A novel stock recommendation system using Guba sentiment analysis

Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss. Existing recommendation systems mostly depend on analysis of trading data and company profit prediction. Though many works show that there is a positive correlation between investors’ sentiment and the finance market trends, few recommendation theories have been built based on sentiment. The primary reason is the difficulty to measure investors’ sentiment. In this work, a novel stock recommendation system is developed based on a proposed theory concerning the correlation between Guba-based sentiment of the retail investors and the stock market trends in China. To verify four hypotheses of the theory, a novel method is proposed to measure the investors’ sentiment by exploiting the large volumes of emotion enriched texts posted in Guba, which is online social platform for individual investors to share news and opinions concerning their favorite stocks. Results show the correctness of the proposed theory: (1) there is a positive correlation between Guba-based sentiment and the stock market trends; 2) the higher the post volumes and agreement, more proficiency the bullishness would be; and (3) a long-lasting negative Guba-based sentiment indicates the arrival of the bear market. The proposed recommendation system consists of three criteria accordingly to ensure the portfolio to meet requirements of the theory. Finally, experiments are implemented using the real data of Chinese stock market from March 2009 to March 2016 and the results show the effectiveness of the proposed system in recommending lucrative stocks and the theoretical cumulate profit is about eight times of the CSI300 in the period.

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

Access this article

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Rent this article via DeepDyve

Similar content being viewed by others

A Stock Recommendation Strategy Based on M-LDA Model

Chapter © 2016

The Influence of Investor Sentiment on Stock Market Based on Sentiment Analysis

Chapter © 2022

Online Stock Forum Sentiment Analysis

Chapter © 2015

Explore related subjects

References

  1. Cho V (2010) MISMIS - A comprehensive decision support system for stock market investment. Elsevier Science Publishers B. V.
  2. Chien Y-WC, Chen Y-L (2010) Mining associative classification rules with stock trading data—a ga-based method. Knowl-Based Syst 23(6):605–614 ArticleGoogle Scholar
  3. Chou SC, Yang CC, Chan CH, Lai F (1996) A rule-based neural stock trading decision support system. In: 1996 Proceedings of the IEEE/IAFE conference on computational intelligence for financial engineering, pp 148–154
  4. Wen Q, Yang Z, Song Y, Jia P (2010) Automatic stock decision support system based on box theory and svm algorithm. Expert Syst Appl 37(2):1015–1022 ArticleGoogle Scholar
  5. Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news:the azfin text system. ACM Trans Infor Syst 27(2):1–19 ArticleGoogle Scholar
  6. Geva T, Zahavi J (2014) Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decis Support Syst 57(3):212–223 ArticleGoogle Scholar
  7. Bonde GS (2012) Extracting the best features from multi-company stock data to improve stock price prediction (Doctoral dissertation, University of Georgia)
  8. Eickhoff M, Muntermann J (2015) Stock analysts vs. the crowd: a study on mutual prediction. In PACIS (p. 144)
  9. Lucey BM, Dowling M (2005) The role of feelings in investor decision-making. J Econ Surv 19(2):211–237 ArticleGoogle Scholar
  10. Torres BP, González AG (2017) Evolution of the semantic Web towards the intelligent Web: from conceptualization to personalization of contents. Media and metamedia management. Springer international publishing
  11. Skourletopoulos G, Mavromoustakis CX, Mastorakis G, Batalla JM, Dobre C, Panagiotakis S et al. (2017) Towards mobile cloud computing in 5G mobile networks: applications, big data services and future opportunities. Advances in mobile cloud computing and big data in the 5G Era. Springer international publishing
  12. Forgas JP (1995) Mood and judgment: the affect infusion model (aim). Psychol Bull 117(1):39–66 ArticleGoogle Scholar
  13. Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psychol Bull 2:127 Google Scholar
  14. Tetlock PC (2007) Giving content to investor sentiment: the role of media in the stock market. J Financ 62 (3):1139–1168 ArticleGoogle Scholar
  15. Bollen J, Mao H (2011) Twitter mood as a stock market predictor. Computer 44(10):91–94 ArticleGoogle Scholar
  16. Chen R, Lazer M (2013) Sentiment analysis of twitter feeds for the prediction of stock market movement. stanford edu Retrieved January 25:2013 Google Scholar
  17. Rao T, Srivastava S (2012) Analyzing stock market movements using twitter sentiment analysis. In: International conference on advances in social networks analysis and mining, pp 119–123
  18. Oh C, Sheng O (2011) Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement. In Icis (pp. 1–19)
  19. Khatri SK, Singhal H, Johri P (2015) Sentiment analysis to predict bombay stock exchange using artificial neural network. In: International conference on reliability, INFOCOM technologies and optimization, pp 1–5
  20. Bing L, Chan KCC, Ou C (2014) Public sentiment analysis in twitter data for prediction of a company’s stock price movements. In: IEEE international conference on E-Business engineering, pp 232–239
  21. Mao H, Counts S, Bollen J (2015) Quantifying the effects of online bullishness on international financial markets(No. 9). ECB Statistics Paper
  22. Zhou Z, Zhao J, Xu K (2016) Can Online Emotions Predict the Stock Market in China?. Web Information Systems Engineering – WISE 2016. Springer international publishing
  23. Chi L, Zhuang X, Song D (2012) Investor sentiment in the chinese stock market: an empirical analysis. Appl Econ Lett 19(4):345–348 ArticleGoogle Scholar
  24. Gupte A, Joshi S, Gadgul P, Kadam A, Gupte A (2014) Comparative study of classification algorithms used in sentiment analysis. Internal Electron J Comput Sci Inf Technol 5(5):6261–6264 Google Scholar
  25. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: International conference on language resources and evaluation, Lrec 2010, 17-23 May 2010, Valletta, Malta
  26. Zou H, Tang X, Xie B, Liu B (2015) Sentiment classification using machine learning techniques with syntax features. In: International conference on computational science and computational intelligence, pp 175–179
  27. Chen KJ, Huang SL, Shih YY, Chen YJ (2005) Extended-HowNet: a representational framework for concepts. Proceedings of OntoLex 2005-Ontologies and Lexical Resources
  28. Bollen J, Mao H, Zeng X (2010) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8 ArticleMathSciNetGoogle Scholar
  29. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Springer, US BookMATHGoogle Scholar
  30. Zibriczky12 D (2016) Recommender systems meet finance: a literature review
  31. Treynor JL, Black F (1973) How to use security analysis to improve portfolio selection. J Bus 46(1):66–86 ArticleGoogle Scholar
  32. Metcalf GE, Malkiel BG (1994) The Wall Street Journal contests: The experts, the darts, and the efficient market hypothesis. Appl Financ Econ 4(5):371–374 ArticleGoogle Scholar
  33. Ha YM, Park S, Kim SW, Won JI, Yoon JH (2009) A stock recommendation system exploiting rule discovery in stock databases. Inf Softw Technol 51(7):1140–1149 ArticleGoogle Scholar
  34. Zhang ZY, Shi C, Zhang SL, Shi ZZ (2006) Stock time series forecasting using support vector machines employing analyst recommendations. In: International conference on advances in neural networks, pp 452–457
  35. Tapjinda T, Vechpanich P, Leelasupakul N, Prompoon N, Patanothai C (2015) An automated stock recommendation system from stock investment research using domain specific information extraction. In: International joint conference on computer science and software engineering, pp 30–35
  36. Manber U, Myers G (1990) Suffix arrays: a new method for on-line string searches. In: Acm-Siam symposium on discrete algorithms, pp 319–327
  37. Zhou M, Tompa FW (1998) The suffix-signature method for searching for phrases in text. Inf Syst 23 (8):567–588 ArticleGoogle Scholar
  38. Gallé M, Peterlongo P, Coste F (2009) In-place update of suffix array while recoding words. Int J Found Comput Sci 20(06):1025–1045 ArticleMathSciNetMATHGoogle Scholar
  39. Jieba project for Chinese text segmentation, https://github.com/fxsjy/jieba, [Retrieved March 20, 2017]
  40. Antweiler W, Frank MZ (2004) Is all that talk just noise? the information content of internet stock message boards. J Financ 59(3):1259–1294 ArticleGoogle Scholar

Funding

This work is supported by National Natural Science Foundation of China under Grant NO. 61371185.

Author information

Authors and Affiliations

  1. Business School, Beijing Normal University, Beijing, 100875, China Yunchuan Sun
  2. School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, China Mengting Fang & Xinyu Wang
  1. Yunchuan Sun