Zombie Board: Board Tenure and Firm Performance

We show that board tenure exhibits an inverted U‐shaped relation with firm value and accounting performance. The quality of corporate decisions, such as M&A, financial reporting quality, and CEO compensation, also has a quadratic relation with board tenure. Our results are consistent with the interpretation that directors’ on‐the‐job learning improves firm value up to a threshold, at which point entrenchment dominates and firm performance suffers. To address endogeneity concerns, we use a sample of firms in which an outside director suffered a sudden death, and find that sudden deaths that move board tenure away from (toward) the empirically observed optimum level in the cross‐section are associated with negative (positive) announcement returns. The quality of corporate decisions also follows an inverted U‐shaped pattern in a sample of firms affected by the death of a director.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

Learning minimum variance discrete hedging directly from the market

Option hedging is a critical risk management problem in finance. In the Black–Scholes model, it has been recognized that computing a hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing variance of the option hedge risk, as it fails to capture the model parameter dependence on the underlying price (see e.g. Coleman et al., J. Risk, 2001, 5(6), 63–89; Hull and White, J. Bank. Finance, 2017, 82, 180–190). In this paper, we demonstrate that this issue can exist generally when determining hedging position from the sensitivity of the option function, either calibrated from a parametric model from current option prices or estimated nonparametricaly from historical option prices. Consequently, the sensitivity of the estimated model option function typically does not minimize variance of the hedge risk, even instantaneously. We propose a data-driven approach to directly learn a hedging function from the market data by minimizing variance of the local hedge risk. Using the S&P 500 index daily option data for more than a decade ending in August 2015, we show that the proposed method outperforms the parametric minimum variance hedging method proposed in Hull and White [J. Bank. Finance, 2017, 82, 180–190], as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied BS delta hedging for weekly and monthly hedging.     

Are there major differences in the attitudes and service quality of standard and seasonal employees? An empirical examination and implications for practice

Despite the increased use of seasonal employees by organizations, few studies have been completed on the attitudes and service quality of seasonal office workers. Using Lautsch's classification model, we analyzed the organizational context in which the standard and seasonal workers in this study were employed. Hypotheses were developed based upon the organizational analysis and social exchange theory. Results from archival data obtained from a web‐based organizational survey of 205 clerical and professional workers indicated contrary to expectations, standard and seasonal employees did not significantly differ in terms of perceptions of overall job conditions, perceived organizational support (POS), or job engagement. However, seasonal employees did report significantly fewer opportunities to work on challenging tasks, less comfortable physical working conditions, and less job security than the standard workers. As predicted, standard employees reported significantly higher levels of service quality performance than seasonal employees. Additionally, job engagement mediated the relationship between POS and service quality for both the standard and seasonal employees. Implications for managing seasonal employees are discussed.     

Forecasting U.S. money growth using economic uncertainty measures and regularisation techniques

This paper examines the out-of-sample forecasting properties of six different economic uncertainty variables for the growth of the real M2 and real M4 Divisia money series for the U.S. using monthly data. The core contention is that information on economic uncertainty improves the forecasting accuracy. We estimate vector autoregressive models using the iterated rolling-window forecasting scheme, in combination with modern regularisation techniques from the field of machine learning. Applying the Hansen-Lunde-Nason model confidence set approach under two different loss functions reveals strong evidence that uncertainty variables that are related to financial markets, the state of the macroeconomy or economic policy provide additional informational content when forecasting monetary dynamics. The use of regularisation techniques improves the forecast accuracy substantially.     

Government Engagement,Environmental Policy,and Environmental Performance: Evidence from the Most Polluting Chinese Listed Firms

This study empirically examines the implementation of environmental policies and how government engagement impacts on a firm's environmental performance based on a sample of Chinese listed firms in the eight most polluting industries over a 10‐year period. The findings of the study demonstrate that government engagement, measured as ownership structure, is positively correlated with environmental performance, measured by environmental capital expenditure, for state‐owned firms, but no significant relation is found for non‐state‐owned firms. In addition, non‐state‐owned firms are more likely to perform better in terms of environmental investment after the 2006 enactment of a new policy explicitly linking environmental issues with political incentives to regional governments. This study also reports that corporate environmental performance impairs firm value for state‐owned firms but has no impact on firm value for non‐state‐owned firms, suggesting that investors negatively respond to environmental investments made by state‐owned firms as a result of government engagement/political pressure. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment.     

Why Do People Leave? a Study Of Nonsupervisory Restaurant Employees’ Polychronic Time Use Preference and Turnover Intention

ABSTRACT

The purpose of the study was to present a comprehensive view on the associations among polychronicity, job satisfaction, work engagement, and turnover intention within a restaurant context. Using a sample of 252 servers in full-service restaurants, structural equation modeling results found that polychronicity positively linked with job satisfaction, work engagement, and turnover intention. An indirect effect from polychronicity to job satisfaction to turnover intention was found. Results can assist restaurant managers in selecting candidates that best fit their organization. It will also assist employees in determining which career path best matches their personality traits.     

Stakeholder Engagement: Defining Strategic Advantage for Sustainable Construction

Although sustainable development is increasingly becoming a part of business plans, it is unclear what makes the economic, social and environmental dynamics strategically compatible. This research examines which of the following in sustainable development – government policy, managerial attitude and stakeholder engagement – is the most influential on the profitability of companies in the UK construction sector. Quantitative and qualitative analyses were rendered through a survey and semi‐structured interviews. Patterns of ambiguity in legislation were discovered as an obstacle for changing the sector's mind‐set. Stakeholder engagement was identified as the defining factor increasing managers' awareness, helping legislation to be effectively implemented and making sustainability highly appealing to clients. These findings indicate that to gain competitive advantage, companies should embark on long‐term strategic alliances which adopt the proposals of environmental non‐governmental organisations and closely follow public opinion. This, strengthens brand equity, allows for premium pricing, increases market share and maximizes profit. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment.     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Does reading scenarios of future land use changes affect willingness to participate in land use planning?

Scenarios of future outcomes often provide context for policy decisions and can be a form of science communication, translating complex and uncertain relationships into stories for a broader audience. We conducted a survey experiment (n = 270) to test the effects of reading land use change scenarios on willingness to participate in land use planning activities. In the experiment, we tested three combinations of scenarios across two time periods, comparing survey responses of individuals reading a set of scenarios with individuals who did not read scenarios. Reading scenario narratives increased willingness to participate in land use planning activities and perceived self-efficacy. Measures of interest and sense of community also increased willingness to participate. Tests of an indirect mediation model found self-efficacy partially mediated the effect of reading scenarios on willingness to participate. This latter relationship may be a mechanistic explanation for the effect of reading scenarios. Envisioning the future with brief, bulleted scenarios of land use change in a print format appears to increase self-efficacy in planning for the future. Our results suggest scenarios can serve as a vehicle for changing public participation in land use planning.