Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Does educational diversity of managers matter for the performance of team‐managed funds?

We study the effect of the educational diversity of managers on the performance of team‐managed mutual funds using a large sample of U.S. equity funds from 1994 to 2013. We consider diversity in terms of both final educational degree and field of educational specialisation. We find that, in general, both types of diversity have a positive impact on fund performance, and our results are robust over a wide range of performance metrics and changes in market conditions.     

Unfolding the impact of leadership and management on sustainability performance: Green and lean practices and guanxi as the dual mediators

This study aims to empirically evaluate the predictors that influence sustainability performance among manufacturing firms. Leadership and management, green and lean practices, and guanxi were examined to determine whether these predictors are directly and/or indirectly affecting sustainability performance; 160 valid responses were collected and partial-least-squares-structural-equation-modeling (PLS-SEM) was used to analyze the data. The results showed that leadership and management positively influenced green and lean practices and green and lean practices positively influenced sustainability performance. Leadership and management also positively influenced guanxi. Interestingly, leadership and management and guanxi do not exert a significant direct influence on sustainability performance. The findings contributed to the development of the resource-based-view theory further by empirically exploring the significance of leadership and management coupled with green and lean practices as competencies and capability to drive sustainability performance. The testing of the dual mediators' effects further added value to this study.     

Contracting and Reporting Conservatism around a Change in Fiduciary Duties*

We exploit an influential 1991 Delaware court ruling to examine simultaneously two types of conservatism that play important roles in resolving creditor–owner agency conflicts: contracting conservatism and reporting conservatism. The ruling expanded managerial fiduciary duties in favor of creditors for Delaware-incorporated firms in the vicinity of insolvency. In those firms, following the ruling, debt contracts are less likely to include conservative adjustments to accounting numbers used for covenant compliance (i.e., contracting conservatism decreases), while public financial reporting becomes more conservative (i.e., reporting conservatism increases). The decrease in contracting conservatism is concentrated in firms that exhibit a greater increase in reporting conservatism, suggesting that reporting conservatism is more cost-effective in resolving agency conflicts. In addition, the substitution effect is more pronounced in firms facing greater business uncertainty and firms with greater board independence.     

Change within purchasing and supply management organisations – Assessing the claims from maturity models

It is a wide-held assumption that professional development and change within purchasing and supply management (PSM) organisations can be explained and guided by a maturity model. In this paper the guidance which the maturity model concept offers to understand a PSM organisation's performance is assessed. The methodology is based on the outcomes of a literature review of PSM maturity models, development of an organisational change framework and the learning from three qualitative case studies. An alternative understanding of the development of the PSM organisation is offered through an organisational change framework, composing 1) movement transitions, 2) scalability of change, 3) acceptability of change, and 4) the substantive element of change. The research found that extant PSM maturity models are too rigid for PSM managers to apply, and although maturity models are commonly accepted in PSM literature, in practice, they may produce the opposite effect of what is promised. The PSM maturity models suggest that their application will lead to increased status and influence of PSM within the organisation; expectations that may not be met. PSM organisations’ change processes are subjected to a range of situational and contextual power relations which must be considered in order to advance the specific PSM organisation roles and responsibilities.     

Abuse of Market Dominance Under China’s Anti-Monopoly Law: The Case of Tetra Pak

Review of Industrial Organization - In this paper, we analyze a recent antitrust case of abuse of dominance that was decided by a Chinese administrative enforcement agency under China’s...     

Non-GAAP reporting following debt covenant violations

Review of Accounting Studies - We investigate whether firms change their non-GAAP reporting practices after debt covenant violations. We find that the likelihood that a firm will disclose non-GAAP...     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.