{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:47:56Z","timestamp":1767340076271,"version":"build-2065373602"},"reference-count":12,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T00:00:00Z","timestamp":1681344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>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\u2019 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.<\/jats:p>","DOI":"10.3390\/axioms12040372","type":"journal-article","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T04:33:26Z","timestamp":1681360406000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Classification of Financial Events and Its Effects on Other Financial Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Maria C.","family":"Mariani","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences and Computational Science Program, University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0180-9464","authenticated-orcid":false,"given":"Osei K.","family":"Tweneboah","sequence":"additional","affiliation":[{"name":"Department of Data Science, Ramapo College of New Jersey, Mahwah, NJ 07430, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7927-0673","authenticated-orcid":false,"given":"Md Al Masum","family":"Bhuiyan","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Austin Peay State University, Clarksville, TN 37044, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria P.","family":"Beccar-Varela","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4225-9008","authenticated-orcid":false,"given":"Ionut","family":"Florescu","sequence":"additional","affiliation":[{"name":"School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"ref_1","unstructured":"Luhby, T. 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A Stat. Mech. Appl."},{"key":"ref_5","unstructured":"Vuorenmaa, T.A. (2005). Noise and Fluctuations in Econophysics and Finance, SPIE. Bank of Finland Research Discussion Paper No. 27\/2005."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.18267\/j.pep.439","article-title":"Interdependence between some major European stock markets-A wavelet lead\/lag analysis","volume":"22","author":"Dajcman","year":"2013","journal-title":"Prague Econ. Pap."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shumway, R.H., and Stoffer, D.S. (2010). Time Series Analysis and Its Applications with R Examples, Springer.","DOI":"10.1007\/978-1-4419-7865-3"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cryer, J.D., and Chan, K.S. (2008). Time Series Analysis with its Applications in R, Springer.","DOI":"10.1007\/978-0-387-75959-3"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kaufman, L., and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons.","DOI":"10.1002\/9780470316801"},{"key":"ref_10","first-page":"39","article-title":"The Lehman Brothers Bankruptcy A: Overview","volume":"1","author":"Wiggins","year":"2019","journal-title":"J. Financ. Cris."},{"key":"ref_11","unstructured":"NANEX (2022, December 15). 2010 Flash Crash Analysis, Final Conclusion. Available online: http:\/\/www.nanex.net\/FlashCrashFinal\/FlashCrashAnalysis_Theory.html."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0304-4076(92)90104-Y","article-title":"Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root","volume":"54","author":"Kwiatkowski","year":"1992","journal-title":"J. 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