Skip to main content
SpringerLink
Log in

Discovery of trading points based on Bayesian modeling of trading rules

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Mining hidden patterns with different technical indicators from the historical financial data has been regarded as an efficient way to determine the trading decisions in the financial market. Technical analysis has shown that a number of specific combinations of technical indicators could be treated as trading patterns for forecasting efficient trading directions. However, it is a challenging assignment to discover those combinations. In this paper, we innovatively propose to use a biclustering algorithm to detect the trading patterns. The discovered trading patterns are then utilized to forecast the market movement based on the Naive Bayesian algorithm. Finally, the Adaboost algorithm is applied to improve the accuracy of the forecasts. The proposed method was implemented on seven historical stock datasets and the average performance was compared with that of four existing algorithms. Experimental results demonstrated that the proposed algorithm outperforms the other four algorithms and can provide a valuable reference in the financial investments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Agrawal, S., Jindal, M., Pillai, G.: Momentum analysis based stock market prediction using Adaptive Neuro-Fuzzy Inference System (ANFIS). In: Proceedings of the international multiconference of engineers and computer scientists (2010)

  2. Atsalakis, G.S., Valavanis, K.P.: Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst. Appl. 36(7), 10696–10707 (2009)

    Article  Google Scholar 

  3. Cai, G., Xu, W., Zhang, W., Wang, P.: Application of E-Bayes method in stock forecast. In: 2011 Fourth International Conference on Information and Computing (ICIC), pp. 504–506. (2011)

  4. de Castro, P.A., de França, F.O., Ferreira, H.M., Von Zuben, F.J.: Applying biclustering to text mining: an immune-inspired approach. In: Artificial immune systems, pp. 83–94. Springer, (2007)

  5. Ceci, M., Appice, A., Malerba, D.: Mr-SBC: a multi-relational naive bayes classifier. In: European conference on principles of data mining and knowledge discovery, pp. 95–106. Springer, (2003)

  6. Chang, P.-C., Liu, C.-H.: A TSK type fuzzy rule based system for stock price prediction. Expert Syst. Appl. 34(1), 135–144 (2008)

    Article  Google Scholar 

  7. Chang, P.-C., Fan, C.-Y., Liu, C.-H.: Integrating a piecewise linear representation method and a neural network model for stock trading points prediction. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(1), 80–92 (2009)

    Article  Google Scholar 

  8. Chang, P.-C., Liao, T.W., Lin, J.-J., Fan, C.-Y.: A dynamic threshold decision system for stock trading signal detection. Appl. Soft Comput. 11(5), 3998–4010 (2011)

    Article  Google Scholar 

  9. Chang, P.-C., Wu, J.-L., Lin, J.-J.: A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting. Appl. Soft Comput. 38, 831–842 (2016)

    Article  Google Scholar 

  10. Cheng, Y., Church, G.M.: Biclustering of expression data. In: International conference on intelligent system for molecular biology, vol. 8, pp. 93–103. (2000)

  11. Elkan, C.: Boosting and naive Bayesian learning. In. Proceddings of the International conference on knowledge discovery and data mining CS97–557, University of California, San Diego, (1997)

  12. Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory, pp. 23–37. Springer, (1995)

  13. Ghimire, D., Lee, J.: Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines. Sensors. 13(6), 7714–7734 (2013)

    Article  Google Scholar 

  14. Goswami, M.M., Bhensdadia, C.K., Ganatra, A.: Candlestick analysis based short term prediction of stock price fluctuation using SOM-CBR. In: IEEE International on Advance Computing Conference (IACC 2009), pp. 1448–1452. IEEE, (2009)

  15. Hu, W., Hu, W., Maybank, S.: Adaboost-based algorithm for network intrusion detection. IEEE Trans. Syst. Man Cybern. B Cybern. 38(2), 577–583 (2008)

    Article  Google Scholar 

  16. Huang, Q., Zeng, Z.: A review on real-time 3D ultrasound imaging technology. Bio Med Research International. 2017, 6027029 (2017)

    Google Scholar 

  17. Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Comput. Oper. Res. 32(10), 2513–2522 (2005)

    Article  Google Scholar 

  18. Huang, Q., Wang, T., Tao, D., Li, X.: Biclustering learning of trading rules. IEEE Transactions on Cybernetics. 45(10), 2287–2298 (2015)

    Article  Google Scholar 

  19. Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In: Proceddings of the international conference on knowledge and discovery and dara mining, vol 98, pp. 202–207. Citeseer, (1996)

  20. Lee, J.-J., Lee, P.-H., Lee, S.-W., Yuille, A., Koch, C.: Adaboost for text detection in natural scene. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 429–434. IEEE, (2011)

  21. Leigh, W., Modani, N., Purvis, R., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Syst. Appl. 23(2), 155–159 (2002)

    Article  Google Scholar 

  22. Li, X., Wang, L., Sung, E.: Ada boost with SVM-based component classifiers. Eng. Appl. Artif. Intell. 21(5), 785–795 (2008)

    Article  Google Scholar 

  23. Li, Y., Liu, W., Li, X., Huang, Q., Li, X.: GA-SIFT: a new scale invariant feature transform for multispectral image using geometric algebra. Inf. Sci. 281, 559–572 (2014)

    Article  MathSciNet  Google Scholar 

  24. Li, Y., Huang, Q., Xie, W., Li, X.: A novel visual codebook model based on fuzzy geometry for large-scale image classification. Pattern Recogn. 48(10), 3125–3134 (2015)

    Article  Google Scholar 

  25. Li, Y., Liu, W., Huang, Q.: Traffic anomaly detection based on image descriptor in videos. Multimedia Tools and Applications. 75(5), 2487–2505 (2016)

    Article  Google Scholar 

  26. Li, Y., Liu, W., Huang, Q., Li, X.: Fuzzy bag of words for social image description. Multimedia Tools and Applications. 75(3), 1371–1390 (2016)

    Article  Google Scholar 

  27. Li, Y., Xia, R., Huang, Q., Xie, W., Li, X.: Survey of spatio-temporal interest point detection algorithms in video. IEEE Access. 5, 10323–10331 (2017)

    Article  Google Scholar 

  28. Liu, H., Li, X., Zhang, S.: Learning instance correlation functions for multilabel classification. IEEE Transactions on Cybernetics. 47(2), 499–510 (2016)

    Article  Google Scholar 

  29. Lv, F., Nevatia, R.: Recognition and segmentation of 3-d human action using hmm and multi-class adaboost. Computer Vision–ECCV. 2006, 359–372 (2006)

    Google Scholar 

  30. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 1(1), 24–45 (2004)

    Article  Google Scholar 

  31. Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn. 39(12), 2464–2477 (2006)

    Article  Google Scholar 

  32. Pai, P.-F., Lin, C.-S.: A hybrid ARIMA and support vector machines model in stock price forecasting. Omega. 33(6), 497–505 (2005)

    Article  Google Scholar 

  33. Pang, Y., Zhu, H., Li, X., Li, X.: Classifying discriminative features for blur detection. IEEE Transactions on Cybernetics. 46(10), 2220–2227 (2015)

    Article  Google Scholar 

  34. Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 22, pp. 41–46. (2001)

  35. Schierholt, K., Dagli, C.H.: Stock market prediction using different neural network classification architectures. In: Proceedings of the IEEE/IAFE 1996, Conference on computational intelligence for financial engineering, pp. 72–78. (1996)

  36. Song, E., Huang, D., Ma, G., Hung, C.-C.: Semi-supervised multi-class Adaboost by exploiting unlabeled data. Expert Syst. Appl. 38(6), 6720–6726 (2011)

    Article  Google Scholar 

  37. Tilakaratne, C., Morris, S., Mammadov, M., Hurst, C.: Predicting stock market index trading signals using neural networks. In: Proceedings of the 14th Annual Global Finance Conference (GFC’07), pp. 171–179. (2007)

  38. Upadhyay, A., Bandyopadhyay, G., Dutta, A.: Forecasting stock performance in indian market using multinomial logistic regression. Journal of Business Studies Quarterly. 3(3), 16 (2012)

    Google Scholar 

  39. Wang, Y., Ai, H., Wu, B., Huang, C.: Real time facial expression recognition with adaboost. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), pp. 926–929. IEEE, (2004)

  40. White, H.: Economic prediction using neural networks: the case of IBM daily stock returns. In: IEEE International Conference on Neural Networks, vol 2, pp.451–458. (1988)

  41. Yang, F., Huang, Q., Jin, L., Liew, W.C.: Segmentation and recognition of multi-model photo event. Neurocomputing. 172(C), 159–167 (2016)

    Article  Google Scholar 

  42. Yubao, W.U., Zhu, X., Fan, W., Fan, W., Jin, R., Zhang, X.: Mining dual networks: models, algorithms, and applications. ACM Trans. Knowl. Discov. Data. 10(4), 40 (2016)

    Google Scholar 

  43. Zadrozny, B., Elkan, C.: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers. In: Eighteenth International Conference on Machine Learning, pp. 609–616. (2001)

  44. Zarandi, M.F., Rezaee, B., Turksen, I., Neshat, E.: A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst. Appl. 36(1), 139–154 (2009)

    Article  Google Scholar 

  45. Zhang, H., Su, J.: Naive bayesian classifiers for ranking. In: European conference on machine learning, vol 3201, pp. 501–512. 2004

    Chapter  Google Scholar 

  46. Zhang, D., Jiang, Q., Li, X.: Application of neural networks in financial data mining. Int. J. Comput. Intell. 1(2), 116–119 (2004)

    Google Scholar 

  47. Zhang, S., Li, X., Zong, M., Zhu, X., Cheng, D.: Learning k for kNN classification. ACM Trans. Intell. Syst. Technol. 8(3), 43 (2017)

    Google Scholar 

  48. Zhu, J., Zou, H., Rosset, S., Hastie, T.: Multi-class adaboost. Statistics and its Interface. 2(3), 349–360 (2009)

    Article  MathSciNet  Google Scholar 

  49. Zhu, X., Luo, X., Xu, C.: Editorial learning for multimodal data. Neurocomputing. vol 207, pp. 684–692. (2017)

    Google Scholar 

  50. Zuo, Y., Kita, E.: Stock price forecast using Bayesian network. Expert Syst. Appl. 39(8), 6729–6737 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by National Natural Science Foundation of China (Nos. 61372007 and 61571193), Natural Science Foundation of Guangdong Province, China (nos. 2017A030312006 and 2015A030313210), Fundamental Research Funds for the Central Universities (NO.2015ZM138), Project of Science and Technology Department of Guangdong province (2014A050503020, 2016A010101021, 2016A010101022 and 2016A010101023), and Science and Technology Program of Guangzhou (no. 201704020134).

Author information

Authors and Affiliations

  1. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China

    Qinghua Huang & Yanshan Li

  2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China

    Qinghua Huang, Zhoufan Kong & Jie Yang

  3. School of Mechanical Engineering, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China

    Qinghua Huang

  4. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi, 710119, China

    Xuelong Li

Authors
  1. Qinghua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhoufan Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanshan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuelong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinghua Huang.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Q., Kong, Z., Li, Y. et al. Discovery of trading points based on Bayesian modeling of trading rules. World Wide Web 21, 1473–1490 (2018). https://doi.org/10.1007/s11280-018-0534-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-018-0534-9

Keywords

Search

Navigation