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Published in: International Journal of Data Science and Analytics 3/2022

12-05-2022 | Regular Paper

Path signature-based phase space reconstruction for stock trend prediction

Authors: Cuiting Li, Ke Liu

Published in: International Journal of Data Science and Analytics | Issue 3/2022

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Abstract

In the field of quantitative finance, stock price trend prediction has always been one of the concerns of people. Stock price time series is the most important calculation basis in stock price prediction. The usual research idea is to take the one-dimensional single variable stock price time series to construct sample matrix through phase space reconstruction (PSR), and use it as the feature input of machine learning algorithm to predict the stock price. However, we often need a relatively large embedded dimension to obtain more useful information, which easily leads to the problems of high matrix dimension, over-fitting of prediction and low computational efficiency. To solve the above problems, we propose a path signature-based phase space reconstruction (PSR-PS) feature engineering approach for stock trend prediction, which can effectively extract features, reduce the dimension of high-dimensional price series data information and capture the necessary and effective information. Extensive experiments on several benchmark data sets from diverse real financial markets show that PSR-PS outperforms PSR and PSR-PCA (Principal component analysis-based PSR) in accuracy, precision and computational time, combined with different machine learning algorithms. It suggests that the proposed PSR-PS is effective.
Literature
1.
go back to reference Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a time series. Phys. Rev. Lett. 45(3), 712–716 (1980) CrossRef Packard, N.H., Crutchfield, J.P., Farmer, J.D., Shaw, R.S.: Geometry from a time series. Phys. Rev. Lett. 45(3), 712–716 (1980) CrossRef
2.
go back to reference Takens, F.: Detecting strange attractors in turbulence. Lect. Notes Math. 898(2), 361–381 (1981) MathSciNetMATH Takens, F.: Detecting strange attractors in turbulence. Lect. Notes Math. 898(2), 361–381 (1981) MathSciNetMATH
3.
go back to reference Henrique, B.M., Sobreiro, V.A., Kimura, H.: Literature review: machine learning techniques applied to financial market prediction. Expert Syst. Appl. 124, 226–251 (2019) CrossRef Henrique, B.M., Sobreiro, V.A., Kimura, H.: Literature review: machine learning techniques applied to financial market prediction. Expert Syst. Appl. 124, 226–251 (2019) CrossRef
4.
go back to reference Giamouridis, D.: Systematic investment strategies. Financ. Anal. J. 73(4), 10–14 (2017) CrossRef Giamouridis, D.: Systematic investment strategies. Financ. Anal. J. 73(4), 10–14 (2017) CrossRef
6.
go back to reference Yang, W., Jin, L.: The Representation and Recognition of Trajectory Data Based on Path Signature Feature and Deep Learning Methods, pp. 24–27. South China University of Technology, Guangzhou (2018) Yang, W., Jin, L.: The Representation and Recognition of Trajectory Data Based on Path Signature Feature and Deep Learning Methods, pp. 24–27. South China University of Technology, Guangzhou (2018)
7.
go back to reference Flint, G., Hambly, B., Lyons, T.: Discretely sampled signals and the rough Hoff process. Stoch. Process. Appl. 126(9), 2593–2614 (2016) MathSciNetCrossRef Flint, G., Hambly, B., Lyons, T.: Discretely sampled signals and the rough Hoff process. Stoch. Process. Appl. 126(9), 2593–2614 (2016) MathSciNetCrossRef
8.
go back to reference Tomohiro, T., Sugeno, M.: Fuzzy identification of system and its aplication to modeling and control. Trans. Syst. Man Cybern. 15(1), 116–132 (1985) MATH Tomohiro, T., Sugeno, M.: Fuzzy identification of system and its aplication to modeling and control. Trans. Syst. Man Cybern. 15(1), 116–132 (1985) MATH
9.
go back to reference Zhang, H., Liang, J., Chai, Z.: Stock prediction based on phase space reconstruction and Echo state networks. J. Algorithms Comput. Technol. 7(1), 87–99 (2012) Zhang, H., Liang, J., Chai, Z.: Stock prediction based on phase space reconstruction and Echo state networks. J. Algorithms Comput. Technol. 7(1), 87–99 (2012)
10.
go back to reference Kazema, A., Sharifia, E., Hussainb, F.K., Saberic, M., Hussaind, O.K.: Support vector regression with Chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13(2), 947–958 (2013) CrossRef Kazema, A., Sharifia, E., Hussainb, F.K., Saberic, M., Hussaind, O.K.: Support vector regression with Chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13(2), 947–958 (2013) CrossRef
11.
go back to reference Zhao, S.: Integrated stock market forecasting based on phase space reconstruction and nonparametric neural network. J. Guangxi Univer. Natl. 17(3), 1158 (2018) Zhao, S.: Integrated stock market forecasting based on phase space reconstruction and nonparametric neural network. J. Guangxi Univer. Natl. 17(3), 1158 (2018)
12.
go back to reference Chen, K.-S.: Integration of paths a faithful representation of paths by non-commutative formal power series. Trans. Am. Math. Soc. 89(2), 395–407 (1958) MathSciNetMATH Chen, K.-S.: Integration of paths a faithful representation of paths by non-commutative formal power series. Trans. Am. Math. Soc. 89(2), 395–407 (1958) MathSciNetMATH
13.
go back to reference Lyons, T.J.: Differential equations driven by rough signals. Revista Matematica Iberoamericana 14(2), 215–310 (1998) MathSciNetCrossRef Lyons, T.J.: Differential equations driven by rough signals. Revista Matematica Iberoamericana 14(2), 215–310 (1998) MathSciNetCrossRef
14.
go back to reference Levin, D., Lyons, T., Ni, H.: Learning from the past, predicting the statistics for the future, learning an evolving system, p. 0260. arXiv preprint arXiv:​1309.​0260 (2013) Levin, D., Lyons, T., Ni, H.: Learning from the past, predicting the statistics for the future, learning an evolving system, p. 0260. arXiv preprint arXiv:​1309.​0260 (2013)
15.
go back to reference Xie, Z., Sun, Z., Jin, L., Ni, H., Lyons, T.: Learning spatialsemantic context with fully convolutional recurrent network for online handwritten chinese text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1903–1917 (2018) CrossRef Xie, Z., Sun, Z., Jin, L., Ni, H., Lyons, T.: Learning spatialsemantic context with fully convolutional recurrent network for online handwritten chinese text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1903–1917 (2018) CrossRef
16.
go back to reference Yang, W., Lyons, T., Ni, H., Schmid, C., Jin, L., Chang, J.: Leveraging the path signature for skeleton-based human action recognition, p. 03993. arXiv preprint arXiv:​1707.​03993 (2017) Yang, W., Lyons, T., Ni, H., Schmid, C., Jin, L., Chang, J.: Leveraging the path signature for skeleton-based human action recognition, p. 03993. arXiv preprint arXiv:​1707.​03993 (2017)
17.
go back to reference Gyurko, L.G., Lyons, T., Kontkowski, M., Field, J.: Extracting information from the signature of a financial data stream. Quant. Finance 2, 10048 (2013) Gyurko, L.G., Lyons, T., Kontkowski, M., Field, J.: Extracting information from the signature of a financial data stream. Quant. Finance 2, 10048 (2013)
18.
go back to reference Arribas, I.P., Saunders, K., Goodwin, G., Lyons, T.: A signature-based machine learning model for bipolar disorder and borderline personality disorder. Transl. Psychiatr. 2, 274 (2018) CrossRef Arribas, I.P., Saunders, K., Goodwin, G., Lyons, T.: A signature-based machine learning model for bipolar disorder and borderline personality disorder. Transl. Psychiatr. 2, 274 (2018) CrossRef
19.
go back to reference Santoso, M., Sutjiadi, R., Lim, R.: Indonesian stock prediction using support vector machine (SVM). MATEC Web Conf. 164, 01031 (2018) CrossRef Santoso, M., Sutjiadi, R., Lim, R.: Indonesian stock prediction using support vector machine (SVM). MATEC Web Conf. 164, 01031 (2018) CrossRef
20.
go back to reference Jaiwang, G., Jeatrakul, P.: A forecast model for stock trading using support vector machine. In: International Computer Science and Engineering Conference (ICSEC) (2016) Jaiwang, G., Jeatrakul, P.: A forecast model for stock trading using support vector machine. In: International Computer Science and Engineering Conference (ICSEC) (2016)
21.
go back to reference Guo, H., Gao, B., Lu, H.: Research on stock return rate based on Boruta-PSO-SVM. Sens. Microsyst. 37(3), 51–53 (2018) Guo, H., Gao, B., Lu, H.: Research on stock return rate based on Boruta-PSO-SVM. Sens. Microsyst. 37(3), 51–53 (2018)
22.
go back to reference Deng, J., Li, L.: Application of parametric optimization random forests in stock forecasting. Comput. Eng. Softw. 41(01), 178–182 (2020) Deng, J., Li, L.: Application of parametric optimization random forests in stock forecasting. Comput. Eng. Softw. 41(01), 178–182 (2020)
23.
go back to reference Wang, S.Y., Cao, Z.F., Chen, M.Z.: Research on application of random forests in the quantitative stock selection model. Oper. Res. Manag. Sci. 25(3), 1447 (2016) Wang, S.Y., Cao, Z.F., Chen, M.Z.: Research on application of random forests in the quantitative stock selection model. Oper. Res. Manag. Sci. 25(3), 1447 (2016)
24.
go back to reference Lin, N., Qin, J.T.: Forecast of A-share stock change based on random forest. J. Univ. Shanghai Sci. Technol. 40(3), 1147 (2018) Lin, N., Qin, J.T.: Forecast of A-share stock change based on random forest. J. Univ. Shanghai Sci. Technol. 40(3), 1147 (2018)
25.
go back to reference Wang, Y., Guo, Y.K.: Application of improved XGboost model in stock forecasting. Comput. Eng. Appl. 55(20), 202–207 (2019) Wang, Y., Guo, Y.K.: Application of improved XGboost model in stock forecasting. Comput. Eng. Appl. 55(20), 202–207 (2019)
26.
go back to reference Chen, J., Liu, D.X., Wu, D.S.: Stock index forecasting method based on feature selection and LSTM model. Comput. Eng. Appl. 55(6), 108–112 (2019) Chen, J., Liu, D.X., Wu, D.S.: Stock index forecasting method based on feature selection and LSTM model. Comput. Eng. Appl. 55(6), 108–112 (2019)
27.
go back to reference Liu, S., Tang, G., Pang, B.: Rolling bearing fault diagnosis based on phase space reconstruction and stationary subspace analysis. Vib. Shock 34(22), 187–191 (2015) Liu, S., Tang, G., Pang, B.: Rolling bearing fault diagnosis based on phase space reconstruction and stationary subspace analysis. Vib. Shock 34(22), 187–191 (2015)
28.
go back to reference Cao, L.Y.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D Nonlinear Phen. 110, 43–50 (1997) CrossRef Cao, L.Y.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D Nonlinear Phen. 110, 43–50 (1997) CrossRef
29.
go back to reference Fathima, T.A., Jothiprakash, V.: Behavioural analysis of a time series-a Chaotic approach. Sadhana 39(3), 676–695 (2014) CrossRef Fathima, T.A., Jothiprakash, V.: Behavioural analysis of a time series-a Chaotic approach. Sadhana 39(3), 676–695 (2014) CrossRef
30.
go back to reference Gong, Z.P.: Comparison of the calculating methods of delay time in the reconstructed phase space of manufacturing quality information system. Syst. Eng. 29(3), 81–85 (2011) Gong, Z.P.: Comparison of the calculating methods of delay time in the reconstructed phase space of manufacturing quality information system. Syst. Eng. 29(3), 81–85 (2011)
33.
go back to reference Gyurko, L.G., Lyons, T.: Rough paths based numerical algorithms in computational finance, mathematics in finance. In: Santiago, C.M. and Jose L.F.P. (eds) American Mathematical Society, Real Sociedad Matematica Española, pp. 397–405 (2010) Gyurko, L.G., Lyons, T.: Rough paths based numerical algorithms in computational finance, mathematics in finance. In: Santiago, C.M. and Jose L.F.P. (eds) American Mathematical Society, Real Sociedad Matematica Española, pp. 397–405 (2010)
34.
go back to reference Lyons, T., Ni, H., Levin, D.: Learning from the past, predicting the statistics of the future. Learn. Evol. Syst. 2, 1700 (2013) Lyons, T., Ni, H., Levin, D.: Learning from the past, predicting the statistics of the future. Learn. Evol. Syst. 2, 1700 (2013)
35.
go back to reference Lyons, T.: Rough paths, signatures and the modelling of functions on streams. In: Proceedings of the International Congress of Mathematicians, pp. 163–184 (2014) Lyons, T.: Rough paths, signatures and the modelling of functions on streams. In: Proceedings of the International Congress of Mathematicians, pp. 163–184 (2014)
Metadata
Title
Path signature-based phase space reconstruction for stock trend prediction
Authors
Cuiting Li
Ke Liu
Publication date
12-05-2022
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics / Issue 3/2022
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-022-00326-z

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