Stochastic dominance, efficiency and separation in financial markets

A portfolio problem exhibits separation when all of its solutions can be expressed as affine combinations of a small number of mutual funds. The concept of separation is one of the cornerstones of modern portfolio theory, underlying everything from the mean-variance portfolio selection rule of [7. and 8.] and [11.] to the equilibrium pricing model of [10.], [6.] and [2.]. A great deal of effort has been put into investigating conditions which validate separation assumptions: [3.] as well as [4.] study this problem in terms of utility functions while [9.] takes a distributional approach. The purpose of this note is to show that for the distributional approach, the so-called weak and strong forms of separation are actually equivalent.     

Using the Delphi method to assess community health needs

Comprehensive health planning must ultimately be based upon information about the people to be served. Their needs as potential patients or clients of the various service systems being planned must be identified and evaluated. Regional cooperation on meeting these health care needs is premised on some agreement amongst the population as to the existing problems and their relative priorities. In order to investigate how such a health needs assessment might be undertaken a study was conducted using a panel of citizens selected from a 10-country central Texas region. The results demonstrate the fesibility of employing the Delphi method for achieving consensus on some health care issues; the information obtained can become valuable input to the overall health planning process.     

A simple algebraic estimation procedure for innovation diffusion models of new product acceptance

A simple algebraic estimation procedure is developed to estimate the parameters of diffusion models of new product acceptance. The procedure required knowledge of the occurrence of the point of inflection (based on actual data, analogous products, or management judgments). It is conceptually easy to use and can be implemented by using a hand calculator. Since the procedure does not employ period-by-period time-series diffusion data, it is not expected to provide the best fit to the data as compared to the maximum likelihood and nonlinear least squares estimation procedures. However, the procedure can provide very reasonable estimates about the relative magnitudes of the parameter estimates. In that respect, the procedure can be used to generate good starting values for the maximum likelihood and nonlinear least squares estimation procedures. In the absence of data, using management judgments about the point of inflection, the procedure can be implemented in a decision-support system to develop conditional diffusion curves for a new product. Data from four diverse innovations are used to illustrate the procedure.     

Indexing and Stock Price Efficiency

Indexing has experienced substantial growth over the last two decades because it is an effective way of holding a diversified portfolio while minimizing trading costs and taxes. In this article, we focus on one negative externality of indexing: the effect on the efficiency of stock prices. Based on a sample of large and liquid US stocks, we find that greater indexing leads to less efficient stock prices, as indicated by stronger post‐earnings‐announcement drift and greater deviations of stock prices from the random walk. We conjecture that reduced incentives for information acquisition and arbitrage induced by indexing and passive trading are probably the main causes for degradation in price efficiency.     

An Approach for Determining Optimal Product Sampling for the Diffusion of a New Product

Free samples are an effective means for introducing and promoting a new product. However, product sampling is also expensive. As a result, careful consideration must be given to the question of how many samples should be distributed. To encourage product adoption in any target market, a company needs to determine the “right” amount of sampling. In other words, a firm needs to determine the optimal number of samples that must be available for trial by the innovators, early adopters, and other key consumers who influence the adoption rate of the new product. With too few samples, the product might not reach enough of these key consumers to generate the word-of-mouth recommendations necessary for market success. On the other hand, offering too many free samples is a waste of a company's resources. Dipak Jain, Vijay Mahajan, and Eitan Muller propose a framework for determining the optimal levels of product sampling. In addition to identifying the upper bounds for the sampling levels of both durable and nondurable products, their model identifies the optimal size of product sampling based on such parameters as the coefficients of innovation and imitation, market potential, discount rate, and gross margin. Several observations are made regarding the relationships between the optimal sampling level and the various parameters used in the model. For example, a high sampling level is not appropriate for a product with a high coefficient of innovation. On the other hand, if a product has a high coefficient of imitation, the sampling level should be high because a significant number of trials are necessary for word of mouth to be effective. High sampling levels are also indicated by a high discount rate or gross margin. For durable goods, the optimal level of neutral sampling (i.e., sampling that does not specifically target innovators and early adopters) rarely exceeds 5%, and the maximum level is 7%. The optimal target sampling level is always higher than the corresponding neutral case, but, in most cases, only marginally so. For the parameter ranges chosen in this article, the maximum level for target sampling is approximately 9%. However, it is important to note that the theoretical upper bounds are no more than benchmarks for the maximum possible level of sampling. In practical situations, the optimal level may be considerably lower than these upper bounds. In such cases, the actual values will depend on the values for the various parameters used in the model.     

Agility and flexibility in international business research: A comprehensive review and future research directions

This study explores 134 articles on agility and flexibility research published in the top-nine international business (IB) journals. Accordingly, we critically analyze, map and structure flexibility and agility research in IB, and synthesize the findings into an integrative framework. The paper identifies a wide range of definitions of the concept and a lot of work on its conceptualization. However, studies at the individual and institutional levels of analyses are scarce, and the concept is under-theorized, as there is a dearth of real theoretical underpinnings in this research stream. Thus, in addressing this gap, more qualitative and interdisciplinary research is needed.     

Understanding the quality–quantity conundrum of customer referral programs: effects of contribution margin,extraversion, and opinion leadership

Firms can substantially profit from customer referrals, but they must understand the different stages of the referral process to determine what drives the number of referrals (first stage), conversion (second stage), and average contribution margin per referral (third stage). Applying a framework that integrates perceptual and behavioral drivers, this study uses a financial services company’s customer survey and transaction data to investigate how the effect of contribution margins of referring customers at all three stages depends on their perceived extraversion and opinion leadership. Extreme extraversion and opinion leadership diminish the positive effect of the contribution margins of referring customers on the number of referrals; their effect on the number of successful referrals is insignificant. In terms of the contribution margin of successful referrals, extraversion has a negative and opinion leadership a positive moderating effect.     

Risky short positions and investor sentiment: Evidence from the weekend effect in futures markets

This paper examines the weekend effect in futures markets and presents rational and behavioral reasons for its existence. Specifically, we document a weekend effect (Friday's return minus the following Monday's return) in futures markets. The weekend effect occurs partly because of asymmetric risk between long and short positions around weekends; the weekend effect increases when short positions are relatively more risky. In addition, we find that both lagged and contemporaneous changes in investor sentiment are related to the weekend effect. These results are consistent with the investor sentiment literature that finds that mood improves on Fridays but deteriorates on Mondays.