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Çok Değişkenli Uyarlanabilir Regresyon Uzanımları (MARS) Modeli Kullanılarak Türkiye’de Borsa Fiyatının Makroekonomik Değişkenler İle Tahmin Edilmesi

Yıl 2020, , 759 - 771, 31.10.2020

Buğra Bağcı , Ferhat Çıtak

https://doi.org/10.19168/jyasar.743931

Öz

Bu çalışma, Türkiye’de Ocak 2010'dan Aralık 2019'a kadar geçen sürede Çok Değişkenli Uyarlanabilir Regresyon Spline (MARS) Modelini kullanarak, Borsa İstanbul kapanış fiyatının (BIST 100) makroekonomik belirleyicilerini tahmin etmeyi amaçlamaktadır. Bu çalışmada,. MARS modelini kullanarak hisse senedi fiyatını tahmin etmek için 10 makroekonomik değişken kullanılmıştır. Sonuçlarımız enflasyon oranı, altın fiyatları, sanayi üretim endeksi, para arzı, döviz kuru, kredi hacmi ve iç borç stoku gibi değişkenlerin BIST 100 kapanış fiyatının tahmini için önemli olduğunu göstermektedir.

Anahtar Kelimeler

Çok Değişkenli Uyarlanabilir Regresyon Uzanımları (MARS) Modeli BIST 100 Makroekonomik Değişkenler

Kaynakça

  • Adusel, M. (2014). “The Inflation-Stock Market Returns Nexus: Evidence from the Ghana Stock Exchange,” Journal of Economics and International Finance, 6(2): 38-46. DOI: 10.5897/JEIF2013.0556.
  • Afsal, E. and Haque, M. (2016). “Market Interactions in Gold and Stock Markets: Evidences from Saudi Arabia,” International Journal of Economics and Financial, 6(3): 1025-1034.
  • Ahmed, F., Islam, K. and Khan, M. (2016). “Relationship between Inflation and Stock Market Returns: Evidence from Bangladesh,” Journal of Business and Economics, 9(1): 1-9.
  • Ahmed, S. (2008). “Aggregate Economic Variables and Stock Market in India,” International Research Journal of Finance and Economics,” 14: 14-64.
  • Al Mukit, M. (2013). “The Effects of Interest Rates Volatility on Stock Returns: Evidence from Bangladesh,” International Journal in Management Business Research, 3(3): 269-279.
  • Al-Ameer, M., Hammad, W., Ismail, A. and Hamdan, A. (2018). “The Relationship of Gold Price with the Stock Market: The Case of Frankfurt Stock Exchange,” International Journal of Energy Economics and Policy, 8(5): 357-371.
  • Al-Hajj, E., Al-Mulali, U. and Solarin, S. (2017). “The Influence of Oil Price Shocks on Stock Market Returns: Fresh Evidence from Malaysia,” International Journal of Energy Economics and Policy, 7(5): 235-244.
  • Anari , A. and Kolari, J. (2001). “Stock Prices and Inflation,” Journal of Financial Research, 24(4): 587-602. DOI: 10.1111/j.1475-6803.2001.tb00832.x.
  • Anyalechi, K., Ezeaku, H., Onwumere, J. and Okereke, E. (2019). “Does Oil Price Fluctuation Affect Stock Market Returns in Nigeria?,” International Journal of Energy Economics and Policy, 9(1): 194-199.
  • Arouri, M. and Rault, C. (2011). “Do Structural Oil-Market Shocks Affect Stock Prices?,” Energy Economics, 31: 569-575. DOI: 10.1016/j.eneco.2009.03.001.
  • Asmy, M., Rohilina, W., Hassama, A. and Fouad, M. (2009). “Effects of Macroeconomic Variables on Stock Prices in Malaysia: An Approach of Error Correction Model,” MPRA Paper No. 20970, University Library of Munich, Germany.
  • Başarır, Ç. (2019). “Altın ve Hisse Senedi Getirileri Arasındaki Nedensellik Ilişkisi: Turkiye Örneği,” Trakya Üniversitesi Sosyal Bilimler Dergisi, 21 (2): 475-490. DOI: 10.26468/trakyasobed.472190.
  • Basher, S. and Sadorsky, P. (2006). “Oil Price Risk and Emerging Stock Markets,” Global Finance Journal, 17: 224–51. DOI: 10.1016/j.gfj.2006.04.001.
  • Bauy, D. and Lucey, B. (2010). “Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold,” Financial Review, 45(2): 217-229. DOI: 10.1111/j.1540-6288.2010.00244.x.
  • Bekaert, G. and Engstrom, E. (2009). “Inflation and the Stock Market: Understanding the “Fed Model”, Cambridge: NBER Working Paper Series. DOI: 10.1016/j.jmoneco.2010.02.004.
  • Brahmasrene, T. and Jiranyakul, K. (2007). “Cointegration and Causality Between Stock Index and Macroeconomic Variables in a Emerging Markets,” Academy of Acounting and Financial Studies Journal.
  • Chen, S. S. (2010). “Do Higher Oil Prices Push the Stock Market into Bear Territory?,” Energy Economics, 32: 490–95. DOI: 10.1016/j.eneco.2009.08.018.
  • Ciner, C. (2001). “Energy Shocks and Financial Markets: Nonlinear Linkages,” Studies in Nonlinear Dynamics and Econometrics, 5: 203–12. DOI: 10.2202/1558-3708.1079.
  • Ciner, C. (2013). “Oil and Stock Returns: Frequency Domain Evidence,” Journal of International Financial Markets Institutions and Money, 23: 1–11. DOI: 10.1016/j.intfin.2012.09.002.
  • Crosby, M. (2002). “Stock Returns and Inflation,” Australian Economic Papers, 40(2):156-165. DOI: 10.1111/1467-8454.00119.
  • Çağlı, E., Taşkın, D. and Halaç, U. (2010). “Testing Long-Run Relationship between Stock Market and Macroeconomic Variables in the Presence of Structural Breaks: The Turkish Case,” International Research Journal of Finance and Economics, 48(48): 49-60.
  • Diaz, E. and De Gracia, F. (2017). “Oil Price Shocks and Stock Returns of Oil and Gas Corporations,” Finance Research Letters, 20: 75-80. DOI: 10.1016/j.frl.2016.09.010.
  • Doğru, B. and Uysal, M. (2015). “Bir Yatırım Aracı Olarak Altın ile Hisse Senedi Endeksi Arasındaki Ilişkinin Analizi: Türkiye Uzerine Ampirik Uygulama,” Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24(1): 239-254.
  • Driesprong, G., Jacobsen, B. and Benjamin, M. (2008). “Striking oil: Another puzzle?,” Journal of Financial Economics, 89: 307–27. DOI: 10.1016/j.jfineco.2007.07.008.
  • Dutta, A., Nikkinen, J. and Rothovius, T. (2017). “Impact of Oil Price Uncertainty on Middle East and African Stock Markets,” Energy, 123: 189-197. DOI: 10.1016/j.energy.2017.01.126.
  • El-Nader, H. and Alraimony, A. (2012). “The Impact of Macroeconomic Factors on Amman Stock Market Returns,” International Journal of Economics and Finance, 4(12): 202-213. DOI: 10.5539/ijef.v4n12p202.
  • Filis, G., Degiannakis, S. and Floros, C. (2011). “Dynamic Correlation Between Stock Market and Oil Prices: The Case of Oil-Importing and Oil-Exporting Countries,” International Review of Financial Analysis, 20: 152-164. DOI: 10.1016/j.irfa.2011.02.014.
  • Friedman J.H., (1991a). “Multivariate Adaptive Regression Splines,” Annals of Statistics, 19(1): 1-141.
  • Gan, C., Lee, M., Yong, H. and Zhang, J. (2006). “Macroeconomic Variables and Stock Market Interactions: New Zealand Evidence,” Investment Management and Financial Innovation, 3(4): 89-101.
  • Gregoriou , A. and Kontonikas, A. (2006). “Inflation Targeting and The Stationarity Of Inflation: New Results from an ESTAR Unit Root Test,” Bulletin of Economic Research , 58(4): 309-322. DOI: 10.1111/j.0307-3378.2006.00246.x.
  • Hammoudeh, S. and Eleisa, L. (2004). “Dynamic Relationship Among GCC Stock Markets And NYMEX Oil Future,”Contemporary Economic Policy, 22: 250-269. DOI: 10.1093/cep/byh018.
  • Hampe, A. and Macmillan, P. (2009). “Can Macroeconomic Variables Explain Long-Term Stock Market Movements? A Comparison Of The US and Japan,” Applied Financial Economics, 19: 111-119. DOI: 10.1080/09603100701748956.
  • Hanousek, J. and Filler, R. (2000). “The Relationship Between Economic Factors and Equity Markets in Central Europe,” Economics of transition, 8 (3): 623-638. DOI: 10.1111/1468-0351.00058.
  • Koruzomi, T. and Kimura, T. (2003). “Optimal Monetary Policy in a Micro Founded Model With Parameter Uncertainty,” Finance and Economics Discussion Series. Board of Governors of the Federal Reserve System (U.S.). DOI: 10.1016/j.jedc.2005.10.003.
  • Lee, Y. and Chiou, J. (2011). “Oil Sensitivity and its Asymmetric Impact On The Stock Market,” Energy, 36(1): 168-174. DOI: 10.1016/j.energy.2010.10.057.
  • Miller, J. and Ronald, A. (2009). “Crude Oil and Stock Markets: Stability, Instability, and Bubbles,” Energy Economics, 31: 559–68. DOI: 10.1016/j.eneco.2009.01.009.
  • Narayan, P. and Sharma, S. (2011). “New Evidence on Oil Price and Firm Returns,” Journal of Banking and Finance, 35: 3253-3262. DOI: 10.1016/j.jbankfin.2011.05.010.
  • Papapetrou, E. (2001). “Oil Price Shocks, Stock Market, Economic Activity and Employment in Greece,” Energy Economics, 23: 511–32. DOI: 10.1016/S0140-9883(01)00078-0.
  • Park, J. and Ratti, R. (2008). “Oil Price Shocks and the Stock Markets in the U.S. and 13 European Countries,” Energy Economics, 30: 2587–608. DOI: 10.1016/j.eneco.2008.04.003.
  • Patel, S. (2013). “Causal Relationship between Stock Market Indices and Gold Price: Evidence from India,” The IUP Journal of Applied Finance, 19(1), 99-109.
  • Pearce, D. and Roley, V. (1988). “Firm Characteristics, Unanticipated Inflation, and Stock Returns,” The Journal of Finance, 43(4): 965-981. DOI: 10.1111/j.1540-6261.1988.tb02615.x.
  • Rahman, A. Abdul, Z. N., Sidek, M. and Fauziah, H. (2009). “Macroeconomic Determinants of Malaysian Stock Market,” African Journal of Business Management, 3(3): 95-106.
  • Ratanapakorn, O. and Sharma, S. (2007). “Dynamic analysis between the US stock returns and the Macroeconomic Variables,” Applied Financial Economics, 17(5): 369-377. DOI: 10.1080/09603100600638944.
  • Ray, S. (2013). “Causal Nexus between Gold Price Movement and Stock Market: Evidence from Indian Stock Market,” Econometrics, 1: 12-19.
  • Shiblee, L. (2009). The Impact of Inflation, GDP, Unemployment, and Money Supply On Stock Prices. Available at SSRN: https://ssrn.com/abstract=1529254
  • Smith, G. (2001). “The Price Of Gold and Stock Price Indices for the United States,” The World Gold Council, 8(1): 1-16.
  • Sohail, N. and Hussain, Z. (2009). “Long-Run and Short-Run Relationship between Macroeconomic Variables and Stock Prices in Pakistan,” Pakistan Economic and Social Review, 47(2):183-198.
  • Srinivasan, P. (2014). “Gold Price, Stock Price and Exchange Rate Nexus: The Case of India,” The Romanian Economic Journal, 17(52).
  • Şahin, E. E. Bitcoin Fiyatina Etki Eden Faktörlerin Mars Metodu İle Belirlenmesi/Determination Of Factors Affecting Bitcoin Price By MARS Method. Uluslararası Ekonomi İşletme ve Politika Dergisi, 4(1), 171-184, DOI: 10.29216/ueip.657407.
  • Wei, C. (2003). “Energy, the Stock Market, and the Putty-Clay Investment Model,” American Economic Review, 93: 311-323. DOI: 10.1257/000282803321455313.

Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model

Yıl 2020, , 759 - 771, 31.10.2020

Buğra Bağcı , Ferhat Çıtak

https://doi.org/10.19168/jyasar.743931

Öz

This empirical investigation aims at forecasting the macroeconomic determinants of Istanbul Stock Price (XU 100) in Turkey by using the Multivariate Adaptive Regression Splines (MARS) Model over the period spanning from the January 2010 to December 2019. In this study, we used 10 macroeconomic variables for forecasting stock price using the MARS model. Our results indicate that variables such as inflation rate, gold prices, industrial production index, money supply, exchange rate, credit volume, and internal debt stock were found to be important for forecasting XU100 price.

Anahtar Kelimeler

Multivariate Adaptive Regression Splines (MARS) Model XU 100 Macroeconomic Variables

Kaynakça

  • Adusel, M. (2014). “The Inflation-Stock Market Returns Nexus: Evidence from the Ghana Stock Exchange,” Journal of Economics and International Finance, 6(2): 38-46. DOI: 10.5897/JEIF2013.0556.
  • Afsal, E. and Haque, M. (2016). “Market Interactions in Gold and Stock Markets: Evidences from Saudi Arabia,” International Journal of Economics and Financial, 6(3): 1025-1034.
  • Ahmed, F., Islam, K. and Khan, M. (2016). “Relationship between Inflation and Stock Market Returns: Evidence from Bangladesh,” Journal of Business and Economics, 9(1): 1-9.
  • Ahmed, S. (2008). “Aggregate Economic Variables and Stock Market in India,” International Research Journal of Finance and Economics,” 14: 14-64.
  • Al Mukit, M. (2013). “The Effects of Interest Rates Volatility on Stock Returns: Evidence from Bangladesh,” International Journal in Management Business Research, 3(3): 269-279.
  • Al-Ameer, M., Hammad, W., Ismail, A. and Hamdan, A. (2018). “The Relationship of Gold Price with the Stock Market: The Case of Frankfurt Stock Exchange,” International Journal of Energy Economics and Policy, 8(5): 357-371.
  • Al-Hajj, E., Al-Mulali, U. and Solarin, S. (2017). “The Influence of Oil Price Shocks on Stock Market Returns: Fresh Evidence from Malaysia,” International Journal of Energy Economics and Policy, 7(5): 235-244.
  • Anari , A. and Kolari, J. (2001). “Stock Prices and Inflation,” Journal of Financial Research, 24(4): 587-602. DOI: 10.1111/j.1475-6803.2001.tb00832.x.
  • Anyalechi, K., Ezeaku, H., Onwumere, J. and Okereke, E. (2019). “Does Oil Price Fluctuation Affect Stock Market Returns in Nigeria?,” International Journal of Energy Economics and Policy, 9(1): 194-199.
  • Arouri, M. and Rault, C. (2011). “Do Structural Oil-Market Shocks Affect Stock Prices?,” Energy Economics, 31: 569-575. DOI: 10.1016/j.eneco.2009.03.001.
  • Asmy, M., Rohilina, W., Hassama, A. and Fouad, M. (2009). “Effects of Macroeconomic Variables on Stock Prices in Malaysia: An Approach of Error Correction Model,” MPRA Paper No. 20970, University Library of Munich, Germany.
  • Başarır, Ç. (2019). “Altın ve Hisse Senedi Getirileri Arasındaki Nedensellik Ilişkisi: Turkiye Örneği,” Trakya Üniversitesi Sosyal Bilimler Dergisi, 21 (2): 475-490. DOI: 10.26468/trakyasobed.472190.
  • Basher, S. and Sadorsky, P. (2006). “Oil Price Risk and Emerging Stock Markets,” Global Finance Journal, 17: 224–51. DOI: 10.1016/j.gfj.2006.04.001.
  • Bauy, D. and Lucey, B. (2010). “Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold,” Financial Review, 45(2): 217-229. DOI: 10.1111/j.1540-6288.2010.00244.x.
  • Bekaert, G. and Engstrom, E. (2009). “Inflation and the Stock Market: Understanding the “Fed Model”, Cambridge: NBER Working Paper Series. DOI: 10.1016/j.jmoneco.2010.02.004.
  • Brahmasrene, T. and Jiranyakul, K. (2007). “Cointegration and Causality Between Stock Index and Macroeconomic Variables in a Emerging Markets,” Academy of Acounting and Financial Studies Journal.
  • Chen, S. S. (2010). “Do Higher Oil Prices Push the Stock Market into Bear Territory?,” Energy Economics, 32: 490–95. DOI: 10.1016/j.eneco.2009.08.018.
  • Ciner, C. (2001). “Energy Shocks and Financial Markets: Nonlinear Linkages,” Studies in Nonlinear Dynamics and Econometrics, 5: 203–12. DOI: 10.2202/1558-3708.1079.
  • Ciner, C. (2013). “Oil and Stock Returns: Frequency Domain Evidence,” Journal of International Financial Markets Institutions and Money, 23: 1–11. DOI: 10.1016/j.intfin.2012.09.002.
  • Crosby, M. (2002). “Stock Returns and Inflation,” Australian Economic Papers, 40(2):156-165. DOI: 10.1111/1467-8454.00119.
  • Çağlı, E., Taşkın, D. and Halaç, U. (2010). “Testing Long-Run Relationship between Stock Market and Macroeconomic Variables in the Presence of Structural Breaks: The Turkish Case,” International Research Journal of Finance and Economics, 48(48): 49-60.
  • Diaz, E. and De Gracia, F. (2017). “Oil Price Shocks and Stock Returns of Oil and Gas Corporations,” Finance Research Letters, 20: 75-80. DOI: 10.1016/j.frl.2016.09.010.
  • Doğru, B. and Uysal, M. (2015). “Bir Yatırım Aracı Olarak Altın ile Hisse Senedi Endeksi Arasındaki Ilişkinin Analizi: Türkiye Uzerine Ampirik Uygulama,” Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 24(1): 239-254.
  • Driesprong, G., Jacobsen, B. and Benjamin, M. (2008). “Striking oil: Another puzzle?,” Journal of Financial Economics, 89: 307–27. DOI: 10.1016/j.jfineco.2007.07.008.
  • Dutta, A., Nikkinen, J. and Rothovius, T. (2017). “Impact of Oil Price Uncertainty on Middle East and African Stock Markets,” Energy, 123: 189-197. DOI: 10.1016/j.energy.2017.01.126.
  • El-Nader, H. and Alraimony, A. (2012). “The Impact of Macroeconomic Factors on Amman Stock Market Returns,” International Journal of Economics and Finance, 4(12): 202-213. DOI: 10.5539/ijef.v4n12p202.
  • Filis, G., Degiannakis, S. and Floros, C. (2011). “Dynamic Correlation Between Stock Market and Oil Prices: The Case of Oil-Importing and Oil-Exporting Countries,” International Review of Financial Analysis, 20: 152-164. DOI: 10.1016/j.irfa.2011.02.014.
  • Friedman J.H., (1991a). “Multivariate Adaptive Regression Splines,” Annals of Statistics, 19(1): 1-141.
  • Gan, C., Lee, M., Yong, H. and Zhang, J. (2006). “Macroeconomic Variables and Stock Market Interactions: New Zealand Evidence,” Investment Management and Financial Innovation, 3(4): 89-101.
  • Gregoriou , A. and Kontonikas, A. (2006). “Inflation Targeting and The Stationarity Of Inflation: New Results from an ESTAR Unit Root Test,” Bulletin of Economic Research , 58(4): 309-322. DOI: 10.1111/j.0307-3378.2006.00246.x.
  • Hammoudeh, S. and Eleisa, L. (2004). “Dynamic Relationship Among GCC Stock Markets And NYMEX Oil Future,”Contemporary Economic Policy, 22: 250-269. DOI: 10.1093/cep/byh018.
  • Hampe, A. and Macmillan, P. (2009). “Can Macroeconomic Variables Explain Long-Term Stock Market Movements? A Comparison Of The US and Japan,” Applied Financial Economics, 19: 111-119. DOI: 10.1080/09603100701748956.
  • Hanousek, J. and Filler, R. (2000). “The Relationship Between Economic Factors and Equity Markets in Central Europe,” Economics of transition, 8 (3): 623-638. DOI: 10.1111/1468-0351.00058.
  • Koruzomi, T. and Kimura, T. (2003). “Optimal Monetary Policy in a Micro Founded Model With Parameter Uncertainty,” Finance and Economics Discussion Series. Board of Governors of the Federal Reserve System (U.S.). DOI: 10.1016/j.jedc.2005.10.003.
  • Lee, Y. and Chiou, J. (2011). “Oil Sensitivity and its Asymmetric Impact On The Stock Market,” Energy, 36(1): 168-174. DOI: 10.1016/j.energy.2010.10.057.
  • Miller, J. and Ronald, A. (2009). “Crude Oil and Stock Markets: Stability, Instability, and Bubbles,” Energy Economics, 31: 559–68. DOI: 10.1016/j.eneco.2009.01.009.
  • Narayan, P. and Sharma, S. (2011). “New Evidence on Oil Price and Firm Returns,” Journal of Banking and Finance, 35: 3253-3262. DOI: 10.1016/j.jbankfin.2011.05.010.
  • Papapetrou, E. (2001). “Oil Price Shocks, Stock Market, Economic Activity and Employment in Greece,” Energy Economics, 23: 511–32. DOI: 10.1016/S0140-9883(01)00078-0.
  • Park, J. and Ratti, R. (2008). “Oil Price Shocks and the Stock Markets in the U.S. and 13 European Countries,” Energy Economics, 30: 2587–608. DOI: 10.1016/j.eneco.2008.04.003.
  • Patel, S. (2013). “Causal Relationship between Stock Market Indices and Gold Price: Evidence from India,” The IUP Journal of Applied Finance, 19(1), 99-109.
  • Pearce, D. and Roley, V. (1988). “Firm Characteristics, Unanticipated Inflation, and Stock Returns,” The Journal of Finance, 43(4): 965-981. DOI: 10.1111/j.1540-6261.1988.tb02615.x.
  • Rahman, A. Abdul, Z. N., Sidek, M. and Fauziah, H. (2009). “Macroeconomic Determinants of Malaysian Stock Market,” African Journal of Business Management, 3(3): 95-106.
  • Ratanapakorn, O. and Sharma, S. (2007). “Dynamic analysis between the US stock returns and the Macroeconomic Variables,” Applied Financial Economics, 17(5): 369-377. DOI: 10.1080/09603100600638944.
  • Ray, S. (2013). “Causal Nexus between Gold Price Movement and Stock Market: Evidence from Indian Stock Market,” Econometrics, 1: 12-19.
  • Shiblee, L. (2009). The Impact of Inflation, GDP, Unemployment, and Money Supply On Stock Prices. Available at SSRN: https://ssrn.com/abstract=1529254
  • Smith, G. (2001). “The Price Of Gold and Stock Price Indices for the United States,” The World Gold Council, 8(1): 1-16.
  • Sohail, N. and Hussain, Z. (2009). “Long-Run and Short-Run Relationship between Macroeconomic Variables and Stock Prices in Pakistan,” Pakistan Economic and Social Review, 47(2):183-198.
  • Srinivasan, P. (2014). “Gold Price, Stock Price and Exchange Rate Nexus: The Case of India,” The Romanian Economic Journal, 17(52).
  • Şahin, E. E. Bitcoin Fiyatina Etki Eden Faktörlerin Mars Metodu İle Belirlenmesi/Determination Of Factors Affecting Bitcoin Price By MARS Method. Uluslararası Ekonomi İşletme ve Politika Dergisi, 4(1), 171-184, DOI: 10.29216/ueip.657407.
  • Wei, C. (2003). “Energy, the Stock Market, and the Putty-Clay Investment Model,” American Economic Review, 93: 311-323. DOI: 10.1257/000282803321455313.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Buğra Bağcı 0000-0002-3268-3702

Ferhat Çıtak 0000-0003-4978-5251

Yayımlanma Tarihi 31 Ekim 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Bağcı, B., & Çıtak, F. (2020). Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model. Yaşar Üniversitesi E-Dergisi, 15(60), 759-771. https://doi.org/10.19168/jyasar.743931
AMA Bağcı B, Çıtak F. Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model. Yaşar Üniversitesi E-Dergisi. Ekim 2020;15(60):759-771. doi:10.19168/jyasar.743931
Chicago Bağcı, Buğra, ve Ferhat Çıtak. “Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model”. Yaşar Üniversitesi E-Dergisi 15, sy. 60 (Ekim 2020): 759-71. https://doi.org/10.19168/jyasar.743931.
EndNote Bağcı B, Çıtak F (01 Ekim 2020) Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model. Yaşar Üniversitesi E-Dergisi 15 60 759–771.
IEEE B. Bağcı ve F. Çıtak, “Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model”, Yaşar Üniversitesi E-Dergisi, c. 15, sy. 60, ss. 759–771, 2020, doi: 10.19168/jyasar.743931.
ISNAD Bağcı, Buğra - Çıtak, Ferhat. “Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model”. Yaşar Üniversitesi E-Dergisi 15/60 (Ekim 2020), 759-771. https://doi.org/10.19168/jyasar.743931.
JAMA Bağcı B, Çıtak F. Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model. Yaşar Üniversitesi E-Dergisi. 2020;15:759–771.
MLA Bağcı, Buğra ve Ferhat Çıtak. “Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model”. Yaşar Üniversitesi E-Dergisi, c. 15, sy. 60, 2020, ss. 759-71, doi:10.19168/jyasar.743931.
Vancouver Bağcı B, Çıtak F. Forecasting Turkish Stock Market Price With Macroeconomic Variables From The Multivariate Adaptive Regression Splines (Mars) Model. Yaşar Üniversitesi E-Dergisi. 2020;15(60):759-71.