Identifying inter-firm relationships is critical in understanding the industry landscape. However, due to the dynamic nature of such relationships, it is challenging to capture corporate social networks in a scalable and timely manner. To address this issue, this research develops a framework to build corporate social network representations by applying natural language processing (NLP) techniques on a corpus of 10-K filings, describing the reporting firms’ perceived relationships with other firms. Our framework uses named-entity recognition (NER) to locate the corporate names in the text, topic modeling to identify types of relationships included, and BERT to predict the type of relationship described in each sentence. To show the value of the network measures created by the proposed framework, we conduct two empirical analyses to see their impacts on firm performance. The first study shows that competition relationship and in-degree measurements on all relationship types have prediction power in estimating future earnings. The second study focuses on the difference between individual perspectives in an inter-firm social network. Such a difference is measured by the direction of mentions and is an indicator of a firm’s success in network governance. Receiving more mentions from other firms is a positive signal to network governance and it shows a significant positive correlation with firm performance next year.
In this paper, we build on the network structural hole concept of organizational theory to theorize an individual firm-specific strategic competitive positioning (SCP) construct. We use unsupervised document embedding approaches to operationalize the SCP construct by capturing each firm’s relative competitive and strategic positioning in a strategic similarity matrix of all existing U.S. publicly traded firms’ annual corporate filings. This approach enables us to construct a theoretically driven firm-level SCP measure with minimal human expert intervention. Our construct dynamically captures competitive positioning across different firms and years without using artificially bounded and often outdated industry classification systems. We illustrate how the dynamic measure captures industry-level and cross-industry strategic changes. Then, we demonstrate the effectiveness of our construct with an empirical analysis showing the imprinting and dynamic effects of SCP on firm performance. The results show that our dynamic SCP measure outperforms existing competition measures and successfully predicts post-IPO performance. This paper makes significant contributions to the information systems and organizations literatures by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning. The construct can be easily applied to firm-specific, industry-level, and cross-industry research questions in many contexts across many disciplines.
IT risk, especially cybersecurity risk, has rapidly increased and become a top concern for researchers, regulators, firm managers, and investors. This study creates a novel firm-level IT risk measure applicable to all US-listed firms by applying the BERTopic topic modeling to risk factors reported in Item 1A of the 10-K annual reports. We validate the measure with multiple approaches including cross-validations, presenting illustrative excerpts of IT risk factors, conducting cross-sectional and over-time distribution analyses, and analyzing firm characteristics associated with IT risk. The measure is found to be heightened in IT-intensive industries and for firms with larger sizes, higher profits, and better growth potential, and it can predict future data breaches. Using this ex-ante IT risk measure, we examine the relation between IT risk and stock price crash risk, which reflects a firm’s propensity to stock price crashes. Our findings suggest that IT risk is positively associated with crash risk, and we also identify that downward operating risk and predictability for data breaches are two mechanisms for the crash risk effect of IT risk. By decomposing IT risk into cybersecurity risk and non-cybersecurity IT risk, we find that both types of IT risk increase crash risk, but the effect of cybersecurity risk is stronger than that of non-cybersecurity IT risk, consistent with their different risk natures. We further observe that the novelty and readability of IT risk factors strengthen the crash risk effects of IT risk, consistent with the notion that the novelty represents updated and increased IT risk, and readability improves the understanding of IT risk. Lastly, difference-in-differences analyses reveal that IT risk increases stock price crash risk, not the other way around. We conclude the paper by discussing academic contributions and practical implications in the context of the SEC’s directives on reporting and managing IT risk and cybersecurity risk.
Presented at UKC (2017), KISTI (2017), WITS (2017), Rutgers Business School (2018)
There are increasing needs for understanding and fathoming of the business management environment through big data analysis at the industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm-level analyses using publicly available company disclosure data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels.
Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries’ topic trend, software and hardware industries are compared in recent 20 years. Also, the changes in management subject at the firm level are observed with a comparison of two companies in the software industry. The changes in topic trends provide a lens for identifying decreasing and growing management subjects at industrial and firm-level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at the firm level in the software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades.
For suggesting a methodology to develop an analytical model based on public management data at the industrial and corporate level, there may be contributions in terms of making the ground of practical methodology to identifying changes of management subjects. However, there are required further researches to provide a microscopic analytical model with regard to the relation of technology management strategy between management performance in case of related to the various pattern of management topics as of frequent changes of management subject or their momentum. Also, more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.