Abstract
In today’s modern society and global economy, decision making processes are increasingly supported by data. Especially in financial businesses it is essential to know about how the players in our global or national market are connected. In this work we compare different approaches for creating company relationship graphs. In our evaluation we see similarities in relationships extracted from Bloomberg and Reuters business news and correlations in historic stock market data.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
References
Akita, R., Yoshihara, A., Matsubara, T., Uehara, K.: Deep learning for stock prediction using numerical and textual information. In: International Conference on Computer and Information Science, pp. 1–6 (2016)
Bachelier, L.: Theory of speculation. In: Annales scientifiques de l’École normale supérieure (1900)
Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc.: Ser. B (Methodol.) 26, 211–243 (1964)
Box, G.E.P., Jenkins, G.M.: Time series analysis forecasting and control. J. Time Ser. Anal. (1970)
Chen, Y., Wei, Z., Huang, X.: Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp. 1655–1658. ACM (2018)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Using structured events to predict stock price movement: an empirical investigation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1415–1425 (2014)
Dionisio, A., Menezes, R., Mendes, D.A.: Mutual information: a measure of dependency for nonlinear time series. Phys. A: Stat. Mech. Appl. 344, 326–329 (2004)
Engle, R.F., Granger, C.W.J.: Co-integration and error correction: representation, estimation, and testing. Econometrica 55, 251–276 (1987)
Fama, E.F.: Random walks in stock market prices. Financ. Anal. J. 21, 55–59 (1965)
Fleming, J., Ostdiek, B., Whaley, R.E.: Predicting stock market volatility: a new measure. J. Future Mark. 15, 265–302 (1995)
Franke, J., Härdle, W.K., Hafner, C.M.: Statistics of Financial Markets (2010)
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438 (1969)
Granger, C.W.J., Newbold, P.: Spurious regressions in econometrics. J. Econ. 2, 111–120 (1974)
Hallin, M.: Gauss-Markov Theorem in Statistics. Wiley (2006). ISBN 9781118445112
Hong, H., et al.: Trading and Returns Under Periodic Market Closures (1998)
Hsu, M.W., Lessmann, S., Sung, M.C., Ma, T., Johnson, J.E.: Bridging the divide in financial market forecasting: machine learners vs. financial economists. Expert Syst. Appl. 61, 215–234 (2016)
Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., Ngo, D.C.L.: Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst. Appl. 42, 306–324 (2015)
Khadjeh Nassirtoussi, A., Aghabozorgi, S., Wah, T., Ngo, D.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41, 7653–7670 (2014)
Kim, S.: A cross-correlation-based stock forecasting model. In: Proceedings of The National Conference On Undergraduate Research (2016)
Kosapattarapim, C.: Granger causality between stock prices and currency exchange rates in Thailand. In: AIP Conference Proceedings, vol. 1905, p. 50025, March 2017
Lee, H., Surdeanu, M., MacCartney, B., Jurafsky, D.: On the importance of text analysis for stock price prediction. In: Proceedings of the Language Resources and Evaluation Conference, pp. 1170–1175 (2014)
Li, B., Chan, K.C., Ou, C., Ruifeng, S.: Discovering public sentiment in social media for predicting stock movement of publicly listed companies. Inf. Syst. 69, 81–92 (2017)
Li, X., Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. Knowl.-Based Syst. 69, 14–23 (2014)
Lipenkova, J.: A system for fine-grained aspect-based sentiment analysis of Chinese. In: ACL-IJCNLP, pp. 55–60. ACL (2015)
Millo, G.: Robust standard error estimators for panel models: a unifying approach. J. Stat. Softw. 82, 1–27 (2017)
Morgan, I.G.: Stock prices and heteroscedasticity. J. Bus. 49, 496–508 (1976)
Peng, Y., Jiang, H.: Leverage financial news to predict stock price movements using word embeddings and deep neural networks. In: Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2016)
Lopez de Prado, M.: Advances in Financial Machine Learning. Wiley, Hoboken (2018)
Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A., Jaimes, A.: Correlating financial time series with micro-blogging activity. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM (2012)
Sun, A., Lachanski, M., Fabozzi, F.J.: Trade the tweet: social media text mining and sparse matrix factorization for stock market prediction. Int. Rev. Financ. Anal. 48, 272–281 (2016)
Vlastakis, N., Markellos, R.N.: Information demand and stock market volatility. J. Bank. Finance 36, 1808–1821 (2012)
Wang, F., Shieh, S.J., Havlin, S., Stanley, H.E.: Statistical analysis of the overnight and daytime return. Phys. Rev. E 79, 056109 (2009)
Yule, G.U.: Why do we sometimes get nonsense-correlations between Time-Series?-a study in sampling and the nature of time-series. J. R. Stat. Soc. 89, 1–63 (1926)
Zhai, Y., Hsu, A., Halgamuge, S.K.: Combining news and technical indicators in daily stock price trends prediction. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4493, pp. 1087–1096. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72395-0_132
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kellermeier, T., Repke, T., Krestel, R. (2020). Mining Business Relationships from Stocks and News. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds) Mining Data for Financial Applications. MIDAS 2019. Lecture Notes in Computer Science(), vol 11985. Springer, Cham. https://doi.org/10.1007/978-3-030-37720-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-37720-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37719-9
Online ISBN: 978-3-030-37720-5
eBook Packages: Computer ScienceComputer Science (R0)