SHIFTS IN BORSA ISTANBUL RETURN BEHAVIOUR: AN ANALYSIS WITH CHANGE-POINT DETECTION

Yıl 2023, Cilt: 16 Sayı: 1, 211 - 212, 01.02.2023

Hazar Altınbas

https://doi.org/10.17261/Pressacademia.2023.1694

Öz

Purpose- In Turkey, there is an enormous increase in local investors’ stock market interest for the last several years (Altınbaş, 2021: 36). Increasing inflation, deteriorating welfare, lack of alternative investments seems to be the foremost reasons of this attention. As of November 2022, Borsa Istanbul 100 (BIST 100) index value level reached to historic highs (tripled in one year) and this trend is expected to continue. The index is exponentially increasing in overall and there are several retracement periods over the time. What drives, strengthens or changes this behaviour of market? Do political developments, economic news or any other events influence investors? To answer these questions, change points in statistical parameters of market return distributions are examined and detected dates are evaluated. Analysis period covers the dates between January 2009 and November 2022.
Methodology- Change points/breakpoints in statistical properties of time series can be informative for specific events that influence the behaviour (Beltratti & Morana, 2006; Chatzikonstanti, 2017; Eizaguirre, Biscarri, & de Gracia Hidalgo, 2004; Khuong Nguyen & Bellalah, 2008; Malik, 2011). In this study, change points determination is done with pruned exact linear time (PELT) method (Killick, Fearnhead, & Eckley, 2012). This method is applied to daily return data of the index, and it estimates a single or multiple points at which the statistical properties change. Analysis is conducted in R statistical software (R Core Team, 2021) with package changepoint.np (Haynes, Killick, Fearnhead, Eckley, & Grose, 2022). This is a modified version of original change point detection technique which is used in cases where there is no assumption on the statistical properties of observations (Haynes, Fearnhead, & Eckley, 2017).
Findings- Many changepoints detected. Some of these coincide with explicit/significant events, such as coup attempt in July 2016 or Covid-19 pandemic announcements. Interestingly, frequency of change points increased over the last one and a half year and most of them are very close to Monetary Policy Committee meetings. But some changes seem not to be directly related with important events, and some important events did not significantly change market behaviour.
Conclusion- Findings indicate it is neither possible to generalize market behaviour shifts with specific event types nor certainly expect a change after an event. Deeper analysis and interpretation of individual events and their impact may further provide further insights.

Anahtar Kelimeler

Stock market, investor sentiment, pruned exact linear time, change-point detection.

Kaynakça

• Altınbas, H., (2021). Salgının ve geleceğe ilişkin belirsizliğin, bireylerin finansal karar verme süreçlerine etkisi ile sonuçları üzerine bir değerlendirme ve öneriler. In Covid-19’un Ekonomi Politiği (pp. 21–46).
• Beltratti, A., & Morana, C. (2006). Breaks and persistency: Macroeconomic causes of stock market volatility. Journal of Econometrics, 131(1–2), 151–177. https://doi.org/10.1016/j.jeconom.2005.01.007
• Chatzikonstanti, V. (2017). Breaks and outliers when modelling the volatility of the U.S. stock market. Applied Economics, 49(46), 4704–4717. https://doi.org/10.1080/00036846.2017.1293785
• Eizaguirre, J. C., Biscarri, J. G., & de Gracia Hidalgo, F. P. (2004). Structural changes in volatility and stock market development: Evidence for Spain. Journal of Banking & Finance, 28(7), 1745–1773.
• Haynes, K., Fearnhead, P., & Eckley, I. A. (2017). A computationally efficient nonparametric approach for changepoint detection. Statistics and Computing, 27(5), 1293–1305. https://doi.org/10.1007/s11222-016-9687-5
• Khuong Nguyen, D., & Bellalah, M. (2008). Stock market liberalization, structural breaks and dynamic changes in emerging market volatility. Review of Accounting and Finance, 7(4), 396–411. https://doi.org/10.1108/14757700810920784
• Killick, R., Fearnhead, P., & Eckley, I. A. (2012). Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association, 107(500), 1590–1598. https://doi.org/10.1080/01621459.2012.737745
• Malik, F. (2011). Estimating the impact of good news on stock market volatility. Applied Financial Economics, 21(8), 545–554. https://doi.org/10.1080/09603107.2010.534063
• R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/