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2016 | OriginalPaper | Chapter

Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach

Authors : Petr Hájek, Jana Boháčová

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

This paper aims to extract both sentiment and bag-of-words information from the annual reports of U.S. banks. The sentiment analysis is based on two commonly used finance-specific dictionaries, while the bag-of-words are selected according to their tf-idf. We combine these features with financial indicators to predict abnormal bank stock returns using a neural network with dropout regularization and rectified linear units. We show that this method outperforms other machine learning algorithms (Naïve Bayes, Support Vector Machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns. Thus, this neural network seems to be well suited for text classification tasks working with sparse high-dimensional data. We also show that the quality of the prediction significantly increased when using the combination of financial indicators and bigrams and trigrams, respectively.

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Literature
1.
Henry, E.: Are investors influenced by how earnings press releases are written? J. Bus. Commun. 45(4), 363–407 (2008)CrossRef
2.
Tetlock, P.C., Saar-Tsechansky, M., Macskassy, S.: More than words: quantifying language to measure firms’ fundamentals. J. Financ. 63(3), 1437–1467 (2008)CrossRef
3.
Doran, J.S., Peterson, D.R., Price, S.M.: Earnings conference call content and stock price: the case of REITs. J. Real Estate Financ. Econ. 45(2), 402–434 (2012)CrossRef
4.
Antweiler, W., Frank, M.Z.: Is all that talk just noise? the information content of internet stock message boards. J. Financ. 59(3), 1259–1294 (2004)CrossRef
5.
Tetlock, P.C.: Giving content to investor sentiment: the role of media in the stock market. J. Financ. 62, 1139–1168 (2007)CrossRef
6.
Loughran, T., McDonald, B.: When is a liability not a liability? textual analysis, dictionaries, and 10-Ks. J. Financ. 66(1), 35–65 (2011)CrossRef
7.
Hájek, P., Olej, V.: Evaluating sentiment in annual reports for financial distress prediction using neural networks and support vector machines. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part II. CCIS, vol. 384, pp. 1–10. Springer, Heidelberg (2013)CrossRef
8.
Hajek, P., Olej, V., Myskova, R.: Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technol. Econ. Dev. Econ. 20(4), 721–738 (2014)CrossRef
9.
Hájek, P., Olej, V.: Intuitionistic fuzzy neural network: the case of credit scoring using text information. In: Iliadis, L., et al. (eds.) EANN 2015. CCIS, vol. 517, pp. 337–346. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-23983-5_​31 CrossRef
10.
Li, F.: Do Stock Market Investors Understand the Risk Sentiment of Corporate Annual Reports? (2006). SSRN 898181
11.
Li, F.: Annual report readability, current earnings, and earnings persistence. J. Account. Econ. 45(2), 221–247 (2008)CrossRef
12.
Feldman, R., Govindaraj, S., Livnat, J., Segal, B.: Management’s tone change, post earnings announcement drift and accruals. Rev. Account. Stud. 15(4), 915–953 (2010)CrossRef
13.
Balakrishnan, R., Qiu, X.Y., Srinivasan, P.: On the predictive ability of narrative disclosures in annual reports. Eur. J. Oper. Res. 202(3), 789–801 (2010)CrossRefMATH
14.
Price, S.M., Doran, J.S., Peterson, D.R., Bliss, B.A.: Earnings conference calls and stock returns: the incremental informativeness of textual tone. J. Bank. Financ. 36(4), 992–1011 (2012)CrossRef
15.
Loughran, T., McDonald, B.: The use of word lists in textual analysis. J. Behav. Financ. 16(1), 1–11 (2015)CrossRef
16.
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv preprint arXiv:​1207.​0580
17.
Baharudin, B., Lee, L.H., Khan, K.: A review of machine learning algorithms for text-documents classification. J. Adv. Inf. Technol. 1(1), 4–20 (2010)
18.
Kearney, C., Liu, S.: Textual sentiment in finance: a survey of methods and models. Int. Rev. Finan. Anal. 23(33), 171–185 (2014)CrossRef
19.
Li, F.: The information content of forward-looking statements in corporate filings - a naïve Bayesian machine learning approach. J. Account. Res. 48(5), 1049–1102 (2010)CrossRef
20.
Demers, E.A., Vega, C.: Soft Information in Earnings Announcements: News or Noise? Working paper. In: INSEAD (2010)
21.
Davis, A.K., Piger, J.M., Sedor, L.M.: Beyond the numbers: measuring the information content of earnings press release language. Contemp. Account. Res. 29(3), 845–868 (2012)CrossRef
22.
Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans. Inf. Syst. (TOIS) 27(2), 12 (2009)CrossRef
23.
Jiang, S., Pang, G., Wu, M., Kuang, L.: An improved K-nearest-neighbor algorithm for text categorization. Expert Syst. Appl. 39(1), 1503–1509 (2012)CrossRef
24.
Schumaker, R.P., Zhang, Y., Huang, C.N., Chen, H.: Evaluating sentiment in financial news articles. Decis. Support Syst. 53(3), 458–464 (2012)CrossRef
25.
Li, Q., Wang, T., Gong, Q., Chen, Y., Lin, Z., Song, S.K.: Media-aware quantitative trading based on public web information. Decis. Support Syst. 61, 93–105 (2014)CrossRef
26.
Yu, Y., Duan, W., Cao, Q.: The impact of social and conventional media on firm equity value: a sentiment analysis approach. Decis. Support Syst. 55(4), 919–926 (2013)CrossRef
27.
Kothari, S.P., Li, X., Short, J.E.: The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: a study using content analysis. Account. Rev. 84(5), 1639–1670 (2009)CrossRef
28.
Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. J. Finan. Econ. 33(1), 3–56 (1993)CrossRefMATH
29.
Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41(16), 7653–7670 (2014)CrossRef
30.
Nam, J., Kim, J., Mencía, E.L., Gurevych, I., Fürnkranz, J.: Large-scale multi-label text classification - revisiting neural networks. In: Calders, T., Esposito, F., Hullermeier, E., Meo, R. (eds.) Machine Learning and Knowledge Discovery in Databases, pp. 437–452. Springer, Heidelberg (2014)
31.
Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30th International Conference on Machine Learning (ICML), vol. 30, pp. 1–6. Atlanta, Georgia (2013)
32.
Jaitly, N., Hinton, G.: Learning a better representation of speech soundwaves using restricted boltzmann machines. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5884–5887 (2011)
33.
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM Sigkdd Explor. Newsl. 6(1), 1–6 (2004)CrossRef
34.
Yin, L., Ge, Y., Xiao, K., Wang, X., Quan, X.: Feature selection for high-dimensional imbalanced data. Neurocomputing 105, 3–11 (2013)CrossRef
Metadata
Title
Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach
Authors
Petr Hájek
Jana Boháčová
Copyright Year
2016
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44188-7_5

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