Kontaktujte nás | Jazyk: čeština English
dc.title | Usage of control charts for time series analysis in financial management | en |
dc.contributor.author | Kovářík, Martin | |
dc.contributor.author | Sarga, Libor | |
dc.contributor.author | Klímek, Petr | |
dc.relation.ispartof | Journal of Business Economics and Management | |
dc.identifier.issn | 1611-1699 Scopus Sources, Sherpa/RoMEO, JCR | |
dc.date.issued | 2015 | |
utb.relation.volume | 16 | |
utb.relation.issue | 1 | |
dc.citation.spage | 138 | |
dc.citation.epage | 158 | |
dc.type | article | |
dc.language.iso | en | |
dc.publisher | Vilnius Gediminas Technical University - Taylor and Francis Inc. | |
dc.identifier.doi | 10.3846/16111699.2012.732106 | |
dc.relation.uri | https://journals.vgtu.lt/index.php/JBEM/article/view/2716 | |
dc.relation.uri | http://www.tandfonline.com/doi/abs/10.3846/16111699.2012.732106 | |
dc.subject | autocorrelation | en |
dc.subject | control chart CUSUM | en |
dc.subject | statistical process control | en |
dc.subject | control chart ARIMA | en |
dc.subject | Shewhart's control charts | en |
dc.subject | control chart EWMA | en |
dc.description.abstract | We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951-1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest. | en |
utb.faculty | Faculty of Management and Economics | |
dc.identifier.uri | http://hdl.handle.net/10563/1004116 | |
utb.identifier.obdid | 43872080 | |
utb.identifier.scopus | 2-s2.0-84925935959 | |
utb.identifier.wok | 000346357900008 | |
utb.source | j-wok | |
dc.date.accessioned | 2015-01-29T11:35:05Z | |
dc.date.available | 2015-01-29T11:35:05Z | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.rights.access | openAccess | |
utb.contributor.internalauthor | Kovářík, Martin | |
utb.contributor.internalauthor | Sarga, Libor | |
utb.contributor.internalauthor | Klímek, Petr | |
utb.fulltext.affiliation | Martin KOVÁŘÍK1, Libor SARGA2, Petr KLÍMEK3 Department of Statistics and Quantitative Methods, Faculty of Management and Economics, Tomas Bata University, Mostní 5139, 760 01 Zlín, Czech Republic E-mails: 1kovarik.fame@seznam.cz; 2sarga@fame.utb.cz (corresponding author); 3klimek@fame.utb.cz Martin KOVÁŘÍK, PhD, graduated at the Faculty of Management and Economics, Tomas Bata University in Zlin, where he is lecturing at the Department of Statistics and Quantitative Methods since 2009. He also graduated at Faculty of Applied Informatics, Tomas Bata University in Zlin in the field of Information Technology. Author and co-author of 7 books and 5 lecture notes, his research is focused on mathematical and statistical methods in quality management and computationally-intensive statistical data analyses with results published in numerous peer-reviewed journals and presented at conferences in the Czech Republic as well as internationally. Martin Kovářík is also a consultant of statistical data analysis, application of statistical methods in quality management and questionnairebased surveys data processing. Libor SARGA, Ing., is a PhD candidate at the Department of Statistics and Quantitative Methods, Faculty of Management and Economics, Tomas Bata University in Zlin, Czech Republic. His professional interests include information technology, data security policies, and quantitative data processing. His dissertation will focus on setting a suitable data security framework in organizations. Petr KLÍMEK, Associate Professor, is a university teacher and a scientific researcher currently with the Department of Statistics and Quantitative Methods, Faculty of Management and Economics, Tomas Bata University in Zlín. He is the author or co-author of many publications in the field of statistical data analysis in books, teaching scripts, and scientific articles. | |
utb.fulltext.dates | Received 10 July 2012; accepted 17 September 2012 | |
utb.fulltext.references | Atienza, O. O.; Tang, L. C.; Ang, B. W. 1997. ARL properties of a sample autocorrelation chart, Computers and Industrial Engineering 33(3–4): 733–736. http://dx.doi.org/10.1016/S0360-8352(97)00234-9 Alwan, L. C. 1992. Effects of autocorrelation on control chart performance, Communications in Statistics – Theory and Methods 21(4): 1025–1049. http://dx.doi.org/10.1080/03610929208830829 Apley, D. W.; Tsung, F. 2002. The autoregressive T2 chart for monitoring univariate autocorrelated processes, Journal of Quality Technology 34(1): 80–96. Chambers, D. S.; Wheeler, D. J. 1992. Understanding statistical process control. 2nd ed. Tennessee: SPC Press, Inc. Chandra, M. J. 2001. Statistical quality control. Florida: CRC Press, LLC. Dyer, J. N.; Conerly, D. M.; Adams, B. M. 2003. A simulation study and evaluation of multivariate forecast based control charts applied to ARMA processes, Journal of Statistical Computation and Simulation 73(10): 709–724. http://dx.doi.org/10.1080/0094965031000062168 Gervini, D. 2003. A robust and efficient adaptive reweighted estimator of multivariate location and scatter, Journal of Multivariate Analysis 84(1): 116–144. http://dx.doi.org/10.1016/S0047-259X(02)00018-0 Grubbs, F. E. 1969. Procedures for detecting outlying observations in samples, Technometrics 11(1): 1–21. http://dx.doi.org/10.1080/00401706.1969.10490657 Harris, T. J.; Ross, W. H. 1991. Statistical process control procedures for correlated observations, Canadian Journal of Chemical Engineering 69(1): 48–57. http://dx.doi.org/10.1002/cjce.5450690106 Hušek, R. 2007. Ekonometrická analýza [Econometric analysis]. Praha: Oeconomica. Kovarik, M.; Kral, M. 2011. Carry trade. 1st ed. Zilina: Georg, Inc. Kovarik, M. 2012. Usage of control charts and stochastic differential equations for volatility change point detection in time series. 1st ed. Zilina: Georg, Inc. Kovarik, M. 2013a. Research in the field of monitoring autocorrelated processes using of control charts. 1st ed. Zilina: Georg, Inc. Kovarik, M. 2013b. Volatility change point detection using stochastic differential equations and time series control charts, International Journal of Mathematical Models and Methods in Applied Sciences 2(7): 121–132. Kovarik, M.; Klimek, P. 2013. Survey and analysis of the use statistical process control methods in selected Czech manufacturing companies, International Journal of Mathematical Models and Methods in Applied Sciences 4(7): 358–369. Kovarik, M.; Sarga, L. 2014. Implementing control charts to corporate financial management, WSEAS Transactions on Mathematics 13: 246–255. Krieger, C. A.; Champ, C. W.; Alwan, L. C. 1992. Monitoring an autocorrelated process. Presented at the Pittsburgh Conference on Modeling and Simulation, Pittsburgh, PA. Lu, C. W.; Reynolds, M. R. 1999a. EWMA control charts for monitoring the mean of autocorrelated processes, Journal of Quality Technology 31(2): 166–188. Lu, C. W.; Reynolds, M. R. 1999b. Control charts for monitoring the mean and variance of autocorrelated processes, Journal of Quality Technology 31(3): 259–274. Meloun, M.; Militký, J. 2006. Kompendium statistického zpracování dat [Statistical data processing compendium]. Praha: Academia. Montgomery, D. C, Friedman, D. J. 1989. Statistical process control in computer integrated manufacturing environment, in J. B. Keats, N. F. Hubele (Eds.). Statistical process control in automated manufacturing. New York: Marcel Dekker. Montgomery, D. C.; Mastrangelo, C. M. 1991. Some statistical process control methods for autocorrelated data, Journal of Quality Technology 23(3): 179–204. Noskievičová, D. 2008. Vybrané metody statistické regulace procesu pro autokorelovaná data [Selected methods for statistical process regulation with autocorrelated data], AUTOMA 9(10): 40–43. Runger, G. C.; Willemain, T. R. 1996. Batch-means control charts for autocorrelated data, IIE Transactions 28(6): 483–487. Sun, J.; Xu, L. 2004. Batch average control chart, ASQ Annual Quality Congress Proceedings, 24–26 May 2004, Toronto, Ontario 58: 85–96. Yourstone, S. A.; Montgomery, D. C. 1991. Detection of process upsets – sample autocorrelation control chart and group autocorrelation control chart applications, Quality and Reliability Engineering International 7(3): 133–140. http://dx.doi.org/10.1002/qre.4680070304 | |
utb.fulltext.sponsorship | - | |
utb.fulltext.projects | - |