Top

Neural Computing and Applications

Published in:

13-03-2018 | Original Article

Market impact analysis via deep learned architectures

Authors: Xiaodong Li, Jingjing Cao, Zhaoqing Pan

Published in: Neural Computing and Applications | Issue 10/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Perturbation wavelet neural sliding mode position control for a voice coil motor driver

next article Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

www.finet.hk.

www.hkex.com.hk.

www.hsi.com.hk.

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

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

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 38:2177–2186CrossRef

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

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

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

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

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

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

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

10.

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

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):520–531CrossRef

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):616–624CrossRef

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

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

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):3–12MathSciNetCrossRef

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):689–702CrossRef

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 83:187–205CrossRef

27.

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

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):411–419CrossRef

29.

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

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):452–463CrossRef

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

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):1174–1185CrossRef

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):67–78CrossRef

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):1–19CrossRef

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):26–34CrossRef

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):809–821CrossRef

40.

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

41.

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

42.

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

43.

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

44.

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

45.

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

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):596–608MathSciNetCrossRef

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 142:228–238CrossRef

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

49.

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

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):513–529CrossRef

52.

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

Title: Market impact analysis via deep learned architectures
Authors: Xiaodong Li
Jingjing Cao
Zhaoqing Pan
Publication date: 13-03-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3415-3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 10/2019

Adaptive lasso echo state network based on modified Bayesian information criterion for nonlinear system modeling

Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid

Deriving the priority weights from multiplicative consistent single-valued neutrosophic preference relations

Structure regularized self-paced learning for robust semi-supervised pattern classification

A neural dynamic system for solving convex nonlinear optimization problems with hybrid constraints

Perturbation wavelet neural sliding mode position control for a voice coil motor driver

Premium Partner