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Purpose -Basel III regulations require banks to protect themselves against strategic risk. This paper aims to provide a comprehensive and measurable definition of this risk and proposes a framework to estimate economic capital requirements.Design/methodology/approach -The paper studies the literature and solicits expert opinion in formulating a comprehensive and measurable definition of strategic risk. The paper postulates that the economic capital for a bank's strategic risk should be estimated using the cost of equity as the profitability threshold, rather than zero and develops a simulation-based framework to estimate economic capital.Findings -The framework closely matches the actual economic capital outlay for strategic risk from our case study of ABN AMRO. It is shown that a bank's strategic growth plans can fall into one of two scenarios based on risk-return characteristics. In one scenario, the required economic capital outlay will increase, and decrease in the other.Practical implications -This framework is generalizable and makes use of widely accepted and used practices in banks, making it readily implementable in practice. It does not introduce errors resulting from model selection, parameterizations or complex calculations.Social implications -Society would be worse off in the absence of banking and lending services. Banks need to take risks to grow and stay competitive. The framework facilitates better strategic risk management, protecting banks from collapse and reducing the need for taxpayer-funded bailouts.Originality/value -The paper provides a measurable and practitioner-verified definition of strategic risk and proposes a simple framework to estimate economic capital requirements, a crucial topic, given the threats and increased levels of strategic risk facing banks.

Passing is an important and crucial aspect of winning a soccer match. It plays a big role in important decisions made by managers and owners when buying/selling players, picking offensive/defensive strategies and defining a style of play. In this article we show how to support these decisions by analyzing the unique passing behaviors (motifs) of players and teams from the patterns in their passing-possession data. We analyze individual players as well as teams based on the diversity and frequency of their involvement in different motifs. We gather passing and possession data from 4 seasons (2012-15) of 6 big European leagues with 8219 matches, 3532 unique players and 155 unique teams and apply the network motif concept to study the patterns. By introducing an expected goals model we build on the motif concept to measure the effectiveness of styles of play. We also make use of a novel way to represent the motif data (the radar graph) to make comparisons between players and teams across multiple seasons. We show how this analysis can support scouting for players and managers, identifying unique players/teams, finding relationships between position and style and in finding a suitable replacement for La Computadora.

Purpose The credit ratings issued by the Big 3 ratings agencies are inaccurate and slow to respond to market changes. This paper aims to develop a rigorous, transparent and robust credit assessment and rating scheme for sovereigns. Design/methodology/approach This paper develops a regression-based model using credit default swap (CDS) data, and data on financial and macroeconomic variables to estimate sovereign CDS spreads. Using these spreads, the default probabilities of sovereigns can be estimated. The new ratings scheme is then used in conjunction with these default probabilities to assign credit ratings to sovereigns. Findings The developed model accurately estimates CDS spreads (based on RMSE values). Credit ratings issued retrospectively using the new scheme reflect reality better. Research limitations/implications This paper reveals that both macroeconomic and financial factors affect both systemic and idiosyncratic risks for sovereigns. Practical implications The developed credit assessment and ratings scheme can be used to evaluate the creditworthiness of sovereigns and subsequently assign robust credit ratings. Social implications The transparency and rigor of the new scheme will result in better and trustworthy indications of a sovereign’s financial health. Investors and monetary authorities can make better informed decisions. The episodes that occurred during the debt crisis could be avoided. Originality/value This paper uses both financial and macroeconomic data to estimate CDS spreads and demonstrates that both financial and macroeconomic factors affect sovereign systemic and idiosyncratic risk. The proposed credit assessment and ratings schemes could supplement or potentially replace the credit ratings issued by the Big 3 ratings agencies.

Many ranking algorithms rank a set of alternatives based on their performance in a set of pairwise comparisons. In this study, a special scenario is observed in which the objective is to rate and rank a set of groups in a traditional recruiting situation, in which the groups extend offers to the set of individuals, and the individuals will select one of their available offers. The new ranking method, Crowd-Ranking, uses collective wisdom and decision-making in conjunction with Markov chains to create competitive matches between alternatives and ultimately provide a ranking of the alternatives. First, the method is evaluated by its performance in a perfect season scenario. Next, it is applied to the case of NCAA football recruiting in the power conferences (ACC, Big Ten, Big 12, Pac 12 and SEC) in the Football Bowl Subdivision. For the Big Ten conference, the method performs significantly better than popular existing services at predicting future team performance based on past recruiting rankings. For a comprehensive national ranking of the power conferences, there is no statistically significant difference between Crowd-Ranking and the other methods.