The Recurrence Quantification Analysis (RQA), a pattern recognition-based time series analysis method, can be successfully utilized for short, nonstationary, nonlinear, and chaotic time series. These RQA measures quantify several properties of time series, including predictability, regularity, stability, randomness, and complexity. In this regard, first, we analyzed the intraday seasonality with RQA and demonstrated how RQA measures change among the intraday periods by using 160 million row matched orders of 100 shares from Borsa Istanbul Equity Market between 2019M10 and 2020M02. We selected 50 stocks from the BIST50 Index group and 50 stocks from outside of the BIST100 Index group. Since these two share groups exhibit similar intraday RQA seasonality, our results are robust. Second, we explained intraday volatility with RQA measures and found a relationship between RQA measures and intraday volatility using a regression model.

Keywords:

Remember to check out the Most Cited Articles!
Check out our Bifurcation & Chaos

References

Abad, D., Sanabria, S. & Yagüe, J. [2009] “ Strategic timing of annual earnings announcements: Evidence from an order-driven market,” Rev. Quant. Finan. Account. 32, 287–308. Crossref, Google Scholar
Abhyankar, A., Ghosh, D., Levin, E. & Limmack, R. J. [1997] “ Bid-ask spreads, trading volume and volatility: Intra-day evidence from the London Stock Exchange,” J. Busin. Finan. Account. 24, 343–362. Crossref, Google Scholar
Abu, Y. & Mostafa, A. A. [1996] “ Introduction to financial forecasting,” Appl. Intell. 6, 205–213. Crossref, Web of Science, Google Scholar
Ahn, H.-J., Bae, K.-H. & Chan, K. [2001] “ Limit orders, depth, and volatility: Evidence from the stock exchange of Hong Kong,” The J. Finan. 56, 767–788. Crossref, Web of Science, Google Scholar
Amihud, Y. & Mendelson, H. [1987] “ Trading mechanisms and stock returns: An empirical investigation,” The J. Finan. 42, 533–553. Crossref, Web of Science, Google Scholar
Andersen, T. G. & Bollerslev, T. [1997a] “ Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns,” The J. Finan. 52, 975–1005. Crossref, Web of Science, Google Scholar
Andersen, T. G. & Bollerslev, T. [1997b] “ Intraday periodicity and volatility persistence in financial markets,” J. Empir. Finan. 4, 115–158. Crossref, Google Scholar
Andersen, T. G., Bollerslev, T. & Cai, J. [2000] “ Intraday and interday volatility in the Japanese stock market,” J. Int. Finan. Mark. Instit. Money 10, 107–130. Crossref, Google Scholar
Andersen, T. G., Bollerslev, T., Diebold, F. X. & Ebens, H. [2001a] “ The distribution of realized stock return volatility,” J. Finan. Econ. 61, 43–76. Crossref, Web of Science, Google Scholar
Andersen, T. G., Bollerslev, T., Diebold, F. X. & Labys, P. [2001b] “ The distribution of realized exchange rate volatility,” J. Ameri. Statist. Assoc. 96, 42–55. Crossref, Web of Science, Google Scholar
Andersen, T. G., Bollerslev, T., Diebold, F. X. & Labys, P. [2003] “ Modeling and forecasting realized volatility,” Econometrica 71, 579–625. Crossref, Web of Science, Google Scholar
Aslan, A. & Sensoy, A. [2019] “ Intraday efficiency-frequency nexus in the cryptocurrency markets,” Finan. Res. Lett., https://doi.org/10.1016/j.frl.2019.09.013. Web of Science, Google Scholar
Atay, F. M. & Altıntaş, Y. [1999] “ Recovering smooth dynamics from time series with the aid of recurrence plots,” Phys. Rev. E 59, 6593–6598. Crossref, Web of Science, Google Scholar
Bastos, J. A. & Caiado, J. [2011] “ Recurrence quantification analysis of global stock markets,” Physica A 390, 1315–1325. Crossref, Web of Science, Google Scholar
Bildik, R. [2001] “ Intra-day seasonalities on stock returns: Evidence from the Turkish stock market,” Emerging Mark. Rev. 2, 387–417. Crossref, Google Scholar
Çelik, M. & Afşar, K. [2010] “ Finansal zaman serilerinde yineleme haritalari analizi: İMKB örneği,” Dumlupınar Üniversitesi Sosyal Bilimler Dergisi 22, 1–8. Google Scholar
Chen, W.-S. [2011] “ Use of recurrence plot and recurrence quantification analysis in Taiwan unemployment rate time series,” Physica A 390, 1332–1342. Crossref, Web of Science, Google Scholar
Cootner, P. H. [1964] The Random Character of Stock Market Prices (MIT Press, Boston). Google Scholar
Eaves, J. & Williams, J. [2010] “ Are intraday volume and volatility U-shaped after accounting for public information?” Amer. J. Agricul. Econ. 92, 212–227. Crossref, Web of Science, Google Scholar
Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. [1987] “ Recurrence plots of dynamical systems,” Europhys. Lett. 4, 973–977. Crossref, Web of Science, Google Scholar
Elaut, G., Frömmel, M. & Lampaert, K. [2018] “ Intraday momentum in FX markets: Disentangling informed trading from liquidity provision,” J. Finan. Mark. 37, 35–51. Crossref, Web of Science, Google Scholar
Engle, R. F. & Sokalska, M. E. [2012] “ Forecasting intraday volatility in the US equity market. Multiplicative component GARCH,” J. Finan. Econom. 10, 54–83. Web of Science, Google Scholar
Fabretti, A. & Ausloos, M. [2005] “ Recurrence plot and recurrence quantification analysis techniques for detecting a critical regime,” Int. J. Mod. Phys. C 16, 671–706. Link, Web of Science, Google Scholar
Fama, E. F. [1965] “ Random walks in stock market prices,” Finan. Anal. J. 21. https://doi.org/10.2307/4469865. Crossref, Google Scholar
Fama, E. F. [1970] “ Efficient capital markets: A review of theory and empirical work,” The J. Finan. 25, 383. Crossref, Web of Science, Google Scholar
Gao, L., Han, Y., Zhengzi Li, S. & Zhou, G. [2018] “ Market intraday momentum,” J. Finan. Econ. 129, 394–414. Crossref, Web of Science, Google Scholar
Hamao, Y. & Hasbrouck, J. [1995] “ Securities trading in the absence of dealers: Trades and quotes on the Tokyo stock exchange,” The Rev. Finan. Stud. 8, 849–878. Crossref, Web of Science, Google Scholar
Haniff, M. N. & Pok, W. C. [2010] “ Intraday volatility and periodicity in the Malaysian stock returns,” Res. Int. Busi. Finan. 24, 329–343. Crossref, Google Scholar
Harju, K. & Hussain, S. M. [2011] “ Intraday seasonalities and macroeconomic news announcements,” Europ. Finan. Manag. 17, 367–390. Crossref, Web of Science, Google Scholar
Harris, L. [1986] “ A transaction data study of weekly and intradaily patterns in stock returns,” J. Finan. Econ. 16, 99–117. Crossref, Web of Science, Google Scholar
Harris, L. [1989] “ A day-end transaction price anomaly,” The J. Finan. Quant. Anal. 24, 29–45. Crossref, Web of Science, Google Scholar
Ho, T., Lv, J. R. & Schultz, E. [2021] “ Market intraday momentum in Australia,” Pacific-Basin Finan. J. 65, 101499. Crossref, Web of Science, Google Scholar
Hua, J., Peng, L., Schwartz, R. A. & Alan, N. S. [2020] “ Resiliency and stock returns,” The Rev. Finan. Stud. 33, 747–782. Crossref, Web of Science, Google Scholar
Huang, Y. C. & Chan, S. H. [2014] “ The trading behavior of attention securities with different closing mechanisms: Evidence from Taiwan,” Rev. Pacific Basin Finan. Mark. Poli. 17, 1–16. Google Scholar
Hughes, M. P., Smith, S. D. & Winters, D. B. [2007] “ An empirical examination of intraday volatility in on-the-run U.S. treasury bills,” J. Econ. Bussine. 59, 487–499. Crossref, Google Scholar
Inci, A. C. & Ozenbas, D. [2017] “ Intraday volatility and the implementation of a closing call auction at Borsa Istanbul,” Emerg. Mark. Rev. 33, 79–89. Crossref, Web of Science, Google Scholar
Jiang, C., Likitapiwat, T. & Mcinish, T. H. [2012] “ Information content of earnings announcements: Evidence from after-hours trading,” J. Finan. Quant. Anal. 47, 1303–1330. Crossref, Web of Science, Google Scholar
Kadioglu, E. & Küçükkocaoğlu, G. [2015] “ Borsa İstanbul’da Güniçi Getiri ve Volatilite Yapısıile Tek Fiyatlı Açılış ve Kapanış Seanslarının Etkisi,” BDDK Bankacılık ve Finansal Piyasalar Dergisi 9, 103–126. Google Scholar
Kadioglu, E., Ocal, N. & Bostanci, F. [2020] “ Borçlanma Araçları Piyasasında Öğle Arasının Kaldırılmasının Güniçi Getiri, Volatilite ve İşlem Hacmine Etkisi,” Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 23, 937–958. Crossref, Google Scholar
Kadioglu, E. [2021] “ Intraday analysis of regulation change in microstructure: Evidence from an emerging market,” Int. J. Emerg. Mark. Web of Science, Google Scholar
Küçükkocaoğlu, G. [2005] Borsa İstanbul’da Gün İçi Getiri, Volatilite ve Kapanış Fiyatı Manipülasyonu. Ankara: Sermaye PiyasasıKurulu. Google Scholar
Kyle, A. S. [1985] “ Continuous auctions and insider trading,” Econometrica 53, 1315–1335. Crossref, Web of Science, Google Scholar
Lowengrub, P. & Melvin, M. [2002] “ Before and after international cross-listing: An intraday examination of volume and volatility,” J. Int. Finan. Mark. Instit. Money 12, 139–155. Crossref, Google Scholar
Malkiel, B. G. [1973] A Random Walk Down Wall Street (Norton & Co., NY). Google Scholar
Marwan, N. & Kurths, J. [2002] “ Nonlinear analysis of bivariate data with cross recurrence plots,” Phys. Lett. A 302, 299–307. Crossref, Web of Science, Google Scholar
Marwan, N., Carmenromano, M., Thiel, M. & Kurths, J. [2007] “ Recurrence plots for the analysis of complex systems,” Phys. Rep. 438, 237–329. Crossref, Web of Science, Google Scholar
Miralles-Quirós, J. L., Miralles-Quirós, M. d. M. & Daza-Izquierdo, J. [2015] “ Intraday patterns and trading strategies in the Spanish stock market,” Appl. Econ. 47, 88–99. Crossref, Web of Science, Google Scholar
Miwa, K. & Ueda, K. [2017] “ Is the extension of trading hours always beneficial? An artificial agent-based analysis,” Computat. Econ. 50, 595–627. Crossref, Web of Science, Google Scholar
Orlando, G. & Zimatore, G. [2018] “ Recurrence quantification analysis of business cycles,” Chaos Solit. Fract. 110, 82–94. Crossref, Web of Science, Google Scholar
Packard, N. H., Crutchfield, J. P., Farmer, J. D. & Shaw, R. S. [1980] “ Geometry from a time series,” Phys. Rev. Lett. 45, 712–716. Crossref, Web of Science, Google Scholar
Paye, B. S. [2012] “ ‘Déjà Vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables,” J. Finan. Econ. 106, 527–546. Crossref, Web of Science, Google Scholar
Sasikumar, A. & Kamaiah, B. [2014] “ A complex dynamical analysis of the Indian stock market,” Econ. Res. Int. 2014, 1–6. Crossref, Google Scholar
Schwert, G. W. [1989] “ Why does stock market volatility change over time?” The J. Finan. 44, 1115–1153. Crossref, Web of Science, Google Scholar
Selçuk, F. & Gençay, R. [2006] “ Intraday dynamics of stock market returns and volatility,” Physica A 367, 375–387. Crossref, Web of Science, Google Scholar
Sensoy, A. & Serdengecti, S. [2019] “ Intraday volume-volatility nexus in the FX markets: Evidence from an emerging market,” Int. Rev. Finan. Anal. 64, 1–12. Crossref, Web of Science, Google Scholar
Sévi, B. [2014] “ Forecasting the volatility of crude oil futures using intraday data,” Europ. J. Oper. Res. 235, 643–659. Crossref, Web of Science, Google Scholar
Strozzi, F., Zaldívar, J.-M. & Zbilut, J. P. [2007] “ Recurrence quantification analysis and state space divergence reconstruction for financial time series analysis,” Physica A 376, 487–499. Crossref, Web of Science, Google Scholar
Strozzi, F., Gutiérrez, E., Noè, C., Rossi, T., Serati, M. & Zaldívar, J. M. [2008] “ Measuring volatility in the Nordic spot electricity market using recurrence quantification analysis,” European Phys. J. Special Topics 164, 105–115. Crossref, Web of Science, Google Scholar
Takens, F. [1981] “ Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, eds. Rand, D. & Young, L. S. , Vol. 898 (Springer, Berlin, Heidelberg). Crossref, Google Scholar
Taylor, S. J. [1986] Modelling Financial Time Series (John Wiley). Google Scholar
Tian, G. G. & Guo, M. [2007] “ Interday and intraday volatility: Additional evidence from the Shanghai stock exchange,” Rev. Quant. Finan. Account. 28, 287–306. Crossref, Google Scholar
Trulla, L. L., Giuliani, A., Zbilut, J. P. & Webber, C. L. [1996] “ Recurrence quantification analysis of the logistic equation with transients,” Phys. Lett. A 223, 255–260. Crossref, Web of Science, Google Scholar
Wang, Y., Wei, Y., Wu, C. & Yin, L. [2018] “ Oil and the short-term predictability of stock return volatility,” J. Empir. Finan. 47, 90–104. Crossref, Web of Science, Google Scholar
Webber, C. L., Jr., & Zbilut, J. P. [1994] “ Dynamical assessment of physiological systems and states using recurrence plot strategies,” J. Appl. Physiol. 76, 965–973. Crossref, Web of Science, Google Scholar
Wood, R. A., McInish, T. H. & Ord, J. K. [1985] “ An investigation of transactions data for Nyse stocks,” The J. Finan. 40, 723–739. Crossref, Web of Science, Google Scholar
Xiao, X. & Yamamoto, R. [2020] “ Price discovery, order submission, and tick size during preopen period,” Pacific-Basin Finance J. 63, 101428. Crossref, Web of Science, Google Scholar
Xing, Y. & Wang, J. [2020] “ Linkages between global crude oil market volatility and financial market by complexity synchronization,” Empir. Econ. 59, 2405–2421. Crossref, Web of Science, Google Scholar
Zbilut, J. P. & Webber, C. L. [1992] “ Embeddings and delays as derived from quantification of recurrence plots,” Phys. Lett. A 171, 199–203. Crossref, Web of Science, Google Scholar
Zbilut, J. P. [2005] “ Use of recurrence quantification analysis in economic time series,” in Economics: Complex Windows, eds. Salzano, M. & Kirman, A. (Springer, Milano), pp. 91–104. Crossref, Google Scholar
Zhang, H. [2018] “ Intraday patterns in foreign exchange returns and realized volatility,” Finan. Res. Lett. 27, 99–104. Crossref, Web of Science, Google Scholar
Zhang, Y. J., Ma, F. & Zhu, B. [2019] “ Intraday momentum and stock return predictability: Evidence from China,” Econ. Modell. 76, 319–329. Crossref, Web of Science, Google Scholar