Abstract
Aim: The paper aims to automatically generate commentaries on financial reports. Background: Analysing and commenting financial reports is critical to evaluate the performance of a company so that management may change course to meet the targets. Generating commentaries is a task that relies on the expertise of analysts. Methodology: We propose an encoder-decoder architecture based on Recurrent Neural Networks (RNN) that are trained on both financial reports and commentaries. This architecture learns to generate those commentaries from the detected patterns on data. The proposed model is assessed on both synthetic and real data. We compare different neural network combinations on both encoder and decoder, namely GRU, LSTM and one layer neural networks. Results: The accuracy of the generated commentaries is evaluated using BLEU, ROUGE and METEOR scores and probability of commentary generation. The results show that a combination of one layer neural network and an LSTM as encoder and decoder respectively provides a higher accuracy. Conclusion: We observe that the LSTM highly depends on long term memory particularly in learning from real commentaries.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sutskever, I., Vinyals, O., Le, Q.V.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Farhadi, A., et al.: Every picture tells a story: generating sentences from images. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 15–29. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_2
Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: data, models and evaluation metrics. JAIR 47, 853–899 (2013)
Mao, J., Xu, W., Yang, Y., Wang, J., Yuille, A.: Deep captioning with multimodal recurrent neural networks (M-RNN). arXiv:1412.6632 (2014)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell, a neural image caption generator. arXiv:1411.4555 (2014)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. arXiv:1411.4389v2 (2014)
Karpathy, A., Li, F.-F.: Deep visual-semantic alignments for generating image descriptions. arXiv:1412.2306 (2014)
Chen, L., He, Y., Fan, L.: Let the robot tell: describe car image with natural language via LSTM. Pattern Recogn. Lett. 98, 75–82 (2017)
Fang, H., et al.: From captions to visual concepts and back. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 1473–1482 (2015)
Jia, X., Gavves, E., Fernando, B., Tuytelaars, T.: Guiding long-short term memory for image caption generation. In: Proceedings of the ICCV (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs with relationships to statistical pattern recognition. In: Soulié, F.F., Hérault, J. (eds.) Neurocomputing: Algorithms, Architectures and Applications, pp. 227–236. Springer, Heidelberg (1990). https://doi.org/10.1007/978-3-642-76153-9_28
Tieleman, T., Hinton, G.: Lecture 6.5-RMSprop: divide the gradient by a running average of its recent magnitude. COURSERA 4(2), 26–31 (2012)
Keras Documentation. https://keras.io/optimizers/. Accessed 28 Jan 2019
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: ACL (2002)
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81 (2004)
Elliott, D., Keller, F.: Image description using visual dependency representations. In: EMNLP, pp. 1292–1302. ACL (2013)
Kilickaya, M., Erdem, A., Ikizler-Cinbis, N., Erdem, E.: Re-evaluating automatic metrics for image captioning. In: Proceedings of EACL 2017, pp. 199–209 (2017)
Sharma, S., El Asri, L., Schulz, H., Zumer, J.: Relevance of unsupervised metrics in task-oriented dialogue for evaluating natural language generation. CoRR, vol. abs/1706.09799 (2017). http://arxiv.org/abs/1706.09799
Dangeti, P.: Statistics for Machine Learning, 1st edn. Packt Publishing Ltd., Birmingham (2017)
Acknowledgement
This work is supported by a grant from Smart Computing For Innovation (SOSCIP) consortium, Toronto, Canada.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
El Mokhtari, K., Maidens, J., Bener, A. (2019). Predicting Commentaries on a Financial Report with Recurrent Neural Networks. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)