Contributions published at Department of Banking and Finance (Pablo Koch Medina)

We need more computing capacity for the next growth cycle, and computers with conventional transistor technology are reaching their limits. So new ideas are required. The quantum computer, which overcomes the binary system and is not based on silicon microchips, could be a solution. This technology will continue to develop exponentially and transform science, the economy, and society. Furthermore, the paradigm of quantum communication offers an entirely novel possibility of distributed computing by allowing quantum computers to be networked via quantum channels to intrinsically secure communication. This article explains how quantum computers exploit new phenomena that do not occur in classical physics. Along the four primary application areas identified (optimization, simulation, machine learning, and cryptography), we describe possible applications in various industries. Our critical appraisal presents the technical challenges that still hold the potential for quantum computing to complement traditional computing systems. Accordingly, small and mid-sized companies do not necessarily need to invest in quantum computers but in their use. Quantum as a service can be the first step for visionary leaders to get familiar with it and gain a competitive advantage early on.

Digitalization and sustainability have been the core drivers of transformation of the financial industry in recent years. In this context, green fintech plays a major role, which, however, is still an unexplored field in business, information systems and finance research. This paper conducts a systematic literature analysis and develops a research agenda based on a framework, which is derived from clustering 74 academic research papers. The framework consists of the four clusters strategy, organization, technology, and potentials along nine dimensions. The research agenda reveals that green fintech is still a very premature field of research. The analysis shows that areas like customer- and government-related services, insurance-oriented approaches and SDGs which focus on life on land and life below water are still rare and that most of the approaches focus on blockchain technology, while other financial technologies like artificial intelligence are still underrepresented.

Aus Asien kommt der Trend Social Commerce, der diverse Branchen und disruptive Technologien verbindet und so ein neues Kundenerlebnis schafft. Was bedeutet dieses Phänomen, und sind wir bereit dafür?

We use BERT, an AI-based algorithm for language understanding, to quantify regulatory climate risk disclosures and analyze their impact on the term structure in the credit default swap (CDS) market. Risk disclosures can either increase or decrease CDS spreads, depending on whether the disclosure reveals new risks or reduces uncertainty. Training BERT to differentiate between transition and physical climate risks, we find that disclosing transition risks increases CDS spreads after the Paris Climate Agreement of 2015, while disclosing physical risks decreases the spreads. In addition, we also find that the election of Trump had a negative impact on CDS spreads for firms exposed to transition risk. These impacts are consistent with theoretical predictions and economically and statistically significant.

Over fifty countries have introduced regulatory sandboxes to foster financial innovation. This article conducts the first evaluation of their ability to improve fintechs’ access to capital and attendant real effects. Exploiting the staggered introduction of the UK sandbox, we establish that firms entering the sandbox see an increase of 15% in capital raised post-entry. Their probability of raising capital increases by 50%. Sandbox entry also has a significant positive effect on survival rates and patenting. Investigating the mechanism, we present evidence consistent with lower asymmetric information and regulatory costs.

Climate change poses new challenges for portfolio management. In our not-yet-low carbon world, investors face a trade-off between minimizing their exposure to climate risks and maximizing the benefits of portfolio diversification. This paper investigates how investors and financial intermediaries navigate this trade-off. After the release of Morningstar's novel carbon risk metrics in April 2018, mutual funds labeled as "low carbon" experienced a significant increase in investor demand, especially those with high risk-adjusted returns. Fund managers actively reduced their exposure to firms with high carbon risk scores, especially stocks with returns that correlated more with the funds' portfolios and were thus less useful for diversification. These findings shed light on whether and how climate-related information can re-orient capital flows in a low carbon direction.

While the crypto market has experienced volatility and skepticism, the underlying blockchain technology has continually evolved since the introduction of Bitcoin in 2009. Though Bitcoin has doubled since last year, the focus has shifted to non-fungible tokens (NFTs) and infrastructure protocols like Chainlink and Graph.

This paper examines the potential of ChatGPT, a large language model, as a financial advisor for listed firm performance forecasts. We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’ forecasts and the realised values. Our findings suggest that ChatGPT can correct the optimistic biases of human analysts. This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making.

Why do residential mortgages carry a fixed or an adjustable interest rate? To answer this question we study unique data from 103 banks belonging to 73 different banking groups across twelve countries in the euro area. To explain the large cross-country and time variations observed, we distinguish between household conditions that determine the local demand for credit and the characteristics of banks that supply credit. As bank funding mostly occurs at the group level, we disentangle these two sets of factors by comparing the outcome observed for the same banking group across the different countries. Local household conditions dominate. In particular we find that the share of new loans with a fixed rate is larger when: (1) the historical volatility of inflation is lower, (2) the correlation between unemployment and the short-term interest rate is higher, (3) households’ financial literacy is lower, and (4) the use of local mortgages to back covered bonds and of mortgage-backed securities is more widespread.

This chapter investigates the pricing of single-asset autocallable barrier reverse convertibles in the Heston local-stochastic volatility (LSV) model. Despite their complexity, autocallable structured notes are the most traded equity-linked exotic derivatives. The autocallable payoff embeds an early redemption feature generating strong path and model dependency. Consequently, the commonly used local volatility (LV) model is overly simplified for pricing and risk management. Given its ability to match the implied volatility smile and reproduce its realistic dynamics, the LSV model is, in contrast, better suited for exotic derivatives, such as autocallables. We use quasi-Monte Carlo methods to study the pricing given the Heston LSV model and compare it with the LV model. In particular, we establish the sensitivity of the valuation differences of autocallables between the two models with respect to pay-off features, model.

We study bank branching dynamics across 3,143 US counties and 26 years. During the last decade, banks closed their branches at an unprecedented rate. At its peak in 2009, there were 90,783 branches. By 2020, this number has fallen by 12 percent. While technological factors correlate with these branching dynamics, bank fragility and consolidation are also strongly associated with changes in the number of branches (and their openings and closures). Interestingly, technological capabilities to service customers, such as online banking, seem less tightly linked to de-branching than technological capabilities to process internal information. Our analysis shows that large banks rely on internal technology to shed branches, while small banks close branches when they are vulnerable or consolidate.

Firms’ investments in green technology are crucial for investors’ alignment to the Net Zero target. However, it is still unclear whether firms that invest in green technologies are rewarded by the market, particularly in the long run. Using a science-based technological measure of greenness, we find that the adoption of sustainable technologies is associated with better future financial and operating performance. Firms with greener technologies do not just appeal to investors’ pro-social preferences but also represent better firms. The results are especially strong in countries characterized by higher financial development, and for firms with better climate-related disclosure.

In this study, we use earnings management to examine (1) how investors regard a CEO’s commitment to honesty and (2) the impact of their perceptions, in light of their own moral values, on their investment decisions. In two laboratory experiments using students as investor proxies, we find that investors perceive a CEO as being more committed to honesty when they believe the CEO has engaged less in earnings management. A one standard deviation increase in a CEO’s perceived commitment to honesty, compared to that of another CEO, leads to a 40% reduction in the importance the investors assigned, when making investment decisions, to differences in the two CEOs’ claimed future returns. This effect is particularly pronounced among investors with a proself value orientation. For prosocial investors, their moral values and those they attribute to the CEO directly influence their investment decisions, with returns playing a secondary role. Our findings contrast with the idea, implicit in the literature on ‘sin’ stocks, that morality is a niche concern. By contrast, we find that moral values play a significant role for distinct types of investors and that they influence investment decisions for both moral and pecuniary reasons.

Künstliche Intelligenz hat die Effizienz der Datenanalyse revolutioniert und bietet die Möglichkeit, das Investment Research und Portfoliomanagement zu automatisieren. Ihr Einsatz ist aber kein uneingeschränkter Erfolgsgarant.

"Breit diversifizieren": Der Botschafter des FuW-Börsenspiels Thorsten Hens wich während der Spieldauer von seiner eigentlichen Anlagephilosophie ab. Nach Startschwierigkeiten schnitt er dennoch unter den besten 20% des Wettbewerbs ab. Für den Finanzprofessor und Verhaltensökonomen steht die breite Diversifikation im Vordergrund beim Aufbau eines langfristig angelegten Portfolios. Auf Einzeltitel setzt er in der Regel nicht. Für das kommende Jahr empfiehlt er Schweizer Aktien, weil der heimische Markt grosses Aufholpotenzial aufweise. Doch gefeit vor systematischen Anlagefehlern sei auch er nicht.

In the battle against unethical behavior in organizations, fostering employees' moral courage proves vital beyond conventional regulation and compliance efforts. To propel this frontier and empower individuals to uphold moral values, a robust measure of workplace moral courage becomes imperative. Drawing upon a competency-based approach, this paper introduces the Workplace Moral Courage Scale (WMCS). Unlike previous measures, the WMCS stands out by acknowledging the diverse ways in which moral courage can manifest within workplace settings. Building on data of two diverse German employee samples (total N = 995), we unveil five distinct factors: challenging colleague and supervisor misconduct, opposing unethical orders, confessing mistakes, and initiating positive changes. The WMCS exhibits good psychometric properties and convergent and discriminant validity. Confirming its concurrent validity, the WMCS effectively predicts various forms of employee silence, even after controlling for organizational influences. The paper concludes with a discussion of the results and implications of the WMCS.

Online investing is often facilitated by digital platforms, where the information of peer top performers can be widely accessible and distributed. However, the influence of such information on retail investors’ psychology, their trading behaviour and potential risks they may be prone to is poorly understood. We investigate the impact of upward social comparison on risk-taking, trading activity and investor satisfaction using a tailored experiment with 807 experienced retail investors trading on a dynamically evolving simulated stock market, designed to systematically measure various facets of trading activity. We find that investors presented with an upward social comparison take more risk and trade more actively, and they report significantly lower satisfaction with their own performance. Our findings demonstrate the pitfalls of modern investment platforms with peer information and social trading. The broad implications of this study also provide guidelines for improving retail investor satisfaction and protection.

Professor Marc Chesney of the University of Zurich says that the foul stench oil, gas, and coal emanated from global climate summit COP28.

Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available at www.chatclimate.ai, and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution.

Belief in the effects of climate change remains stubbornly regionally specific. This column discusses how banks assess environmental risks in syndicated loan markets in the US. The deregulation following US withdrawal from the Paris Agreement in 2017 prompted banks to reduce the environmental-risk sensitivity of their loan pricing in Republican states, while lenders charged higher rates to borrowers causing severe environmental damage only in states where climate denial is low. The price of environmental risk in bank lending, the authors suggest, is driven by local beliefs and regulatory enforcement practices.