Top

Neural Computing and Applications

Published in:

21-11-2016 | Original Article

Investor sentiment identification based on the universum SVM

Authors: Wen Long, Ye-ran Tang, Ying-jie Tian

Published in: Neural Computing and Applications | Issue 2/2018

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

search-config

AI-assisted search

Off

Abstract

Universum refers to additional samples which contain priori knowledge for classification but belonging to none of the class. It has been proved that universum positioned “in between” the two classes obtain better results. Since opinions on stock market defined as investor sentiment involve quite a number of neutral views, these neutral views can be used as universum samples to better identify investor sentiment. With universum samples, this paper uses support vector machine (SVM) to classify posts on stock forum. We define bullish views as positive samples, define bearish views as negative samples, and also further discuss the situation of a 3-class problem with neutral views. Compared with standard SVM, the empirical studies with universum samples in this paper show better performance for both 2- and 3-class classifications.

previous article CRHS: clustering and routing in wireless sensor networks using harmony search algorithm

next article An improved ranking method for comparing trapezoidal intuitionistic fuzzy numbers and its applications to multicriteria decision making

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

Long JBD, Waldmann RJ (1990) Noise trader risk in financial markets. J Bradford De Longs working papers 98(4):703–738

Lee CMC, Shleifer A, Thaler RH (1991) Investor sentiment and the closed-end fund puzzle. J Financ 46(1):75–109CrossRef

Nagel S (2005) Short sales, institutional investors and the cross-section of stock returns. J Financ Econ 78(2):277–309CrossRef

Barberis N, Xiong W (2010) Realization utility. J Financ Econ 104(2):251–271CrossRef

Otoo MW (1999) Consumer sentiment and the stock market. Working Paper, Board of Governors of the Federal Reserve System, Washington, DC, pp 1–16

Charoenrook A (2006) Does sentiment matter? Working Paper, Ahlbrandt University

Lemmon M, Portniaguina E (2006) Consumer confidence and asset prices: some empirical evidence. Rev Financ Stud 19(4):1499–1529CrossRef

Schmeling M (2009) Investor sentiment and stock returns: some international evidence. J Empir Financ 16(3):394–408CrossRef

Wheatley SM, Neal R (1998) Do measures of investor sentiment predict returns? J Financ Quant Anal 33:523–547CrossRef

10.

Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. Soc Sci Electron Publ 61(4):1645–1680

11.

Baker M, Wurgler J (2007) Investor sentiment in the stock market. Soc Sci Electron Publ 21(2):129–151

12.

Baker M, Wurgler J, Yuan Y (2012) Global, local, and contagious investor sentiment. J Financ Econ 104(2):272–287CrossRef

13.

Stambaugh RF, Yu J, Yuan Y (2012) The short of it: investor sentiment and anomalies. J Financ Econ 104(2):288–302CrossRef

14.

Stambaugh RF, Yu J, Yuan Y (2015) Arbitrage asymmetry and the idiosyncratic volatility puzzle. J Financ 70(5):1903–1948CrossRef

15.

Berger D, Turtle HJ (2015) Sentiment bubbles. J Financ Mark 23:59–74CrossRef

16.

Werner Antweiler, Frank Murray Z (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59(3):1259–1294CrossRef

17.

Das SR, Chen MY (2007) Yahoo! for Amazon: sentiment extraction from small talk on the web. Manage Sci 53:1375–1388CrossRef

18.

Kim SH, Kim D (2014) Investor sentiment from internet message postings and the predictability of stock returns. J Econ Behav Organ 107(PB):708–729CrossRef

19.

Wu DD, Zheng L, Olson DL (2014) A decision support approach for online stock forum sentiment analysis. IEEE Trans Syst Man Cybern Syst 44(8):1077–1087CrossRef

20.

Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATH

21.

Vapnik V (2006) Estimation of dependences based on empirical data, 2nd edn. Springer, BerlinMATH

22.

Weston J, Collobert R, Sinz F, Bottou L, Vapnik V (2006) Inference with the universum. In: International conference, vol 2006, pp 1009–1016

23.

Sinz FH, Chapelle O, Agarwal A, Schölkopf B (2007) An analysis of inference with the universum. Adv Neural Inf Process Syst 20(2008):1369–1376

24.

Cherkassky V, Dai W (2009) Empirical study of the universum SVM learning for high-dimensional data. In: International conference on artificial neural networks—ICANN 2009, vol 5768, pp 932–941

25.

Cherkassky V, Dhar S, Dai W (2011) Practical conditions for effectiveness of the universum learning. IEEE Trans Neural Netw 22(8):1241–1255CrossRef

26.

Dhar S, Cherkassky V (2015) Development and evaluation of cost-sensitive universum-SVM. IEEE Trans Cybern 45(4):806–818CrossRef

27.

Zhang D, Wang J, Wang F, Zhang C (2008) Semi-supervised classification with universum. In: Siam international conference on data mining, SDM 2008, April 24–26, 2008, Atlanta, Georgia, USA, vol 2, pp 340–344

28.

Chen S, Zhang C (2009) Selecting informative universum sample for semi-supervised learning. In: International joint conference on artificial intelligence, vol 18, pp 111–122

29.

Shen C, Wang P, Shen F, Wang H (2011) Uboost: boosting with the universum. IEEE Trans Pattern Anal Mach Intell 34(4):825–832CrossRef

30.

Qi Z, Tian Y, Yong S (2012) Twin support vector machine with universum data. Neural Netw 36C(3):112–119CrossRefMATH

31.

Qi Z, Tian Y, Shi Y (2014) A nonparallel support vector machine for a classification problem with universum learning. J Comput Appl Math 263(263):288–298MathSciNetCrossRefMATH

32.

Lu S, Tong L (2015) Weighted twin support vector machine with universum. Adv Comput Sci Int J 3(2):17–23

33.

Xu Y, Chen M, Li G (2015) Least squares twin support vector machine with universum data for classification. Int J Syst Sci 47(15):3637–3645

34.

Liu CL, Hsaio WH, Lee CH, Chang TH (2015) Semi-supervised text classification with universum learning. IEEE Trans Cybern 46(2):1CrossRef

35.

Xu Y, Chen M, Yang Z, Li G (2016) ν-twin support vector machine with universum data for classification. Appl Intell 44(4):956–968

36.

Pan S, Wu J, Zhu X, Long G, Zhang C (2016) Boosting for graph classification with universum. Knowl Inf Syst 1–25. doi:10.1007/s10115-016-0934-z

37.

Zhu C (2016) Double-fold localized multiple matrix learning machine with universum. Form Pattern Anal Appl 1–28. doi:10.1007/s10044-016-0548-9

38.

Gao T, Tian Y, Shao X, Deng N (2008) Accurate prediction of translation initiation sites by universum SVM. J Chem Eng Jpn 42(8):570–575

39.

Chen S, Zhang C (2009) Image classification via SVM using in-between universum samples. In: 16th IEEE international conference on image processing (ICIP), pp 1421–1424

40.

Jiao Y, Zhang X, Zhuo L, Chen M (2010) Tongue image classification based on Universum SVM. In: IEEE international conference on biomedical engineering and informatics, vol 2, pp 657–660

41.

Hao X, Zhang D (2013) Ensemble universum SVM learning for multimodal classification of Alzheimer’s disease. Mach Learn Med Imaging 8184(2013):227–234

42.

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH

43.

Vapnik VN (1996) The nature of statistical learning theory. Springer, New YorkMATH

44.

Trafalis TB, Ince H (2000) Support vector machine for regression and applications to financial forecasting. IEEE-Inns-Enns international joint conference on neural networks, vol 6, pp 6348–6348

45.

Schölkopf B, Tsuda K, Vert J (2004) Support vector machine applications in computational biology. Kernel methods in computational biology. MIT Press, Cambridge

46.

Goh KS, Chang EY, Li B (2005) Using one-class and two-class svms for multiclass image annotation. IEEE Trans Knowl Data Eng 17(10):1333–1346CrossRef

47.

Isa D, Lee LH, Kallimani VP, Rajkumar R (2008) Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Trans Knowl Data Eng 20(9):1264–1272CrossRef

48.

Borgwardt KM (2011) Kernel methods in bioinformatics. Handbook of statistical bioinformatics. Springer, Berlin

49.

Deng N, Tian Y, Zhang C (2012) Support vector machines. Optimization based theory, algorithms, and extensions. CRC Press, New YorkMATH

50.

Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(10):613–620CrossRefMATH

51.

Harris ZS (1954) Distributional structure. Synthese Language Library 10:146–162

52.

Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manage 24(88):513–523CrossRef

Title: Investor sentiment identification based on the universum SVM
Authors: Wen Long
Ye-ran Tang
Ying-jie Tian
Publication date: 21-11-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2684-y

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 2/2018

Peristaltic flow of MHD Jeffery nanofluid in curved channel with convective boundary conditions: a numerical study

Study on electrical synapse coupling synchronization of Hindmarsh-Rose neurons under Gaussian white noise

Improvement on projection twin support vector machine

Hydro-thermal generation scheduling using integrated gravitational search algorithm and predator–prey optimization technique

Modified cuckoo search algorithm and the prediction of flashover voltage of insulators

Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

Premium Partner