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.     

Examining the success of women of color-owned small and medium-sized enterprises in the United States: A system dynamics perspective

The objective of this study was to unravel the challenges confronting women of color (WoC)-owned small and medium-sized enterprises (SMEs) in the United States. This is based on findings that most WoC-owned SMEs fail within the first few years of establishment. The impact of the global financial crisis resulting from the COVID-19 pandemic on WoC-owned SMEs was also explored. System Dynamics (SD) is a computational modeling approach useful for understanding changes in a system over time and is applied in this study to illustrate WoC entrepreneurs' navigation through the startup and maturation of SMEs. The authors calibrated and validated the model with publicly available data. Findings revealed that more emphasis should be placed on failure reduction in the early years of establishment of these businesses. Also, there is the need for early intervention rather than focusing on the improvement of the successful business exit from the system. Results indicated that the creation of new businesses by WoC after the failure of existing businesses produced an increase in the number of failed enterprises. The authors assert that attention must be paid at the individual level through support to the entrepreneur. This study contributes to the extant literature by providing the first known SD model useful in depicting the SME system for WoC entrepreneurs in the US. The model serves as a potentially useful tool for informing effective policy making, education, and programmatic approaches to support the success of WoC entrepreneurs in the US.

     

The role of satisfaction in labor diaspora dynamics: An analysis of BREXIT effects

In diaspora research, people's international mobility is often understood as a response to pull-push forces on an economic macro-level or as part of diasporic waves. However, labor diaspora formations are also influenced by micro-level (i.e., individual perceptions) drivers related to work per se, such as satisfaction. This explorative qualitative study takes a novel angle and focuses on the role of Portuguese nurses’ satisfaction with the evaluation of the Brexit scenario and its effects on this labor diaspora's mobility and stability. The findings illustrate that satisfaction, although affected by uncertainty, mainly relates to perceived working conditions and legislation and the possibilities to communicate with locals and other diasporans. This paper provides suggestions for future research and contributes to the development of theory on staying and human stickiness, explaining the dynamics of exit and entry in (re-)migration decision making.     

The productivity growth of euro area banks

Journal of Productivity Analysis - We study the performance of the banking system in the Eurozone over the period 2006–2017 as measured by total factor productivity growth (TFPG) and its...     

The socio-economic impact of certification schemes in conflict-affected regions: The case of arabica coffee in the Eastern DRC

The paper investigates the benefits of certification of cooperatives in conflict-affected areas. We study whether and how certification may contribute to attenuate the impact of conflicts on the members of coffee cooperatives in the Eastern DRC. We use an inverse probability weighted difference-in-difference approach to study data collected between December 2017 and October 2019 from four NGO-supported cooperatives. Two of these cooperatives got organic certification in 2018, of which one got an additional Small Producers Symbol (SPP) certification in 2018. Certified cooperatives allow Arabica coffee producers to access markets and buffer against drops in market prices. Results indicate that certified cooperatives were better able to resist the international price drop than non-certified cooperatives and the alternative informal markets. The results also suggest that certified cooperative members increased their sales to the cooperative instead of turning to informal side-selling. We find a decrease in reported food deficits by the members of certified cooperatives and an increase in the importance of coffee in their household's income. We conclude that certification is an effective way forward for smallholder coffee producers and cooperatives in unstable regions.     

How Wealthy are the Rich?

Underreporting and undersampling biases in top tail wealth, although widely acknowledged, have not been statistically quantified so far, essentially because they are not readily observable. Here we exploit the functional form of power law-like regimes in top tail wealth to derive analytical expressions for these biases, and use German microdata from a popular survey and rich list to illustrate that tiny differences in non-response rates lead to tail wealth estimates that differ by an order of magnitude, in our case ranging from 1 to 9 trillion euros. Underreporting seriously compounds the problem, and we find that the estimation of totals in scale-free systems oftentimes tends to be spurious. Our findings also suggest that recent debates on the existence of scale- or type-dependence in returns to wealth are ill-posed because the available data cannot discriminate between scale- or type-dependence, on one hand, and statistical biases, on the other hand. Yet both economic theory and mathematical formalism indicate that sampling and reporting biases are more plausible explanations for the observed data than scale- or type-dependence.