Contributions in Merlin

We study the effect of using fractional returns instead of log returns on improving portfolio optimization. The fractional return is defined as a fractional difference of log prices. The rationale behind the substitution lies in the fractional return’s similarity to log returns and its memory richness nature. We conduct extensive experiments on 8 groups of stocks with Gaussian and COMFORT models, with the optimization objective of maximizing the Sharpe ratio, with or without short-selling. Our findings are the following. First, qualitatively, portfolios using fractional returns behave generally more turbulent. Second, the Sharpe ratio demonstrates a pattern of first increasing and then decreasing with respect to degrees of fractional returns. Third, application of fractional returns yields improved Sharpe ratios in most groups. Fourth, the improvement effect is greater for portfolios with short selling. We also give an interpretation of the findings and conclude with future directions.

Nachhaltige Firmenkredite sind ein aufkommendes Thema im Schweizer Kreditmarkt. Zusätzlich zur Kreditfähigkeit und -würdigkeit beinhaltet die Bonitätsprüfung eine Nachhaltigkeitskomponente, wodurch die Informationengrundmenge steigt. Somit ist die Forschungsfrage naheliegend, ob sich die Informationsasymmetrien zwischen Kreditgeber und -nehmer vergrössern, sobald ein Kredit als nachhaltig deklariert wird. Zur Beantwortung der Forschungsfrage wird eine qualitative Forschungsmethode angewendet, wobei Experten aus vier verschiedenen Schweizer Banken interviewt werden. Der Fokus liegt auf den Nachhaltigkeitskriterien und deren Einfluss auf die Informationsasymmetrien. Die Ergebnisse zeigen, dass die Informationsasymmetrien bei nachhaltigen Krediten tendenziell schwächer wirken, da das zu finanzierende Unternehmen genauer analysiert wird und somit eine engere Kundenbeziehung entsteht.

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Das Ziel dieser Arbeit ist es, dass die Leser*innen ein tieferes Verständnis bezüglich Zombie-Unternehmen erhalten und sich deren Gefahren in der Wirtschaft bewusst werden. Im Rahmen dessen wird anfangs erklärt, welche Kennzahlen ein Zombie-Unternehmen charakterisieren, wobei im Allgemeinen gesagt werden kann, dass diese Unternehmen eine hohe Verschuldung, sowie eine geringe Rentabilität aufweisen. Dabei begünstigen finanziell schwache Banken, ein ineffizientes Insolvenzregime, sowie ein niedriges Zinsniveau und Finanzkrisen die Entstehung und Aufrechthaltung von nicht lebensfähigen Unternehmen. In den meisten Fällen betrifft dies kleine und mittlere Unternehmen mit einem Umsatz von weniger als 500 Millionen US-Dollar. Im Vergleich zu gesunden Unternehmen weisen die Zombie-Firmen eine geringere Investitionstätigkeit wie eine halb so hohe Arbeits- und Gesamtproduktivität auf. Zugleich binden sie begrenzte Ressourcen, wie Human- und Finanzkapital in der Branche, was zur Folge hat, dass gesunde Unternehmen ihr Potenzial nicht ausschöpfen können. Durch die Verzerrung des Marktes gibt es ein Überangebot, daraus resultiert eine geringere Gewinnmarge und höhere Barrieren für Markteinsteiger. Die Immobilienbranche weist mit rund acht Prozent den höchsten Anteil an Zombie-Unternehmen in der Wirtschaft auf. Viele Wissenschaftler*innen sehen mit der Verbesserung der Insolvenzreglungen und der Stabilisierung der schwachen Banken essenzielle Massnahmen, um die Entstehung beziehungsweise Aufrechthaltung der Zombie-Unternehmen zu hemmen.

We hypothesize that financial risk-taking originates in preindustrial interpersonal population diversity. We use data on immigrants residing in the United States and show that controlling for all known determinants of portfolio decisions and more than 100 control variables, diversity in the country of immigrants’ origin positively affects stock market participation and the level of risky asset holdings. Our results remain robust when instrumenting diversity with plant variety. We also identify the channels through which the effect of diversity operates (mostly individualism and human capital), but also conclude that diversity exerts an independent effect.

The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of operational platforms, IoT spectrum sensors are vulnerable to heterogeneous malware. The usage of behavioral fingerprinting and machine learning has shown merit in detecting cyberattacks. Still, there exist challenges in terms of (i) designing, deploying, and evaluating ML-based fingerprinting solutions able to detect malware attacks affecting real IoT spectrum sensors, (ii) analyzing the suitability of kernel events to create stable and precise fingerprints of spectrum sensors, and (iii) detecting recent malware samples affecting real IoT spectrum sensors of crowdsensing platforms. Thus, this work presents a detection framework that applies device behavioral fingerprinting and machine learning to detect anomalies and classify different botnets, rootkits, backdoors, ransomware and cryptojackers affecting real IoT spectrum sensors. Kernel events from CPU, memory, network,file system, scheduler, drivers, and random number generation have been analyzed, selected, and monitored to create device behavioral fingerprints. During testing, an IoT spectrum sensor of the ElectroSense platform has been infected with ten recent malware samples (two botnets, three rootkits, three backdoors, one ransomware, and one cryptojacker) to measure the detection performance of the framework in two different network configurations. Both supervised and semi-supervised approaches provided promising results when detecting and classifying malicious behaviors from the eight previous malware and seven normal behaviors. In particular, the framework obtained 0.88–0.90 true positive rate when detecting the previous malicious behaviors as unseen or zero-day attacks and 0.94–0.96 F1-score when classifying them

In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse-selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse-selection costs for major cryptocurrencies. Also, our results suggest that adverse-selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse-selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse-selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.

Employees with relevant knowledge and skills for digitalization have become increasingly important for the competitiveness of MNCs. However, the shortage of such digital human capital in many host countries is putting pressure on MNC subsidiaries to prevent these employees from leaving. We theorize that the retention of digital human capital in MNC subsidiaries does not merely depend on salaries but crucially on the learning opportunities that subsidiaries offer. By integrating mechanisms from the literature on subsidiary-specific advantages into theoretical models explaining voluntary mobility constraints of employees, we reason that the opportunities for acquiring new skills in subsidiaries with advanced digital expertise will reduce the odds of losing these valuable employees. We test our theoretical predictions for 11,598 employees with digital human capital working for 866 foreign MNC subsidiaries in Denmark observed between 2002 and 2012. We find that digital expertise helps retaining digital human capital. The effect is stronger if subsidiaries have an internationally diverse workforce and when they possess patented technologies. Both factors provide distinct learning opportunities from digital expertise. The effect is weaker if the subsidiary is located in regional clusters of digital expertise since alternative employers may offer similar learning opportunities.

The degree of certainty that decision-makers have about their evaluations of available choice alternatives and their confidence about selecting the subjectively best alternative are important factors that affect current and future value-based choices. Assessments of the alternatives in a given choice set are rarely unidimensional; their values are usually derived from a combination of multiple distinct attributes. For example, the taste, texture, quantity, and nutritional content of a snack food may all be considered when determining whether to consume it. We examined how certainty about the levels of individual attributes of an option relates to certainty about the overall value of that option as a whole and/or to confidence in having chosen the subjectively best available option. We found that certainty and confidence are derived from unequally weighted combinations of attribute certainties rather than simple, equal combinations of all sources of uncertainty. Attributes that matter more in determining choice outcomes also are weighted more in metacognitive evaluations of certainty or confidence. Moreover, we found that the process of deciding between two alternatives leads to refinements in both attribute estimations and the degree of certainty in those estimates. Attributes that are more important in determining choice outcomes are refined more during the decision process in terms of both estimates and certainty. Although certainty and confidence are typically treated as unidimensional, our results indicate that they, like value estimates, are subjective, multidimensional constructs.

We exploit the exogenous Covid-19 shock in a bicultural area of Italy to identify cultural differences in the way companies respond to economic shocks. Firms with managers of diverse cultural backgrounds resort to different forms of government aid, diverge in their investment decisions, and have different growth rates. These findings are consistent with cultural differences in time preferences and debt aversion. Specifically, we find that the response of managers belonging to a more long-term oriented culture is characterized by a lower recourse to debt, more investments and higher growth rates. Overall, our results show that the cultural origin of managers significantly affects firms' reaction to economic shocks and real economic outcomes.