Contributions in Merlin

This paper presents findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In this paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for ad-hoc search based tasks at Video Browser Showdown is introduced.

While second-tier stars lack popularity compared to superstars, their marginal contribution to team performance on the pitch relative to that of superstars is unknown. Relying on league-specific preseason market value distributions to define superstars and second-tier stars, we compare the marginal contributions of superstars and second-tier stars to team performance on the pitch in the top five European football leagues. Examining the impact of unexpected injury-related absences, we find that second-tier stars’ marginal contribution is at least equal to that of superstars. Thus, the players with arguably the highest costs for clubs do not contribute accordingly to short-run sportive success.

This study examines the stock price reactions of environmentally responsible stocks during the onset of the 2023 banking crisis, triggered by the collapse of Silicon Valley Bank (SVB). Our findings indicate that stocks poised to benefit from the shift to a low-carbon economy underperformed during the 2023 crisis. This suggests that investors anticipate a slowdown in climate tech development due to distress in the banking sector. Our results underscore the significance of considering not only the influence of the climate crisis on financial stability, but also the pivotal role that financial stability plays in ensuring a successful energy transition.

Purpose: In this paper, the authors investigate whether coronavirus disease 2019 (COVID-19) impacts household finances, like household debt repayments in the UK. Design/methodology/approach: This paper employs a vector autoregressive (VAR) model that nests neural networks and uses Mixed Data Sampling (MIDAS) techniques. The authors use data information related to COVID-19, financial markets and household finances. Findings: The authors' results show that household debt repayments' response to the first principal component of COVID-19 shocks is negative, albeit of low magnitude. However, when the authors employ specific COVID-19-related data like vaccines and tests the responses are positive, insinuating the underlying dynamic complexities. Overall, confirmed deaths and hospitalisations negatively affect household debt repayments. The authors also report low persistence in household debt repayments. Generalised impulse response functions (IRFs) confirm the main results. As draconian measures, the lockdowns are eased and the COVID-19 shocks are diminishing, and household financial data converge to the levels prior to the pandemic albeit with some lags. Originality/value: To the best of the authors' knowledge, this is the first study that examines the impact of the pandemic on household debt repayments. The authors' findings show that policy response in the future should prioritise innovation of new vaccines and testing.

We estimate the indirect costs of financial distress due to lost sales by exploiting real estate shocks and cross-supplier variation in real estate assets and leverage. We show that for the same client buying from different suppliers, the client’s purchases from distressed suppliers decline by an additional 13% following a drop in local real estate prices. The effect is more pronounced in more competitive industries, manufacturing, durable goods, less-specific goods, and when the costs of switching suppliers are low. Our results suggest that clients reduce their exposure to suppliers in financial distress.

Die Hinterziehung von Steuern soll durch den international eingeführten automatischen Informationsaustausch (AIA) aufgedeckt und eingedämmt werden. Zudem besteht in der Schweiz seit 2010 die straflose Selbstanzeige. Im Zusammenhang mit der internationalen Einführung des AIA ist ein deutlicher Anstieg der straflosen Selbstanzeigen zu beobachten. Dabei werden erhebliche kantonale Unterschiede sichtbar. So ist die Anzahl strafloser Selbstanzeigen pro zehntausend Steuerpflichtige in Kantonen mit hohen Vermögensteuern, getrieben durch die bevölkerungsstarken Genferseekantone, nach der Einführung des AIA tendenziell höher. Da das Selbstanzeigeverhalten im Zuge des AIA vorwiegend ausländisches Vermögen und Einkommen betrifft, könnte auch der Anteil der zugewanderten Wohnbevölkerung eine Rolle spielen.

In the digital paradigm, collaborative value creation takes place across organisational boundaries while the consumer moves to the centre. Value will be created in the future through the combination of artificial intelligence, augmented reality, the internet of things, blockchain, cloud and quantum computing, and the customer journey will span different sectors. The golden triangle of digital ecosystems includes commerce, social media, and finance. Companies that understand the benefits of the platform economy can achieve exponential growth and become winners of the digital transformation. They can share knowledge and resources across organisational boundaries, react quickly to market changes and find the balance between existing businesses and new markets. The future of work and leadership in open and digital ecosystems requires rethinking and new dynamic capabilities such as openness, agility, and ambidexterity. The book is based on years of research. It expands traditional management models with pragmatic strategy and action concepts, which are explained with diverse practical examples of tech companies from the USA, cross-industry ecosystems from China and healthcare platforms from Switzerland.

To remain competitive, organizations tend to change their established ways of working, their strategy, the core values, and the organizational structure. Such thorough changes are referred to as transformational change. Unfortunately, transformational change is often unsuccessful because organizational members do not always welcome the change. Although organizations often expect their supervisors to be successful role-models and change-agents during the transformational change process, we argue that initiating transformational change could increase supervisors' hindrance stress levels, which may result in abusive behaviors towards employees. More specifically, in a multi-source survey and an experimental study, we find evidence that transformational change is associated with supervisors' experienced hindrance stress, which subsequently led to more abusive behaviors towards employees.

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil.

The internal ratings-based (IRB) approach maps bank risk profiles more adequately than the standardized approach. After switching to IRB, banks’ risk-weighted asset (RWA) densities are thus expected to diverge, especially across countries with different supervisory strictness and risk levels. However, when examining 52 listed banks headquartered in 14 European countries that adopted the IRB approach, we observe a convergence of their RWA densities over time. We test whether this convergence can be entirely explained by differences in the size of the banks, loss levels, country risk, and/or time of IRB implementation. Our findings indicate that this is not the case. Whereas banks in high-risk countries with lax regulation, reduce their RWA densities, banks elsewhere increase theirs. Especially for banks in high-risk countries, RWA densities underestimate banks’ economic risk. Hence, the IRB approach enables regulatory arbitrage, whereby authorities only enforce strict supervision on capital requirements if they do not jeopardize bank viability.