Mining Public Business Knowledge: A Case Study in SEC's EDGAR

Han, Meng; Liang, Yi; Duan, Zhuojun; Wang, Yingjie

doi:10.1109/bdcloud-socialcom-sustaincom.2016.65

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…With the growth of the Internet, the exponential growth of data volumes become publicly available to researchers; there has been a flood of new information, and big data. There are many researchers used data-driven approach in advanced technology domains, including enterprise management [15], Internet of Things [16], online social networks [17, 18], social networks influence [19], traffic monitoring [20], media applications, collective intelligence and data pravicy [21, 22]. Machine learning and deep learning are popular research topics recently.…”

Section: Introductionmentioning

confidence: 99%

Mild cognitive impairment understanding: an empirical study by data-driven approach

Liu

Han

et al. 2019

BMC Bioinformatics

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BackgroundCognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy.ResultsIn this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined.ConclusionBy incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

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Section: Introductionmentioning

confidence: 99%

Mild cognitive impairment understanding: an empirical study by data-driven approach

Liu

Han

et al. 2019

BMC Bioinformatics

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“…In contrast, data mining focuses on finding patterns and trends in the existing data. Therefore, data mining techniques serve as input for ML, while ML adopts data mining algorithms to set up models (Hasan, 2019; Han et al , 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approaches in FinTech: a survey

Tian

Han

2021

IDD

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Purpose This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.

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“…Matthies and Coners (2015) evaluated two text analysis strategies -dictionary and statistical approach and concluded that they complement each other. Han et al (2016) provides general data extraction and analysis resolution for mining the business knowledge from EDGAR. Focusing on each company's annual meeting date from the 'DEF 14A' form, they automatically scanned 546,451 documents and extracted 82,872 annual meeting date records of 10,417 companies.…”

Section: Content Analysismentioning

confidence: 99%

Text analysis in financial disclosures

Ravula¹

2021

Preprint

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Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas.Keywords NLP • bag-of-words • Disclosures • Machine learning • EDGAR • Text analysis 1 SEC History https://www.sec.gov/about/reports/sec-fy2015-agency-mission-information.pdf

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Mining Public Business Knowledge: A Case Study in SEC's EDGAR

Cited by 8 publications

References 10 publications

Mild cognitive impairment understanding: an empirical study by data-driven approach

Mild cognitive impairment understanding: an empirical study by data-driven approach

Data-driven approaches in FinTech: a survey

Text analysis in financial disclosures

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