Proceedings of the Second Workshop on Economics and Natural Language Processing

Udo Hahn, Véronique Hoste, Zhu Zhang (Editors)

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Hong Kong
Association for Computational Linguistics
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Proceedings of the Second Workshop on Economics and Natural Language Processing
Udo Hahn | Véronique Hoste | Zhu Zhang

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Financial Event Extraction Using Wikipedia-Based Weak SupervisionWikipedia-Based Weak Supervision
Liat Ein-Dor | Ariel Gera | Orith Toledo-Ronen | Alon Halfon | Benjamin Sznajder | Lena Dankin | Yonatan Bilu | Yoav Katz | Noam Slonim

Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This work is in line with this latter approach, leveraging relevant Wikipedia sections to extract weak labels for sentences describing economic events. Whereas previous weakly supervised approaches required a knowledge-base of such events, or corresponding financial figures, our approach requires no such additional data, and can be employed to extract economic events related to companies which are not even mentioned in the training data.

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Forecasting Firm Material Events from 8-K Reports
Shuang (Sophie) Zhai | Zhu (Drew) Zhang

In this paper, we show deep learning models can be used to forecast firm material event sequences based on the contents in the company’s 8-K Current Reports. Specifically, we exploit state-of-the-art neural architectures, including sequence-to-sequence (Seq2Seq) architecture and attention mechanisms, in the model. Our 8K-powered deep learning model demonstrates promising performance in forecasting firm future event sequences. The model is poised to benefit various stakeholders, including management and investors, by facilitating risk management and decision making.

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Incorporating Fine-grained Events in Stock Movement Prediction
Deli Chen | Yanyan Zou | Keiko Harimoto | Ruihan Bao | Xuancheng Ren | Xu Sun

Considering event structure information has proven helpful in text-based stock movement prediction. However, existing works mainly adopt the coarse-grained events, which loses the specific semantic information of diverse event types. In this work, we propose to incorporate the fine-grained events in stock movement prediction. Firstly, we propose a professional finance event dictionary built by domain experts and use it to extract fine-grained events automatically from finance news. Then we design a neural model to combine finance news with fine-grained event structure and stock trade data to predict the stock movement. Besides, in order to improve the generalizability of the proposed method, we design an advanced model that uses the extracted fine-grained events as the distant supervised label to train a multi-task framework of event extraction and stock prediction. The experimental results show that our method outperforms all the baselines and has good generalizability.

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Group, Extract and Aggregate : Summarizing a Large Amount of Finance News for Forex Movement Prediction
Deli Chen | Shuming Ma | Keiko Harimoto | Ruihan Bao | Qi Su | Xu Sun

Incorporating related text information has proven successful in stock market prediction. However, it is a huge challenge to utilize texts in the enormous forex (foreign currency exchange) market because the associated texts are too redundant. In this work, we propose a BERT-based Hierarchical Aggregation Model to summarize a large amount of finance news to predict forex movement. We firstly group news from different aspects : time, topic and category. Then we extract the most crucial news in each group by the SOTA extractive summarization method. Finally, we conduct interaction between the news and the trade data with attention to predict the forex movement. The experimental results show that the category based method performs best among three grouping methods and outperforms all the baselines. Besides, we study the influence of essential news attributes (category and region) by statistical analysis and summarize the influence patterns for different currency pairs.

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Complaint Analysis and Classification for Economic and Food Safety
João Filgueiras | Luís Barbosa | Gil Rocha | Henrique Lopes Cardoso | Luís Paulo Reis | João Pedro Machado | Ana Maria Oliveira

Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems : target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70 %, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints.