Spain

Public companies are obliged to include financial and non-financial information within their corporate filings under Regulation S-K, in the United States . However, the requirements still allow for manager’s discretion. This raises the question to which extent the information is actually included and if this information is at all relevant for investors. We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the “Management’s Discussion and Analysis of Financial Conditions and Results of Operations” (MD&amp;A) section of 10-K (yearly) and 10-Q (quarterly) filings. We further analyse the mediating effect of the environmental performance on the relationship between the company’s disclosures and financial performance. Hereby, we address the results of previous studies regarding environmental performance. We find that the textual information contained within the MD&amp;A section does not allow for conclusions about the future (corporate) financial performance. However, there is evidence that the environmental performance can be extracted by natural language processing
methods.

COLING 2020

A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

Public companies are obliged to include financial and non-financial information within their corporate filings under Regulation S-K, in the United States . However, the requirements still allow for manager’s discretion. This raises the question to which extent the information is actually included and if this information is at all relevant for investors. We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the “Management’s Discussion and Analysis of Financial Conditions and Results of Operations” (MD&A) section of 10-K (yearly) and 10-Q (quarterly) filings. We further analyse the mediating effect of the environmental performance on the relationship between the company’s disclosures and financial performance. Hereby, we address the results of previous studies regarding environmental performance. We find that the textual information contained within the MD&A section does not allow for conclusions about the future (corporate) financial performance. However, there is evidence that the environmental performance can be extracted by natural language processing
methods.

technical paper

We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data. The system’s building blocks are domain agnostic, making it universally applicable. In this paper, we describe each compo-nent of the system and how it extracts relevant economic information from U.S. Federal Open Market Committee (FOMC) statements. Our methodology achieves an impressive 96% accuracy for identifying relevant information for a set of seven economic indicators: household spending, inflation, unemployment, economic activity, fixed investment, fed-eral funds rate, and labor market.

Information Extraction from Federal Open Market Committee Statements



We present a transfer learning approach for Title Detection in FinToC 2020 challenge. Our proposed approach relies on the premise that the geometric layout and character features of the titles and non-titles can be learnt separately from a large corpus, and their learning can then be transferred to a domain-specific dataset. On a domain-specific dataset, we train a Deep Neural Net on the text of the document along with a pre-trained model for geometric and character features. We achieved an F-Score of 83.25 on the test set and secured top rank in the title detection task in FinToC 2020 (Bentabet et al., 2020).


Investigating Transfer Learning for Title Detection in Table of Contents Generation

We rely on the Apache PDFBox library to extract text and all additional information. 
Our constrained system based on Maximum Entropy classifier uses only the provided training data without any additional external resources. 
Our system achieves F1 score 81\% and \#1 place in the French track and F1 score 77\% and \#2 place among 5 participating teams in the English track.


UWB@FinTOC-2020 Shared Task: Financial Document Title Detection

Financial causality detection is centered on identifying connections between different assets from financial news in order to improve trading strategies. FinCausal 2020 - Causality Identificationin Financial Documents – is a competition targeting to boost results in financial causality by obtaining an explanation of how different individual events or chain of events interact and generate subsequent events in a financial environment. The competition is divided into two tasks: (a) a binary classification task for determining whether sentences are causal or not, and (b) a sequence labeling task aimed at identifying elements related to cause and effect. Various Transformer-based language models were fine-tuned for the first task and we obtained the second place in the competition with an F1-score of 97.55% using an ensemble of five such language models. Subsequently, a BERT model was fine-tuned for the second task and a Conditional Random Fieldmodel was used on top of the generated language features; the system managed to identify the cause and effect relationships with an F1-score of 73.10%. We open-sourced the code and made it available at:https://github.com/avramandrei/FinCausal2020.

Causality Analysis in Financial Documents using Pretrained Language Models

This paper reports on an approach to increase multi-token-term recall in a parsing task. We use a compliance-domain parser to extract, during the process of parsing raw text, terms that are unlisted in the terminology. The parser uses a similarity measure (Generalized Dice Coefficient) between listed terms and unlisted term candidates to (i) determine term status, (ii) serve puta- tive terms to the parser, (iii) decrease parsing complexity by glomming multi-tokens as lexical singletons, and (iv) automatically augment the terminology. We illustrate an experiment with examples from the tax-and-regulations domain. Bootstrapping the parsing process to detect out- of-vocabulary terms at runtime increases parsing accuracy in addition to producing other benefits to a natural-language-processing pipeline, which translates arithmetic calculations written in En- glish into computer-executable operations

Mitigating Silence in Compliance Terminology during Parsing of Utterances

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds a hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.

Hierarchical summarization of financial reports with RUNNER

In this paper, we perform modality prediction in financial dialogue. To this end, we introduce a new dataset and develop a binary classifier to detect strong or weak modal answers depending on surface, lexical, and semantic representations of the preceding question and financial features. To do so, we contrast different algorithms, feature categories, and fusion methods. Perhaps counter-intuitively, our results indicate that the strongest features for the given task are financial uncertainty measures such as market and individual firm risk.

Predicting Modality in Financial Dialogue

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50% are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.

Extracting Fine-Grained Economic Events from Business News

In this presentation we describe our system submitted to the FinTOC-2020 shared task on financial document structure extraction. We propose a two-step approach to identify titles in financial documents and to extract their table of contents (TOC). First, we identify text blocks as candidates for titles using unsupervised learning based on character-level information of each document. Then, we apply supervised learning on a self-constructed regression task to predict the depth of each text block in the document structure hierarchy using transfer learning combined with document features and layout features. It is noteworthy that our single multilingual model performs well on both tasks and on different languages, which indicates the usefulness of transfer learning for title detection and TOC generation. Moreover, our approach is independent of the presence of actual TOC pages in the documents. It is also one of the few submissions to the FinTOC-2020 shared task addressing both subtasks in both languages, English and French, with one single model.

Multilingual Document Structure Extraction using Transfer Learning

This paper presents our participation to the FinCausal-2020 Shared Task whose ultimate aim is toextract cause-effect relations from a given financial text. Our participation includes two systemsfor the two sub-tasks of the FinCausal-2020 Shared Task. The first sub-task (Task-1) consists ofthe binary classification of the given sentences as causal meaningful (1) or causal meaningless(0). Our approach for the Task-1 includes applying linear support vector machines after trans-forming the input sentences into vector representations using term frequency-inverse documentfrequency scheme with 3-grams. The second sub-task (Task-2) consists of the identification ofthe cause-effect relations in the sentences, which are detected as causal meaningful. Our ap-proach for the Task-2 is a CRF-based model which uses linguistically informed features. For theTask-1, the obtained results show that there is a small difference between the proposed approachbased on linear support vector machines (F-score 94%) , which requires less time compared to theBERT-based baseline (F-score 95%). For the Task-2, although a minor modifications such as thelearning algorithm type and the feature representations are made in the conditional random fieldsbased baseline (F-score 52%), we have obtained better results (F-score 60%). The source codesfor the both tasks are available online (https://github.com/ozenirgokberk/FinCausal2020.git/).

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