1. bookVolumen 62 (2016): Edición 1 (March 2016)
Detalles de la revista
Primera edición
22 Feb 2015
Calendario de la edición
4 veces al año
Acceso abierto

Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading

Publicado en línea: 19 Mar 2016
Volumen & Edición: Volumen 62 (2016) - Edición 1 (March 2016)
Páginas: 12 - 26
Recibido: 01 Oct 2015
Aceptado: 01 Feb 2016
Detalles de la revista
Primera edición
22 Feb 2015
Calendario de la edición
4 veces al año

1. Alexander, S. S. (1961). Price movements in speculative markets: Trends or random walk. Industrial Management Review, 2(2), 7–26.Search in Google Scholar

2. Alexander, S. S. (1964). Price movements in speculative markets: Trends or random walk, number 2. Industrial Management Review, 5(2), 25–46.Search in Google Scholar

3. Almenberg, J., & Dreber, A. (2012). Gender, stock market participation and financial literacy. Retrieved from http://swopec.hhs.se/hastef/papers/hastef0737.pdfhttp://dx.doi.org/10.2139/ssrn.1880909Search in Google Scholar

4. Alwan, L. C. (1991). Autocorrelation: Fixed versus variable control limits. Quality Engineering, 4(2), 167–188. http://dx.doi.org/10.1080/08982119108918904Search in Google Scholar

5. Alwan, L. C., & Roberts, H. V. (1988). Time-series modeling for statistical process control. Journal of Business and Economic Statistics, 6(1), 87–95. http://dx.doi.org/10.1080/07350015.1988.10509640Search in Google Scholar

6. Benić, V., & Franić, I. (2008). Stock market liquidity: Comparative analysis of Croatian and regional markets. Financial Theory and Practice, 32(4), 477–498.Search in Google Scholar

7. Best, M., & Neuhauser, D. (2006). Walter A. Shewhart, 1924, and the Hawthorne factory. Quality & Safety in Health Care, 15(2), 142–143. http://dx.doi.org/10.1136/qshc.2006.018093Search in Google Scholar

8. Bogan, V. (2008). Stock market participation and the internet. Journal of Financial and Quantitative Analysis, 43(1), 191–212. http://dx.doi.org/10.1017/S0022109000002799Search in Google Scholar

9. Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis, forecasting and control. San Francisco: Holden-Day.Search in Google Scholar

10. Box, G. E. P., Luceno, A., & Paniagua-Quinones, M. D. C. (2009). Statistical control by monitoring and adjustment. Hoboken, NJ: John Wiley & Sons. http://dx.doi.org/10.1002/9781118164532Search in Google Scholar

11. Caporale, G. M., Howells, P. G. A., & Soliman, A. M. (2004). Stock market development and economic growth: The causal linkage. Journal of Economic Development, 29(1), 33–50.Search in Google Scholar

12. Corrado, J. C., & Lee, S. H. (1992). Filter rule tests of the economic significance of serial dependencies in daily stock returns. Journal of Financial Research, 15(4), 369–387. http://dx.doi.org/10.1111/j.1475-6803.1992.tb00119.xSearch in Google Scholar

13. del Castillo, E. (2002). Statistical process adjustment for quality control. New York: John Wiley & Sons.Search in Google Scholar

14. Dryden, M. M. (1969). A source of bias in filter tests of share prices. Journal of Business, 42(3), 321–325.10.1086/295200Search in Google Scholar

15. Dumičić, K., & Žmuk, B. (2011a). Metode statističke kontrole kvalitete. In K. Dumičić & V. Bahovec (Eds.), Poslovna statistika (pp. 459–539). Zagreb: Element.Search in Google Scholar

16. Dumičić, K., & Žmuk, B. (2011b). Monitoring delivery time with control charts. In B. Katalinić (Ed.), Annals of DAAAM for 2011 & Proceedings of the 22nd DAAAM International World Symposium (pp. 1199–1200). Vienna: DAAAM International.10.2507/22nd.daaam.proceedings.584Search in Google Scholar

17. Fama, E. F., & Blume, M. E. (1965). Filter rules and stock-market trading. Journal of Business, 39(1), 226–241.Search in Google Scholar

18. Gandy, A. (2012). Performance monitoring of credit portfolios using survival analysis. International Journal of Forecasting, 28(1), 139–144. http://dx.doi.org/10.1016/j.ijforecast.2010.08.006Search in Google Scholar

19. Guiso, L., Sapienza, P., & Zingales, L. (2008). Trusting the stock market. The Journal of Finance, 63(6), 2557–2600. http://dx.doi.org/10.1111/j.1540-6261.2008.01408.xSearch in Google Scholar

20. Harris, T. J., & Ross, W. H. (1991). Statistical process control procedures for autocorrelated observations. Canadian Journal of Chemical Engineering, 69(1), 48–57. http://dx.doi.org/10.1002/cjce.5450690106Search in Google Scholar

21. Hubbard, C. L. (1967). A control chart for postwar stock price levels. Financial Analysts Journal, 23(6), 139–145. http://dx.doi.org/10.2469/faj.v23.n6.139Search in Google Scholar

22. Hunter, J. S. (1986). The exponentially weighted moving average. Journal of Quality Technology, 18(4), 203–210.10.1080/00224065.1986.11979014Search in Google Scholar

23. Hyndman, R. J. (2001). ARIMA processes. Retrieved from https://datajobs.com/data-science-repo/ARIMA-Intro-[Hyndman].pdfSearch in Google Scholar

24. Kovarik, M., & Klimek, P. (2012). The usage of time series control charts for financial process analysis. Journal of Competitiveness, 4(3), 29–45. http://dx.doi.org/10.7441/joc.2012.03.03Search in Google Scholar

25. Kovarik, M., & Sarga, L. (2014). Implementing control charts to corporate financial management. WSEAS Transactions on Mathematics, 13, 246–255.Search in Google Scholar

26. Levich, R. M., & Rizzo, R. C. (1998). Alternative tests for time series dependence based on autocorrelation coefficients. Retrieved from http://pages.stern.nyu.edu/~rlevich/wp/LR1.pdfSearch in Google Scholar

27. Lewellen, J. (2002). Momentum and autocorrelation in stock returns. The Review of Financial Studies, 15(2), 533–563. http://dx.doi.org/10.1093/rfs/15.2.533Search in Google Scholar

28. Lillo, F., & Farmer, J. D. (2004). The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics, 8(3), 1–35. http://dx.doi.org/10.2202/1558-3708.1226Search in Google Scholar

29. Liu, C. S., & Tien, F. C. (2011). An evaluation of single-featured EWMA-X (SFEWMA-X) control chart with process mean shifts and standard deviation changes. International Journal of Applied Science and Engineering, 9(2), 111–121.Search in Google Scholar

30. Lu, C. W., & Reynolds, M. R. (1999a). Control chart for monitoring the mean and variance of autocorrelated processes. Journal of Quality Technology, 31(3), 259–274.10.1080/00224065.1999.11979925Search in Google Scholar

31. Lu, C. W., & Reynolds, M. R. (1999b). EWMA control charts for monitoring the mean of autocorrelated processes. Journal of Quality Technology, 31(2), 166–188.10.1080/00224065.1999.11979913Search in Google Scholar

32. Lu, C. W., & Reynolds, M. R. (2001). CUSUM chart for monitoring an autocorrelated process. Journal of Quality Technology, 33(3), 316–334.10.1080/00224065.2001.11980082Search in Google Scholar

33. Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32(1), 1–12. http://dx.doi.org/10.1080/00401706.1990.10484583Search in Google Scholar

34. Manas, A. T. (2005). The increasing relevance of the stock market in the world: A new scenario. Retrieved from http://www2.uah.es/iaes/publicaciones/DT_01_05.pdfSearch in Google Scholar

35. McLeod, A. I., & Sales, P. R. H. (1983). Algorithm AS 191: An algorithm for approximate likelihood calculation of ARMA and seasonal ARMA models. Applied Statistics, 32(2), 211–223. http://dx.doi.org/10.2307/2347301Search in Google Scholar

36. McNeese, W., & Wilson, W. (2002). Using time series charts to analyse financial data. Retrieved from http://www.spcforexcel.com/files/timeseriesfinancial.pdfSearch in Google Scholar

37. Montgomery, D. C. (2013). Statistical quality control: A modern introduction. Singapore: John Wiley & Sons.Search in Google Scholar

38. 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 (pp. 67–88). New York: Marcel Dekker.Search in Google Scholar

39. Montgomery, D. C., Jennings, C. L., & Pfund, M. E. (2011). Managing, controlling, and improving quality. Hoboken, NJ: John Wiley & Sons.Search in Google Scholar

40. Montgomery, D. C., & Runger, G. C. (2011). Applied statistics and probability for engineers. Hoboken, NJ: John Wiley & Sons.Search in Google Scholar

41. Moskowitz, H., Wardell, D. G., & Plante, R. D. (1994). Run-length distributions of special-cause control charts for correlated processes. Technometrics, 36(1), 3–27. http://dx.doi.org/10.1080/00401706.1994.10485393Search in Google Scholar

42. NIST/SEMATECH. (2013). EWMA control charts. Retrieved from http://www.itl.nist.gov/div898/handbook/pmc/section3/pmc324.htmSearch in Google Scholar

43. Noskievičová, D. (2007). Control chart limits setting when data are autocorrelated. Retrieved from http://www.ep.liu.se/ecp/026/120/ecp0726120.pdfSearch in Google Scholar

44. Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41(1–2), 100–115. http://dx.doi.org/10.1093/biomet/41.1-2.100Search in Google Scholar

45. Rebisz, B. (2015). Appliance of quality control charts for sovereign risk modelling. Journal of Applied Economics and Business Research, 5(3), 148–160.Search in Google Scholar

46. Riaz, M., Abbas, N., & Does, R. J. M. M. (2011). Improving the performance of CUSUM charts. Quality and Reliability Engineering International, 27(4), 415–424. http://dx.doi.org/10.1002/qre.1124Search in Google Scholar

47. Roberts, H. V. (1959). Stock market “patterns” and financial analysis: Methodological suggestions. Journal of Finance, 14(1), 1–10. http://dx.doi.org/10.1111/j.1540-6261.1959.tb00481.xSearch in Google Scholar

48. Roberts, S. W. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239–250. http://dx.doi.org/10.1080/00401706.1959.10489860Search in Google Scholar

49. Ryu, J. H., Wan, H., & Kim, S. (2010). Optimal design of a CUSUM chart for a mean shift of unknown size. Journal of Quality Technology, 42(3), 311–326.10.1080/00224065.2010.11917826Search in Google Scholar

50. SAS Institute. (2014). Statistical details for CUSUM control charts. Retrieved from http://www.jmp.com/support/help/Statistical_Details_for_CUSUM_Control_Charts.shtmlSearch in Google Scholar

51. Schmid, W. (1995). On the run length of a Shewhart chart for correlated data. Statistical Papers, 36(1), 111–130. http://dx.doi.org/10.1007/BF02926025Search in Google Scholar

52. Schmid, W., & Schone, A. (1997). Some properties of the EWMA control chart in presence of autocorrelation. Annals of Statistics, 25(3), 1277–1283. http://dx.doi.org/10.1214/aos/1069362748Search in Google Scholar

53. Sewell, M. (2011). Characterization of financial time series. Retrieved from http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/images/Research_Student_Information/RN_11_01.pdfSearch in Google Scholar

54. Sullivan, R., Timmermann, A., & White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. The Journal of Finance, 54(5), 1647–1691. http://dx.doi.org/10.1111/0022-1082.00163Search in Google Scholar

55. Sweeney, J. R. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285–300. http://dx.doi.org/10.2307/2331068Search in Google Scholar

56. Tachiwou, A. M. (2010). Stock market development and economic growth: The case of West African Monetary Union. International Journal of Economics and Finance, 2(3), 97–103. http://dx.doi.org/10.5539/ijef.v2n3p97Search in Google Scholar

57. Tolvi, J. (2002). Outliers and predictability in monthly stock market index returns. Finnish Journal of Business Economics, 6(4), 369–380.Search in Google Scholar

58. van Rooij, M., Lusardi, A., & Alessie, R. (2011). Financial literacy and stock market participation. Journal of Financial Economics, 101(2), 449–472. http://dx.doi.org/10.1016/j.jfineco.2011.03.006Search in Google Scholar

59. Vanbrackle, L. N., & Reynolds, M. R. (1997). EWMA and CUSUM control charts in the presence of correlation. Communications in Statistics—Simulation and Computation, 26(3), 979–1008. http://dx.doi.org/10.1080/03610919708813421Search in Google Scholar

60. Vasipoulos, A. V., & Stamboulis, A. P. (1978). Modification of control chart limits in the presence of data correlation. Journal of Quality Technology, 10(1), 20–30.10.1080/00224065.1978.11980809Search in Google Scholar

61. Venkataramani, C. (2003). Random walk hypotheses and profitability of momentum based trading rules. Retrieved from http://www-stat.wharton.upenn.edu/~steele/HoldingPen/Mouli's%20Dissertation.pdfSearch in Google Scholar

62. Wild, C. J., & Seber, G. A. F. (1999). Chance encounters: A first course in data analysis and inference. New York: Wiley.Search in Google Scholar

63. Woodall, W. H., & Faltin, F. W. (1993). Autocorrelated data and SPC. ASQC Statistics Division Newsletter, 13(4), 18–21.Search in Google Scholar

64. Zagreb Stock Exchange. (2014a). 2013 trading summary. Retrieved from http://zse.hr/UserDocsImages/reports/ZSE-2013-eng.pdfSearch in Google Scholar

65. Zagreb Stock Exchange. (2014b). Historical overview. Retrieved from http://zse.hr/default.aspx?id=32877Search in Google Scholar

66. Zagreb Stock Exchange. (2014c). Index CROBEX. Retrieved from http://zse.hr/default.aspx?id=44102&index=CROBEXSearch in Google Scholar

67. Zagreb Stock Exchange. (2014d). Index CROBEX10. Retrieved from http://zse.hr/default.aspx?id=44102&index=CROBEX10Search in Google Scholar

68. Zagreb Stock Exchange. (2014e). Indices. Retrieved from http://zse.hr/default.aspx?id=43539Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo