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The purpose of this study is to evaluate whether the classification of the equity mutual fund depends on the performance measure used.

The sample for this study includes stock mutual funds for the USA, Europe and emerging market economies covering the period 2010 to 2020. Using more than 20 performance measures the results are compared using the Sharpe ratio as the reference.

The results show that performance measures based on absolute reward–risk ratios like Sortino, Treynor, etc. have similar rankings, because in general the numerator (mean excess return) is the same. However, when the authors employ other types of performance measures, results may be significantly different, especially in the case of metrics for “incremental returns”, i.e. alphas. Focussing on markets, their results show that choosing performance measures is more relevant for emerging markets.

The sample is only limited to the USA, Europe and the emerging market, and there are other performance metrics in the literature which have not been covered in this work.

The ordering of equity mutual funds depends on the measure used, specially if investors employ factor models to measure excess returns (alphas). Hence, policy formulation on disclosure of mutual fund performance should encourage the use of several metrics from different families. Investors must be aware of the different rankings made and the most appropriate metrics based on their preferences.

This paper focusses specifically on the effect that performance metrics have on relative fund performance. Previous studies have ignored alpha metrics to rank funds, which are commonly employed by investors. The authors’ study performs an analysis for three different markets considering the two main developed ones (the American and European equity markets), as well as the emerging one, largely ignored until now.

Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model.

This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level).

“Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability.

In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC.

By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc.

This research investigates the effect of mergers and acquisition (M&A) transactions in the pharmaceutical sector. The study assesses the short-term value creation or destruction for shareholders of pharmaceutical companies involved in M&A activities on the acquiring side.

The empirical analysis is carried out by applying the event study methodology in order to define the cumulative abnormal return for each transaction observed. Then, the correlations between abnormal returns and economic metrics are determined building a multiple regression model. These metrics refers to the acquirer, target or to the deal itself.

Evidence show a short-term value creation for shareholders of pharmaceutical companies involved in M&A transactions on the acquiring side. On the one hand, the analysis suggests a negative correlation between the value creation and the acquiring firm's level of indebtedness. On the other hand, the value creation is positively correlated with target's metrics such as Return on Equity (ROE), Return on Assets (ROA) and Research and Development (R&D) intensity. Value creation is also tied to deal-specific characteristics regarding the cash used in the transaction and the comparative extent of the deal.

This analysis allows to predict returns around an announcement day considering the described indicators of value creation or destruction. M&As play a key role in the strategy implementation as reaction to exogenous shocks and endogenous needs.

This study enriches the literature of corporate finance applied to the pharmaceutical sector. Indeed, this industry is gaining increasing relevance in the M&A panorama. Thus, the related dynamics need to be assessed considering the uniqueness of the pharmaceutical sector in terms of regulation, stakeholders and social impact.

An acute need exists for a practical quantitative risk management‐based real estate investment underwriting methodology that clearly helps guide decision making and addresses the shortcomings of discounted cash flow (DCF) modeling by evaluating the full range of probable outcomes. This paper seeks to address this issue.

The simulation‐based excess return model (SERM) is an original methodology developed based on an application of Monte Carlo simulation to project risk assessment combined with the widely practiced DCF modeling. A case study is provided where results of the modeling are compared with traditional DCF risk models and with prior projects with known outcomes.

This paper lays out a practical method for stochastic quantitative risk management modeling for real estate development projects and illustrates that for identical projects risk‐adjusted returns derived with the use of SERM may differ significantly from returns provided by traditional discounted cash flow analysis. SERM corrects serious shortcomings in the DCF methodology by incorporating stochastic tools for the measurement of the universe of outcomes. It further serves to condense the results of Monte Carlo simulations into a simplified metric that can guide practitioners and which is easily communicational to decision makers for making project funding decisions.

SERM offers a simple, practical decision‐making method for underwriting projects that addresses the limitations of the prevailing methodologies via: stochastic assessment of the range of outcomes; interdependence of input variables; and objective risk premium metrics.

This paper presents an original methodology for making project‐funding decisions for real estate development projects that is based on Monte Carlo simulation combined with DCF analysis. The methodology presented here will have value for real estate developers, investors, project underwriters, and lenders looking for a practical and objective method for project valuation and risk management than is offered by traditional DCF analysis. A review of literature did not reveal analogous methodologies for risk management.

Establishing the strength of a novel variable–mortgage debt as a fraction of US gross domestic product (GDP)–on forecasting capitalisation rates in both the US office and multifamily sectors.

The authors specify a vector error correction model (VECM) to the data. VECM are used to address the nonstationarity issues of financial variables while maintaining the information embedded in the levels of the data, as opposed to their differences. The cap rate series used are from Green Street Advisors and represent transaction cap rates which avoids the problem of artificial smoothness found in appraisal-based cap rates.

Using a VECM specified with the novel variable, unemployment and past cap rates contains enough information to produce more robust forecasts than the traditional variables (return expectations and risk premiums). The method is robust both in and out of sample.

This has direct implications for governmental policy, offering a path to real estate price stability and growth through mortgage access–functions largely influenced by the Fed and the quasi-federal agencies Fannie Mae and Freddie Mac. It also offers a timely alternative to interest rate-based forecasting models, which are likely to be less useful as interest rates are to be held low for the foreseeable future.

This study offers a new and highly explanatory variable to the literature while being among the only to model either (1) transactional cap rates (versus appraisal) (2) out-of-sample data (versus in-sample) (3) without the use of the traditional variables thought to be integral to cap rate modelling (return expectations and risk premiums).

Marketing and brand managers are under more pressure than ever before to demonstrate the impact of the managers' strategies and actions on company value, especially in an emerging market. In this context, the authors investigate the relationship between brand equity and company performance using the rankings of most valued brands from Brand Finance (BF), Brand Analytics (BZ) and Interbrand (IB).

The authors use used a panel from the period between 1990 and 2018 (29 years), consisting of a sample of 689 companies with shares traded in an emerging market representing 7,970 observations with unbalanced data. The authors applied a dynamic Differences-in-Differences Ordinary Last Squares (DID OLS) method.

The main finding of this study is that brands ranked as valuable significantly increased the brands' companies' intangible assets, return on assets, free cash flow (FCF) and market value.

The present study helps brand and marketing managers show to chief executive officers (CEOs) and shareholders the importance of brand development. In addition, valuable brand companies of an emerging market may represent an interesting opportunity for market investors.

This study contributes to the marketing literature, addressing the fields of marketing and finance, by analyzing the performance of companies separately over a long period, with different metrics, an unconventional model in the marketing area and different rankings of valuable brand names.

Companies and organizations use various innovation governance structures, processes and metrics to make decisions about allocation of resources to the development of an innovative idea. Although many metrics measuring the process of innovation and the performance of the enterprise have been developed, a fundamentally solid and complete metric speaking to the quality and viability of the innovative idea itself is lacking. The business, applied innovation, creativity, unmet user needs and problem-solving (BACUP) model of innovation quality is proposed as such a metric based on viewing innovation from the five different viewpoints mentioned in its definition. BACUP is shown to facilitate discussion and analysis in innovation theory and is proposed as a tool allowing any innovation governance structure to achieve innovation assurance by mitigating risk and uncertainty and maximizing an innovation’s chance for success.

The BACUP framework was inspired by researching definitions of innovation and coming upon a survey in which different definitions were obtained from several different roles in companies and organizations. To use BACUP as a metric, the author and research assistants made qualitative judgments about innovations. Several judgments were obtained independently and consensus was plotted on the BACUP graphs.

BACUP can be used to illustrate and discuss major concepts in innovation theory. BACUP can be used to compare the relative viability of different innovative ideas. BACUP can be used to detect vulnerabilities in innovative ideas and provide information to innovation management and governance so that corrective measures can be taken. BACUP can be extended by other researchers and practitioners.

In its current form, BACUP is not a quantitative tool; however, the authors envision other researchers applying existing quantitative tools and incorporating them into the BACUP framework.

BACUP is an innovative idea quality metric employable in any existing innovation management/governance structure or methodology. BACUP also gives practitioners a way to engineer innovative ideas into successful innovations.

BACUP can lead to predictable and repeatable improved innovation outcomes, resulting in superior solutions to problems in all domains.

The BACUP framework is a novel, multi-dimensional view of innovation. Application of BACUP as a metric yields a new type of capability for innovation governance called innovation assurance.

The analysis aims to explore how momentum return changes with alternative computational methods and the extent to which the portfolio structure is important in the momentum context.

The focus reflected in the prior research emphasises the method used by Jegadeesh and Titman and various extensions to test whether momentum returns exist. This study uses alternative methods of buying previous Winners and short‐selling previous Losers to determine if this significantly changes the returns.

The current study clarifies the impact of several contributory factors that impact upon estimated momentum returns. The large sample of cleaned data upon which this study is based provides a higher degree of confidence that the findings are sound and not just a statistical anomaly.

The research is important from a practitioner perspective as details of momentum return are presented for each country using different methods, providing information regarding the most profitable country in which to invest and whether the momentum return is sustainable under different formative approaches.

One of the important contributions of this study is a detailed empirical analysis, presenting results in a global context rather than on a single country basis.

Many observers believe that industry experience of entrepreneurs drives successful new entrepreneurial firms. However, whenever it comes to disruptive digital ventures such as Financial Technologies (Fintechs), the picture may be different due to the cross-industry nature of digital firms. The purpose of this study is to disentangle the impacts of finance, banking and information technology (IT) experiences of founders on performance of European Fintechs around venture capital (VC) investment.

Based on a data set of 105 Fintechs from European countries, including UK, which are involved in 201 VC rounds between 2006 and 2019, the authors adopt a Bayesian quantile approach to link founders’ experience with two performance measures that identify market success (return on sales) and investment outcome (return on equity).

The findings indicate that finance and IT-specific experiences seem to matter more often than banking experience and that the extent of their impact depends on level and metric of performance. More specifically, Fintechs in Europe and UK are more able to achieve market success with both finance and IT experiences of their founders, but that does not necessarily transform into higher returns for investors.

This study provides new evidence that not all aspects of industry experience matter for digital ventures, as they must fit to a certain firm, cycle and industry. For Fintech, as the name says, finance and IT experiences matter.