Top

Cognitive Computation

Published in:

08-03-2019

Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns

Authors: Jianjun Sun, Qinghua Huang, Xuelong Li

Published in: Cognitive Computation | Issue 6/2019

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

search-config

AI-assisted search

Off

Abstract

Due to the effects of many deterministic and stochastic factors, it has always been a challenging goal to gain good profits from the stock market. Many methods based on different theories have been proposed in the past decades. However, there has been little research about determining the temporal investment style (i.e., short term, middle term, or long term) for the stock. In this paper, we propose a method to find suitable stock investment styles in terms of investment time. Firstly, biclustering is applied to a matrix that is composed of technical indicators of each trading day to discover trading patterns (regarded as trading rules). Subsequently a k-nearest neighbor (KNN) algorithm is employed to transform the trading rules to the trading actions (i.e., the buy, sell, or no-action signals). Finally, a min-max and quantization strategy is designed for determination of the temporal investment style of the stock. The proposed method was tested on 30 stocks from US bear, bull, and flat markets. The experimental results validate its usefulness.

previous article Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm

next article A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

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

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"

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

Wang F, Zhang Y, Rao Q, Li K, Zhang H. Exploring mutual information-based sentimental analysis with kernel-based extreme learning machine for stock prediction. Soft Comput. 2017;21(12):3193–205.CrossRef

Chavarnakul T, Enke D. Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Syst Appl. 2008;34(2):1004–17.CrossRef

Chang PC, Fan CY, Liu CH. 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. 2009;39(1):80–92.CrossRef

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. 2014;281:559–72.CrossRef

Li Y, Liu W, Huang Q. Traffic anomaly detection based on image descriptor in videos. Multimed Tools Appl. 2016;75(5):2487–505.CrossRef

Wang YH. Using neural network to forecast stock index option price: a new hybrid GARCH approach. Qual Quant. 2009;43(5):833–43.CrossRef

Tilakaratne CD, Morris SA, Mammadov MA, Hurst C P. Predicting stock market index trading signals using neural networks. In: Proceedings of the 14th annual global finance conference (GFC’07); 2007. pp. 171-179.

White H. Economic prediction using neural networks: the case of IBM daily stock returns. In: IEEE International Conference on Neural Networks; 1988. vol. 2, pp451–458.

Wu Y, Zhu X, Li L, et al. Mining dual networks: models, algorithms, and applications. ACM Trans Knowl Discov Data. 2016;10(4):40.CrossRef

10.

Zhang D, Jiang Q, and Li X (2004) Application of neural networks in financial data mining. In: International Conference on Computational Intelligence, pp. 392-395.

11.

Zhang S, Li X, Zong M, et al. Learning k for knn classification. ACM Trans Intell Syst Technol. 2017;8(3):43.

12.

Potvin JY, Soriano P, Vallée M. Generating trading rules on the stock markets with genetic programming. Comput Oper Res. 2004;31(7):1033–1047.4.CrossRef

13.

Hilliard JE, Hilliard, J. Rebalancing versus buy and hold: theory, simulation and empirical analysis. Rev Quant Finan Acc. 2018;50(1):1–32.CrossRef

14.

Chang PC, Liu CH. A TSK type fuzzy rule based system for stock price prediction. Expert Syst Appl. 2008;34(1):135–44.CrossRef

15.

Briza AC, Naval PC Jr. Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Appl Soft Comput. 2011;11(1):1191–201.CrossRef

16.

Leigh W, Modani N, Purvis R, Roberts T. Stock market trading rule discovery using technical charting heuristics. Expert Syst Appl. 2002;23(2):155–9.CrossRef

17.

Huang W, Nakamori Y, Wang SY. Forecasting stock market movement direction with support vector machine. Comput Oper Res. 2005;32(10):2513–22.CrossRef

18.

Yang F, Huang Q, Jin L, Wee-Chung Liew A. Segmentation and recognition of multi-model photo event. Neurocomputing. 2016;172:159–67.CrossRef

19.

Zarandi MHF, Rezaee B, Turksen IB, et al. A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Syst Appl. 2009;36(1):139–54.CrossRef

20.

Zhang H, Su J. Naive bayesian classifiers for ranking. In: European conference on machine learning. Berlin: Springer; 2004. p. 501–12.

21.

Upadhyay A, Bandyopadhyay G, Dutta A. Forecasting stock performance in Indian market using multinomial logistic regression. J Bus Stud Q. 2012;3(3):16.

22.

Asghar MZ, Khan A, Bibi A, Kundi FM, Ahmad H. Sentence-level emotion detection framework using rule-based classification. Cogn Comput. 2017;9(6):868–94.CrossRef

23.

Sun X, Peng X, Ding S. Emotional human-machine conversation generation based on long short-term memory. Cogn Comput. 2018;10(3):389–97.CrossRef

24.

Ding S, Zhang J, Jia H, Qian J. An adaptive density data stream clustering algorithm. Cogn Comput. 2016;8(1):30–8.CrossRef

25.

Huang Q, Wang T, Tao D, Li X. Biclustering learning of trading rules. IEEE Trans Cybern. 2015;45(10):2287–98.PubMedCrossRef

26.

Hussain A, Cambria E. Semi-supervised learning for big social data analysis. Neurocomputing. 2018;275:1662–73.CrossRef

27.

Mahmud M, Kaiser MS, Rahman MM, Rahman MA, Shabut AM, Mamun SA, et al. A brain-inspired trust management model to assure security in a cloud based iot framework for neuroscience applications. Cogn Comput. 2018;9:1–10.

28.

Malik ZK, Hussain A, Wu QMJ. Extracting online information from dual and multiple data streams. Neural Comput & Applic. 2018;30(1):87–98.CrossRef

29.

Gong C, Tao D, Maybank SJ, Liu W, Kang G, Yang J. Multi-modal curriculum learning for semi-supervised image classification. IEEE Trans Image Process. 2016;25(7):3249–60.PubMedCrossRef

30.

Gong C, Liu T, Tao D, Fu K, Tu E, Yang J. Deformed graph Laplacian for semisupervised learning. IEEE Trans Neural Netw Learn Syst. 2015;26(10):2261–74.PubMedCrossRef

31.

Gong C, Liu T, Tang Y, Yang J, Yang J, Tao D. A regularization approach for instance-based superset label learning. IEEE Trans Cybern. 2018;48(3):967–78.PubMedCrossRef

32.

Wang M, Shang X, Li X, Liu W, Li Z. Efficient mining differential co-expression biclusters in microarray datasets. Gene. 2013;518(1):59–69.PubMedCrossRef

33.

Huang Q. A biclustering technique for mining trading rules in stock markets. Appl Inform Commun. 2011;224:16–24.CrossRef

34.

Huang Q, Huang X, Kong Z, Li X, Tao D. Bi-phase evolutionary searching for biclusters in gene expression data. IEEE Trans Evol Comput. 2018. https://doi.org/10.1109/TEVC.2018.2884521.CrossRef

35.

Huang Q, Chen Y, Liu L, Tao D, Li X. On combining biclustering mining and AdaBoost for breast tumor classification. IEEE Trans Evol Comput. 2019. https://doi.org/10.1109/TKDE.2019.2891622.

36.

Huang Q, Kong Z, Li Y, Yang J, Li X. Discovery of trading points based on Bayesian modeling of trading rules. World Wide Web. 2018;21(6):1473–90.CrossRef

37.

Ratner M, Leal RPC. Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. J Bank Financ. 1999;23(12):1887–905.CrossRef

38.

Xu C, Tao D, Xu C. A survey on multi-view learning. Comput Sci. 2013.

39.

Zhang L, Zhang Q, Zhang L, Tao D, Huang X, Du B. Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding. Pattern Recogn. 2015;48(10):3102–12.CrossRef

Title: Determination of Temporal Stock Investment Styles via Biclustering Trading Patterns
Authors: Jianjun Sun
Qinghua Huang
Xuelong Li
Publication date: 08-03-2019
Publisher: Springer US
Published in: Cognitive Computation / Issue 6/2019
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI: https://doi.org/10.1007/s12559-019-9626-9

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+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 6/2019

Unsupervised Object Transfiguration with Attention

Ensemble p-Laplacian Regularization for Scene Image Recognition

A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network

RGB-D Scene Classification via Multi-modal Feature Learning

Clustering of Remote Sensing Imagery Using a Social Recognition-Based Multi-objective Gravitational Search Algorithm

Cognitive Modelling and Learning for Multimedia Mining and Understanding

Premium Partner