Corporate Culture and Firm Value: Evidence from Crisis

Fang, Yiwei; Fiordelisi, Franco; Hasan, Iftekhar; Leung, Woon Sau; Wong, Gabriel

doi:10.2139/ssrn.3623400

Cited by 1 publication

(7 citation statements)

References 143 publications

(120 reference statements)

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“…Prior studies in finance have relied on textual analysis approaches, such as bag-of-words, to extract corporate or risk culture features from financial reports and other text documents (Fang et al, 2020;Bianchi et al, 2016). In another approach, Li et al (2021) also uses an advanced neural network word embedding model to measure corporate culture values from earning conference calls.…”

Section: Methodsmentioning

confidence: 99%

“…Theoretically, a sound corporate culture should have a positive relationship with firm performance. Fang et al (2020) studied US public firms around the 2008-2009 financial crises and showed that corporate culture created value for firms. Guiso et al (2015) use a unique IJMF 20,2 survey data that assesses the workplace environment of firms and find that corporate culture has a positive relation with firm performance.…”

Section: Risk Culture and Firm Performancementioning

confidence: 99%

“…Thus, rather than taking the traditional approach of using interviews or surveys, recent studies employ big data and unstructured text data analytics to assess risk culture in financial institutions (Bianchi et al. , 2016; Fang et al. , 2020; Nguyen et al.…”

Section: Introductionmentioning

confidence: 99%

“…Our work relates to previous studies that use textual analysis to measure corporate risk culture of publicly traded U.S. firms (Fang et al, 2020), stress tested banks (Agarwal et al, 2019) and risk culture of large European banks (Bianchi et al, 2016). However, our approach to measure and identify risk culture differs in a number of ways.…”

mentioning

confidence: 99%

“…Sheedy and Griffin (2014) observe that consistent measurements of risk culture are hard to construct based on existing data, which are mostly diverse and messy objects from different data resources. Thus, rather than taking the traditional approach of using interviews or surveys, recent studies employ big data and unstructured text data analytics to assess risk culture in financial institutions (Bianchi et al, 2016;Fang et al, 2020;Nguyen et al, 2019). These studies commonly define risk culture using frequencies or scores of words such as "risk," "culture" or "risk culture" extracted from text documents.…”

mentioning

confidence: 99%

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Identifying the risk culture of banks using machine learning

Owusu

Gupta

2023

IJMF

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PurposeAlthough risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.Design/methodology/approachTo convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.FindingsThe PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.Originality/valueThe relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

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Section: Methodsmentioning

confidence: 99%

Section: Risk Culture and Firm Performancementioning

confidence: 99%