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Purpose: The present study aims to test the Quadratic Programming model for Optimal Portfolio selection empirically.

Need for the Study: All the investors who buy financial products are motivated to obtain higher profits or, in other words, to maximise their returns. However, the high returns are often accompanied by higher risks, and avoiding such risks has become the primary concern for all investors. There is a great need for such a model to maximise profits and minimise risk, which can help design an investment portfolio with minimum risk and maximum return. The Quadratic Programming model is one such model which can be applied for selected shares to build an optimised portfolio.

Methodology: This study optimises the stock samples using a two-level screening of correlation coefficient and coefficient of variation. The monthly closing prices of the NSE-listed Indian pharmaceutical stocks from December 2019 to January 2022 have been used as sample data. The Lagrange Multiplier method is used to apply the model to achieve the optimal portfolio solution. Based on the market reality, the transaction costs have also been considered. The Quadratic programming model is further optimised to achieve the optimal portfolio for the select stocks.

Findings: The traditional portfolio theory and the modified quadratic model gives similar and consistent results. In other words, the modified quadratic model asserts the accuracy of the conventional portfolio model. The portfolio constructed in the present study gives a return much higher than the return of the benchmark portfolio of Nifty Fifty, indicating the usefulness of applying the Quadratic Programming model.

Practical Implications: The construction of an optimal portfolio using the traditional or modified Quadratic model can help investors make rational investment decisions for better returns with lower risks.

This chapter examines self-assessed health (SAH) data of 29 European countries using Eurostat data for the years 2009 and 2018. It first computes the indices recently introduced by Seth and Yalonetzky (2020) and provides confidence intervals for these indices. The ranking of these countries for the year 2018 is then summarized by Hasse diagrams. The chapter then examines first- and second-order stochastic dominance, based again on the recent work of Seth and Yalonetzky. Here also bootstrap confidence intervals were computed. The ranking of the countries in 2018 is then translated again into Hasse diagrams. It appears that Hungary and Latvia are respectively the poorest and least poor countries, both in 2009 and 2018, in terms of their SAH condition. While countries like Ireland, Luxembourg, Romania and Portugal are in the poorer segment of the distribution of countries, Norway, the United Kingdom, Denmark, the Netherlands and Austria are located on the less poor portion. As expected, the Hasse diagrams show also that there are quite a few instances where some countries cannot be ranked.

A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

Using data on food insecurity in Israel, this chapter suggests borrowing techniques from the literature on multidimensional poverty to measure food insecurity, a distinction being made between “nominal” and “real” food insecurity. Various counting techniques are then implemented, including the well-known approach of Alkire and Foster. The chapter ends with a section where, following recent work by Dhongde, Li, Pattanaik, and Xu (2016), a distinction is also made between “basic” and “non-basic” dimensions of food insecurity.

The aim of any research is to create knowledge and to generate new insights. For insights from empirical research to be valid, the data from which insights are derived must be valid. Empirical data in tourism and hospitality research is predominantly collected by means of surveys. The chapter discusses a range of dangers to data validity associated with survey research; explains under which circumstances surveys represent a suitable or unsuitable method of data collection; and offers practical recommendations that can easily be adopted by survey researchers to ensure maximum validity of their data.

This chapter introduces four research methods that are not covered in the previous chapters. They are (1) non-parametric statistics, (2) interpretive structural modeling, (3) analytic hierarchy process, and (4) data envelopment analysis. The methods are discussed with examples. The discussion, however, is introductory; so we urge the reader to go through the pertinent references for details.

This paper explored the relationship between the embeddedness of a firm’s managerial values and corporate financial performance in Swiss small and medium-sized enterprises (SMEs) by developing a conceptual maturity model of managerial values (MM-MV). The MM-MV articulates the extent to which managerial values are embedded within organizations, allowing the analysis of the interrelationship between the degree of values-embeddedness and financial performance in SMEs. The findings suggested that as managerial values become more embedded, financial performance increases; therefore, SMEs exhibiting highly embedded managerial values such as customer-minded, team spirit, innovation-driven reliability, persistency, competency, and engagement tend to financially outperform SMEs that have not fully embedded managerial values throughout the firm.