Abstract
With the fast development of natural language processing (NLP), financial text data processing has gained much attention due to its huge potential business value. Deep learning model based on manual perception-based labeling is commonly used to illustrate implicit meanings behind financial text. However, such manual labeling is costly and subjective, and may not perform well due to its weak link with direct financial trading. This paper therefore proposes a novel learning model called Enhancement Learning (EL) on financial text data by using task-based labeling. Financial text often has its application task. The derived financial trading data from such task called task-based label is objective and links to certain characters of financial text. Compared to model trained only on manual labels, EL consists of two models trained by manual perception-based labels and derived task-based labels respectively. Then, the task-based model will be used as main model to produce initial judgment on text, with the perception-based model as a filter to drop cases which are different from common perception. This task-oriented perception-enhanced approach can improve the performance of financial text data due to its direct link with financial task and further verification from perception. This paper illustrates the proposed Enhancement Learning on financial text data by the case in stock return prediction. Numerical experiments show that EL performs better than both the perception-based model and the task-based model.












Similar content being viewed by others
References
Xu Y and Cohen SB (2018) Stock movement prediction from tweets and historical prices, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, pp. 1970–1979, https://doi.org/10.18653/v1/P18-1183
Kazemian S, Zhao S, and Penn G (2016) Evaluating sentiment analysis in the context of securities trading, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2094–2103
Li X, Xie H, Lau RYK, Wong T-L, Wang F-L (2018) Stock prediction via sentimental transfer learning. IEEE Access 6:73110–73118. https://doi.org/10.1109/ACCESS.2018.2881689
Feuerriegel S, Prendinger H (2016) News-based trading strategies. Decis Support Syst 90:65–74
Smailovic J, Grcar M, Lavrac N, Žnidarsic M (2014) Stream-based active learning for sentiment analysis in the financial domain. Inf Sci 285:181–203
Katayama D, Tsuda K (2018) A method of measurement of the impact of Japanese news on stock market. Procedia Comput Sci 126:1336–1343
Nofsinger JR (2005) Social mood and financial economics. J Behav Financ 6(3):144–160. https://doi.org/10.1207/s15427579jpfm0603_4
Chen C and Shih P (2019) A stock trend prediction approach based on Chinese news and technical indicator using genetic algorithms, in 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 1468–1472
“Barra’s Risk Models.” https://www.msci.com/www/research-paper/barra-s-risk-models/014972229 (accessed Mar. 12, 2020)
Van de Kauter M, Breesch D, Hoste V (2015) Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Syst Appl 42(11):4999–5010. https://doi.org/10.1016/j.eswa.2015.02.007
Araci D (2019) FinBERT: financial sentiment analysis with pre-trained language models, Accessed: Mar. 11, 2020. [Online]. Available: https://arxiv.org/abs/1908.10063v1
Loughran T, Mcdonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J Financ 66(1):35–65
Jangid H, Singhal S, Shah RR, and Zimmermann R (2018) Aspect-based financial sentiment analysis using deep learning, in Companion Proceedings of the The Web Conference 2018, Republic and Canton of Geneva, Switzerland, pp. 1961–1966, https://doi.org/10.1145/3184558.3191827
Akhtar MS, Kumar A, Ghosal D, Ekbal A, and Bhattacharyya P (2017) A multilayer perceptron based ensemble technique for fine-grained financial sentiment analysis, in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 540–546, Accessed: Dec. 22, 2018. [Online]. Available: https://www.aclweb.org/anthology/D17-1057
Sohangir S, Petty N, and Wang D (2018) Financial sentiment lexicon analysis, in 2018 IEEE 12th International Conference on Semantic Computing (ICSC), pp. 286–289, https://doi.org/10.1109/ICSC.2018.00052
Yang SEL, Zhang M, and Xiang Y (2018) Aspect-based financial sentiment analysis with deep neural networks, in Companion Proceedings of the The Web Conference 2018, Republic and Canton of Geneva, Switzerland, pp. 1951–1954, https://doi.org/10.1145/3184558.3191825
Devlin J, Chang M-W, Lee K, and Toutanova K (2018) BERT: pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805 [cs], Accessed: Dec. 24, 2018. [Online]. Available: http://arxiv.org/abs/1810.04805
Salunkhe A and Mhaske S (2019) “Aspect based sentiment analysis on financial data using transferred learning approach using pre-trained BERT and regressor model”
Mittermayer M (2004) Forecasting intraday stock price trends with text mining techniques, in In: Proceedings 37th Annual Hawaii Int, Conference on System Sciences (HICSS), Big Island, p. 64
Nassirtoussi AK, Aghabozorgi SR, Wah TY, Ngo DCL (2014) Text mining for market prediction: a systematic review. Expert Syst Appl 41:7653–7670
Kraus M and Feuerriegel S (2017) Decision support from financial disclosures with deep neural networks and transfer learning, arXiv:1710.03954 [cs], https://doi.org/10.1016/j.dss.2017.10.001
Othan D, Kilimci ZH, and Uysal M Financial sentiment analysis for predicting direction of stocks using Bidirectional Encoder Representations from Transformers (BERT) and Deep Learning Models”
Sousa MG, Sakiyama K, de S. Rodrigues L, Moraes PH, Fernandes ER, and Matsubara ET (2019) “BERT for stock market sentiment analysis, in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1597–1601, https://doi.org/10.1109/ICTAI.2019.00231
Luss R, d’Aspremont A (2015) Predicting abnormal returns from news using text classification. Quant Finance 15(6):999–1012
Devlin J, Chang M-W, Lee K, and Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805 [cs], Accessed: Mar. 12, 2020. [Online]. Available: http://arxiv.org/abs/1810.04805
Cui Y et al (2019) Pre-training with Whole Word Masking for Chinese BERT, ArXiv, vol. abs/1906.08101
Zhang C Sentiment analysis and deep reinforcement learning for algorithmic trading
Fama EF, French KR (1993) Common risk factors in the returns on stocks and bonds. J Financ Econ 33(1):3–56. https://doi.org/10.1016/0304-405X(93)90023-5
Sharpe WF (1994) The Sharpe ratio. J Portfolio Manag 21(1):49–58. https://doi.org/10.3905/jpm.1994.409501
Chen H-C, Lai CW, Wu S-C (2016) Mutual fund selection and performance persistence in 401(k) plans. North Am J Econ Finance 35:78–100. https://doi.org/10.1016/j.najef.2015.10.004
Huang A, Wu W, and Yu T (2019) Textual analysis for China’s financial markets: a review and discussion, China Finance Review International
Luo L et al (2018) Beyond polarity: interpretable financial sentiment analysis with hierarchical query-driven attention, in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 4244–4250, https://doi.org/10.24963/ijcai.2018/590
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Man, X., Lin, J. Enhancement learning on financial text data. Pers Ubiquit Comput 26, 1011–1021 (2022). https://doi.org/10.1007/s00779-020-01497-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-020-01497-x