Predictors of ethical decisions regarding insider trading

This paper examines potential predictors of ethical decisions regarding insider trading. An interactionist perspective is taken, in which person variables, situational variables, and the interaction of these two sets of variables are viewed as influencing ethical decisions. The results of our study support such a perspective. Ethical decisions regarding insider trading appear to be a function of a complex set of interacting variables related to both the person and the situation. The implications of these findings are discussed.David E. Terpstra is Hearin-Hess Professor of Management at the University of Mississippi. A frequent contributor to management and psychological journals, his current research interests involve business ethics, sexual harassment, and human resource management issues.Mario G. C. Reyes is an Assistant Professor of Finance at the University of Idaho. His research and publications have been primarily in the areas of market efficiency, portfolio management, and financial econometrics. He has participated in the Authur Anderson & Co. Conference on Teaching Business Ethics.Donald W. Bokor's research interests are in the domain of organizational structure and development with implications on human behavior and strategic management.     

Quantifying knowledge exchange in R&D networks: a data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets, extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. These data are used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in eight dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate μ for an alliance duration τ. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). This is a new measure that takes in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm’s position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency \(\mathcal {\hat {C}}_{n}\). Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.     

Successful and sustainable business partnerships: How to select the right partners

In more and more industries it becomes true that value creating networks compete against each other with relatively stable relations between suppliers inside the network. Managers all over the world are searching for the most efficient and effective coordinative forms for their relations with OEM's or suppliers within such a value-creating networks. This paper gives a normative guideline to decide if or if not a partnership is the right coordinative form for OEM-supplier relations within a value-creating network. Based on the existing mainly positivistic research in this field, two aspects are highlighted as the main drivers for the suitability of a partnership as a well working governance mechanism for value-creating networks: (i) individualization vs. standardization of the delivered components combined with the potential of the end customers to identify quality differences or not and (ii) the possibilities to allocate the revenues made by the value-creating network on the several ‘partners’ within the network. All aspects were integrated in a decision model for managers to find out if partnership as the coordinative form is really the best choice in a given situation.     

BRAND ACTIVISM: A Literature Review and Future Research Agenda

Brand activism is a new and fragmented phenomenon that has been attracting growing attention from scholars. Research on this topic is still at an exploratory stage and in need of advancement and conceptualization. The aim of this study is to systematize the extant literature on brand activism by attempting an in-depth exploration of the antecedents that identify the construct along with the influencing and consequence factors that will need to be tested and validated by future research. This study adopts the guidelines provided by the SPAR-4-SLR protocol and considers 76 papers with no time limitation. To the best of our knowledge, this seems to be the first scientific systematization of brand activism. From the in-depth analysis of the extant literature, two conceptual frameworks were constructed that can guide both researchers, in their understanding and investigation of the phenomenon, and practitioners in their implementation strategies of brand activism. The no time restriction of the analysis has allowed an investigation of the phenomenon from its prodromes until recent years, also highlighting important gaps that will have to be filled by future research.     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.     

Forecasting high-frequency excess stock returns via data analytics and machine learning

Borsa Istanbul introduced data analytics to present additional information about its market conditions. We examine whether this product can be utilized via various machine learning methods to predict intraday excess returns. Accordingly, these analytics provide significant prediction ratios above 50% with ideal profit ratios that can reach up to 33%. Among all the methods considered, XGBoost (logistic regression) performs better in predicting excess returns in the long-term analysis (short-term analysis). Results provide evidence for the benefits of both the analytics and the machine learning methods and raise further discussion on the semistrong market efficiency.     

Competition effects in EU external aid supply tenders funded with the Pre-accession and Neighbourhood instruments

Empirica - This paper investigates empirically the competition effects on procurement costs for Europe Aid funded supply tenders in countries benefiting from the EU Pre-accession (IPA) and...     

Russia’s invasion of Ukraine: assessment of the humanitarian,economic, and financial impact in the short and medium term

What are the economic effects of the Ukraine war for Ukraine, Russia, and the rest of Europe? In this study, the Vienna Institute for International Economic Studies (wiiw) sheds light on the immediate consequences on the one hand, but also on the medium-term structural changes caused by the largest armed conflict in Europe since the Second World War. The Russian invasion of Ukraine has triggered a humanitarian crisis. Pre-war, almost 19 m people lived in those regions that are currently directly affected. Refugee inflows to the rest of Europe are likely to be at least three times greater than in 2015/2016. As Black Sea ports come under Russian assault, Ukraine has lost its ability to sell more than half of its exports, primarily agricultural commodities and metals. Western financial support will become ever more important as the war continues. Turning to Russia, sanctions will have a very serious impact on that country’s economy and financial sector. Despite being partly hamstrung by the fact that a large proportion of Russian reserve assets are frozen in the EU and G7, the central bank managed to stabilise financial markets by a combination of confidence-building and hard-steering measures: capital controls, FX controls, regulatory easing for financial institutions, and a doubling of the key policy rate. The medium-term and long-term outlook is negative. As a result of the war and the sanctions, the rest of Europe faces a surge in already high inflation; this will weigh on real incomes and will depress economic growth. Many European countries rely heavily on Russia for oil and gas imports: import shares are over 75% in Czechia, Latvia, Hungary, Slovakia, and Bulgaria with respect to natural gas; Slovakia, Lithuania, Poland, and Finland with respect to oil and petroleum; and Cyprus, Estonia, Latvia, Denmark, Lithuania, Greece, and Bulgaria with respect to solid fuels. Aside from energy, the fallout via trade for the rest of Europe is likely to be small. Non-energy trade and investment links between Russia and many European countries have declined in importance since 2013. There are four main areas of structural change and lasting impact for the EU (and Europe more broadly) as a result of Russia’s invasion of Ukraine. First, the EU will get more serious about defence. Second, the green transition will gather pace. Third, broader Eurasian economic integration will be unwound. And fourth, the EU accession prospects for countries in Southeast Europe could (and should) improve.

     

Optimal insurance under maxmin expected utility

We examine a problem of demand for insurance indemnification, when the insured is sensitive to ambiguity and behaves according to the maxmin expected utility model of Gilboa and Schmeidler (J. Math. Econ. 18:141–153, 1989), whereas the insurer is a (risk-averse or risk-neutral) expected-utility maximiser. We characterise optimal indemnity functions both with and without the customary ex ante no-sabotage requirement on feasible indemnities, and for both concave and linear utility functions for the two agents. This allows us to provide a unifying framework in which we examine the effects of the no-sabotage condition, of marginal utility of wealth, of belief heterogeneity, as well as of ambiguity (multiplicity of priors) on the structure of optimal indemnity functions. In particular, we show how a singularity in beliefs leads to an optimal indemnity function that involves full insurance on an event to which the insurer assigns zero probability, while the decision maker assigns a positive probability. We examine several illustrative examples, and we provide numerical studies for the case of a Wasserstein and a Rényi ambiguity set.