Non-technical skills for managing risk and performance in financial trading

Recent large-scale failures in financial institutions have been found to be caused, in-part, by human factors-related issues in financial trading. In other environments where risk management and performance are intertwined, a human factors approach is often adopted to understand how the ‘non-technical skills (NTS)’ (leadership (LD), decision-making (DM), situation awareness (SA), teamwork) of organisational actors influence outcomes. Yet, to date, there has been minimal application of human factors research in financial trading. This study (i) identifies ‘real-world’ (i.e. non-laboratory) research studies investigating the NTS important for performance in financial trading, (ii) examines and synthesises data on the NTS found to underpin good or poor performance and (iii) considers the quality and coverage of research investigating NTS in financial trading, and identifies potential areas for future research. Nineteen studies were identified through a systematic literature search and then content-analysed for associations between NTS and performance in financial trading. The review found a range of decision-making (e.g. heuristics and biases, intuitive DM, emotional regulation) and LD skills (e.g. setting standards, monitoring behaviour, encouraging speaking-up) to have been identified as important for managing risk and performance in financial trading environments. Furthermore, SA (e.g. information search and assessment strategies, vigilance, identifying ‘noise’ data) and teamwork (e.g. avoiding ‘role’ conflict, communication between traders) were found to be important, yet remain less explored within the literature, and should be the focus of future research. NTS appear essential for effective risk management within the financial sector, yet further field research is required to examine the context-relevant behaviours that underpin safe activity. This will facilitate the development of evidence-based systems for assessing and training NTS competencies.     

Traffic evacuation time under nonhomogeneous conditions

During many manmade and natural crises such as terrorist threats, floods, hazardous chemical and gas leaks, emergency personnel need to estimate the time in which people can evacuate from the affected urban area. Knowing an estimated evacuation time for a given crisis, emergency personnel can plan and prepare accordingly with the understanding that the actual evacuation time will take longer. Given the urban area to be evacuated, street widths exiting the area's perimeter, the area's population density, average vehicle occupancy, transport mode share and crawl speed, an estimation of traffic evacuation time can be derived. Peak-hour traffic data collected at three, midblock, Mumbai sites of varying geometric features and traffic composition were used in calibrating a model that estimates peak-hour traffic flow rates. Model validation revealed a correlation coefficient of +0.98 between observed and predicted peak-hour flow rates. A methodology is developed that estimates traffic evacuation time using the model.     

Your Governance or Mine?

In response to criticism directed at the resource sector's corporate governance, this paper examines the corporate governance and underlying firm characteristics of resource development stage entities (DSEs) relative to a size‐matched sample of non‐resource firms. We find that resource DSEs have different governance characteristics in the measures of board independence, chair/CEO duality and CEO cash bonuses. Furthermore, there are differences in the information environment measures of analyst following, debt levels, stock market return and stock turnover. Considering we document substantial differences in underlying firm characteristics, corporate governance differences are likely appropriate to the mining industry and should not be uniformly labelled as ‘bad’. Our results suggest that media rankings based on corporate governance scores may not accurately portray the resource sector. Overall, our results are of interest to Australian investors and regulators and contribute to a broader understanding of contextually contingent corporate governance.     

STRATEGIC ASSET ALLOCATION FOR LONG‐TERM INVESTORS: PARAMETER UNCERTAINTY AND PRIOR INFORMATION

We study the effect of parameter uncertainty on the long‐run risk for three asset classes: stocks, bills and bonds. Using a Bayesian vector autoregression with an uninformative prior we find that parameter uncertainty raises the annualized long‐run volatilities of all three asset classes proportionally with the same factor relative to volatilities that are conditional on maximum likelihood parameter estimates. As a result, the horizon effect in optimal asset allocations is much weaker compared to models in which only equity returns are subject to parameter uncertainty. Results are sensitive to alternative informative priors, but generally the term structure of risk for stocks and bonds is relatively flat for investment horizons up to 15 years. Copyright © 2013 John Wiley & Sons, Ltd.