AN EMPIRICAL STUDY FOR EXPLAINING THE RELATIONSHIP BETWEEN EXCHANGE RATES AND STOCK EXCHANGE INDEX IN THE SHORT AND LONG TERM

Yıl 2018, Cilt 36, Sayı 1, 83 - 104, 30.03.2018

Kenan İLARSLAN

https://doi.org/10.17065/huniibf.411132

Cited By: 3

Öz

Determining the price and/or price movements of financial assets such as stocks, bonds and foreign currencies through objective criteria and methods provides various opportunities for individual and institutional investors such as increasing profits or reducing losses. In this study, the reflection of the change in US dollar and Euro currency on BIST 100 index in order to resolve the ambiguity about financial assets within a certain probability by the framework of Bayes Theorem is estimated. In the study, Dollar and Euro exchange rates and BIST 100 index daily closing prices are used as a data covering the period of 2007-2016. As a result of the study, it is found that there is a negative relation between exchange rates and stock exchange index in the short term and a positive relation in the long term. This finding shows that the methods used in determination of the exchange rates such as “Portfolio Balance Approach" in the short term and “Traditional Approach” in the long term are prevalent in Turkey. Besides, it is also found that when Euro exchange rate increases, the BIST 100 index increases with 53,30% probability at the same day and  when Dollar exchange rate increases, the BIST 100 index increases with 52,58% probability at the same day.

KISA VE UZUN DÖNEMDE DÖVİZ KURLARI İLE BORSA ENDEKSİ ARASINDAKİ İLİŞKİNİN AÇIKLANMASINA YÖNELİK AMPİRİK BİR ÇALIŞMA

Öz

Hisse senedi, tahvil ve döviz gibi finansal varlık fiyat ve/veya fiyat davranışlarının objektif ölçüt ve yöntemlerle belirlenmesi bireysel ve kurumsal yatırımcılara kazancı artırma ya da zararı azaltma gibi çeşitli fırsatlar sunar. Bu çalışmada, finansal varlıkların fiyat hareketleri ile ilgili belirsizliğin giderilmesine yönelik olarak Bayes Teoremi çerçevesinde Dolar/TLve Euro/TL kurlarındaki bir değişmenin BIST 100 endeksine nasıl yansıyacağı belli bir olasılık dahilinde tahmin edilmeye çalışılmıştır. Çalışmada, 2007-2016 dönemi Dolar ve Euro kuru ile BIST 100 endeksi günlük kapanış fiyatları veri olarak kullanılmıştır. Yapılan analizler sonucunda döviz kurları ile borsa endeksi arasında kısa dönemde negatif, uzun dönemde pozitif yönlü bir ilişkinin bulunduğu belirlenmiştir. Bu bulgu ise Türkiye’de kısa dönemde, döviz kurlarını belirlemeye yönelik yaklaşımlardan olan “Portföy Dengesi Yaklaşımın” uzun dönemde ise “Geleneksel Yaklaşımın” geçerli olduğunu göstermektedir. Çalışmada ayrıca Dolar kurunun artması durumunda aynı gün itibariyle BIST 100 endeksinin %52,58 olasılıkla yükseleceği, Euro kurunun artması durumunda aynı gün itibariyle BIST 100 endeksinin %53,30 olasılıkla yükseleceği sonucuna ulaşılmıştır. 

Anahtar Kelimeler

Bayes Teorisi, döviz kurları, borsa, tahmin

Kaynakça

• Akdeniz, F. (2016), Olasılık ve İstatistik, Adana: Nobel Kitapevi.
• Akel, V., S. Gazel (2014),“Döviz Kurları İle BIST Sanayi Endeksi Arasındaki Eşbütünleşme İlişkisi: Bir ARDL Sınır Testi Yaklaşımı”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 44, 23-41.
• Akıncı, G.Y., F. Küçükçaylı, (2016), “Hisse Senedi Piyasası ve Döviz Kuru Mekanizmaları Üzerine Bir Panel Veri Analizi”, Muhasebe ve Finansman Dergisi, 71, 127-148.
• Anderson, D.R., D.J. Sweeney, T.A. Williams (2011), Essentials of Statistics for Business and Economics, USA: South-Western Cengage Learning,
• Andreu, E., M. Matsi, A. Savvides (2013), “Stockand Foreign Exchange Market Linkages in Emerging Economies”, Int. Fin. Markets, Inst. and Money, 27, 248-268.
• Aydemir, O., E. Demirhan (2009), “The Relationship Between StockPrices and Exchange Rates Evidence fromTurkey”, International Research Journal of Finance and Economics, 23, 2007-215.
• Beck, K., B. Niendorf, P. Peterson (2012), “The Use of Bayesian Methods in Financial Research”, Investment Management and Financial Innovations, 9(3), 68-75.
• Bello, Z. (2013), “The Association Between Exchange Rates and Stock Returns”, Investment Management and Financial Innovations, 10(3), 40-45.
• Berke, B. (2012), “Döviz Kuru ve İMKB 100 Endeksi İlişkisi: Yeni Bir Test”, Maliye Dergisi, 163, 243-254.
• Bola, A.A., A.G. Adesola, O.E. Olusayove, A.A. Adebisi (2013), “Forecasting Movement of the Nigerian Stock Exchange All Share Index Using Artificial Neural and Bayesian Networks”, Journal of Finance and Investment Analysis, 2(1), 41-59.
• Boyacıoğlu, M.A., D.Çürük (2016), “Döviz Kuru Değişimlerinin Hisse Senedi Getirisine Etkisi: Borsa İstanbul 100 Endeksi Üzerine Bir uygulama”, Muhasebe ve Finansman Dergisi, 70, 143-156.
• Chairakwattana,K., S. Nathaphan (2014), “Stock Return Predictability by Bayesian Model Averaging: Evidence from Stock Exchange of Thailand”, International Journal of Economic Sciences, 3(1), 47-63. Da Silva, F.M., D.A. Coronel, K.M. Vieria (2014), “Causality and Cointegration Analysis Between Macroeconomic Variable sand the Bovespa”, Plos One, 9(2), 1-9.
• Dimitrova, D. (2005),“The Relationship Between Exchange Ratesand Stock Prices: Studied in a Multivariate Model”, Issues in PoliticalEconomy, 14.
• Doğru, B., M. Recepoğlu (2014), “Türkiye’de Hisse Senedi Fiyatları ve Döviz Kuru Arasında Doğrusal ve Doğrusal Olmayan Eş Bütünleşme İlişkisi”, Dumlupınar Üniversitesi Sosyal Bilimler Dergis, EYİ Özel Sayısı, 17-34.
• DosSantos, A.S., A.R. Neto, E.C. Araujo, L. Oliviera, M.B. Abrita (2013), “Interaction between Macroeconomics Variable sand IBOVESPA, Brazilian Stock Market Index”, Transnational Corporations Review, 5(4), 81-95.
• Elmas, B., Ö. Esen (2011), “Hisse Senedi Fiyatları ile Döviz Kuru Arasındaki Dinamik İlişkinin Belirlenmesi; Farklı Ülke Piyasaları İçin Bir Araştırma”, Muhasebe ve Finansman Dergisi, Ekim, 153-170.
• Erer, D., E. Erer, T.C. Güleç (2016), “Fractional Cointegration Analysis of Stok Market and Exchange Rates: The Case of Turkey”, Financial Studies, 3, 80-94. Ersoy, H., A. Koy (2016), “Euro ve ABD Doları Kurları ile Pay Senedi Endeksleri Arasındaki İlişkinin İncelenmesi: Borsa İstanbul Verileri Üzerine Ampirik Bir Çalışma”, Finans ve Bankacılık Çalışmaları Dergisi, 5(2), 21-36.
• Gerlein, E., A. McGinnity, M.,A. Belatreche, S. Coleman (2016), “Evaluating Machine Learning Classification for Financial Trading: An Empirical Approach”, Expert Systems With Applications, 54, 193-207.
• Gulati, D., M. Kakhani (2012), “Relationship Between Stock Market and Foreign Exchange Market in India: An Empirical Study”, Pacific Business Review International, 5(5), 66-71.
• Güriş, S., İ.S. Saçıldı (2011), “İstanbul Menkul Kıymetler Borsası’nda Hisse Senedi Getiri Volatilitesinin Klasik ve Bayesyen GARCH Modelleri ile Analizi”, Trakya Üniversitesi Sosyal Bilimler Dergisi, 13(2), 153-172.
• Harjito, A., C.B. McGowan (2007), “Stock Price and Exchange Rate Causality: The Case of Four Asean Countries”, Southwestern Economic Review, 34, 103-114.
• Harman, A., Y. Akgündüz (2014), “Efes Ören Yeri Ziyaretçilerinin Müze Deneyimi Beklentilerini Belirlemeye Dönük Bir Araştırma”, Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 16(2), 113-133.
• Jiang, Y., L.H. Wu (2009), “Credit Scoring Model Based on Simple Naive Bayesian Classifier and a Rough Set”, International Conference on Computational Intelligenceand Software Engineering, 1-4.
• Kaltenbrunner, A. (2015), “Financial Integration and Exchange Rate Determination: A Brazilian Case Study”, International Review of Applied Economics, 29(2), 129-149.
• Khan, A., Z. Abbas (2015), “Portfolio Balance Approach: An Empirical Testing”,Journal of Economicsand International Finance, 7(6), 137-142.
• Kita, E., Y. Zuo, M. Harada, T. Mizuno (2012), “Application of Bayesian Network to Stock Price Prediction”, Artificial Intelligence Research, 1(2), 171-184.
• Koulakiotis, A., A. Kiohos, V. Babalos (2015), “Exploring the Interaction Between Stock Price Index and Exchange Rates: An Asymmetric Threshold Approach”, Applied Economics, 47(13), 1273-1285.
• Kurihara, Y., A. Fukushima (2014), “Exchange Rates, Stock Prices and Commodity Prices: Are There Any Relationship?”, Advances in Social Sciences Research Journal, 1(5), 107-115.
• Küçük, E. (2011), “Planlanmış Davranış Teorisi Çerçevesinde Mali Müşavir (SMMM) Olma Niyetinin Altında Yatan Faktörlerin Analizi”, ZKÜ Sosyal Bilimler Dergisi, 7(14), 145-162.
• Lee, W.C. (2012), “A Study of the Causal Relationship Between Real Exchange Rate of Renminbi and Hong Kong Stock Market Index”, Modern Economy, 3, 563-566.
• Lee, Y.M., K.M Wang (2015), “Dynamic Heterogeneous Panel Analysis of the Correlation Between Stock Prices and Exchange Rates”, Economic Research-Ekonomska Istraživanja, 28(1), 749-772.
• Liang, C.C., L.B. Lin, H.C. Hsu (2013), “Reexamining the Relationships Between Stock Prices and Exchange Rates in ASEAN-5 Using Panel GrangerCausalityApproach”, Economic Modelling, 32, 560-563.
• Meissner, G. (2014), Correlation Risk Modeling and Management, Singapore: John Wiley&Sons.
• Miller, M.B. (2014), Mathematics and Statistics for Financial Risk Management, NJ: John WileySons.
• Mishra, S. (2016), “The Quantile Regression Approach to Analysis of Dynamic Interaction Between Exchange Rate and StockReturns in Emerging Markets: Case of BRIC Nations”, The IUP Journal of Financial Risk Management, 13(1), 7-27.
• Moore, T., P. Wang (2014), “Dynamic Linkage Between Real Exchange Ratesand Stock Prices: Evidence From Developed and Emerging Asian Markets”, International Review of Economicsand Finance, 29, 1-11.
• Pan, M.S., R.C.W. Fok, Y.A. Liu (2007), “Dynamic Linkages Between Exchange Ratesand Stock Prices: Evidence From East Asian Markets”, International Review of Economic sand Finance, 16, 503-520.
• Patel, J., S. Shah, P. Thakkar, K. Kotecha (2015), “Predicting Stockand Stock Price Index Movement Using Trend Deterministic Data Preparation and Machine Learning Techniques”, Expert Systems with Applications, 42, 259-268.
• Polson, N.G., B.V. Tew (2000), “Bayesian Portfolio Selection: An Empirical Analysis of the S&P500 Index1970-1996”, Journal of Business &EconomicStatistics, 18(2), 164-173.
• Rachev, S.T., J.S. Hsu, B.S. Bagasheva, F.J. Fabozzi (2008), Bayesian Methods in Finance, NJ: John WileySons.
• Reboredo, J.C., M.A. Rivera-Castro, A. Ugolini (2016), “Downside and Upside Risk Spillovers Between Exchange Rates and Stock Prices”, Journal of Banking& Finance, 62, 76-96.
• Sarkar, S., R.S. Sriram (2001), “Bayesian Models for Early Warning of Bank Failures”, Management Science, 47(11), 1457-1475.
• Sui, L., L. Sun (2016), “Spillover Effects Between Exchange Ratesand Stock Prices: Evidence from BRICS Around the Recent Global Financial Crisis”, Research in International Business and Finance, 36, 459-471.
• Theofilatos, K., S. Likothanassis, A. Karathanasopoulos (2012), “Modeling and Trading the EUR/USD Exchange Rate Using Machine Learning Techniques”, Engineering, Technology & Applied Science Research, 2(5), 269-272.
• Tsagkanos, A., C. Siriopoulos (2013), “A Long-Run Relationship Between Stock Price Index and Exchange Rate: A Structural Nonparametric Cointegrating Regression Approach”, International Financial Markets, Institutionsand Money, 25, 106-118.
• Ünver, Ö., H. Gamgam, B. Altunkaynak (2013), Temel İstatistik Yöntemler, Ankara: Seçkin Yayınları.
• Wu, K.J., C.C. Lu, H. Jono, I. Perez (2012), “Interrelationship Between Philippine Stock Exchange Index and USD Exchange Rate”, Procedia – Socialand Behavioral Sciences, 40, 768-782.
• Yıldız, A. (2014), “Döviz Kuru ile Sektörel Hisse Senedi Endeksleri Arasındaki İlişki”, Finans Politik & Ekonomik Yorumlar, 51(593), 77-91.
• Yurttançıkmaz, Z.Y. (2012), “Döviz Kuru ve Enflasyonun Hisse Senedi Getirileri Üzerindeki Etkisi”, EKEV Akademi Dergisi, 16(51), 393-410.
• Zuo, Y., E. Kita (2012), “Up/Down Analysis of Stock Index by Using Bayesian Network”, Engineering Management Research, 1(2), 46-52.
• Zügül, M., C. Şahin (2009), “İMKB 100 Endeksi ile Bazı Makroekonomik Değişkenler Arasındaki İlişkiyi İncelemeye Yönelik Bir Uygulama”, Akademik Bakış, Nisan, 1-16.