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Advances in Machine Learning (ML) led organizations to increasingly implement predictive decision aids intended to improve employees’ decision-making performance. While such systems improve organizational efficiency in many contexts, they might be a double-edged sword when there is the danger of a system discontinuance. Following cognitive theories, the provision of ML-based predictions can adversely affect the development of decision-making skills that come to light when people lose access to the system. The purpose of this study is to put this assertion to the test. Using a novel experiment specifically tailored to deal with organizational obstacles and endogeneity concerns, we show that the initial provision of ML decision aids can latently prevent the development of decision-making skills which later becomes apparent when the system gets discontinued. We also find that the degree to which individuals 'blindly' trust observed predictions determines the ultimate performance drop in the post-discontinuance phase. Our results suggest that making it clear to people that ML decision aids are imperfect can have its benefits especially if there is a reasonable danger of (temporary) system discontinuances.
In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.
Incentives, self-selection, and coordination of motivated agents for the production of social goods
(2021)
We study, theoretically and empirically, the effects of incentives on the self-selection and coordination of motivated agents to produce a social good. Agents join teams where they allocate effort to either generate individual monetary rewards (selfish effort) or contribute to the production of a social good with positive effort complementarities (social effort). Agents differ in their motivation to exert social effort. Our model predicts that lowering incentives for selfish effort in one team increases social good production by selectively attracting and coordinating motivated agents. We test this prediction in a lab experiment allowing us to cleanly separate the selection effect from other effects of low incentives. Results show that social good production more than doubles in the low- incentive team, but only if self-selection is possible. Our analysis highlights the important role of incentives in the matching of motivated agents engaged in social good production.
Der Einsatz von Künstliche Intelligenz (KI) – Technologien eröffnet viele Chancen, birgt aber auch viele Risiken – insbesondere in der Finanzbranche. Dieses Whitepaper gibt einen Überblick über den aktuellen Stand der Anwendung und Regulierung von KI-Technologien in der Finanzbranche, und diskutiert Chancen und Risiken von KI. KI findet in der Finanzbranche zahlreiche Anwendungsgebiete. Dazu gehören Chatbots, intelligente Assistenten für Kunden, automatischer Hochfrequenzhandel, automatisierte Betrugserkennung, Überwachung der Compliance, Gesichtserkennungssoftware zur Kundenidentifikation u. v. m. Auch Finanzaufsichtsbehörden setzen zunehmend KI-Anwendungen ein, um große und komplexe Datenmengen (Big Data) automatisiert und skalierbar auf Muster zu untersuchen und ihren Aufsichtspflichten nachzukommen.
Die Regulierung von KI in der Finanzbranche ist ein Balanceakt. Auf der einen Seite gibt es eine Notwendigkeit Flexibilität zu gewährleisten, um Innovationen nicht einzudämmen und im internationalen Wettbewerb nicht abgehängt zu werden. Strenge Auflagen können in diesem Zusammenhang als Barriere für die erfolgreiche Weiter-)Entwicklung von KI-Applikationen in der Finanzbranche wirken. Auf der anderen Seite müssen Persönlichkeitsrechte geschützt und Entscheidungsprozesse nachvollziehbar bleiben. Die fehlende Erklärbarkeit und Interpretierbarkeit von KI-Modellen entsteht in erster Linie durch Intransparenz bei einem Großteil heutiger KI-Anwendungen, bei welchen zwar die Natur der Ein- und Ausgaben beobachtbar und verständlich ist, nicht jedoch die genauen Verarbeitungsschritte dazwischen (Blackbox Prinzip).
Dieses Spannungsfeld zeigt sich auch im aktuellen regulatorischen Ansatz verschiedener Behörden. So werden einerseits die positiven Seiten von KI betont, wie Effizienz- und Effektivitätsgewinne sowie Rentabilitäts- und Qualitätssteigerungen (Bundesregierung, 2019) oder neue Methoden der Gefahrenanalyse in der Finanzmarktregulierung (BaFin, 2018a). Andererseits, wird darauf verwiesen, dass durch KI getroffene Entscheidungen immer von Menschen verantwortet werden müssen (EU Art. 22 DSGVO) und demokratische Rahmenbedingungen des Rechtsstaats zu wahren seien (FinTechRat, 2017).
Für die Zukunft sehen wir die Notwendigkeit internationale Regularien prinzipienbasiert, vereinheitlicht und technologieneutral weiterzuentwickeln, ohne dabei die Entwicklung neuer KIbasierter Geschäftsmodelle zu bremsen. Im globalen Wettstreit sollte Europa bei der Regulierung des KI-Einsatzes eine Vorreiterrolle einnehmen und damit seine demokratischen Werte der digitalen Freiheit, Selbstbestimmung und das Recht auf Information weltweit exportieren. Förderprogramme sollten einen stärkeren Fokus auf die Entwicklung nachhaltiger und verantwortungsvoller KI in Banken legen. Dazu zählt insbesondere die (Weiter-)Entwicklung breit einsetzbarer Methoden, die es erlauben, menschen-interpretierbare Erklärungen für erzeugte Ausgaben bereitzustellen und Problemen wie dem Blackbox Prinzip entgegenzuwirken.
Aus Sicht der Unternehmen in der Finanzbranche könnte eine Kooperation mit BigTech-Unternehmen sinnvoll sein, um gemeinsam das Potential der Technologie bestmöglich ausschöpfen zu können. Nützlich wäre auch ein gemeinsames semantisches Metadatenmodell zur Beschreibung der in der Finanzbranche anfallenden Daten. In Zukunft könnten künstliche Intelligenzen Daten aus sozialen Netzwerken berücksichtigen oder Smart Contracts aushandeln. Eine der größten Herausforderungen der Zukunft wird das Anwerben geeigneten Personals darstellen.
How does group identity affect belief formation? To address this question, we conduct a series of online experiments with a representative sample of individuals in the US. Using the setting of the 2020 US presidential election, we find evidence of intergroup preference across three distinct components of the belief formation cycle: a biased prior belief, avoid-ance of outgroup information sources, and a belief-updating process that places greater (less) weight on prior (new) information. We further find that an intervention reducing the salience of information sources decreases outgroup information avoidance by 50%. In a social learn-ing context in wave 2, we find participants place 33% more weight on ingroup than outgroup guesses. Through two waves of interventions, we identify source utility as the mechanism driving group effects in belief formation. Our analyses indicate that our observed effects are driven by groupy participants who exhibit stable and consistent intergroup preferences in both allocation decisions and belief formation across all three waves. These results suggest that policymakers could reduce the salience of group and partisan identity associated with a policy to decrease outgroup information avoidance and increase policy uptake.
Using a novel experimental design, I test how the exposure to information about a group’s relative performance causally affects the members’ level of identification and thereby their propensity to harm affiliates of comparison groups. I find that both, being informed about a high and poor relative performance of the ingroup similarly fosters identification. Stronger ingroup identification creates increased hostility against the group of comparison. In cases where participants learn about poor relative performance, there appears to be a direct level effect additionally elevating hostile discrimination. My findings shed light on a specific channel through which social media may contribute to intergroup fragmentation and polarization.
This paper aims at an improved understanding of the relationship between monetary policy and racial inequality. We investigate the distributional effects of monetary policy in a unified framework, linking monetary policy shocks both to earnings and wealth differentials between black and white households. Specifically, we show that, although a more accommodative monetary policy increases employment of black households more than white households, the overall effects are small. At the same time, an accommodative monetary policy shock exacerbates the wealth difference between black and white households, because black households own less financial assets that appreciate in value. Over multi-year time horizons, the employment effects are substantially smaller than the countervailing portfolio effects. We conclude that there is little reason to think that accommodative monetary policy plays a significant role in reducing racial inequities in the way often discussed. On the contrary, it may well accentuate inequalities for extended periods.
From 1963 through 2015, idiosyncratic risk (IR) is high when market risk (MR) is high. We show that the positive relation between IR and MR is highly stable through time and is robust across exchanges, firm size, liquidity, and market-to-book groupings. Though stock liquidity affects the strength of the relation, the relation is strong for the most liquid stocks. The relation has roots in fundamentals as higher market risk predicts greater idiosyncratic earnings volatility and as firm characteristics related to the ability of firms to adjust to higher uncertainty help explain the strength of the relation. Consistent with the view that growth options provide a hedge against macroeconomic uncertainty, we find evidence that the relation is weaker for firms with more growth options.
Public employees in many developing economies earn much higher wages than similar privatesector workers. These wage premia may reflect an efficient return to effort or unobserved skills, or an inefficient rent causing labor misallocation. To distinguish these explanations, we exploit the Kenyan government’s algorithm for hiring eighteen-thousand new teachers in 2010 in a regression discontinuity design. Fuzzy regression discontinuity estimates yield a civil-service wage premium of over 100 percent (not attributable to observed or unobserved skills), but no effect on motivation, suggesting rent-sharing as the most plausible explanation for the wage premium.
We examine how a firms' investment behavior affects the investment of a neighboring firm. Economic theory yields ambiguous predictions regarding the direction of firm peer effects and consistent with earlier work, we find that firms display similar investment behavior within an area using OLS analysis. Exploiting time-variation in the rise of U.S. states' corporate income taxes and utilizing heterogeneity in firms' exposure to increases in corporate income tax rates, we identify the causal impact of local firms' investments. Using this as an instrumental variable in a 2SLS estimation, we find that an increases in local firms' investment reduces the investment of a local peer firm. This effect is more pronounced if local competition among firms is stronger and supports theories that firm investments are strategic substitutes due to competition.
This paper studies the use of performance pricing (PP) provisions in debt contracts and compares accounting-based with rating-based pricing designs. We find that rating-based provisions are used by volatile-growth borrowers and allow for stronger spread increases over the credit period. Accounting-based provisions are employed by opaque-growth borrowers and stipulate stronger spread reductions. Further, a higher spread-increase potential in rating-based contracts lowers the spread at the loan’s inception and improves the borrower’s performance later on. In contrast, a higher spread-decrease potential in accounting-based contracts lowers the initial spread and raises the borrower’s leverage afterwards. The evidence indicates that rating-based contracts are indeed employed for different reasons than accounting-based contracts: the former to signal a borrower’s quality, the latter to mitigate investment inefficiencies.
In this paper, we propose a model of credit rating agencies using the global games framework to incorporate information and coordination problems. We introduce a refined utility function of a credit rating agency that, additional to reputation maximization, also embeds aspects of competition and feedback effects of the rating on the rated firms. Apart from hinting at explanations for several hypotheses with regard to agencies' optimal rating assessments, our model suggests that the existence of rating agencies may decrease the incidence of multiple equilibria. If investors have discretionary power over the precision of their private information, we can prove that public rating announcements and private information collection are complements rather than substitutes in order to secure uniqueness of equilibrium. In this respect, rating agencies may spark off a virtuous circle that increases the efficiency of the market outcome.
We examine firms’ simultaneous choice of investment, debt financing and liquidity in a large sample of US corporates between 1980 and 2014. We partition the sample according to the firms’ financial constraints and their needs to hedge against future shortfalls in operating income. In contrast to earlier work, our joint estimation approach shows that cash flows affect the corporate decisions of unconstrained firms more strongly than those of constrained firms. Investment-cash flow sensitivities are particularly intense for unconstrained firms with high hedging needs. Investment opportunities (as proxied by Q), however, play a larger role for constrained firms with the effects being strongest in case of low hedging needs. Interestingly, constrained firms with low hedging needs are found to employ more debt to finance their investment opportunities and build up significant cash holdings at the same time. Our results hence indicate overinvestment behavior for unconstrained firms but no underinvestment for constrained firms if they have low hedging needs.
We analyze the market reaction to the sentiment of the CEO speech at the Annual General Meeting (AGM). As the AGM is typically preceded by several information disclosures, the CEO speech may be expected to contribute only marginally to investors’ decision-making. Surprisingly, however, we observe from the transcripts of 338 CEO speeches of German corporates between 2008 and 2016 that their sentiment is significantly related to abnormal stock returns and trading volumes following the AGM. Using a novel business-specific German dictionary based on Loughran and McDonald (2011), we find a negative association of the post-AGM returns with the speeches’ negativity and a positive association with the speeches’ relative positivity (i.e. positivity relative to negativity). Relative positivity moreover corresponds with a lower trading volume in a short time window surrounding the AGM. Investors hence seem to perceive the sentiment of CEO speeches at AGMs as a valuable indicator of future firm performance.
This study examines the role of actual and perceived financial sophistication (i.e., financial literacy and confidence) for individuals' wealth accumulation. Using survey data from the German SAVE initiative, we find strong gender- and education-related differences in the distribution of the two variables and their effects on wealth: As financial literacy rises in formal education, whereas confidence increases in education for men but decreases for women, we observe that women become strongly underconfident with higher education, while men remain overconfident.Regarding wealth accumulation, we show that financial literacy has a positive effect that is stronger for women than for men and that is increasing (decreasing) in education for women (men). Confidence, however, supports only highly-educated men's wealth. When considering different channels for wealth accumulation, we observe that financial literacy is more important for current financial market participation, whereas confidence is more strongly associated with future-oriented financial planning. Overall, we demonstrate that highly-educated men's wealth levels benefit from their overconfidence via all financial decisions considered, but highly-educated women's financial planning suffers from their underconfidence. This may impair their wealth levels in old age.
This paper provides new insights into the nature of loan securitization. We analyze the use of collateralized loan obligation (CLO) transactions by European banks from 1997 to 2004 andtry to identify the influence that various firm-specific and macroeconomic factors may have on an institution's securitization decision. We find that not only regulatory capital arbitrage under Basel I has been driving the market. Rather, our results suggest that loan securitization is an appropriate funding tool for banks with high risk and low liquidity. It may also have been used by commercial banks to indirectly access investment-bank activities and the associated gains.
This paper examines the effect of imperfect labor market competition on the efficiency of compensation schemes in a setting with moral hazard, private information and risk-averse agents. Two vertically differentiated firrms compete for agents by offering contracts with fixed and variable payments. Vertical differentiation between firms leads to endogenous, type-dependent exit options for agents. In contrast to screening models with perfect competition, we find that existence of equilibria does not depend on whether the least-cost separating allocation is interim efficient. Rather, vertical differentiation allows the inferior firm to offer (cross-)subsidizing fixed payments even above the interim efficient level. We further show that the efficiency of variable pay depends on the degree of competition for agents: For small degrees of competition, low-ability agents are under-incentivized and exert too little effort. For large degrees of competition, high-ability agents are over-incentivized and bear too much risk. For intermediate degrees of competition, however, contracts are second-best despite private information.
Open-end real estate funds are of particular importance in the German bank-dominated financial system. However, recently the German open-end fund industry came under severe distress which triggered a broad discussion of required regulatory interventions. This paper gives a detailed description of the institutional structure of these funds and of the events that led to the crisis. Furthermore, it applies recent banking theory to openend real estate funds in order to understand why the open-end fund structure was so prevalent in Germany. Based on these theoretical insights we evaluate the various policy recommendations that have been raised.
Open-end real estate funds are of particular importance in the German bankdominated financial system. However, recently the German open-end fund industry came under severe distress which triggered a broad discussion of required regulatory interventions. This paper gives a detailed description of the institutional structure of these funds and of the events that led to the crisis. Furthermore, it applies recent banking theory to open-end real estate funds in order to understand why the open-end fund structure was so prevalent in Germany. Based on these theoretical insights we evaluate the various policy recommendation that have been raised.
Doing safe by doing good : ESG investing and corporate social responsibility in the U.S. and Europe
(2019)
This paper examines the profitability of investing according to environmental, social and governance (ESG) criteria in the U.S. and Europe. Based on data from 2003 to 2017, we show that a portfolio long in stocks with the highest ESG scores and short in those with the lowest scores yields a significantly negative abnormal return. Interestingly, this is caused by the strong positive return of firms with the lowest ESG activity. As we find that increasing ESG scores reduce firm risk (particularly downside risk), this hints at an insurance-like character of corporate social responsibility: Firms with low ESG activity need to offer a corresponding risk premium. The perception of ESG as an insurance can be shown to be stronger in more volatile capital markets for U.S. firms, but not for European firms. Socially responsible investment may therefore be of varying attractiveness in different market phases.