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Although 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.

To 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.

The 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.

The 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.

Given the serious question raised by the subprime of the 2008 global financial crisis over the rising practices of excessive rewarding of executives in the USA and European firms, the executive pay-performance nexus has emerged as a popular topic of debate in the contemporary corporate finance research. Conducted mostly on the Anglo-Saxon contexts, research outcomes have been inconclusive and dichotomous. Considering this backdrop, this study aims to investigate the endogenous relationship between executive compensation and risk taking in the context of the USA.

Using a large sample of non-financial firms from 2010 to 2020 based on panel data and two-stage least square regression. In this study, the riskier corporate decision is measured as book leverage and ratio of R&D expense to total assets. Chief executive officers’ (CEO) experience and age are used as instrumental variables, and these are expected to influence compensation incentives and, hence, affect firm riskiness indirectly. Firm size, return on assets and CEO turnover are reported to affect compensation and corporate decisions, therefore, included as control variables. Given that higher executive compensation is related to riskier corporate decision in firms, this study incorporates total wealth (i.e. accumulated equity related compensation) as an additional proxy of compensation, and this selection is justifiable by the perfect contracting notion of the agency theory.

The results of this study show a significant positive and increasing nexus among compensation and riskier corporate decisions. Besides, the compensation level proxied through the percentage of each form of compensation in total compensation is very important as greater equity and greater salary diminishes risk taking.

The outcomes of this study have useful implications for firm stakeholders and policymakers.

The level of pay measured by the percentage of each type of compensation in total compensation is of utmost importance as it can increase or decrease risk taking in corporate decisions.

This paper examines the short-term market impact of the beginning of the military conflict between Russia and Ukraine (February 24, 2022) on the world’s largest defense firms.

The authors examine the world’s 100 largest listed defense firms at and around the beginning of the military conflict between Russia and Ukraine using an event-study methodology.

We observe a positive and statistically significant stock price reaction at and around the beginning of the military conflict. These results are consistent with the asset-pricing perspective/expected cash flow hypothesis. Consistent with the captured regulator theory, we find superior market returns for the two portfolios with a greater weight of defense sales. Superior market returns are also found for defense firms with higher R&D and capital expenditure intensity. Finally, these reactions are reinforced or mitigated by other firm-specific characteristics such as size, profitability and institutional ownership.

The effect of the war on stock markets has been relatively little examined in the financial theory. This study intends to fill this gap in the literature.