Adaptif Piyasa Hipotezi ve Getiri Öngörülebilirliği: Borsa İstanbul İçin Bir Gizli Markov Modeli Uygulaması

Year 2021, Volume: 29 Issue: 48, 31 - 58, 28.04.2021

Hasan Arda Burhan , Eylem Acar

https://doi.org/10.17233/sosyoekonomi.2021.02.02

Cited By: 3

Abstract

Adaptif piyasa hipotezi (APH) güncel finansal literatürde belirgin bir ilgi görmektedir. Bu durum APH’nin yine finansal literatürde sıklıkla araştırma konusu olan etkin piyasa hipotezine bir alternatif olarak ortaya çıkmış olması ile bağlantılıdır. Bu doğrultuda çalışmada, ilk olarak Borsa İstanbul hisse senedi piyasası BIST100 endeksi için APH, getiri öngörülebilirliğinin test edilmesi yoluyla incelenmiştir. Bu bağlamda Ocak 1988 - Aralık 2017 arası günlük kapanış fiyatı verilerine otomatik portmanteau ve genelleştirilmiş spektral (GS) testleri uygulanmıştır. Analizin devamında bu testlerin sonuçları, getiri öngörülebilirliği sağlayan dönemleri incelemek için bir gizli Markov model (GMM) uygulamasında kullanılmıştır. Sonuçlara göre Borsa İstanbul’un APH'ne güçlü bir şekilde uyum sağladığı görülmüştür. Ek olarak, GMM uygulamasının sonuçları, endeksin belirleyicileri ile ilgili olarak da periyodik öngörülebilirliği doğrulamıştır.

Keywords

Adaptif Piyasa Hipotezi, Getiri Öngörülebilirliği, Gizli Markov Modelleri, Borsa İstanbul, BIST 100

Adaptive Market Hypothesis and Return Predictability: A Hidden Markov Model Application in Borsa Istanbul

Abstract

The adaptive market hypothesis (AMH) has recently attracted significant interest in the financial literature. The AMH has started to be considered an alternative to the efficient market hypothesis. In this respect, this study, first of all, examines the AMH for the BIST100 index of Turkey’s Borsa Istanbul stock exchange market by testing the return predictability. The applications are performed via automatic portmanteau and the generalized spectral (GS) tests using daily closing price data between January 1988 and December 2017. Secondly, the results of these tests are utilized for a hidden Markov model (HMM) application to examine the periods that yield return predictability. According to the results, it is observed that there is strong evidence for the validity of AMH within the scope of Borsa Istanbul’s BIST100. Additionally, the results of the HMM application confirm the periodic predictability regarding the determinants of the index.

Keywords

Adaptive Market Hypothesis, Return Predictability, Hidden Markov Models, Borsa Istanbul, BIST 100

References

• Aga, M. & B. Kocaman (2011), “Efficient Market Hypothesis and Emerging Capital Markets: Empirical Evidence from Istanbul Stock Exchange”, Journal of Financial Markets Research, 3, 44-57.
• Al-Khazali, O. & A. Mirzaei (2017), “Stock Market Anomalies, Market Efficiency, and The Adaptive Market Hypothesis: Evidence from Islamic Stock Indices”, Journal of International Financial Markets, Institutions & Money, 51, 190-208.
• Alvarez-Ramirez, J. & E. Rodriguez & G. Espinosa-Paredes (2012), “Is The US Stock Market Becoming Weakly Efficient Over Time? Evidence from 80-Year-Long Data”, Physica A, 391, 5643-5647.
• Balaban, E. & H. Baturalp & K. Kunter (1996), Stock Market Efficiency in a Developing Economy: Evidence from Turkey, Central Bank of Republic of Turkey, Ankara, Türkiye.
• Bhar, R. & S. Hamori (2004), Hidden Markov Models: Applications to Financial Economics, Kluwer Academic Publishers, Dordrecht, Netherlands.
• Boratav, K. (2013), Türkiye İktisat Tarihi 1908-2015, İmge Kitabevi Yayınları, İstanbul, Türkiye.
• Boya, C.M. (2019), “From Efficient Markets to Adaptive Markets: Evidence from the French Stock Exchange”, Research in International Business and Finance, 49, 156-165.
• Buguk, C. & B.W. Brorsen (2003), “Testing Weak-Form Market Efficiency: Evidence from the Istanbul Stock Exchange”, International Review of Financial Analysis, 12, 579-590.
• Butler, M. & D. Kazakov (2012), “Testing Implications of the Adaptive Market Hypothesis Via Computational Intelligence”, IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 1-8.
• Campbell, J.Y. & A.W. Lo & A.C. MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press, New Jersey, USA.
• Charles, A. & O. Darné & J.H. Kim (2012), “Exchange-Rate Return Predictability and The Adaptive Markets Hypothesis: Evidence from Major Foreign Exchange Rates”, Journal of International Money and Finance, 31, 1607-1626.
• Charles, A. & O. Darné & J.H. Kim (2017), “Adaptive Markets Hypothesis for Islamic Stock Indices: Evidence from Dow Jones Size and Sector-Indices”, International Economics, 151, 100-112.
• Ching, W.K. & X. Huang & M.K. Ng & T.K. Siu (2013), Markov Chains: Models, Algorithms and Applications, Springer, New York, USA.
• Dağlıoğlu, C. & G. Kıral (2018), “Hisse Senedi Piyasa Fiyatlarının Saklı Markov Modeli ile Tahmin Edilmesi: Türkiye Örneği”, Uluslararası Ekonomi ve Yenilik Dergisi, 4(1), 61-75.
• Dias, J.G. & J.K. Vermunt & S. Ramos (2015), “Clustering Financial Time Series: New Insights from an Extended Hidden Markov Model”, European Journal of Operational Research, 243(3), 852-864.
• Dionne, G. & S.S. Hassani (2015), “Hidden Markov regimes in operational loss data: Application to the recent financial crisis”, Journal of Operational Risk, 1-40.
• Elliott, R.J. & W.C. Hunter & B.M. Jamieson (1998), “Drift and Volatility Estimation in Discrete Time”, Journal of Economic Dynamics and Control, 22(2), 209-218.
• Ertaş, F.C. & O. Özkan (2018), “Piyasa Etkinliği Açısından Adaptif Piyasa Hipotezinin Test Edilmesi: Türkiye ve ABD Hisse Senedi Piyasaları Örneği”, Finans Politik & Ekonomik Yorumlar, 642, 23-40.
• Escanciano, J.C. & C. Velasco (2006), “Generalized Spectral Tests for the Martingale Difference Hypothesis”, Journal of Econometrics, 134(1), 151-185.
• Escanciano, J.C. & I.N. Lobato (2009), “An Automatic Portmanteau Test for Serial Correlation”, Journal of Econometrics, 151(2), 140-149.
• Eyüpoğlu, K. & S. Eyüpoğlu (2020), “Borsa İstanbul Endekslerinde Adaptif Piyasa Hipotezinin Geçerliliğinin Test Edilmesi”, Journal of Yasar University, 15(59), 642-654.
• Fama, E.F. (1965), “The Behavior of Stock-Market Prices”, The Journal of Business, 38(1), 34-105.
• Fama, E.F. (1970), “Efficient Capital Markets: A Review of the Theory and Empirical Work”, Journal of Finance, 25(2), 383-417.
• Farmer, J.D. & A.W. Lo (1999), “Frontiers of Finance: Evolution and Efficient Markets”, Proceedings of the National Academy of Sciences of the United States of America, 96, 9991-9992.
• Ghazani, M.M. & M.K. Araghi (2014), “Evaluation of the Adaptive Market Hypothesis as an Evolutionary Perspective on Market Efficiency: Evidence from the Tehran Stock Exchange”, Research in International Business and Finance, 32, 50-59.
• Ghazani, M.M. & S.B. Ebrahimi (2019), “Testing the Adaptive Market Hypothesis as an Evolutionary Perspective on Market Efficiency: Evidence from The Crude Oil Prices”, Finance Research Letters, 30, 60-68.
• Grossman, S.J. & J.E. Stiglitz (1980), “On the Impossibility of Informationally Efficient Markets”, The American Economic Review, 70(3), 393-408.
• Gyamfi, E.N. (2018), “Adaptive Market Hypothesis: Evidence from the Ghanaian Stock Market”, Journal of African Business, 19(2), 195-209.
• Hatiboğlu, Z. & M. Aysan (1994), Türkiye Ekonomisinde 1994 Bunalımı, Beta Basım Yayım, İstanbul, Türkiye.
• Hiremath, G.S. & J. Kumari (2014), “Stock Returns Predictability and the Adaptive Market Hypothesis in Emerging Markets: Evidence from India”, SpringerPlus, 3(428), 1-14.
• Hiremath, G.S. & S. Narayan (2016), “Testing the Adaptive Market Hypothesis and Its Determinants for the Indian Stock Markets”, Finance Research Letters, 19, 173-180.
• Huang, M. & Y. Huang & K. He (2019), “Estimation and testing nonhomogeneity of hidden Markov model with application in financial time series”, Statistics and Its Interface, 12(2), 215-225.
• Ibe, O.C. (2013), Markov Processes for Stochastic Modelling, Elsevier, Massachusetts, USA.
• Ito, M. & A. Noda & T. Wada (2014), “International Stock Market Efficiency: A Non-Bayesian Time-Varying Model Approach”, Applied Economics, 43(23), 2744-2754.
• Ito, M. & A. Noda & T. Wada (2016), “The Evolution of Stock Market Efficiency in the US: A Non-Bayesian Time-Varying Model Approach”, Applied Economics, 48(7), 621-635.
• Kahraman, D. & M. Erkan (2005), “İstanbul Menkul Kıymetler Borsası’nda Tesadüfi Yürüyüş Testi”, Yönetim ve Ekonomi, 12(1), 11-19.
• Khuntia, S. & J.K. Pattanayak (2018), “Adaptive Market Hypothesis and Evolving Predictability of Bitcoin”. Economic Letters, 167, 26-28.
• Khursheed, A. & M. Naeem & S. Ahmed & F. Mustafa (2020), “Adaptive Market Hypothesis: An Empirical Analysis of Time-Varying Market Efficiency of Cryptocurrencies”, Cogent Economics & Finance, 8(1), 1719574.
• Kılıç, Y. & M.F. Buğan (2016), “The Efficient Market Hypothesis: Evidence from Turkey”, International Journal of Academic Research in Business and Social Sciences, 6(10), 262-272.
• Kim, J.H. & A. Shamsuddin & K.-P. Lim (2011), “Stock Return Predictability and the Adaptive Market Hypothesis: Evidence from Century-Long U.S. Data”, Journal of Empirical Finance, 18, 868-879.
• Kołatka, M. (2020), “Testing the Adaptive Market Hypothesis on the WIG Stock Index: 1994-2019”, Research Papers of Wroclaw University of Economics and Business, 64(1), 131-142.
• Langrock, R. & I.L. MacDonald & W. Zucchini (2012), “Some Nonstandard Stochastic Volatility Models and Their Estimation Using Structured Hidden Markov Models”, Journal of Empirical Finance, 19(1), 147-161.
• Lazăr, D. & A. Todea & D. Filip (2012), “Martingale Difference Hypothesis and Financial Crisis: Empirical Evidence from European Emerging Foreign Exchange Markets”, Economic Systems, 36, 338-350.
• Lekhal, E. & A. El Oubani (2020), “Does the Adaptive Market Hypothesis Explain the Evolution of Emerging Markets Efficiency? Evidence from the Moroccan Financial Market”, Heliyon, 6(7), e04429, 1-12.
• Li, N. (2016), Hidden Markov model and financial application, The University of Texas in Austin, Austin, USA. Lim, K.-P. & R.D. Brooks (2006), The Evolving and Relative Efficiencies of Stock Markets: Empirical Evidence from Rolling Bicorrelation Test Statistics, <https://ssrn.com/abstract=931071/>, 13.02.2019.
• Lim, K.-P. & W. Luo & J.H. Kim (2013), “Are US Stock Index Returns Predictable? Evidence from Automatic Autocorrelation-Based Tests”, Applied Economics, 45(8), 953-962.
• Lim, K.-P. (2007), “Ranking Market Efficiency for Stock Markets: A Nonlinear Perspective”, Physica A, 376, 445-454.
• Lin, S.-K. & S.-Y. Wang & P.-L. Tsai (2009), “Application of Hidden Markov Switching Moving Average Model in the Stock Markets: Theory and Empirical Evidence”, International Review of Economics & Finance, 18(2), 306-317.
• Lo, A.W. & A.C. MacKinlay (1999), A Non-Random Walk Down Wall Street, Princeton University Press, New Jersey, USA.
• Lo, A.W. (2004), “The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective”, Journal of Portfolio Management, 30, 15-29.
• Lo, A.W. (2005), “Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis”, The Journal of Investment Consulting, 7(2), 1-24.
• Lo, A.W. (2012), “Adaptive Markets and the New World Order”, Financial Analysts Journal, 68(2), 18-29.
• Lobato, I. & J.C. Nankervis & N.E. Savin (2001), “Testing for Autocorrelation Using a Modified Box‐Pierce Q Test”, International Economic Review, 42(1), 187-205.
• Madhavan, V. & R. Arrawatia (2016) “Relative Efficiency of G8 Sovereign Credit Default Swaps and Bond Scrips: An Adaptive Market Hypothesis Perspective”, Studies in Microeconomics, 4(2), 1-24.
• Meng, Q.-B. & X. Zhang & J.-N. Bi (2017), “On Optimal Proportional Reinsurance and Investment in a Hidden Markov Financial Market”, Acta Mathematicae Applicatae Sinica English Series, 33(1), 53-62.
• Nguyen, N. & D. Nguyen (2015), “Hidden Markov model for stock selection”, Risks, 3, 455-473.
• Noda, A. (2016), “A Test of the Adaptive Market Hypothesis Using a Time-Varying AR Model in Japan”, Finance Research Letters, 14, 66-71.
• Nurunnabi, M. (2012), “Testing Weak-Form Efficiency of Emerging Economies: A Critical Review of Literature”, Journal of Business Economics and Management, 13(1), 167-188.
• Nystrup, P. & H. Madsen & E. Lindström (2015), “Stylised Facts of Financial Time Series and Hidden Markov Models in Continuous Time”, Quantitative Finance, 15(9), 1531-1541.
• Nystrup, P. & H. Madsen & E. Lindström (2017), “Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters”, Journal of Forecasting, 36(8), 989-1002.
• Obalade, A.A. & P.F. Muzindutsi (2020), “Validating the Adaptive Market Hypothesis in the Tunisian Stock Market”, International Journal of Trade and Global Markets, 13(1), 42-51.
• Öz, E. (2009), “Saklı Markov Modelleri ve Finansal Bir Uygulama”, İstanbul: Yayınlanmamış Doktora Tezi, Marmara Üniversitesi Sosyal Bilimler Enstitüsü.
• Patil, A. & S. Rastogi (2020), “Multifractal Analysis of Time-Varying Market Efficiency: Implications for Adaptive Market Hypothesis”, Test Engineering and Management, 83, 16646-60.
• Popović, S. & A. Mugoša & Đ. Andrija (2013), “Adaptive Markets Hypothesis: Empirical Evidence from Montenegro Equity Market”, Economic Research, 26(3), 31-46.
• Rabiner, L.R. & B.H. Juang (1986), “An Introduction to Hidden Markov Models”, IEEE ASSP Magazine, 3(1), 4-16.
• Rahman, M.L. & D. Lee & A. Shamsuddin (2017), “Time-Varying Return Predictability in South Asian Equity Markets”, International Review of Economics and Finance, 48, 179-200.
• Ramírez, S.C. & P.L. Arellano & O. Rojas (2015), “Adaptive Market Efficiency of Agricultural Commodity Futures Contracts”, Contaduría y Administración, 60(2), 389-401.
• Reilly, F.K. & K.C. Brown (2012), Investment Analysis & Portfolio Management, South-Western Cengage Learning, Ohio, USA.
• Rosini, L. & V. Shenai (2020), “Stock Returns and Calendar Anomalies on the London Stock Exchange in the Dynamic Perspective of the Adaptive Market Hypothesis: A Study of FTSE100 & FTSE250 Indices over a Ten-Year Period”, Quantitative Finance and Economics, 4(1), 121-147.
• Rossi, A. & G.M. Gallo (2006), “Volatility Estimation Via Hidden Markov Models”, Journal of Empirical Finance, 13(2), 203-230.
• Şahin, H. (2009), Türkiye Ekonomisi, Seçkin Yayıncılık, Ankara, Türkiye.
• Shahid, M.N. & K. Latif & G.M. Chaudhary & R. Kouser (2020), “Vacillating Behavior of TOM Effect and Adaptive Market Hypothesis: A Firm-Level Evidence from Emerging Stock Market of Pakistan”, Journal of Business and Social Review in Emerging Economies, 6(2), 517-529.
• Shiller, R.J. (2003), “From Efficient Markets Theory to Behavioral Finance”, The Journal of Economic Perspectives, 17(1), 83-104.
• Simon, H.A. (1955), “A Behavioral Model of Rational Choice”, The Quarterly Journal of Economics, 69(1), 99-118.
• Smith, G. (2012), “The Changing and Relative Efficiency of European Emerging Stock Markets”, The European Journal of Finance, 18(8), 689-708.
• Thomas L.C. & D.E. Allen & N. Morkel-Kingsbury (2002), “A Hidden Markov Chain Model for the Term Structure of Bond Credit Risk Spreads”, International Review of Financial Analysis, 11(3), 311-329.
• Ţiţan, A.G. (2015), “The Efficient Market Hypothesis: Review of Specialized Literature and Empirical Research”, Procedia Economics and Finance, 32, 442-449.
• Todea, A. & M. Ulici & S. Silaghi (2009), “Adaptive Markets Hypothesis: Evidence from Asia-Pacific Financial Markets”, The Review of Finance and Banking, 1(1), 7-13.
• Tripathi A. & V. Vipul & A. Dixit (2020), “Adaptive Market Hypothesis and Investor Sentiments: Global Evidence”, Managerial Finance, 46(11), 1407-1436.
• Urquhart, A. & F. McGroarty (2016), “Are Stock Markets Really Efficient? Evidence of the Adaptive Market Hypothesis”, International Review of Financial Analysis, 47, 39-49.
• Urquhart, A. & R. Hudson (2013), “Efficient or Adaptive Markets? Evidence from Major Stock Markets Using Very Long Run Historic Data”, International Review of Financial Analysis, 28, 130-142.
• Verheyden, T. & F.V. Bossche & L.D. Moor (2013), “Towards a New Framework on Efficient Markets: A Rolling Variance Ratio Test of the Adaptive Market Hypothesis”, Research in International Business and Finance, 34, 294-308.
• Yılmaz, N.T. & T. Can (2016), “The Analysis of Foreign Direct Investment with Hidden Markov Model: Evidence from Turkey”, International Journal of Economic Perspectives, 10(2), 117-133.
• Yılmaz, N.T. (2015), Türkiye’ye Gelen Doğrudan Yabancı Sermaye Yatırımları Üzerine Gizli Markov Modeli Uygulaması, Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul, Türkiye.
• Zhang, B. (2013), “Are the Crude Oil Markets Becoming More Efficient Over Time? New Evidence from a Generalized Spectral Test”, Energy Economics, 40, 875-881.
• Zhang, M. & X. Jiang & Z. Fang & Y. Zeng & K. Xu (2019), “High-Order Hidden Markov Model for Trend Prediction in Financial Time Series”, Physica A: Statistical Mechanics and its Applications, 517, 1-12.
• Zhou, J. & J.M. Lee (2013), “Adaptive Market Hypothesis: Evidence from The REIT Market”, Applied Financial Economics, 23(21), 1649-1662.