The Impact of Power Distance and Uncertainty Avoidance on Real Options Exercise: Potential for Suboptimal Time Delays and Value Destruction

Real options (RO) valuation has been promoted as a way to evaluate investment opportunities and make investment decisions that takes into account the value of managerial flexibility in the face of uncertainty. Although RO enjoys a substantial body of literature considering its application and suitability in different situations, the impact of national culture on the application of RO has received little attention. Values embedded in national culture affect the behavior of managers. In particular, these values play a role in how managers frame information, communicate, and make decisions, directly and indirectly affecting behavior through cultural layers such as organizational culture. National culture, therefore, can systematically affect the application of RO, potentially leading to suboptimal outcomes. In this article, we hypothesize how 2 particularly relevant dimensions of culture, power distance and uncertainty avoidance, affect RO exercise, and estimate the magnitude suboptimality of these effects using a model we develop for the case of a put option. Our estimates suggest RO may not always be a superior valuation approach to net present value for national cultures with certain characteristics.     

Migration decisions under ambiguity: Migration distance effects revisited

There is ample evidence that distance has a strong negative effect on migration. Despite its significance in migration decisions, scholars rarely explore the migration distance effect deeper than the first level of interpretation derived from the simple neoclassical theory of migration. This study revisits the migration distance effect in the spirit of Knightian distinction between risk and uncertainty. To this end, this study attempts to construct estimates of the risk premium migrants are willing to pay to avoid the risk associated with migration distance. The results show that the magnitude of the distance effect is not rationalized by risk aversion in the Arrow–Pratt sense (Arrow, 1965, The theory of risk aversion, Yrjo Jahnssonin Saatio, Helsinki, Finland, 90; Pratt, 1964, Econometrica, 32, 122). In particular, the risk premium demanded by distant rural residents is unjustifiably higher compared with that of those living closer to urban areas in their migration decisions. The results suggest that the migration distance effect is analogous to the equity home bias puzzle and the Ellsberg paradox in that the assumption of subjective expected utility can lead to seemingly irrational human behaviors. Some of the findings in this study shed light on the role of subjective aspects such as perception, confidence, and pessimism in migration decisions, which have been neglected in the literature.     

Interorganisational project dynamics: A longitudinal study of perceptual distance and client-contractor collaborative relationships

Despite the importance of good collaborative relationships in interorganisational projects, clients and contractors often develop adversarial relationships due to perceptual distance about key project issues. In this case study research, we investigated how perceptual distance emerges and changes over time, and how the collaborative relationship between client and contractor develops alongside these dynamics. In this exploration, we built upon agency theory and stewardship theory as complementary perspectives for understanding client-contractor collaborative relationships. We gathered quantitative and qualitative data in two projects, conducting three assessments in about one year. We found that perceptual distance increased and decreased over time, and that a reduction was typically associated with the collaborative relationship being characterized by stewardship rather than agency. These findings suggest that a regular assessment and evaluation of partners’ perceptions of critical project issues is warranted to timely detect and counteract perceptual distance. Moreover, partners would best adopt a stewardship orientation to reduce perceptual distance, although this may take considerable effort given the distributive nature of many pre-project negotiations.     

Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.     

Predictive regressions under asymmetric loss: Factor augmentation and model selection

This paper discusses the specifics of forecasting using factor-augmented predictive regressions under general loss functions. In line with the literature, we employ principal component analysis to extract factors from the set of predictors. In addition, we also extract information on the volatility of the series to be predicted, since the volatility is forecast-relevant under non-quadratic loss functions. We ensure asymptotic unbiasedness of the forecasts under the relevant loss by estimating the predictive regression through the minimization of the in-sample average loss. Finally, we select the most promising predictors for the series to be forecast by employing an information criterion that is tailored to the relevant loss. Using a large monthly data set for the US economy, we assess the proposed adjustments in a pseudo out-of-sample forecasting exercise for various variables. As expected, the use of estimation under the relevant loss is found to be effective. Using an additional volatility proxy as the predictor and conducting model selection that is tailored to the relevant loss function enhances the forecast performance significantly.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

信息技术提高企业组织敏捷性的作用机理

从考察企业组织的敏捷性与交易成本的相互关系入手,对企业组织敏捷性的影响因素进行了剖析。详细分析了信息技术(IT)对提高企业组织敏捷性的作用机理。指出IT正是通过降低企业内、外部的交易成本、缩短流程时间、提高人的认识理性、扼制机会主义、促进企业组织结构、决策方式     

Is Volatility the Best Predictor of Market Crashes?

The objective of this paper is to determine the best predictor of equity market crashes by focusing particularly on volatility and market liquidity. In finance, volatility has traditionally been regarded as the best measure of market risk. However, this paper shows that the forecast value of market liquidity, in particular our modified calculated market depth, predicts equity market crashes much more accurately than does the forecast values of EGARCH or Implied Volatility.     

Generating Enhanced Fishery Rents by Internalizing Product Quality Characteristics

This paper demonstrates howadditional rents are generated in a fisherycharacterized by intraseasonal variation infish characteristics, including size,condition, and composition. Based on anexpanded conceptual model of the optimalharvest rule, fish characteristics affect preand post harvest production yields and outputprices. A dynamic empirical model, which uses asystem of quality characteristics and anhedonic equation, illustrates the complexrelationships and management choices associatedwith internalizing seafood qualitycharacteristics in a hake fishery. The modelretains the regulated open access managementsystem, but controls intertemporal andintersectoral quotas, production portfolios,and total allowable catch. Results demonstratethat including revenue-side effects frominternalizing fish quality can generatesignificantly greater rents and reduce therelative benefits of increased productionyields. If excluded, bioeconomic models canunderestimate the level of regulatory rentdissipation and overemphasize managementobjectives such as full utilization, whichcould misdirect processing decisions and resultin a suboptimal resource management plan.Implications for data collection,multidisciplinary analysis, and improvements inmarine resource management are discussed.