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A fuzzy method for evaluating similar behavior between assets

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Abstract

In this paper, we propose a fuzzy method to investigate the interconnection between equity markets in the form of similar behavior. It has been proved before that the trend cycle of time series can be well estimated using the fuzzy transform. In the suggested method, first, we approximate the local behavior of stocks as a sequence of their trend cycles. Then we measure the distance between these local trend cycles conducting similar practices between different assets. Two experiments are performed to demonstrate the advantages of the suggested method. This method is easy to calculate, well interpretable, and in addition to statistical co-relation, the measure can assist investors in gaining more intuition about the behavior of their assets.

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Notes

  1. Of course, certain formal requirements must be fulfilled. They are omitted here and can be found in the cited literature.

  2. In general, higher degree F-transform.

  3. Modulus of continuity is in our case defined as \(\omega (h, { T\!C})=\max _{\begin{array}{c} |x-y|<h\\ x,y\in [c_1, c_{n-1}] \end{array}}|{ T\!C}(x)-{ T\!C}(y)|\).

  4. A t-norm is a special operation that in fuzzy logic models logical conjunction.

  5. It is necessary to emphasize, that we can work with estimations \({ \widetilde{T\!C}}_X\) and \({ \widetilde{T\!C}}_Y\) of the trend cycle only, because we do not know the real ones.

  6. https://www.nasdaq.com/Second footnote

  7. https://www.nasdaq.com/Second footnote.

  8. www.finance.yahoo.com.

References

  • Anděl J (1976) Statistical analysis of time series. SNTL, Praha ( (in Czech))

    MATH  Google Scholar 

  • Bekaert G, Harvey CR (2003) Market integration and contagion. Tech. rep. National Bureau of Economic Research

  • Bernanke B (2016) The relationship between stocks and oil prices. Ben Bernanke’s Blog on Brookings posted on February, vol 19

  • Cha B, Oh S (2000) The relationship between developed equity markets and the pacific basin’s emerging equity markets. Int Rev Econ Finance 9(4):299–322

    Article  Google Scholar 

  • Chan WS (2003) Stock price reaction to news and no-news: drift and reversal after headlines. J Financ Econ 70(2):223–260

    Article  Google Scholar 

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 2:224–227

    Article  Google Scholar 

  • Devlin SJ, Gnanadesikan R, Kettenring JR (1975) Robust estimation and outlier detection with correlation coefficients. Biometrika 62(3):531–545

    Article  Google Scholar 

  • Eun CS, Shim S (1989) International transmission of stock market movements. J Financ Quantitat Anal 24(2):241–256

    Article  Google Scholar 

  • Fu T-C (2011) A review on time series data mining. Eng Appl Artif Intell 24(1):164–181

    Article  Google Scholar 

  • Granger CW (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica J Econ Soc, pp 424–438

  • Granger CW (1980) Testing for causality: a personal viewpoint. J Econ Dyn Control 2:329–352

    Article  MathSciNet  Google Scholar 

  • Hamao Y, Masulis RW, Ng V (1990) Correlations in price changes and volatility across international stock markets. Rev Financial Stud 3(2):281–307

    Article  Google Scholar 

  • Hamilton J (1994) Time series analysis. Princeton University Press, Princeton

    Book  Google Scholar 

  • Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam

    MATH  Google Scholar 

  • Herbst AF, McCormack JP, West EN (1987) Investigation of a lead-lag relationship between spot stock indices and their futures contracts. J Futures Markets 7(4):373–381

    Article  Google Scholar 

  • Hilliard JE (1979) The relationship between equity indices on world exchanges. J Finance 34(1):103–114

    Article  Google Scholar 

  • Hou K (2007) Industry information diffusion and the lead-lag effect in stock returns. Rev Financial Stud 20(4):1113–1138

    Article  Google Scholar 

  • Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis, vol 344. Wiley, Hoboken

    MATH  Google Scholar 

  • Keogh E, Kasetty S (2003) On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Mining knowl Discovery 7(4):349–371

    Article  MathSciNet  Google Scholar 

  • Kullmann L, Kertész J, Kaski K (2002) Time-dependent cross-correlations between different stock returns: a directed network of influence. Phys Rev E 66(2):026125

    Article  Google Scholar 

  • Liao TW (2005) Clustering of time series data-a survey. Pattern Recognit 38(11):1857–1874

    Article  Google Scholar 

  • Lo AW, MacKinlay AC (1990) When are contrarian profits due to stock market overreaction? Rev Financial Stud 3(2):175–205

    Article  Google Scholar 

  • Mandel L, Wolf E (1976) Spectral coherence and the concept of cross-spectral purity. JOSA 66(6):529–535

    Article  MathSciNet  Google Scholar 

  • Martens M, Poon S-H (2001) Returns synchronization and daily correlation dynamics between international stock markets. J Bank Finance 25(10):1805–1827

    Article  Google Scholar 

  • McQueen G, Pinegar M, Thorley S (1996) Delayed reaction to good news and the cross-autocorrelation of portfolio returns. J Finance 51(3):889–919

    Article  Google Scholar 

  • Mech TS (1993) Portfolio return autocorrelation. J Financial Econ 34(3):307–344

    Article  Google Scholar 

  • Mining WID (2006) Data mining: Concepts and techniques. Morgan Kaufinann

  • Mirshahi S, Novák V (2020) A fuzzy approach for similarity measurement in time series, case study for stocks. In: International conference on information processing and management of uncertainty in knowledge-based systems, Springer, pp 567–577

  • Morse MD, Patel JM (2007) An efficient and accurate method for evaluating time series similarity. In: Proceedings of the 2007 ACM SIGMOD international conference on Management of data, ACM, pp 569–580

  • Nguyen L, Novák V (2015) Filtering out high frequencies in time series using F-transform with respect to raised cosine generalized uniform fuzzy partition. In: Proc. Int. Conference FUZZ-IEEE, Istanbul. IEEE Computer Society, CPS, p 2015

  • Nguyen L, Novák V (2018) Forecasting seasonal time series based on fuzzy techniques. Fuzzy Sets and Systems, (to appear)

  • Novák V, Perfilieva I, Močkoř J (1999) Mathematical principles of fuzzy logic. Kluwer, Boston

    Book  Google Scholar 

  • Novák V, Štěpnička M, Dvořák A, Perfilieva I, Pavliska V, Vavříčková L (2010) Analysis of seasonal time series using fuzzy approach. Int J General Syst 39(3):305–328

    Article  MathSciNet  Google Scholar 

  • Novák V, Perfilieva I, Holčapek M, Kreinovich V (2014) Filtering out high frequencies in time series using f-transform. Inf Sci 274:192–209

    Article  MathSciNet  Google Scholar 

  • Novák V, Perfilieva I, Dvořák A (2016) Insight into fuzzy modeling. Wiley, Hoboken

    Book  Google Scholar 

  • Roll R (1992) Industrial structure and the comparative behavior of international stock market indices. J Finance 47(1):3–41

    Article  Google Scholar 

  • Serra J, Arcos JL (2014) An empirical evaluation of similarity measures for time series classification. Knowl Based Syst 67:305–314

    Article  Google Scholar 

  • Statman M, Scheid J (2008) Correlation, return gaps, and the benefits of diversification. J Portfolio Manage 34(3):132–139

    Article  Google Scholar 

  • Vlachos M, Hadjieleftheriou M, Gunopulos D, Keogh E (2006) Indexing multidimensional time-series. VLDB J 15(1):1–20

    Article  Google Scholar 

  • Wang PE (ed) (2001) Computing with Words. J. Wiley, New York

    Google Scholar 

  • Wang Y, Wei Y, Wu C (2010) Cross-correlations between chinese a-share and b-share markets. Physica A Stat Mech Appl 389(23):5468–5478

    Article  Google Scholar 

  • Wang X, Mueen A, Ding H, Trajcevski G, Scheuermann P, Keogh E (2013) Experimental comparison of representation methods and distance measures for time series data. Data Mining Knowl Discovery 26(2):275–309

    Article  MathSciNet  Google Scholar 

  • Wu C, Su Y-C (1998) Dynamic relations among international stock markets. Int Rev Econ Finance 7(1):63–84

    Article  Google Scholar 

  • Zervas G, Ruger SM (1999) The curse of dimensionality and document clustering

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Acknowledgements

The paper has been supported by the grant 18-13951S of GAČR, Czech Republic.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Soheyla Mirshahi and Vilém Novák. The first draft of the manuscript was written by both authors, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Soheyla Mirshahi.

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Communicated by Vladik Kreinovich.

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Mirshahi, S., Novák, V. A fuzzy method for evaluating similar behavior between assets. Soft Comput 25, 7813–7823 (2021). https://doi.org/10.1007/s00500-021-05639-y

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