Automated industry classification with deep learning

Wood, Sam; Muthyala, Rohit; Jin, Yi; Qin, Yixing; Rukadikar, Nilaj; Rai, Amit; Gao, Hua

doi:10.1109/bigdata.2017.8257920

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…31 This compares with 80% when using only EMIR data as source, but this figure is inflated by the fact that one CCP is wrongly identified as a credit institution. 32 On top of the improved coverage, the detailed granularity of our classification enhances the understanding of the market structure (see Fig. 2).…”

Section: Tablementioning

confidence: 99%

“…31 The fact that there is no domicile is indication of missing or misreported LEI. 32 Since CCPs do not report any sector according to the regulation, a single mis-reported trade alters greatly the final classification. Some euro area CCPs have a banking license to facilitate their role in the market, but they cannot provide credit and are exempted from some capital requirements.…”

Section: Tablementioning

confidence: 99%

“…The GICS, however, is not very detailed for the financial sector, so not suitable to fairly describe the derivatives market. More recent works [32] have used deep learning to predict the sector of companies 2 from the database of business contacts.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Counterparty Sector in EMIR Data

Lenoci

Letizia²

2021

Data Science for Economics and Finance

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The data collected under the European Market Infrastructure Regulation (“EMIR data”) provide authorities with voluminous transaction-by-transaction details on derivatives but their use poses numerous challenges. To overcome one major challenge, this chapter draws from eight different data sources and develops a greedy algorithm to obtain a new counterparty sector classification. We classify counterparties’ sector for 96% of the notional value of outstanding contracts in the euro area derivatives market. Our classification is also detailed, comprehensive, and well suited for the analysis of the derivatives market, which we illustrate in four case studies. Overall, we show that our algorithm can become a key building block for a wide range of research- and policy-oriented studies with EMIR data.

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Section: Tablementioning

confidence: 99%

Section: Tablementioning

confidence: 99%

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Classifying Counterparty Sector in EMIR Data

Lenoci

Letizia²

2021

Data Science for Economics and Finance

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“…The industry group, subsector, and sector are represented by the first four, first three, and first two digits of the NAICS code, respectively. The NAICS system encompasses a total of 20 sectors, 99 subsectors, and 311 industry groups [21].…”

Section: Mainstream Standardsmentioning

confidence: 99%

“…Beyond the financial sector, company classification can generate prospective leads for sales and marketing teams, identify new clients for insurance companies, and pinpoint competitors for corporations. Investment banks and venture capital firms can also benefit from company classification by understanding the distribution of companies among different industries [21].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of NLP-Based Models for Company Classification

Rizinski,

Jankov,

Sankaradas

et al. 2024

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The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classification. We conduct a comprehensive comparison among these models to assess their effectiveness in the company classification task. The evaluation uses the Wharton Research Data Services (WRDS) dataset, consisting of textual descriptions of publicly traded companies. Our findings reveal that the RoBERTa and One-vs-Rest classifiers surpass the other methods, achieving F1 scores of 0.81 and 0.80 on the WRDS dataset, respectively. These results demonstrate that deep learning algorithms offer the potential to automate, standardize, and continuously update classification systems in an efficient and cost-effective way. In addition, we introduce several improvements to the multi-class classification techniques: (1) in the zero-shot methodology, we TF-IDF to enhance sector representation, yielding improved accuracy in comparison to standard zero-shot classifiers; (2) next, we use ChatGPT for dataset generation, revealing potential in scenarios where datasets of company descriptions are lacking; and (3) we also employ K-Fold to reduce noise in the WRDS dataset, followed by conducting experiments to assess the impact of noise reduction on the company classification results.

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Deep Learning for Indonesia Standard Industrial Classification

Dwicahyo

Yuniarto²

2020

2020 International Conference on Electrical Engineering and Informatics (ICELTICs)

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Automated industry classification with deep learning

Cited by 7 publications

References 6 publications

Classifying Counterparty Sector in EMIR Data

Classifying Counterparty Sector in EMIR Data

Comparative Analysis of NLP-Based Models for Company Classification

Deep Learning for Indonesia Standard Industrial Classification

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