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The purpose of this paper is to identify the arbitrage opportunities between US industry-level credit and stock markets with a focus on dynamic lead-lag relationships given that these markets involve heterogeneous agents operating over various time horizons.

The authors use daily data of 11 US industries stock markets and their credit counterparts to model the dynamic dependence and casual nexuses using time-frequency approach, namely, wavelet squared coherence (WTC).

The WTC estimation results show that credit and stock markets are out of phase (counter cyclical) and stock markets lead their credit counterparts. The coherence between two markets increases during financial crises. The banks (utilities) industry credit and stock markets have relatively high (low) dependence.

The casual nexuses between stock and credit markets have multilateral dimensions. Greater interest in examining the relationship between stock markets and credit default swap (CDS) spreads emerged as an important albeit a complex area of research, and gained prominence especially at the onset and following the global financial crises of 2007-2008 which clearly showed that the positive views of CDSs contribution in creating a resilient and efficient financial sector was nothing further from the truth.

The arbitrage and hedging opportunities between stock and credit markets are industry dependent and vary over investment time horizons. The utilities industry seems attractive for the investment with the objective to exploit arbitrage, but not for hedging.

The paper, for the first time, employs time-frequency approach to assess the arbitrage opportunities between US industry-level credit and stock markets.

The purpose of this paper is to propose an integrated qualitative and quantitative approach to the development of a balanced scorecard (BSC) for a real life case company KVIC (Khadi and Village Industries Commission, organic food sector, India).

In this paper the semi‐structured interviews with director, managers, professional consultant, review of published reports and observations made during research work are considered as basis.

This paper illustrates how the use of a mix approach of cause and effect diagram, Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP) can address some of the shortcomings related to the development of BSC in the light of a real life case company KVIC (Khadi Village and Industry Commission, organic food sector India). The paper delivers a complete framework of BSC for the case company.

The paper outlines the limitations of proposed approach in regard to validity of present logical relationships among various objectives of organization in the futuristic environment and indicates the need for a computer software system, which can improve the efficiency of proposed approach.

In the paper a number of case studies report the fact that companies have attempted to derive measures from strategy, based on cause‐and‐effect reasoning, but the claimed link between strategy and measures appeared weak in analysis (Malmi, 2001). The paper establishes the basis for integrating organization's strategic intent with the identification of performance measures and at large development of BSC.

The paper shows that present work demonstrates the use of an innovative approach to the development of performance measurement system at one end while to deliver a workable framework of balanced scorecard for a real life case company is the objective of the other end. The present work encapsulates the philosophy of strategy maps using a mix of quantitative and qualitative approach for a real life case.

This paper aims to analyze systemic risk in and the effect of capital regulation on the European insurance sector. In particular, the evolution of an exposure measure (SRISK) and a contribution measure (Delta CoVaR) are analyzed from 1985 to 2016.

With the help of multivariate regressions, the main drivers of systemic risk are identified.

The paper finds an increasing degree of interconnectedness between banks and insurance that correlates with systemic risk exposure. Interconnectedness peaks during periods of crisis but has a long-term influence also during normal times. Moreover, the paper finds that the insurance sector was greatly affected by spillovers from the process of capital regulation in banking. While European insurance companies initially at the start of the Basel process of capital regulation were well capitalized according to the SRISK measure, they started to become capital deficient after the implementation of the model-based approach in banking with increasing speed thereafter.

These findings are highly relevant for the ongoing global process of capital regulation in the insurance sector and potential reforms of Solvency II. Systemic risk is a leading threat to the stability of the global financial system and keeping it under control is a main challenge for policymakers and supervisors.

This paper provides novel tools for supervisors to monitor risk exposures in the insurance sector while taking into account systemic feedback from the financial system and the banking sector in particular. These tools also allow an evidence-based policy evaluation of regulatory measures such as Solvency II.

The authors analyse six actively traded VIX Exchange Traded Products (ETPs) including 1x long, −1x inverse and 2x leveraged products. The authors assess their impact on the VIX Futures index benchmark.

Long-run causal relations between daily price movements in ETPs and futures are established, and the impact of rebalancing activity of leveraged and inverse ETPs evidenced through causal relations in the last 30 min of daily trading.

High frequency lead lag relations are observed, demonstrating opportunities for arbitrage, although these tend to be short-lived and only material in times of market dislocation.

The causal relations between VXX and VIX Futures are well established with leads and lags generally found to be short-lived and arbitrage relations holding. The authors go further to capture 1x long, −1x inverse as well as 2x leveraged ETNs and the corresponding ETFs, to give a broad representation across the ETP market. The authors establish causal relations between inverse and leveraged products where causal relations are not yet documented.

This study examines the effect of the environmental sensitivity of firms on the relationship between integrated reporting (IR) quality and sustainability performance. Prior research works focus on the nexus between IR quality and sustainability performance with little attention to factors that moderate this relationship.

Ordinary least squares (OLS) and other robust estimations are employed to analyse the data of firms on the Johannesburg Stock Exchange (JSE).

This study finds a positive association between IR quality and sustainability performance. However, the strength of this relationship is found to be weaker among environmentally sensitive firms, thereby raising concerns that such firms may be reporting less sustainability information with the mandatory implementation of IR on the JSE.

The findings highlight the need for regulatory bodies to consider additional sustainability disclosure requirements for firms in environmentally sensitive industries.

The findings should make regulatory bodies aware of the possible actions of environmentally sensitive firms in relation to sustainability information within a mandatory setting of IR.

The study extends the existing literature on IR and sustainability performance by considering the effect of firm environmental sensitivity as a moderating factor.

The purpose of this paper is to investigate whether first level measures in the Balanced Scorecard (BSC) declaring a cause-effect relationship by design are composite indices of lower measures, and if they converge into a single factor as is traditionally accepted in the BSC literature.

This study reports results of a quantitative case study that focusses on an Ontario (Canada) community hospital that has been using the BSC.

The results of this study challenge the cause-effect assumption of the BSC, particularly in a cascading context, and suggest that a lack of attention of how composite indices of lower measures converge into a single higher level measure may be the reason for ineffective use of the BSC.

The BSC is a dynamic tool; as such there are several measures that have a very short history, thus limiting the observations available to be used in statistical models.

A key recommendation for practice that emerges from this study is the need to test if lower level metrics do merge naturally in the upper level measure of the BSC; if not, the upper level measure might not be linked to other measures rendering the BSC ineffective in the context of causality.

Although several studies have argued in favour of the cause-effect relationship of the BSC, none of those found in the literature have paid attention to the way in which first level measures are constructed. This may explain why certain measures are linked, while others are not, to those that are calculated as composite indices of several lower level indicators.

This study aims to explore the oil–stock market nexus from a novel angle by examining the predictive role of oil prices over the excess returns associated with the market, size, book-to-market and momentum factors via bivariate cross-quantilograms.

This study makes use of the bivariate cross-quantilogram methodology recently developed by Han et al. (2016) to analyze the predictability patterns across the oil and stock markets by focusing on various quantiles that formally distinguish between normal, bull and bear as well as extreme market states.

The study analysis of systematic risk premia across the four regions shows that crude oil returns indeed capture predictive information regarding excess factor returns in stock markets, particularly those associated with market, size and momentum factors. However, the predictive power of oil return over excess factor returns is asymmetric and primarily concentrated on extreme quantiles, suggesting that large fluctuations in oil prices capture markedly different predictive information over stock market risk premia during up and down states of the oil market.

The findings have significant implications for the profitability of factor- or style-based active portfolio strategies and suggest that the predictive information contained in oil market fluctuations could be used to enhance returns via conditional strategies based on these predictability patterns.

This study contributes to the vast literature on the oil–stock market nexus from a novel perspective by exploring the effect of oil price fluctuations on the risk premia associated with the systematic risk factors including market, size, value and momentum.

Many corporations have annual expenditures in research and development in the range of billions of U.S. dollars. Senior managers have often been frustrated by the lack of innovation in their organizations and have been looking for better ways to implement an innovation strategy. To provide initial evidence on this significant topic, we conduct an empirical examination and contribute to the existing literature in two important areas. First, we examine how managers choose what measures to pay attention to in managing the innovation process – defined as the process of creative definition, development, and commercialization of substantially new products, services or businesses. We find that managers use measures about specific phases of the innovation process together. For example, measures that inform about the execution stage of the innovation process are grouped together rather than being grouped with measures informative about other phases of the innovation process, such as market performance. This pattern “focused” around specific phases is in contrast to the alternative “balanced” pattern where managers would use measures from various phases of the process together. This result provides the first empirical test of how managers combine measures to filter information about business processes. It also provides important new evidence on the use of measures and provides guidance to the design of measurement systems. Second, this paper provides empirical evidence on the relationship between innovation strategy and the use of measures. Though previous studies have linked innovation strategy and the use of management control systems in general, there is little empirical data on the relationship of strategy and the use of measures and on the innovation process. We find that different dimensions of strategy are positively associated with how managers use different types of measures.

In academics and industry, significant efforts have been made to lead planners and control teams in evaluating project performance and control. In this context, numerous control metrics have been devised and put into practice, often with little emphasis on analyzing their underlying concepts. To cover this gap, this research aims to identify and analyze a holistic list of control metrics and their functionalities in the construction industry.

A multi-step analytical approach was conducted to achieve the study’s objectives. First, a holistic list of control metrics and their functionalities in the construction industry was identified. Second, a quantitative analysis based on social network analysis (SNA) was implemented to discover the most important functionalities.

The results revealed that the most important control metrics' functionalities (CMF) could differ depending on the type of metrics (lagging and leading) and levels of control. However, in general, the most significant functionalities include managing project progress and performance, evaluating the look-ahead level’s performance, measuring the reliability and stability of workflow, measuring the make-ready process, constraint management and measuring the quality of construction flow.

This research will assist the project team in getting a comprehensive sensemaking of planning and control systems and their functionalities to plan and control different dynamic aspects of the project.