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The purpose of this paper is to examine the significance of skewness in maximizing the investor utility using the daily data for eight sectors listed on the National Stock Exchange of India.

The analysis is carried out in three different steps. In the first part, the author analyzes the monthly stock returns and the important financial ratios – price-to-book (PB) ratio, price-earnings (PE) ratio and dividend yield (DY). Second, the author tests the sector-wise return predictability using Westerlund and Narayan (2012) flexible generalized least squares estimator. Third, the author compares the mean–variance–skewness (MVS) utility function with the mean–variance (MV) utility function.

The author forecasts the sectoral stock returns using three financial ratios – PB ratio, PE ratio and DY – as predictors. The results indicate that sectoral stock returns are significantly predicted by these financial ratios. The author then formulates trading strategies by including skewness in the utility function and finds that the investor utility is high when the utility function includes skewness as opposed to when the skewness is excluded.

The author extends the MV utility function to the MVS utility function and shows that the Indian stock market is more profitable when the investor uses a MVS utility function which highlights the main contribution to the literature.

The purpose of this paper is to develop an analytical model for appraising and measuring corporate social responsibility (CSR). The theoretical and conceptual grounds that sustain the model are based on previous approaches.

The utility function, which is the basis of a company's economic dimension, is analyzed in its philosophical and ethical setting, concentrating largely on the Utilitarian and Hedonistic schools and a maximizing agent, the “homo oeconomicus”.

The resulting new approach permits an analytical explanation of the behavior of a company and its owners when incorporating both economic rationality (“homo oeconomicus”) and social responsibility.

The quantitative determination of a global indicator for social responsibility is shown, as is the development of a method for calculating a monetary value of CSR.

This chapter presents an exposition of the Generalized Fechner–Thurstone (GFT) direct utility function, the system of demand functions derived from it, other systems of demand functions from which it can be derived, and its purpose and the econometric circumstances that motivated its original development. Its use in econometrics is demonstrated by an application to household consumer survey data which explores the relationship between prices, on the one hand, and expected exogenous preference changers such as household size, schooling of heads of household, and other social factors, on the other.

Measuring risk aversion is sensitive to assumptions about the wealth in subjects’ utility functions. Data from the same subjects in low- and high-stake lottery decisions allow estimating the wealth in a pre-specified one-parameter utility function simultaneously with risk aversion. This paper first shows how wealth estimates can be identified assuming constant relative risk aversion (CRRA). Using the data from a recent experiment by Holt and Laury (2002a), it is shown that most subjects’ behavior is consistent with CRRA at some wealth level. However, for realistic wealth levels most subjects’ behavior implies a decreasing relative risk aversion. An alternative explanation is that subjects do not fully integrate their wealth with income from the experiment. Within-subject data do not allow discriminating between the two hypotheses. Using between-subject data, maximum-likelihood estimates of a hybrid utility function indicate that aggregate behavior can be described by expected utility from income rather than expected utility from final wealth and partial relative risk aversion is increasing in the scale of payoffs.

The purpose of this paper is to establish a deterministic equivalent income model (DEIM) based on the risk cost (RC) and risk aversion of investors. The model fully considers both subjective and objective factors that affect risk investment and reasonably evaluates risk investment schemes to choose the correct investment scheme and gain greater investment returns.

The utility function is used to measure the extent to which an investor is satisfied by investment returns in various scenarios. Risk aversion expresses subjective attitude of investors to risk. RC represents risk loss in currency. This methodology is based on risk aversion function, utility function and RC theory to establish DEIM.

This study shows that investors with different risk preferences have different certainty equivalent returns (CER), so their choices of investment options change accordingly.

In this paper, the authors use DEIM to test an investment case and conclude that the CER and investment scheme both change with different risk preferences. At the same time, case analysis shows that DEIM is reasonable and stable when evaluating risk investment schemes.

In this study, the authors innovate by introducing both the RC and risk aversion degree into risk investment schemes evaluation and by deriving a utility function from the absolute risk aversion function to build a utility decision matrix and establish DEIM. The model combines the subjective and objective factors that influence risk investment decisions.

A bridge network is a major capital asset that requires continuing investment in order to maintain the network within acceptable limits of safety and serviceability. Ranking and prioritizing procedures have been widely used by several departments of transportation to select bridges for intervention and to distribute the available funds among competing projects. The available ranking and prioritizing procedures have various drawbacks, and an improved, rational ranking and prioritizing procedure is needed. The paper aims to address these issues.

The requirements and characteristics of an innovative ranking and prioritizing method are identified during interviews with professionals involved in bridge management. Based on these requirements, multi‐attribute utility theory (MAUT) is selected to develop the method. A technique to develop utility functions based on the analytical hierarchy process (AHP) is discussed. A hierarchy structure that captures the decision‐making elements is presented. A case study is used to demonstrate the applicability and the validity of the proposed ranking method.

The research findings have identified the decision objectives and the criteria essential to rank and prioritize bridge projects, and these are included within a framework to rank and prioritize bridge projects while incorporating experts' input in the process.

The proposed framework includes weights for the various objectives and recommends utility functions to evaluate the different attributes. In addition, the framework provides flexibility to adjust the weights and to modify the utility functions to reflect network‐specific characteristics. This method can be used by departments of transportation to rank bridges in a network, even incorporating conflicting criteria, and it can be integrated within an already implemented bridge management methodology.

Ranking and prioritizing projects are essential steps in bridge management. Current methods for ranking and prioritizing bridge projects are associated with various drawbacks. This paper proposes an innovative ranking method for bridge networks, based on MAUT. This theory provides flexibility for the decision makers in expressing their degree of satisfaction with each bridge attribute.