysis
• clustering analysis
• high-frequency financial data