Predicting risk from financial reports with regression

Kogan, Shimon; Levin, Dimitry; Routledge, Bryan R.; Sagi, Jacob S.; Smith, Noah A.

doi:10.3115/1620754.1620794

Cited by 171 publications

(161 citation statements)

References 31 publications

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“…Forecasting from text requires identifying textual correlates of a response variable revealed in the future, most of which will be weak and many of which will be spurious (Kogan et al, 2009). We consider two such problems.…”

Section: Sentiment Analysismentioning

confidence: 99%

Linguistic Structured Sparsity in Text Categorization

Yogatama¹,

Smith²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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We introduce three linguistically motivated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in feature weights, enabling us to incorporate prior knowledge into conventional bagof-words models. We show that our structured regularizers consistently improve classification accuracies compared to standard regularizers that penalize features in isolation (such as lasso, ridge, and elastic net regularizers) on a range of datasets for various text prediction problems: topic classification, sentiment analysis, and forecasting.

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Section: Sentiment Analysismentioning

confidence: 99%

Linguistic Structured Sparsity in Text Categorization

Yogatama¹,

Smith²

2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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“…The financial dataset consists of 19,395 10-K reports (data available at http://www.ark.cs.cmu.edu/10K/), the annual revenue reports of publicly-traded corporations required by Securities Exchange Commission, published over the period of 1996-2006 from 10,492 companies [7]. Feature extraction is performed on the raw text files using TFIDF, and produces 150,360 features (sparse) for each instance.…”

Section: Application 1 -Financial Risk Rankingmentioning

confidence: 99%

Active Learning to Rank using Pairwise Supervision

Davidson¹,

Qian

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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This paper investigates learning a ranking function using pairwise constraints in the context of human-machine interaction. As the performance of a learnt ranking model is predominantly determined by the quality and quantity of training data, in this work we explore an active learning to rank approach. Furthermore, since humans may not be able to confidently provide an order for a pair of similar instances we explore two types of pairwise supervision: (i) a set of "strongly" ordered pairs which contains confidently ranked instances, and (ii) a set of "weakly" ordered pairs which consists of similar or closely ranked instances. Our active knowledge injection is performed by querying domain experts on pairwise orderings, where informative pairs are located by considering both local and global uncertainties. Under this active scheme, querying of pairs which are uninformative or outliers instances would not occur. We evaluate the proposed approach on three real world datasets and compare with representative methods. The promising experimental results demonstrate the superior performance of our approach, and validate the effectiveness of actively using pairwise orderings to improve ranking performance.

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“…Due to the strong relation between the textual information and numerical measures, there has been a growing body of studies in the fields of finance and data science that adopt the techniques of natural language processing (NLP) and machine learning to examine the interaction between these two types of information (e.g., Kogan et al, 2009;Tsai and Wang, 2017;Rekabsaz et al, 2017). For example, Loughran and McDonald (2011) and Jegadeesh and Wu (2013) investigate how the disclosures of finance sentiment or risk keywords in SEC-mandated financial reports affect investor expectations about a company's future stock prices.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Loughran and McDonald (2011) and Jegadeesh and Wu (2013) investigate how the disclosures of finance sentiment or risk keywords in SEC-mandated financial reports affect investor expectations about a company's future stock prices. Moreover, Kogan et al (2009) and Tsai and Wang (2017) exploit sentiment analysis of 10-K reports for financial risk analysis. Furthermore, in Liu et al (2016), a web-based information system, FIN10K, is proposed for financial report analysis and visualization.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Saphra¹,

Lopez²

2018

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This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom natural language processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf's properties developers and domain experts are able to build domain-specific NLP applications more efficiently. NLPf is Opensource software and available at https:// gitlab.com/schrieveslaach/NLPf. IntroductionNowadays more and more business models rely on the processing of natural language data, e. g. companies extract relevant eCommerce data from domain-specific documents. The required eCommerce data could be related to various domains, e. g. life-science, public utilities, or social media, depending on the companies' business models.Furthermore, the World Wide Web (WWW) provides a huge amount of natural language data that provides a wide variety of knowledge to human readers. This amount of knowledge is unmanageable for humans and applications try to make this knowledge more accessible to humans, e. g. Treude and Robillard (2016) make natural language text about software programming more accessible through a natural language processing (NLP) application.All these approaches have in common that they require domain-specific NLP models that have been trained on a domain-specific and annotated corpus. These models will be trained by using different NLP frameworks and these models have to be evaluated for every annotation layer. For example, named entity recognition (NER) of Stanford CoreNLP (Manning et al., 2014) might work better than NER of OpenNLP (Reese, 2015, Chapter 1); the chosen segmentation tool, e. g. UDPipe (Straka and Straková, 2017), might work better than Stanford CoreNLP's segmentation tool, and so on. Existing studies show that domain specific training and evaluation is a common approach in the NLP community to determine the best-performing NLP pipeline (Buyko et al., 2006; Giesbrecht and Evert, 2009; Neunerdt et al., 2013; Omran and Treude, 2017).Developers of NLP applications are forced to create domain-specific corpora to determine the best-performing NLP pipeline among many NLP frameworks. During this process they face various obstacles:• The training and evaluation of different NLP frameworks requires a lot of effort of scripting or programming because of incompatible APIs.• Domain experts who annotate domainspecific documents with a GUI tool struggle with an insufficient user experience.• There are too many combinations how developers can combine these NLP tools into NLP pipelines.• The generated NLP models as a build artifact have to be integrated manually into the application code.NLP Lean Programming framework (NLPf) addresses these issues. NLPf provides a standardized p...

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Predicting risk from financial reports with regression

Cited by 171 publications

References 31 publications

Linguistic Structured Sparsity in Text Categorization

Linguistic Structured Sparsity in Text Categorization

Active Learning to Rank using Pairwise Supervision

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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