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Seçili Hisse Senedi Piyasalarındaki Teknoloji Endeksleri Arasındaki İlişkilerinin Johansen Eşbütünleşme Yöntemiyle İncelenmesi

Yıl 2025, Cilt: 10 Sayı: 3, 995 - 1017
https://doi.org/10.25229/beta.1609928

Öz

Yeni teknolojik ürün ve yöntemler, verimliliği artırıcı etki yaratabilmelerinin yanında iş süreçlerini değişime zorlamaktadırlar. Küreselleşme süreci, güncel teknolojilerin geliştirilmesi ve kullanımının yayılım hızını artmış ve etki alanı derinleşmiştir. Finans sektöründeki iş süreçleri ve finansal varlıklar bu gelişmelerden önemli derecede etkilenmektedirler. Ayrıca teknoloji şirketlerine ait hisse senetlerinin son yıllarda anlamlı derecede değer kazanmaları yatırımcı ve araştırmacıların ilgisini çekmektedir. Bu çalışmanın amacı, farklı piyasalardaki teknoloji şirketlerine ait hisse senetlerinin değerleri üzerinden hesaplanan endeksler arasında eşbütünleşme ilişkisinin var olup olmadığının araştırılmasıdır. Amerika Birleşik Devletleri NASDAQ 100 Technology TR (NTTR), Avrupa Birliği piyasalarından iShares STOXX Europe 600 Technology UCITS (SX8PEX), Birleşik Krallık FTSE TechMARK All Share (FTTASX) endeksleri incelenmek üzere seçilmiştir. Seçili endekslerin 2018 Aralık ile 2024 Şubat arasındaki dönemdeki aylık kapanış değerleri üzerinden zaman serileri oluşturulmuştur. Birim kök testi sonuçları serilerin tamamının durağanlık derecelerinin 1 (I=1) olduğunu göstermiştir. Seriler arasındaki muhtemel eşbütünleşme ilişkileri Johansen Eşbütünleşme yöntemi ile araştırılmış ve sınama testleri gerçekleştirilmiştir. Çalışmanın sonucunda seçili teknoloji endeksleri serileri arasında anlamlı bir eşbütünleşme ilişkisinin var olmadığı tespit edilmiştir.

Kaynakça

  • Adekoya, O. B., & Adewuyi, J. (2022). Oil and multinational technology stocks: Predicting fear with fear at the first and higher order moments. Finance Research Letters, 46, 102210. https://doi.org/10.1016/j.frl.2021.102210
  • Ben Rejeb, A., & Arfaoui, M. (2016). Financial market interdependencies: A quantile regression analysis of volatility spillover. Research in International Business and Finance, 36, 140–157. https://doi.org/10.1016/j.ribaf.2015.09.022
  • Breidbach, C. F., & Wirtz, B. (2020). Fintech: Research directions to explore the digital transformation of financial service systems. Journal of Service Theory and Practice, 30(1), 79–102. https://doi.org/10.1108/JSTP-08-2018-0185
  • Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(1), 334–355. https://doi.org/10.1111/j.1467-8454.1978.tb00635.x
  • Chu, J., Chan, S., & Zhang, Y. (2021). Bitcoin versus high-performance technology stocks in diversifying against global stock market indices. Physica A: Statistical Mechanics and Its Applications, 580, 126161. https://doi.org/10.1016/j.physa.2021.126161
  • Elliott, G., Rothenberg, T., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836. https://doi.org/10.2307/2171846
  • Emsbo-Mattingly, L., & Hofschire, D. (2017, January 13). The business cycle approach to equity sector investing. Fidelity Investments. https://www.fidelity.com/webcontent/ap101883-markets_sectors-content/18.01.0/business_cycle/Business_Cycle_Sector_Approach.pdf
  • Feltham, G. A., & Ohlson, J. A. (1995). Valuation and clean surplus accounting for operating and financial activities. Contemporary Accounting Research, 11(2), 661–688. https://doi.org/10.1111/j.1911-3846.1995.tb00462.x
  • Forsythe, G. E., & Straus, T. (1952). An extension of Gauss’ transformation for improving the condition of systems of linear equations. Mathematics of Computation, 6(37), 18–34. https://doi.org/10.1090/S0025-5718-1952-0048162-0
  • Gharb, S., Suret, J.-M., & Siala, M. (2014). R&D investments and high-tech firms' stock return volatility. Technological Forecasting and Social Change, 88, 306–312. https://doi.org/10.1016/j.techfore.2013.10.006
  • Govindaraju, C., & Wong, C.-Y. (2012). Technology stocks and economic performance of government-linked companies: The case of Malaysia. Technological and Economic Development of Economy, 18(2), 248–261. https://doi.org/10.3846/20294913.2012.688313
  • Hacker, R. S., & Hatemi-J, A. (2008). Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH. Journal of Applied Statistics, 35(6), 601–615. https://doi.org/10.1080/02664760801920473
  • Hatemi-J, A. (2004). Multivariate tests for autocorrelation in the stable and unstable VAR models. Economic Modelling, 21(4), 661–683. https://doi.org/10.1016/j.econmod.2003.09.005
  • Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
  • Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580. https://doi.org/10.2307/2938278
  • Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Econometric Theory, 14(4), 517–524. https://doi.org/10.1017/S0266466600007123
  • Kinateder, H., Campbell, R., & Ratti, R. (2021). Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets. Finance Research Letters, 43, 101951. https://doi.org/10.1016/j.frl.2021.101951
  • Kocaarslan, B., & Soytas, U. (2019). Dynamic correlations between oil prices and the stock prices of clean energy and technology firms: The role of reserve currency (US dollar). Energy Economics, 84, 104502. https://doi.org/10.1016/j.eneco.2019.104502
  • Kwon, S. S. (2002). Financial analysts' forecast accuracy and dispersion: High-tech versus low-tech stocks. Review of Quantitative Finance and Accounting, 19(1), 65–91. https://doi.org/10.1023/A:1015730325706
  • Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the March 2020 stock market crash: Evidence from S&P 1500. Finance Research Letters, 38, 101690. https://doi.org/10.1016/j.frl.2020.101690
  • Niu, H. (2021). Correlations between crude oil and stock prices of renewable energy and technology companies: A multiscale time-dependent analysis. Energy, 221, 119800. https://doi.org/10.1016/j.energy.2021.119800
  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
  • Qiao, Z., Wong, R., & Kutan, A. M. (2008). Volatility switching and regime interdependence between information technology stocks 1995–2005. Global Finance Journal, 19(2), 139–156. https://doi.org/10.1016/j.gfj.2008.01.003
  • Rašiová, B., & Árvai, P. (2023). Copula approach to market volatility and technology stocks dependence. Finance Research Letters, 52, 103553. https://doi.org/10.1016/j.frl.2022.103553
  • Sadorsky, P. (2003). The macroeconomic determinants of technology stock price volatility. Review of Financial Economics, 12(2), 191–205. https://doi.org/10.1016/S1058-3300(02)00071-X
  • Tiwari, A. K., & Jondeau, E. (2023). Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis. Energy Economics, 118, 106498. https://doi.org/10.1016/j.eneco.2022.106498
  • White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934

Investigation of the Relationship between Technology Indices on Selected Stock Markets Using Johansen Cointegration Test

Yıl 2025, Cilt: 10 Sayı: 3, 995 - 1017
https://doi.org/10.25229/beta.1609928

Öz

New technological products and methods not only have the potential to increase efficiency but also force business processes to change. Globalization has increased the speed of development and dissemination of current technologies, and their impact has deepened. Business processes and financial assets in the finance sector are significantly affected by these developments. Additionally, the significant increase in the value of technology company stocks in recent years has attracted the attention of investors and researchers. The aim of this study is to investigate whether there is a cointegration relationship among the indices formed by the values of technology company stocks in different markets. The indices selected for examination are the NASDAQ 100 Technology TR (NTTR) from the United States, the iShares STOXX Europe 600 Technology UCITS (SX8PEX) from the European Union markets, and the FTSE TechMARK All Share (FTTASX) from the United Kingdom. Time series have been created based on the monthly closing values of selected indices between December 2018 and February 2024. The unit root test results showed that all series have a degree of stationarity of 1 (I=1). The potential cointegration relationships between the series were investigated using the Johansen Cointegration method, and specification tests were conducted. As a result of the study, it was determined that there is no significant cointegration relationship among the selected technology index series.

Kaynakça

  • Adekoya, O. B., & Adewuyi, J. (2022). Oil and multinational technology stocks: Predicting fear with fear at the first and higher order moments. Finance Research Letters, 46, 102210. https://doi.org/10.1016/j.frl.2021.102210
  • Ben Rejeb, A., & Arfaoui, M. (2016). Financial market interdependencies: A quantile regression analysis of volatility spillover. Research in International Business and Finance, 36, 140–157. https://doi.org/10.1016/j.ribaf.2015.09.022
  • Breidbach, C. F., & Wirtz, B. (2020). Fintech: Research directions to explore the digital transformation of financial service systems. Journal of Service Theory and Practice, 30(1), 79–102. https://doi.org/10.1108/JSTP-08-2018-0185
  • Breusch, T. S. (1978). Testing for autocorrelation in dynamic linear models. Australian Economic Papers, 17(1), 334–355. https://doi.org/10.1111/j.1467-8454.1978.tb00635.x
  • Chu, J., Chan, S., & Zhang, Y. (2021). Bitcoin versus high-performance technology stocks in diversifying against global stock market indices. Physica A: Statistical Mechanics and Its Applications, 580, 126161. https://doi.org/10.1016/j.physa.2021.126161
  • Elliott, G., Rothenberg, T., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836. https://doi.org/10.2307/2171846
  • Emsbo-Mattingly, L., & Hofschire, D. (2017, January 13). The business cycle approach to equity sector investing. Fidelity Investments. https://www.fidelity.com/webcontent/ap101883-markets_sectors-content/18.01.0/business_cycle/Business_Cycle_Sector_Approach.pdf
  • Feltham, G. A., & Ohlson, J. A. (1995). Valuation and clean surplus accounting for operating and financial activities. Contemporary Accounting Research, 11(2), 661–688. https://doi.org/10.1111/j.1911-3846.1995.tb00462.x
  • Forsythe, G. E., & Straus, T. (1952). An extension of Gauss’ transformation for improving the condition of systems of linear equations. Mathematics of Computation, 6(37), 18–34. https://doi.org/10.1090/S0025-5718-1952-0048162-0
  • Gharb, S., Suret, J.-M., & Siala, M. (2014). R&D investments and high-tech firms' stock return volatility. Technological Forecasting and Social Change, 88, 306–312. https://doi.org/10.1016/j.techfore.2013.10.006
  • Govindaraju, C., & Wong, C.-Y. (2012). Technology stocks and economic performance of government-linked companies: The case of Malaysia. Technological and Economic Development of Economy, 18(2), 248–261. https://doi.org/10.3846/20294913.2012.688313
  • Hacker, R. S., & Hatemi-J, A. (2008). Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH. Journal of Applied Statistics, 35(6), 601–615. https://doi.org/10.1080/02664760801920473
  • Hatemi-J, A. (2004). Multivariate tests for autocorrelation in the stable and unstable VAR models. Economic Modelling, 21(4), 661–683. https://doi.org/10.1016/j.econmod.2003.09.005
  • Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
  • Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580. https://doi.org/10.2307/2938278
  • Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Econometric Theory, 14(4), 517–524. https://doi.org/10.1017/S0266466600007123
  • Kinateder, H., Campbell, R., & Ratti, R. (2021). Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets. Finance Research Letters, 43, 101951. https://doi.org/10.1016/j.frl.2021.101951
  • Kocaarslan, B., & Soytas, U. (2019). Dynamic correlations between oil prices and the stock prices of clean energy and technology firms: The role of reserve currency (US dollar). Energy Economics, 84, 104502. https://doi.org/10.1016/j.eneco.2019.104502
  • Kwon, S. S. (2002). Financial analysts' forecast accuracy and dispersion: High-tech versus low-tech stocks. Review of Quantitative Finance and Accounting, 19(1), 65–91. https://doi.org/10.1023/A:1015730325706
  • Mazur, M., Dang, M., & Vega, M. (2021). COVID-19 and the March 2020 stock market crash: Evidence from S&P 1500. Finance Research Letters, 38, 101690. https://doi.org/10.1016/j.frl.2020.101690
  • Niu, H. (2021). Correlations between crude oil and stock prices of renewable energy and technology companies: A multiscale time-dependent analysis. Energy, 221, 119800. https://doi.org/10.1016/j.energy.2021.119800
  • Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
  • Qiao, Z., Wong, R., & Kutan, A. M. (2008). Volatility switching and regime interdependence between information technology stocks 1995–2005. Global Finance Journal, 19(2), 139–156. https://doi.org/10.1016/j.gfj.2008.01.003
  • Rašiová, B., & Árvai, P. (2023). Copula approach to market volatility and technology stocks dependence. Finance Research Letters, 52, 103553. https://doi.org/10.1016/j.frl.2022.103553
  • Sadorsky, P. (2003). The macroeconomic determinants of technology stock price volatility. Review of Financial Economics, 12(2), 191–205. https://doi.org/10.1016/S1058-3300(02)00071-X
  • Tiwari, A. K., & Jondeau, E. (2023). Financial technology stocks, green financial assets, and energy markets: A quantile causality and dependence analysis. Energy Economics, 118, 106498. https://doi.org/10.1016/j.eneco.2022.106498
  • White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Zaman Serileri Analizi, Sermaye Piyasaları, Finansal Ekonomi
Bölüm Araştırma Makaleleri
Yazarlar

Ozan Kaymak 0000-0001-5492-2877

Erken Görünüm Tarihi 20 Ekim 2025
Yayımlanma Tarihi 27 Ekim 2025
Gönderilme Tarihi 30 Aralık 2024
Kabul Tarihi 5 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 10 Sayı: 3

Kaynak Göster

APA Kaymak, O. (2025). Investigation of the Relationship between Technology Indices on Selected Stock Markets Using Johansen Cointegration Test. Bulletin of Economic Theory and Analysis, 10(3), 995-1017. https://doi.org/10.25229/beta.1609928

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