Skip to main content

Advertisement

Springer Nature Link
Log in

Market impact analysis via deep learned architectures

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

How to deeply process market data sources and build systems to process accurate market impact analysis is an attractive problem. In this paper, we build up a system that exploits deep learning architecture to improve feature representations, and adopt state-of-the-art supervised learning algorithm—extreme learning machine—to predict market impacts. We empirically evaluate the performance of the system by comparing different configurations of representation learning and classification algorithms, and conduct experiments on the intraday tick-by-tick price data and corresponding commercial news archives of stocks in Hong Kong Stock Exchange. From the results, we find that in order to make system achieve good performance, both the representation learning and the classification algorithm play important roles, and comparing with various benchmark configurations of the system, deep learned feature representation together with extreme learning machine can give the highest market impact prediction accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. www.finet.hk.

  2. www.hkex.com.hk.

  3. www.hsi.com.hk.

  4. http://cn.mathworks.com/matlabcentral/fileexchange/38310-deep-learning-toolbox.

References

  1. Schumaker RP, Chen H (2010) A discrete stock price prediction engine based on financial news. Computer 43(1):51–56

    Article  Google Scholar 

  2. Yeh C-Y, Huang C-W, Lee S-J (2011) A multiple-kernel support vector regression approach for stock market price forecasting. Expert Syst Appl 38:2177–2186

    Article  Google Scholar 

  3. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  4. Huang G-B, Siew C-K Extreme learning machine: RBF network case. In: Control, automation, robotics and vision conference, ICARCV’04

  5. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Article  Google Scholar 

  6. Wei X-K, Li Y-H, Feng Y Comparative study of extreme learning machine and support vector machine. In: Advances in neural networks, ISNN’06

  7. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2(2):107–122

    Article  Google Scholar 

  8. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MathSciNet  Google Scholar 

  9. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  10. Zhang H, Chow TWS, Wu QMJ (2016) Organizing books and authors by multilayer som. IEEE Trans Neural Netw Learn Syst 27(12):2537–2550

    Article  Google Scholar 

  11. Chen Y-N, Han C-C, Wang C-T, Jeng B-S, Fan K-C (2006) The application of a convolution neural network on face and license plate detection. In: International conference on pattern recognition, vol 3, pp 552–555

  12. Ranzato M, Huang FJ, Boureau Y-L, LeCun Y (2007) Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: 2014 IEEE conference on computer vision and pattern recognition, pp 1–8

  13. Larochelle H, Erhan D, Courville A, Bergstra J, Bengio Y (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: Proceedings of the 24th international conference on machine learning, ICML’07, New York, NY, USA. ACM, pp 473–480

  14. Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, ICML’08, New York, NY, USA. ACM, pp 1096–1103

  15. Zhang H, Cao X, Ho JKL, Chow TWS (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520–531

    Article  Google Scholar 

  16. Zhang H, Li J, Ji Y, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inform 13(2):616–624

    Article  Google Scholar 

  17. Huang FJ, LeCun Y (2006) Large-scale learning with svm and convolutional for generic object categorization. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1, pp 284–291

  18. Lee H, Grosse R, Ranganath R, Ng AY (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th annual international conference on machine learning, ICML’09, New York, NY, USA. ACM, pp 609–616

  19. Lee H, Pham PT, Largman Y, Ng AY (2009) Unsupervised feature learning for audio classification using convolutional deep belief networks. Adv Neural Inf Process Syst 22:1096–1104

    Google Scholar 

  20. Socher R, Huang EH, Pennin J, Manning CD, Ng AY (2011) Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. Adv Neural Inf Process Syst 24:801–809

    Google Scholar 

  21. Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML’11

  22. Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the conference on empirical methods in natural language processing, EMNLP’11, Stroudsburg, PA, USA. Association for Computational Linguistics, pp 151–161

  23. Bordes A, Glorot X, Weston J (2012) Joint learning of words and meaning representations for open-text semantic parsing. In: International conference on artificial intelligence and statistics

  24. Heaton JB, Polson NG, Witte JH (2017) Deep learning for finance: deep portfolios. Appl Stoch Models Bus Ind 33(1):3–12

    Article  MathSciNet  Google Scholar 

  25. Krauss C, Do XA, Huck N (2017) Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S&P 500. Eur J Oper Res 259(2):689–702

    Article  Google Scholar 

  26. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187–205

    Article  Google Scholar 

  27. Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  Google Scholar 

  28. Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419

    Article  Google Scholar 

  29. Sun Y, Yuan Y, Wang G (2011) An OS-ELM based distributed ensemble classification framework in P2P networks. Neurocomputing 74(16):2438–2443

    Article  Google Scholar 

  30. Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V Extreme learning machine for predicting HLA-peptide binding. In: Advances in neural networks, ISNN’06

  31. Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton Marit (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Trans Comput Biol Bioinform 8(2):452–463

    Article  Google Scholar 

  32. Huang Z, Yu Y, Ye S, Liu H (2014) Extreme learning machine based traffic sign detection. In: 2014 international conference on Multisensor fusion and information integration for intelligent systems (MFI), pp 1–6

  33. Kasun LLC, Zhou H, Huang G-B, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31–34

    Google Scholar 

  34. Tang J, Deng C, Huang GB, Zhao B (2015) Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans Geosci Remote Sens 53(3):1174–1185

    Article  Google Scholar 

  35. Li X, Xie H, Wang R, Cai Y, Cao J, Wang F, Min H, Deng X (2016) Empirical analysis: stock market prediction via extreme learning machine. Neural Comput Appl 27(1):67–78

    Article  Google Scholar 

  36. Seo Y-W, Giampapa J, Sycara K (2004) Financial news analysis for intelligent portfolio management. Ph.D. thesis, Robotics Institute, Carnegie Mellon University

  37. Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans Inf Syst 27(2):1–19

    Article  Google Scholar 

  38. Li X, Xie H, Song Y, Zhu S, Li Q, Wang FL (2015) Does summarization help stock prediction? A news impact analysis. IEEE Intell Syst 30(03):26–34

    Article  Google Scholar 

  39. Van Gestel T, Suykens JAK, Baestaens D-E, Lambrechts A, Lanckriet G, Vandaele B, De Moor B, Vandewalle J (2001) Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Trans Neural Netw 12(4):809–821

    Article  Google Scholar 

  40. Cao L, Tay FEH (2001) Financial forecasting using support vector machines. Neural Comput Appl 10(2):184–192

    Article  Google Scholar 

  41. Tay FEH, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317

    Article  Google Scholar 

  42. Tay FEH, Cao L (2002) Modified support vector machines in financial time series forecasting. Neurocomputing 48(1–4):847–861

    Article  Google Scholar 

  43. Cao L, Gu Q (2002) Dynamic support vector machines for non-stationary time series forecasting. Intell Data Anal 6(1):67–83

    Article  Google Scholar 

  44. Cao L, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Li X, Deng X, Zhu S, Wang F, Xie H (2014) An intelligent market making strategy in algorithmic trading. Front Comput Sci 8(4):596–608

    Article  MathSciNet  Google Scholar 

  47. Li X, Huang X, Deng X, Zhu S (2014) Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information. Neurocomputing 142:228–238

    Article  Google Scholar 

  48. Fung GPC, Yu JX, Lu H (2005) The predicting power of textual information on financial markets. IEEE Intell Inform Bull 5(1):1–10

    Google Scholar 

  49. Salton G, McGill M (1984) Introduction to modern information retrieval. McGraw-Hill Book Company, New York

    MATH  Google Scholar 

  50. Li X, Wang C, Dong J, Wang F, Deng X, Zhu S (2011) Improving stock market prediction by integrating both market news and stock prices. Database and expert systems applications. Lecture notes in computer science, vol 6861. Springer, Berlin, pp 279–293

  51. Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B 42(2):513–529

    Article  Google Scholar 

  52. Maymin PZ (2011) Behavioral finance has come of age. Risk Decis Anal 2(3):125

    Google Scholar 

Download references

Acknowledgements

The work described in this paper was partially supported by National Natural Science Foundation of China under the Grant Nos. 61602149 and 61502360, partially supported by the Fundamental Research Funds for the Central Universities under the Grant No. 2016B01714, and partially supported by Priority Academic Program Development of Jiangsu Higher Education Institutions.

Author information

Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, China

    Xiaodong Li

  2. School of Logistics Engineering, Wuhan University of Technology, Wuhan, China

    Jingjing Cao

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Zhaoqing Pan

Authors
  1. Xiaodong Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jingjing Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhaoqing Pan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jingjing Cao.

Ethics declarations

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Market Impact Analysis via Deep Learned Architectures.”

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Cao, J. & Pan, Z. Market impact analysis via deep learned architectures. Neural Comput & Applic 31, 5989–6000 (2019). https://doi.org/10.1007/s00521-018-3415-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3415-3

Keywords