Predicting abnormal returns from news using text classification

Quantitative Finance

Volumn 15, Issue 6, 2015, Pages 999-1012

  Luss, Ronny ^a   D’Aspremont, Alexandre ^a  

^a PRINCETON UNIVERSITY   (United States)

Author keywords

Forecasting applications; Optimization; Pattern recognition; Statistical learning theory; Text classification

Indexed keywords

EID: 84929087153     PISSN: 14697688     EISSN: 14697696     Source Type: Journal    
DOI: 10.1080/14697688.2012.672762     Document Type: Article

References (44)

1. Andersen, T.G. and Bollerslev, T., Intraday periodicity and volatility persistence in financial markets. J. Empir. Finance, 1997, 4, 115–158.
2. Atkinson, D.S. and Vaidya, P.M., A cutting plane algorithm for convex programming that uses analytic centers. Math. Program., 1995, 69, 1–43.
3. Austin, M.P., Bates, G., Dempster, M.A.H., Leemans, V. and Williams, S.N., Adaptive systems for foreign exchange trading. Quantit. Finance, 2004, 4, C37–C45.
4. Bach, F.R.Lanckriet, G.R.G. and Jordan, M.I., Multiple kernel learning, conic duality, and the SMO algorithm. Paper presented at the 21st International Conference on Machine Learning, 2004.
5. Bertsekas, D., Nonlinear Programming, 2nd ed., 1999 (Athena Scientific).
6. Blei, D.M., Ng, A.Y. and Jordan, M.I., Latent dirichlet allocation. J. Mach. Learn. Res., 2003, 3, 993–1022.
7. Bollerslev, T., Chou, R.Y. and Kroner, K.F., Arch modeling in finance: A review of the theory and empirical evidence. J. Econometr., 1992, 52, 5–59.
8. Bousquet, O. and Herrmann, D.J.L., On the complexity of learning the kernel matrix. In Advances in Neural Information Processing Systems, 2003 (MIT Press).
9. Canu, S.Grandvalet, Y.Guigue, V. and Rakotomamonjy, A., Svm and kernel methods matlab toolbox. Perception Systémes et Information, INSA de Rouen, Rouen, France, 2005.
10. Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. 2000 (Cambridge University Press: Cambridge).
11. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K. and Harshman, R., Indexing by latent semantic analysis. J. Am. Soc. Inform. Sci., 1990, 41(6), 391–407.
12. Dempster, M.A.H. and Jones, C.M., A real-time adapative trading system using genetic programming. Quantit. Finance, 2001, 1, 397–413.
13. Ding, Z., Granger, C.W.J. and Engle, R.F., A long memory property of stock market returns and a new model. J. Empir. Finance, 1993, 1, 83–106.
14. Dumais, S.Platt, J.Heckerman, D. and Sahami, M., Inductive learning algorithms and representations for text categorizations. Paper presented at ACM0CIKM98, 1998.
15. Ederington, L.H. and Lee, J.H., How markets process information: News releases and volatility. J. Finance, 1993, XLVIII(4), 1161–1191.
16. Fama, E.F., The behavior of stock-market prices. J. Business, 1965, 38, 34–105.
17. Fung, G.P.C.Yu, J.X. and Lam, W., Stock prediction: Integrating text mining approach using real-time news. Paper presented at the IEEE Conference on Computational Intelligence for Financial Engineering, 2003, pp. 395–402.
18. Gavrishchaka, V.V. and Banerjee, S., Support vector machine as an efficient framework for stock market volatility forecasting. Comput. Mgmt Sci., 2006, 3, 147–160.
19. Goffin, J.-L. and Vial, J.-P., Convex nondifferentiable optimization: A survey focused on the analytic center cutting plane method. Optimiz. Meth. Software, 2002, 17(5), 805–867.
20. Gönen, M. and Alpaydin, E., Multiple kernel learning algorithms. J. Mach. Learn. Res., 2011, 12, 2211–2268.
21. Hofmann, T., Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn., 2001, 42, 177–196.
22. Joachims, T., Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms., 2002 (Kluwer Academic: Dordrecht).
23. Joulin, A., Lefevre, A., Grunberg, D. and Bouchaud, J.-P., Stock price jumps: News and volume play a minor role. Wilmott Mag., 2008.
24. Kalev, P.S., Liu, W.-M., Pham, P.K. and Jarnecic, E., Public information arrival and volatility of intraday stock returns. J. Bank. Finance, 2004, 28, 1441–1467.
25. Kogan, S.Levin, D.Routledge, B.R.Sagi, J.S. and Smith, N.A., Paper presented at the North American Association for Computational Linguistics Human Language Technology Conference, Boulder, CO, 2009.
26. Lanckriet, G.R.G., Cristianini, N., Bartlett, P., Ghaoui, L.E. and Jordan, M.I., Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res., 2004a, 5, 27–72.
27. Lanckriet, G.R.G., Bie, T.D., Cristianini, N., Jordan, M.I. and Noble, W.S., A statistical framework for genomic data fusion. Bioinformatics, 2004b, 20, 2626–2635.
28. Lavrenko, V.Schmill, M.Lawrie, D.Ogilvie, P.Jensen, D. and Allan, J., Mining of concurrent text and time series. Paper presented at the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
29. Luenberger, D., Linear and Nonlinear Programming, 2nd ed., 2003 (Kluwer Academic: Dordrecht).
30. Malliaris, M. and Salchenberger, L., Using neural networks to forecast the S&P 100 implied volatility. Neurocomputing, 1996, 10, 183–195.
31. Micchelli, C.A. and Pontil, M., Feature space perspectives for learning the kernel. Mach. Learn., 2007, 66, 297–319.
32. Mitchell, M.L. and Mulherin, J.H., The impact of public information on the stock market. J. Finance, 1994, XLIX(3), 923–950.
33. Mittermayer, M.-A. and Knolmayer, G., Newscats: A news categorization and trading system. Paper presented at the Sixth International Conference on Data Mining, 2006a.
34. Mittermayer, M.-A. and Knolmayer, G., Text mining systems for predicting the market response to news: A survey. Working Paper No. 184, Institute of Information Systems, University of Bern, 2006b.
35. Ong, C.S., Smola, A.J. and Williamson, R.C., Learning the kernel with hyperkernels. J. Mach. Learn. Res., 2005, 6, 1043–1071.
36. Rakotomamonjy, A., Bach, F., Canu, S. and Grandvalet, Y., Simple MKL. J. Mach. Learn. Res., 2008, 9, 2491–2521.
37. Robertson, C.S.Geva, S. and Wolff, R.C., News aware volatility forecasting: Is the content of news important? Paper presented at the 6th Australasian Data Mining Conference (AusDM’07), Gold Coast, Australia, 2007.
38. Sonnenberg, S., Rätsch, G., Schäfer, C. and Schölkopf, B., Large scale multiple kernel learning. J. Mach. Learn. Res., 2006, 7, 1531–1565.
39. Taylor, S., Modelling Financial Time Series., 1986 (Wiley: New York).
40. Taylor, S.J. and Xu, X., The incremental volatility information in one million foreign exchange quotations. J. Empir. Finance, 1997, 4, 317–340.
41. Thomas, J.D., News and trading rules. Dissertation, Carnegie Mellon University, Pittsburgh, PA, 2003.
42. Wood, R.A., McInish, T.H. and Ord, J.K., An investigation of transactions data for nyse stocks. J. Finance, 1985, XL(3), 723–739.
43. Wuthrich, B.Cho, V.Leung, S.Perammunetilleke, D.Sankaran, K.Zhang, J. and Lam, W., Daily prediction of major stock indices from textual web data. Paper presented at the 4th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1998.
44. Zien, A. and Ong, C.S., Multiclass multiple kernel learning. Paper presented at the 24th International Conference on Machine Learning, 2007, pp. 1191–1198.