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Analyzing The Impact of Macroeconomic Indicators On Stock Market Indices Using Wavelet Methods: The Case of KATILIM30 and BIST100

Yıl 2025, Cilt: 14 Sayı: 1, 90 - 119, 30.06.2025
https://doi.org/10.54282/inijoss.1643855

Öz

This study examines the sensitivity of the BIST100 and XK030 indices in the Turkish financial markets to macroeconomic variables such as commercial lending interest rates and exchange rates using wavelet analysis. Following global economic crises, the fluctuations in financial markets are often inadequately analyzed by traditional methods, whereas wavelet analysis provides a more detailed assessment of relationships between variables in both time and frequency domains. In this study, wavelet power spectrum analysis, wavelet coherence analysis, and partial wavelet coherence analysis are used to detail the short, medium, and long-term effects of interest rates and exchange rates on stock indices. The wavelet analysis conducted in this study reveals that the effects of interest rates and exchange rates on the BIST100 and XK030 indices vary across time and frequency domains. The wavelet power spectrum analysis indicates increased volatility in stock indices during specific periods. Global economic fluctuations and changes in Turkey's interest rate policies have had different impacts on the BIST100 and XK030 indices. While long-term volatility is more pronounced in the XK030 index, short-term fluctuations dominate in the BIST100 index. The wavelet coherence analysis shows that interest rates have a strong long-term effect on the XK030 index, whereas their impact on BIST100 is more limited. Regarding exchange rates, the effects of USD/TRY and EUR/TRY fluctuations on stock indices are more evident in the short term, while these effects weaken in the medium and long term. Partial wavelet coherence analysis indicates that when interest rates are held constant, the influence of exchange rates on stock indices increases, whereas when exchange rates are fixed, the relationship between interest rates and stock indices strengthens. The long-term effect of interest rates on the XK030 index is found to be higher than expected, despite the principles of interest-free finance. The study concludes that interest rates are a crucial factor in long-term investment decisions, while exchange rates play a more significant role in short-term investment strategies. It is essential for investors to shape their portfolio strategies according to time scales and for policymakers to make economic decisions based on these dynamics.

Kaynakça

  • Abioğlu, V. (2021). Volatility Spillovers And Correlations Between Oil Prices and Stock Sectors In Turkey: Implications On Portfolio Hedging And Diversification Opportunities. Sosyoekonomi, 29(47), 79-106.
  • https://doi.org/10.17233/sosyoekonomi.2021.01.04 Adebayo, T. S. (2020). New Insights into Export-Growth Nexus: Wavelet and Causality Approaches. Asian Journal of Economics Business and Accounting, 15 (2): 32-44. https://doi.org/10.9734/ajeba/2020/v15i230212.
  • Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and The Macroeconomy: Using Wavelets To Analyze Old İssues. Empirical Economics, 40(3), 645-655.
  • Burhan, B., & Mohammed, F. A. (2024). A Hybrid Model For Financial Forecasting Based On Maximal Overlap Discrete Wavelet Transform: Evidence From Chinese Exchange Rates. Journal of Economics and Administrative Sciences, 30(142), 476-491. https://doi.org/10.33095/hevp1268
  • Chopra, M. and Mehta, C. (2022). Is The Covid-19 Pandemic More Contagious For The Asian Stock Markets? A Comparison With The Asian Financial, The Us Subprime And The Eurozone Debt Crisis. Journal of Asian Economics, 79, 101450. https://doi.org/10.1016/j.asieco.2022.101450
  • Çakır, M. (2021). The impact of Exchange Rates On Stock Markets In Turkey: Evidence From Linear and Non-Linear Ardl Models. IntechOpen. https://doi.org/10.5772/intechopen.96068
  • Çıtak, F. (2023). Dynamic Linkages Between Green Finance, Environmental Responsibility, Clean Energy and Green Technology: Evidence From Partial and Multiple Wavelet
  • Coherence. In K. Çapraz (Ed.), Academic Studies In Social, Humanities and Administrative Sciences (pp. 93–115). Livre de Lyon.
  • Dungey, M. and Martin, V. (2007). Unravelling Financial Market Linkages During Crises. Journal of Applied Econometrics, 22(1), 89-119. https://doi.org/10.1002/jae.936
  • Frimpong, S., Gyamfi, E., Ishaq, Z., Agyei, S., Agyapong, D., & Adam, A. (2021). Can Global Economic Policy Uncertainty Drive The İnterdependence of Agricultural Commodity Prices? Evidence From Partial Wavelet Coherence Analysis. Complexity, 2021(1). https://doi.org/10.1155/2021/8848424
  • Gallegati, M. (2008). Wavelet Analysis of Stock Returns And Aggregate Economic Activity. Computational Statistics & Data Analysis, 52(6), 3061-3074. https://doi.org/10.1016/j.csda.2007.07.019
  • Gençay, R., Selçuk, F., & Whitcher, B. (2005). Multiscale Systematic Risk. Journal of International Money and Finance, 24(1), 55–70. https://doi.org/10.1016/j.jimonfin.2004.10.003
  • Günay, S. and Can, G. (2022). The Source of Financial Contagion And Spillovers: An Evaluation Of The Covid-19 Pandemic and The Global Financial Crisis. Plos One, 17(1), e0261835. https://doi.org/10.1371/journal.pone.0261835
  • Habib, Y., Xia, E., Fareed, Z., & Hashmi, S. (2020). Time–Frequency Co-Movement Between Covid-19, Crude Oil Prices, And Atmospheric Co2 Emissions: Fresh Global Insights From Partial And Multiple Coherence Approach. Environment Development and Sustainability, 23(6), 9397-9417. https://doi.org/10.1007/s10668-020-01031-2
  • Hu, W., & Cheng, B. (2021). Technical note: Improved Partial Wavelet Coherency For Understanding Scale-Specific and Localized Bivariate Relationships In Geosciences. Hydrology and Earth System Sciences, 25(1), 321-331. https://doi.org/10.5194/hess-25-321-2021
  • İlhan, A., & Akdeniz, C. (2020). COVID-19 Döneminde Makroekonomik Değişkenlerin Borsa Üzerindeki Etkisi: Türkiye Örneği. Ekonomi Politika ve Finans Araştırmaları Dergisi, 893-912. https://doi.org/10.30784/epfad.810630
  • Kantar, L. (2022). Testing of Macroeconomic Factors Affecting Capital Markets with Granger Causality Method: Turkey Practice. Journal of Business Research - Turk. https://doi.org/10.20491/isarder.2022.1446
  • Karakuş, T. F., & Vural, G. (2022). Katılım Endeksi İle Faiz Oranı, Döviz Kuru ve BİST 100 Endeksi Arasındaki İlişkinin İncelenmesi. International Journal of Commerce, Industry and Entrepreneurship Studies, 2(1), 0-2.
  • Kırıkkaleli, D. (2019). Time–Frequency Dependency of Financial Risk and Economic Risk: Evidence From Greece. Journal of Economic Structures, 8(1). https://doi.org/10.1186/s40008-019-0173-z
  • Koncak, A. & Nazlıoğlu, E. H. (2024). Küresel Belirsizlikler ve Türkiye Pay Senedi Piyasası Arasındaki İlişki: Dalgacık Uyum Analizinden Kanıtlar. Maliye Dergisi, 186(Ocak-Haziran), 251–274.
  • Kravets, T., & Sytienko, A. (2013). Wavelet Analysis of The Crisis Effects in Stock Index Returns. Ekonomika, 92(1), 78-96. https://doi.org/10.15388/ekon.2013.0.1133
  • Kriechbaumer, T., Angus, A., Parsons, D., & Casado, M. (2014). An Improved Wavelet–ARIMA Approach for Forecasting Metal Prices. Resources Policy, 39, 32–41. https://doi.org/10.1016/j.resourpol.2013.10.005
  • Li, J., Zhu, S., & Liu, Y. (2006). Genetic Programming With Wavelet-Based İndicators For Financial Forecasting. Transactions of the Institute of Measurement and Control, 28(3), 285-297. https://doi.org/10.1191/0142331206tim177oa
  • Machado, J., Duarte, F., & Duarte, G. (2012). Analysis of Stock Market Indices With Multidimensional Scaling And Wavelets. Mathematical Problems in Engineering, 2012(1), 1–10. https://doi.org/10.1155/2012/819503
  • Mandrikova, O., Mandrikova, B., & Rodomanskay, A. (2021). Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition. Mathematics, 9(7), 737. https://doi.org/10.3390/math9070737
  • Mollah, S., Quoreshi, S., & Zafirov, G. (2016). Equity Market Contagion During Global Financial And Eurozone Crises: Evidence From a Dynamic Correlation Analysis. Journal of International Financial Markets, Institutions and Money, 41, 151-167. https://doi.org/10.1016/j.intfin.2015.12.010
  • Özün, A., & Cifter, A. (2010). A Wavelet Network Model For Analysing Exchange Rate Effects On Interest Rates. Journal of Economic Studies, 37(4), 405-418. https://doi.org/10.1108/01443581011073408
  • Rua, A. (2010). A Wavelet Approach For Factor-Augmented Forecasting. Journal of forecasting, 30(7), 666–678. https://doi.org/10.1002/for.1200
  • Schirmer, A., Lo, C., & Wijaya, M. (2021). When The Music’s No Good: Rhythms Prompt Interactional Synchrony But İmpair Affective Communication Outcomes. Communication Research, 50(1), 30-52. https://doi.org/10.1177/00936502211015900
  • Subbotin, A. (2008). A Multi-Horizon Scale For Volatility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1101376
  • Tang, L., Ling-xiao, T., & Hu, S. (2009). Forecasting Volatility Based On Wavelet Support Vector Machine. Expert Systems With Applications, 36(2), 2901-2909. https://doi.org/10.1016/j.eswa.2008.01.047
  • Torrence, C., & Compo, G. P. (1998). A Practical Guide To Wavelet Analysis. Bulletin of the American Meteorological Society, 79(1), 61-78.
  • Torrence, C., & Webster, P. J. (1999). Interdecadal Changes İn The Enso–Monsoon System. Journal of Climate, 12(8), 2679-2690.
  • Türsoy, T. and Mar’I, M. (2020). Lead-lag and Relationship Between Money Growth And Inflation In Turkey: New Evidence From A Wavelet Analysis. Theoretical and Practical Research in Economic Fields, 11(1), 47. https://doi.org/10.14505/tpref.v11.1(21).04
  • Vácha, L. and Baruník, J. (2012). Co-Movement of Energy Commodities Revisited: Evidence From Wavelet Coherence Analysis. Energy Economics, 34(1), 241-247. https://doi.org/10.1016/j.eneco.2011.10.007
  • Xie, Y., Yu, J., & Ranneby, B. (2009). Forecasting Using Locally Stationary Wavelet Processes. Journal of Statistical Computation and Simulation, 79(9), 1067-1082. https://doi.org/10.1080/00949650802087003
  • Yaacob, N., Jaber, J., Pathmanathan, D., Alwadi, S., & Mohamed, I. (2021). Hybrid of The Lee-Carter Model With Maximum Overlap Discrete Wavelet Transform Filters In Forecasting Mortality Rates. Mathematics, 9(18), 2295. https://doi.org/10.3390/math9182295
  • Ye, Q., & Wei, L. (2015). The prediction Of Stock Price Based On İmproved Wavelet Neural Network. Open Journal of Applied Sciences, 5(4), 115-120. https://doi.org/10.4236/ojapps.2015.54012 Yücel, D., Kılıçaslan, E., & Arman Zengi, C. (2023). Siyasal İletişim Bağlamında Türkiye’de Ekonomik Kriz Süreçlerinde Liderlerin Kriz Söylemleri. SDE Akademi, 3(2), 217-238. https://doi.org/10.58375/sde.1269124.

Dalgacık Yöntemleri Kullanılarak Makroekonomik Göstergelerin Hisse Senedi Endeksleri Üzerindeki Etkisinin Analizi: KATILIM30 ve BIST100 Örneği

Yıl 2025, Cilt: 14 Sayı: 1, 90 - 119, 30.06.2025
https://doi.org/10.54282/inijoss.1643855

Öz

Bu çalışma, Türkiye finansal piyasalarındaki BİST100 ve Katılım30 endekslerinin ticari borç verme faiz oranları ve döviz kurları gibi makroekonomik değişkenlere karşı duyarlılıklarını dalgacık analizi ile incelemektedir. Küresel ekonomik krizlerin ardından finansal piyasalarda oluşan dalgalanmalar, geleneksel yöntemlerle analiz edilmekte yetersiz kalırken, dalgacık analizi hem zaman hem de frekans boyutlarında değişkenler arasındaki ilişkileri daha ayrıntılı bir şekilde değerlendirme imkânı sunmaktadır. Çalışmada, dalgacık güç spektrumu analizi, dalgacık uyum analizi ve kısmi dalgacık uyum analizi kullanılarak, faiz oranları ve döviz kurlarının borsa endeksleri üzerindeki kısa, orta ve uzun vadeli etkileri detaylandırılmıştır. Çalışmada yapılan dalgacık analizi, faiz oranları ve döviz kurlarının BİST100 ve Katılım30 endeksleri üzerindeki etkilerinin zaman ve frekans boyutlarında farklılaştığını ortaya koymuştur. Dalgacık güç spektrumu analizi, borsa endekslerinde belirli dönemlerde artan volatiliteyi göstermiştir. Küresel ekonomik dalgalanmalar ve Türkiye’deki faiz politikalarındaki değişimler, BİST100 ve Katılım30 endekslerinde farklı etkiler yaratmıştır. Katılım30 endeksinde uzun vadeli volatilite daha belirgin olurken, BİST100’de kısa vadeli dalgalanmalar daha baskın gözlemlenmiştir. Dalgacık uyum analizi, faiz oranlarının uzun vadede Katılım30 endeksi üzerinde güçlü bir etkisi olduğunu, ancak BİST100 için bu etkinin daha sınırlı kaldığını göstermiştir. Döviz kurları açısından bakıldığında, USD/TL ve EUR/TL değişimlerinin borsa üzerindeki kısa vadeli etkileri belirginleşmiş, orta ve uzun vadede ise bu etkinin zayıfladığı görülmüştür. Kısmi dalgacık uyum analizi, faiz oranları sabit tutulduğunda döviz kurlarının borsa endeksleri üzerindeki etkisinin arttığını, döviz kurları sabit tutulduğunda ise faiz oranlarının borsa ile ilişkisini güçlendirdiğini ortaya koymuştur. Özellikle faiz oranlarının Katılım30 endeksi üzerindeki uzun vadeli etkisi, faizsiz finans ilkelerine rağmen beklenenden daha yüksek bulunmuştur. Çalışma sonucunda, faiz oranlarının uzun vadeli yatırım kararlarında önemli bir faktör olduğunu, döviz kurlarının ise kısa vadeli yatırım stratejilerinde daha belirleyici bir rol oynadığını göstermektedir. Yatırımcıların zaman ölçeklerine göre portföy stratejilerini şekillendirmeleri ve politika yapıcıların ekonomik kararlarını bu dinamiklere göre belirlemeleri gerektiği anlaşılmaktadır.

Kaynakça

  • Abioğlu, V. (2021). Volatility Spillovers And Correlations Between Oil Prices and Stock Sectors In Turkey: Implications On Portfolio Hedging And Diversification Opportunities. Sosyoekonomi, 29(47), 79-106.
  • https://doi.org/10.17233/sosyoekonomi.2021.01.04 Adebayo, T. S. (2020). New Insights into Export-Growth Nexus: Wavelet and Causality Approaches. Asian Journal of Economics Business and Accounting, 15 (2): 32-44. https://doi.org/10.9734/ajeba/2020/v15i230212.
  • Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and The Macroeconomy: Using Wavelets To Analyze Old İssues. Empirical Economics, 40(3), 645-655.
  • Burhan, B., & Mohammed, F. A. (2024). A Hybrid Model For Financial Forecasting Based On Maximal Overlap Discrete Wavelet Transform: Evidence From Chinese Exchange Rates. Journal of Economics and Administrative Sciences, 30(142), 476-491. https://doi.org/10.33095/hevp1268
  • Chopra, M. and Mehta, C. (2022). Is The Covid-19 Pandemic More Contagious For The Asian Stock Markets? A Comparison With The Asian Financial, The Us Subprime And The Eurozone Debt Crisis. Journal of Asian Economics, 79, 101450. https://doi.org/10.1016/j.asieco.2022.101450
  • Çakır, M. (2021). The impact of Exchange Rates On Stock Markets In Turkey: Evidence From Linear and Non-Linear Ardl Models. IntechOpen. https://doi.org/10.5772/intechopen.96068
  • Çıtak, F. (2023). Dynamic Linkages Between Green Finance, Environmental Responsibility, Clean Energy and Green Technology: Evidence From Partial and Multiple Wavelet
  • Coherence. In K. Çapraz (Ed.), Academic Studies In Social, Humanities and Administrative Sciences (pp. 93–115). Livre de Lyon.
  • Dungey, M. and Martin, V. (2007). Unravelling Financial Market Linkages During Crises. Journal of Applied Econometrics, 22(1), 89-119. https://doi.org/10.1002/jae.936
  • Frimpong, S., Gyamfi, E., Ishaq, Z., Agyei, S., Agyapong, D., & Adam, A. (2021). Can Global Economic Policy Uncertainty Drive The İnterdependence of Agricultural Commodity Prices? Evidence From Partial Wavelet Coherence Analysis. Complexity, 2021(1). https://doi.org/10.1155/2021/8848424
  • Gallegati, M. (2008). Wavelet Analysis of Stock Returns And Aggregate Economic Activity. Computational Statistics & Data Analysis, 52(6), 3061-3074. https://doi.org/10.1016/j.csda.2007.07.019
  • Gençay, R., Selçuk, F., & Whitcher, B. (2005). Multiscale Systematic Risk. Journal of International Money and Finance, 24(1), 55–70. https://doi.org/10.1016/j.jimonfin.2004.10.003
  • Günay, S. and Can, G. (2022). The Source of Financial Contagion And Spillovers: An Evaluation Of The Covid-19 Pandemic and The Global Financial Crisis. Plos One, 17(1), e0261835. https://doi.org/10.1371/journal.pone.0261835
  • Habib, Y., Xia, E., Fareed, Z., & Hashmi, S. (2020). Time–Frequency Co-Movement Between Covid-19, Crude Oil Prices, And Atmospheric Co2 Emissions: Fresh Global Insights From Partial And Multiple Coherence Approach. Environment Development and Sustainability, 23(6), 9397-9417. https://doi.org/10.1007/s10668-020-01031-2
  • Hu, W., & Cheng, B. (2021). Technical note: Improved Partial Wavelet Coherency For Understanding Scale-Specific and Localized Bivariate Relationships In Geosciences. Hydrology and Earth System Sciences, 25(1), 321-331. https://doi.org/10.5194/hess-25-321-2021
  • İlhan, A., & Akdeniz, C. (2020). COVID-19 Döneminde Makroekonomik Değişkenlerin Borsa Üzerindeki Etkisi: Türkiye Örneği. Ekonomi Politika ve Finans Araştırmaları Dergisi, 893-912. https://doi.org/10.30784/epfad.810630
  • Kantar, L. (2022). Testing of Macroeconomic Factors Affecting Capital Markets with Granger Causality Method: Turkey Practice. Journal of Business Research - Turk. https://doi.org/10.20491/isarder.2022.1446
  • Karakuş, T. F., & Vural, G. (2022). Katılım Endeksi İle Faiz Oranı, Döviz Kuru ve BİST 100 Endeksi Arasındaki İlişkinin İncelenmesi. International Journal of Commerce, Industry and Entrepreneurship Studies, 2(1), 0-2.
  • Kırıkkaleli, D. (2019). Time–Frequency Dependency of Financial Risk and Economic Risk: Evidence From Greece. Journal of Economic Structures, 8(1). https://doi.org/10.1186/s40008-019-0173-z
  • Koncak, A. & Nazlıoğlu, E. H. (2024). Küresel Belirsizlikler ve Türkiye Pay Senedi Piyasası Arasındaki İlişki: Dalgacık Uyum Analizinden Kanıtlar. Maliye Dergisi, 186(Ocak-Haziran), 251–274.
  • Kravets, T., & Sytienko, A. (2013). Wavelet Analysis of The Crisis Effects in Stock Index Returns. Ekonomika, 92(1), 78-96. https://doi.org/10.15388/ekon.2013.0.1133
  • Kriechbaumer, T., Angus, A., Parsons, D., & Casado, M. (2014). An Improved Wavelet–ARIMA Approach for Forecasting Metal Prices. Resources Policy, 39, 32–41. https://doi.org/10.1016/j.resourpol.2013.10.005
  • Li, J., Zhu, S., & Liu, Y. (2006). Genetic Programming With Wavelet-Based İndicators For Financial Forecasting. Transactions of the Institute of Measurement and Control, 28(3), 285-297. https://doi.org/10.1191/0142331206tim177oa
  • Machado, J., Duarte, F., & Duarte, G. (2012). Analysis of Stock Market Indices With Multidimensional Scaling And Wavelets. Mathematical Problems in Engineering, 2012(1), 1–10. https://doi.org/10.1155/2012/819503
  • Mandrikova, O., Mandrikova, B., & Rodomanskay, A. (2021). Method of Constructing a Nonlinear Approximating Scheme of a Complex Signal: Application Pattern Recognition. Mathematics, 9(7), 737. https://doi.org/10.3390/math9070737
  • Mollah, S., Quoreshi, S., & Zafirov, G. (2016). Equity Market Contagion During Global Financial And Eurozone Crises: Evidence From a Dynamic Correlation Analysis. Journal of International Financial Markets, Institutions and Money, 41, 151-167. https://doi.org/10.1016/j.intfin.2015.12.010
  • Özün, A., & Cifter, A. (2010). A Wavelet Network Model For Analysing Exchange Rate Effects On Interest Rates. Journal of Economic Studies, 37(4), 405-418. https://doi.org/10.1108/01443581011073408
  • Rua, A. (2010). A Wavelet Approach For Factor-Augmented Forecasting. Journal of forecasting, 30(7), 666–678. https://doi.org/10.1002/for.1200
  • Schirmer, A., Lo, C., & Wijaya, M. (2021). When The Music’s No Good: Rhythms Prompt Interactional Synchrony But İmpair Affective Communication Outcomes. Communication Research, 50(1), 30-52. https://doi.org/10.1177/00936502211015900
  • Subbotin, A. (2008). A Multi-Horizon Scale For Volatility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1101376
  • Tang, L., Ling-xiao, T., & Hu, S. (2009). Forecasting Volatility Based On Wavelet Support Vector Machine. Expert Systems With Applications, 36(2), 2901-2909. https://doi.org/10.1016/j.eswa.2008.01.047
  • Torrence, C., & Compo, G. P. (1998). A Practical Guide To Wavelet Analysis. Bulletin of the American Meteorological Society, 79(1), 61-78.
  • Torrence, C., & Webster, P. J. (1999). Interdecadal Changes İn The Enso–Monsoon System. Journal of Climate, 12(8), 2679-2690.
  • Türsoy, T. and Mar’I, M. (2020). Lead-lag and Relationship Between Money Growth And Inflation In Turkey: New Evidence From A Wavelet Analysis. Theoretical and Practical Research in Economic Fields, 11(1), 47. https://doi.org/10.14505/tpref.v11.1(21).04
  • Vácha, L. and Baruník, J. (2012). Co-Movement of Energy Commodities Revisited: Evidence From Wavelet Coherence Analysis. Energy Economics, 34(1), 241-247. https://doi.org/10.1016/j.eneco.2011.10.007
  • Xie, Y., Yu, J., & Ranneby, B. (2009). Forecasting Using Locally Stationary Wavelet Processes. Journal of Statistical Computation and Simulation, 79(9), 1067-1082. https://doi.org/10.1080/00949650802087003
  • Yaacob, N., Jaber, J., Pathmanathan, D., Alwadi, S., & Mohamed, I. (2021). Hybrid of The Lee-Carter Model With Maximum Overlap Discrete Wavelet Transform Filters In Forecasting Mortality Rates. Mathematics, 9(18), 2295. https://doi.org/10.3390/math9182295
  • Ye, Q., & Wei, L. (2015). The prediction Of Stock Price Based On İmproved Wavelet Neural Network. Open Journal of Applied Sciences, 5(4), 115-120. https://doi.org/10.4236/ojapps.2015.54012 Yücel, D., Kılıçaslan, E., & Arman Zengi, C. (2023). Siyasal İletişim Bağlamında Türkiye’de Ekonomik Kriz Süreçlerinde Liderlerin Kriz Söylemleri. SDE Akademi, 3(2), 217-238. https://doi.org/10.58375/sde.1269124.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Katılım Bankacılığı, Finans, Finansal Piyasalar ve Kurumlar
Bölüm Makaleler
Yazarlar

Recep Çakar 0000-0002-4069-7653

Eyyüp Ensari Şahin 0000-0003-2110-7571

Erken Görünüm Tarihi 26 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 20 Şubat 2025
Kabul Tarihi 28 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 14 Sayı: 1

Kaynak Göster

APA Çakar, R., & Şahin, E. E. (2025). Dalgacık Yöntemleri Kullanılarak Makroekonomik Göstergelerin Hisse Senedi Endeksleri Üzerindeki Etkisinin Analizi: KATILIM30 ve BIST100 Örneği. İnönü Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 14(1), 90-119. https://doi.org/10.54282/inijoss.1643855