Purpose This paper aims to determine possible differences in causes or characteristics between men and women in attaining the CEO position in large publicly listed companies in the USA. Design/methodology/approach T-test statistic, correlation analyses and logit model were used to determine the role individual factors (tenure in management roles, age of CEOs, number of children, years of education) and the firm-level factor (number of employees, net income) play in determining the likelihood of having a female CEO. Findings The research results show that years of education, the number of children and the number of employees in the business play significant roles in determining the likelihood of having a female CEO. An increase in the number of children and years spent in education lower the probability of the CEO being a woman, while having greater number of employees raises the likelihood of having a woman CEO. Research limitations/implications The findings are applicable to only the largest publicly traded firms in the USA and are not applicable to mid to small publicly listed, private or non-for-profit companies or institutions. This research is a starting point for future research of women and men CEOs of small and mid-size publicly traded and non-publicly traded firms in the USA. Originality/value Prior research has shown that having children is detrimental for women in management positions; this research specifically identifies this problem for the CEO position. It also reveals that having more of education does not translate to getting to the CEO position for women.

Purpose The purpose of this study is to examine the role of chief financial officers’ (CFOs’) gender in financial risk taking of 58 US companies along with the impact of having women board members. Design/methodology/approach Using a panel data of 58 selected S&P 500 companies during the period 2012-2016, this paper determines whether the gender of CFOs and having women board members play a role in risk-taking behavior of firms. Findings Firms led by female CFOs are smaller in size with lower net income and net revenue. The panel data analysis shows that the impact of female CFOs on firms’ financial risk is mixed, depending on risk measures used, whereas increasing female board members reduces that risk. Research limitations/implications The data used is limited to 58 S&P 500 companies, and two of the three risk-taking measures used in the study, specifically investment in property, plant and equipment (PPE) and debt/equity ratio, may not be applicable to some industries. Practical implications The findings provide mixed evidence of risk aversion by females in executive and leadership positions, depending on the measures used and the management responsibilities they undertake (CFO versus board member) with support for the glass cliff phenomenon in which females may be leading financially precarious organizations. Social implications Female CFOs are found to be leading relatively smaller and financially poor-performing firms compared with the male CFO-led firms, thereby giving support to the glass cliff arguments. Originality/value The paper examines the role of CFOs’ gender and board diversity in risk taking as measured by the investment in PPE, debt/equity ratio and stock return volatility.

This paper explores the long‐run relationship and the associated short‐run dynamics between the U.S. stock market and three major macroeconomic fundamentals, namely the U.S. industrial production index, the U.S. 10‐year Treasury bond yield and the West Texas Intermediate oil price, for the time period covering 1985–2015. The quantile autoregressive distributed lag (QARDL) model presented by Cho et al. (2015) Journal of Econometrics, 188, 281–300, which combines the autoregressive distributed lag model of Pesaran and Shin (1998), Cambridge University Press, and Pesaran et al. (2001) Journal of Applied Econometrics, 16, 289–326, and the quantile regression methodology of Koenker and Bassett (1978), Econometrica, 46, 33–50, is used. This approach analyses the links among the variables over a range of quantiles which represent different states of the equity market. The findings show a significant quantile‐varying cointegration relationship between U.S. equity prices and the three selected macroeconomic factors. The U.S. industrial production index is found to have the largest long‐run influence on the equity market. These findings highlight the importance of market players as well as policymakers to develop a greater understanding and awareness of how macro variables impact stock prices under different stock market conditions, particularly in relation to optimal asset allocation and risk management strategies, among others.