Sustainable Architecture for Finance in Europe (SAFE)
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With open banking, consumers take greater control over their own financial data and share it at their discretion. Using a rich set of loan application data from the largest German FinTech lender in consumer credit, this paper studies what characterizes borrowers who share data and assesses its impact on loan application outcomes. I show that riskier borrowers share data more readily, which subsequently leads to an increase in the probability of loan approval and a reduction in interest rates. The effects hold across all credit risk profiles but are the most pronounced for borrowers with lower credit scores (a higher increase in loan approval rate) and higher credit scores (a larger reduction in interest rate). I also find that standard variables used in credit scoring explain substantially less variation in loan application outcomes when customers share data. Overall, these findings suggest that open banking improves financial inclusion, and also provide policy implications for regulators engaged in the adoption or extension of open banking policies.
With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal, and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers’ online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by “smartly” administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers’ profits.
Short sale bans may improve market quality during crises: new evidence from the 2020 Covid crash
(2022)
In theory, banning short selling stabilizes stock prices but undermines pricing efficiency and has ambiguous impacts on market liquidity. Empirical studies find mixed and conflicting results. This paper leverages cross-country policy variation during the 2020 Covid crisis to assess differential impacts of bans on stock liquidity, prices, and volatility. Results suggest that bans improved liquidity and stabilized prices for illiquid stocks but temporarily diminished liquidity for highly liquid stocks.The findings support theories in which short sale bans may improve liquidity by selectively filtering out informed— potentially predatory—traders. Thus, policies that target the most illiquid stocks may deliver better overall market quality than uniform short sale bans imposed on all stocks.
This note argues that in a situation of an inelastic natural gas supply a restrictive monetary policy in the euro zone could reduce the energy bill and therefore has additional merits. A more hawkish monetary policy may be able to indirectly use monopsony power on the gas market. The welfare benefits of such a policy are diluted to the extent that some of the supply (approximately 10 percent) comes from within the euro zone, which may give rise to distributional concerns.
SAFE Update December 2022
(2022)
Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.
We analyze how market fragmentation affects market quality of SME and other less actively traded stocks. Compared to large stocks, they are less likely to be traded on multiple venues and show, if at all, low levels of fragmentation. Concerning the impact of fragmentation on market quality, we find evidence for a hockey stick effect: Fragmentation has no effect for infrequently traded stocks, a negative effect on liquidity of slightly more active stocks, and increasing benefits for liquidity of large and actively traded stocks. Consequently, being traded on multiple venues is not necessarily harmful for SME stock market quality.
We investigate the impact of uneven transparency regulation across countries and industries on the location of economic activity. Using two distinct sources of regulatory variation—the varying extent of financial-reporting requirements and the staggered introduction of electronic business registers in Europe—, we consistently document that direct exposure to transparency regulation is negatively associated with the focal industry’s economic activity in terms of inputs (e.g., employment) and outputs (e.g., production). By contrast, we find that indirect exposure to supplier and customer industries’ transparency regulation is positively associated with the focal industry’s economic activity. Our evidence suggests uneven transparency regulation can reallocate economic activity from regulated toward unregulated countries and industries, distorting the location of economic activity.
Cryptocurrencies provide a unique opportunity to identify how derivatives impact spot markets. They are fully fungible, trade across multiple spot exchanges at different prices, and futures contracts were selectively introduced on bitcoin (BTC) exchange rates against the USD in December 2017. Following the futures introduction, we find a significantly greater increase in cross-exchange price synchronicity for BTC--USD relative to other exchange rate pairs, as demonstrated by an increase in price correlations and a reduction in arbitrage opportunities and volatility. We also find support for an increase in price efficiency, market quality, and liquidity. The evidence suggests that futures contracts allowed investors to circumvent trading frictions associated with short sale constraints, arbitrage risk associated with block confirmation time, and market segmentation. Overall, our analysis supports the view that the introduction of BTC--USD futures was beneficial to the bitcoin spot market by making the underlying prices more informative.
Using the negotiation process of the Basel Committee on Banking Supervision (BCBS), this paper studies the way regulators form their positions on regulatory issues in the process of international standard-setting and the consequences on the resultant harmonized framework. Leveraging on leaked voting records and corroborating them using machine learning techniques on publicly available speeches, we construct a unique dataset containing the positions of banks and national regulators on the regulatory initiatives of Basel II and III. We document that the probability of a regulator opposing a specific initiative increases by 30% if their domestic national champion opposes the new rule, particularly when the proposed rule disproportionately affects them. We find the effect is driven by regulators who had prior experience of working in large banks – lending support to the private-interest theories of regulation. Meanwhile smaller banks, even when they collectively have a higher share in the domestic market, do not have any impact on regulators’ stand – providing little support to public-interest theories of regulation. Finally, we show this decision-making process manifests into significant watering down of proposed rules, thereby limiting the potential gains from harmonization of international financial regulation.
Resolving financial distress where property rights are not clearly defined: the case of China
(2022)
We use data on financially distressed Chinese companies in order to study a debt market where property rights are crudely defined and poorly enforced. To help with identification we use an event where a business-friendly province published new guidelines regarding the administration and enforcement of assets pledged as collateral. Although by no means a comprehensive reform of bankruptcy law or property rights, by instructing courts to enforce existing, albeit rudimentary, contractual rights the new guidelines virtually eliminated creditors runs and produced a sharp increase in the survival rate of financially-distressed companies. These changes illustrate how piecemeal reforms of property rights and their enforcement may have a significant impact on economic outcomes. Our analysis and results challenge the view that a fully fledged system of private property is a precondition for economic development.
We employ a proprietary transaction-level dataset in Germany to examine how capital requirements affect the liquidity of corporate bonds. Using the 2011 European Banking Authority capital exercise that mandated certain banks to increase regulatory capital, we find that affected banks reduce their inventory holdings, pre-arrange more trades, and have smaller average trade size. While non-bank affiliated dealers increase their market-making activity, they are unable to bridge this gap - aggregate liquidity declines. Our results are stronger for banks with a higher capital shortfall, for non-investment grade bonds, and for bonds where the affected banks were the dominant market-maker.
Supranational supervision
(2022)
We exploit the establishment of a supranational supervisor in Europe (the Single Supervisory Mechanism) to learn how the organizational design of supervisory institutions impacts the enforcement of financial regulation. Banks under supranational supervision are required to increase regulatory capital for exposures to the same firm compared to banks under the local supervisor. Local supervisors provide preferential treatment to larger institutes. The central supervisor removes such biases, which results in an overall standardized behavior. While the central supervisor treats banks more equally, we document a loss in information in banks’ risk models associated with central supervision. The tighter supervision of larger banks results in a shift of particularly risky lending activities to smaller banks. We document lower sales and employment for firms receiving most of their funding from banks that receive a tighter supervisory treatment. Overall, the central supervisor treats banks more equally but has less information about them than the local supervisor.
The loan impairment rules recently introduced by IFRS 9 require banks to estimate their future credit losses by using forward-looking information. We use supervisory loan-level data from Germany to investigate how banks apply their reporting discretion and adjust their lending upon the announcement of the new rules. Our identification strategy exploits a cut-off for the level of provisions at the investment grade threshold based on banks’ internal rating of a borrower. We find that banks required to adopt the new rules assign better internal ratings to exactly the same borrowers compared to banks that do not apply IFRS 9 around this cut-off. This pattern is consistent with a strategic use of the increased reporting discretion that is inherent to rules requiring forward-looking loss estimation. At the same time, banks also reduce their lending exposure to exactly those borrowers at the highest risk of experiencing a rating downgrade below the cutoff. These loans would be associated with additional provisions in future periods, both in the intensive and extensive margin. The lending change thus mitigates some of the negative effects of increased reporting opportunism on banks’ crisis resilience. However, when these firms with internal ratings around the investment grade cut-off obtain less external funding through banks, the introduction of IFRS 9 will likely also be associated with real economic effects
he ECB is independent, but it is also accountable to the European parliament (EP). Yet, how the EP has held the ECB accountable has largely been overlooked. This paper starts addressing this gap by providing descriptive statistics of three accountability modalities. The paper highlights three findings. First, topics of accountability have changed. Climate-related accountability has increased quickly and dramatically since 2017. Second, if the relationship between price stability and climate change remains an object of conflict among MEPs, a majority within the EP has emerged to put pressure for the ECB to take a more active stance against climate change, precisely on behalf of its price stability mandate. Third, MEPs engage with the climate topic in very specific ways. There is a gender divide between the climate and the price stability topics. Women engage more actively with climate-related topics. While the Greens heavily dominate the climate topic, parties from the Right dominate the topic of Price stability. Finally, MEPs adopt a more united strategy and a particularly low confrontational tone in their climate-related interventions.
The authors estimate perceptions about the Fed's monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions is time-varying and cyclical: high during tightening episodes but low during easings. Forecasters update their perceptions about the policy rule in response to monetary policy actions, measured by high-frequency interest rate surprises, suggesting that forecasters have imperfect information about the rule. The perceived rule impacts asset prices crucial for monetary policy transmission, driving how interest rates respond to macroeconomic news and explaining term premia in long-term interest rates.
When the COVID-19 crisis struck, banks using internal-rating based (IRB) models quickly recognized the increase in risk and reduced lending more than banks using a standardized approach. This effect is not driven by borrowers’ quality or by banks in countries with credit booms before the pandemic. The higher risk sensitivity of IRB models does not always result in lower credit provision when risk intensifies. Certain features of the IRB models – the use of a downturn Loss Given Default parameter – can increase banks’ resilience and preserve their intermediation capacity also during downturns. Affected borrowers were not able to fully insulate and decreased corporate investments.
Many nations incentivize retirement saving by letting workers defer taxes on pension contributions, imposing them when retirees withdraw their funds. Using a dynamic life cycle model, we show how ‘Rothification’ – that is, taxing 401(k) contributions rather than payouts – alters saving, investment, consumption, and Social Security claiming patterns. We find that taxing pension contributions instead of withdrawals leads to delayed retirement, somewhat lower lifetime tax payments, and relatively small reductions in consumption. Indeed, the two tax regimes generate quite similar relative inequality metrics: the relative consumption inequality ratio under TEE is only four percent higher than in the EET case. Moreover, results indicate that the Gini measures are also strikingly similar under the EET and the TEE regimes for lifetime consumption, cash on hand, and 401(k) assets, differing by only 1-4 percent. While tax payments are higher early in life under the TEE regime, they are slightly lower in the long run. Moreover, higher EET tax payments are also accompanied by higher volatility. We therefore find few reasons for policymakers to favor either tax approach on egalitarian or revenue-enhancing grounds.
Many people do not understand the concepts of life expectancy and longevity risk, potentially leading them to under-save for retirement or to not purchase longevity insurance, which in turn could reduce wellbeing at older ages. We investigate alternative ways to increase the salience of both concepts, allowing us to assess whether these change peoples’ perceptions and financial decision making. Using randomly-assigned vignettes providing subjects with information about either life expectancy or longevity, we show that merely prompting people to think about financial decisions changes their perceptions regarding subjective survival probabilities. Moreover, this information also boosts respondents’ interest in saving and demand for longevity insurance. In particular, longevity information influences both subjective survival probabilities and financial decisions, while life expectancy information influences only annuity choices. We provide some evidence that many people are simply unaware of longevity risk.
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.
Lack of privacy due to surveillance of personal data, which is becoming ubiquitous around the world, induces persistent conformity to the norms prevalent under the surveillance regime. We document this channel in a unique laboratory---the widespread surveillance of private citizens in East Germany. Exploiting localized variation in the intensity of surveillance before the fall of the Berlin Wall, we show that, at the present day, individuals who lived in high-surveillance counties are more likely to recall they were spied upon, display more conformist beliefs about society and individual interactions, and are hesitant about institutional and social change. Social conformity is accompanied by conformist economic choices: individuals in high-surveillance counties save more and are less likely to take out credit, consistent with norms of frugality. The lack of differences in risk aversion and binding financial constraints by exposure to surveillance helps to support a beliefs channel.
The leading premium
(2022)
In this paper, we consider conditional measures of lead-lag relationships between aggregate growth and industry-level cash-flow growth in the US. Our results show that firms in leading industries pay an average annualized return 3.6\% higher than that of firms in lagging industries. Using both time series and cross sectional tests, we estimate an annual pure timing premium ranging from 1.2% to 1.7%. This finding can be rationalized in a model in which (a) agents price growth news shocks, and (b) leading industries provide valuable resolution of uncertainty about the growth prospects of lagging industries.
Previous studies document a relationship between gambling activity at the aggregate level and investments in securities with lottery-like features. We combine data on individual gambling consumption with portfolio holdings and trading records to examine whether gambling and trading act as substitutes or complements. We find that gamblers are more likely than the average investor to hold lottery stocks, but significantly less likely than active traders who do not gamble. Our results suggest that gambling behavior across domains is less relevant compared to other portfolio characteristics that predict investing in high-risk and high-skew securities, and that gambling on and off the stock market act as substitutes to satisfy the same need, e.g., sensation seeking.
We develop a two-sector incomplete markets integrated assessment model to analyze the effectiveness of green quantitative easing (QE) in complementing fiscal policies for climate change mitigation. We model green QE through an outstanding stock of private assets held by a monetary authority and its portfolio allocation between a clean and a dirty sector of production. Green QE leads to a partial crowding out of private capital in the green sector and to a modest reduction of the global temperature by 0.04 degrees of Celsius until 2100. A moderate global carbon tax of 50 USD per tonne of carbon is 4 times more effective.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Socially responsible investing (SRI) continues to gain momentum in the financial market space for various reasons, starting with the looming effect of climate change and the drive toward a net-zero economy. Existing SRI approaches have included environmental, social, and governance (ESG) criteria as a further dimension to portfolio selection, but these approaches focus on classical investors and do not account for specific aspects of insurance companies. In this paper, we consider the stock selection problem of life insurance companies. In addition to stock risk, our model set-up includes other important market risk categories of insurers, namely interest rate risk and credit risk. In line with common standards in insurance solvency regulation, such as Solvency II, we measure risk using the solvency ratio, i.e. the ratio of the insurer’s market-based equity capital to the Value-at-Risk of all modeled risk categories. As a consequence, we employ a modification of Markowitz’s Portfolio Selection Theory by choosing the “solvency ratio” as a downside risk measure to obtain a feasible set of optimal portfolios in a three-dimensional (risk, return, and ESG) capital allocation plane. We find that for a given solvency ratio, stock portfolios with a moderate ESG level can lead to a higher expected return than those with a low ESG level. A highly ambitious ESG level, however, reduces the expected return. Because of the specific nature of a life insurer’s business model, the impact of the ESG level on the expected return of life insurers can substantially differ from the corresponding impact for classical investors.
This paper utilizes a comprehensive worker-firm panel for the Netherlands to quantifythe impact of ICT capital-skill complementarity on the finance wage premium after the Global Financial Crisis. We apply additive worker and firm fixed-effect models to account for unobserved worker- and firm-heterogeneity and show that firm fixed-effects correct for a downward bias in the estimated finance wage premium. Our results indicate a sizable finance wage premium for both fixed- and full-hourly wages. The complementarity between ICT capital spending and the share of high skill workers at the firm-level reduces the full-wage premium considerably and the fixed-wage premium almost entirely.
SAFE Update October 2022
(2022)
SAFE Update August 2022
(2022)
We investigate the link between Big Five personality traits and the marginal propensity to consume (MPC) for users of a German financial account aggregator app. We use 1,700 survey responses and transaction data of 56,000 app users to assess whether Big Five personality traits help explain MPC heterogeneity. We find that extraversion corresponds to an increase in consumption whereas agreeableness and neuroticism correspond to a decrease in consumption. We test this with trust and risk preferences and find that risk indicates more explanatory power in consumption response than the Big Five. Our findings help policy makers target individuals more efficiently.
Conditional yield skewness is an important summary statistic of the state of the economy. It exhibits pronounced variation over the business cycle and with the stance of monetary policy, and a tight relationship with the slope of the yield curve. Most importantly, variation in yield skewness has substantial forecasting power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and consensus survey forecast errors for the ten-year Treasury yield. The COVID pandemic did not disrupt these relations: historically high skewness correctly anticipated the run-up in long-term Treasury yields starting in late 2020. The connection between skewness, survey forecast errors, excess returns, and departures of yields from normality is consistent with a theoretical framework where one of the agents has biased beliefs.
The authors present evidence of a new propagation mechanism for wealth inequality, based on differential responses, by education, to greater inequality at the start of economic life. The paper is motivated by a novel positive cross-country relationship between wealth inequality and perceptions of opportunity and fairness, which holds only for the more educated. Using unique administrative micro data and a quasi-field experiment of exogenous allocation of households, the authors find that exposure to a greater top 10% wealth share at the start of economic life in the country leads only the more educated placed in locations with above-median wealth mobility to attain higher wealth levels and position in the cohort-specific wealth distribution later on. Underlying this effect is greater participation in risky financial and real assets and in self-employment, with no evidence for a labor income, unemployment risk, or human capital investment channel. This differential response is robust to controlling for initial exposure to fixed or other time-varying local features, including income inequality, and consistent with self-fulfilling responses of the more educated to perceived opportunities, without evidence of imitation or learning from those at the top.
The authors identify U.S. monetary and fiscal dominance regimes using machine learning techniques. The algorithms are trained and verified by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning methods outperform a standard logistic regression concerning the simulated data. Among those the Boosted Ensemble Trees classifier yields the best results. The authors find clear evidence of fiscal dominance before Volcker. Monetary dominance is detected between 1984-1988, before a fiscally led regime turns up around the stock market crash lasting until 1994. Until the beginning of the new century, monetary dominance is established, while the more recent evidence following the financial crisis is mixed with a tendency towards fiscal dominance.
This note argues that the European Central Bank should adjust its strategy in order to consider broader measures of inflation in its policy deliberations and communications. In particular, it points out that a broad measure of domestic goods and services price inflation such as the GDP deflator has increased along with the euro area recovery and the expansion of monetary policy since 2013, while HICP inflation has become more variable and, on average, has declined. Similarly, the cost of owner-occupied housing, which is excluded from the HICP, has risen during this period. Furthermore, it shows that optimal monetary policy at the effective lower bound on nominal interest rates aims to return inflation more slowly to the inflation target from below than in normal times because of uncertainty about the effects and potential side effects of quantitative easing.
Green finance upside down
(2021)
Using hand-collected data on CEO appointments during shareholder activism campaigns, this study examines whether shareholder involvement in CEO recruiting affects frictions in CEO hiring decisions. The results indicate that appointments of CEOs who are recruited with shareholder activist influence are followed by more favorable stock market reactions and stronger profitability improvements than CEO appointments that also occur during activism campaigns but without the influence of activists. I find little evidence that shareholder activists increase hiring frictions by facilitating the recruiting of CEOs who will implement myopic corporate policies. Analyses of recruiting process characteristics reveal that activist influence is associated with more resources being dedicated to the CEO search process and with a higher propensity to recruit CEOs from outside the firm. These findings contribute to the CEO labor market literature, which tends to focus on the decision to remove incumbent CEOs but provides limited insights into CEO recruiting.
We study the design features of disclosure regulations that seek to trigger the green transition of the global economy and ask whether such regulatory interventions are likely to bring about sufficient market discipline to achieve socially optimal climate targets.
We categorize the transparency obligations stipulated in green finance regulation as either compelling the standardized disclosure of raw data, or providing quality labels that signal desirable green characteristics of investment products based on a uniform methodology. Both categories of transparency requirements can be imposed at activity, issuer, and portfolio level.
Finance theory and empirical evidence suggest that investors may prefer “green” over “dirty” assets for both financial and non-financial reasons and may thus demand higher returns from environmentally-harmful investment opportunities. However, the market discipline that this negative cost of capital effect exerts on “dirty” issuers is potentially attenuated by countervailing investor interests and does not automatically lead to socially optimal outcomes.
Mandatory disclosure obligations and their (public) enforcement can play an important role in green finance strategies. They prevent an underproduction of the standardized high-quality information that investors need in order to allocate capital according to their preferences. However, the rationale behind regulatory intervention is not equally strong for all categories and all levels of “green” disclosure obligations. Corporate governance problems and other agency conflicts in intermediated investment chains do not represent a categorical impediment for green finance strategies.
However, the many forces that may prevent markets from achieving socially optimal equilibria render disclosure-centered green finance legislation a second best to more direct forms of regulatory intervention like global carbon taxation and emissions trading schemes. Inherently transnational market-based green finance concepts can play a supporting role in sustainable transition, which is particularly important as long as first-best solutions remain politically unavailable.
Extant research shows that CEO characteristics affect earnings management. This paper studies how investors infer a specific characteristic of CEOs, namely moral commitment to honesty, from earnings management and how this perception – in conjunction with their own social and moral preferences – shapes their investment choices. We conduct two laboratory experiments simulating investment choices. Our results show that participants perceive a CEO to be more committed to honesty when they infer that the CEO engaged less in earnings management. For investment decisions, a one standard deviation increase in a CEO's perceived commitment to honesty compared to another CEO reduces the relevance of differences in the CEOs’ claimed future returns by 40%. This effect is most prominent among investors with a proself value orientation. To prosocial investors, their own honesty values and those attributed to the CEO matter directly, while returns play a secondary role. Overall, perceived CEO honesty matters to different investors for distinct reasons.
This paper argues that the key mechanisms protecting retail investors’ financial stake in their portfolio investments are indirect. They do not rely on actions by the investors or by any private actor directly charged with looking after investors’ interests. Rather, they are provided by the ecosystem that investors (are legally forced to) inhabit, as a byproduct of the mostly self-interested, mutually and legally constrained behavior of third parties without a mandate to help the investors (e.g., speculators, activists). This elucidates key rules, resolves the mandatory vs. enabling tension in corporate/securities law, and exposes passive investing’s fragile reliance on others’ trading.
Increasing the diversity of policy committees has taken center stage worldwide, but whether and why diverse committees are more effective is still unclear. In a randomized control trial that varies the salience of female and minority representation on the Federal Reserve’s monetary policy committee, the FOMC, we test whether diversity affects how Fed information influences consumers’ subjective beliefs. Women and Black respondents form unemployment expectations more in line with FOMC forecasts and trust the Fed more after this intervention. Women are also more likely to acquire Fed-related information when associated with a female official. White men, who are overrepresented on the FOMC, do not react negatively. Heterogeneous taste for diversity can explain these patterns better than homophily. Our results suggest more diverse policy committees are better able to reach underrepresented groups without inducing negative reactions by others, thereby enhancing the effectiveness of policy communication and public trust in the institution.
More European, more uniform
(2021)
The US Tax Cuts and Jobs Act (TCJA) led to a drastic reduction in the corporate tax and improved the treatment of C corporations compared to S corporations. We study the differential effect of the TCJA on these types of corporations using key economic variables of US banks, such as the number of employees, average salaries and benefits, profit/loss before taxes, and net income. Our analysis suggests that the TCJA increased the net-of-tax profits of C corporation banks compared to S corporations and, to a lesser extent, their pre-tax profits. At the same time, the reform triggered no significantly differential effect on the employment and average wages.
Our starting point is the following simple but potentially underappreciated observation: When assessing willingness to pay (WTP) for hedonic features of a product, the results of such measurement are influenced by the context in which the consumer makes her real or hypothetical choice or in which the questions to which she replies are set (such as in a contingent valuation analysis). This observation is of particular relevance when WTP regards sustainability, the “non-use value” of which does not derive from a direct (physical) sensation and where perceived benefits depend heavily on available information and deliberations. The recognition of such context sensitivity paves the way for a broader conception of consumer welfare (CW), and our proposed standard of “reflective WTP” may materially change the scope for private market initiatives with regards to sustainability, while keeping the analytical framework within the realm of the CW paradigm. In terms of practical implications, we argue, for instance, that actual purchasing decisions may prove insufficient to measure consumer appreciation of sustainability, as they may rather echo learnt but unreflected heuristics and may be subject to the specific shopping context, such as heavy price promotions. Also, while it may reflect current social norm, the latter may change considerably over time as more consumers adopt their behavior.
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.
Si bien el discurso sobre los derechos humanos se volvió fundamental para desafiar la austeridad en el período que siguió a la Gran Crisis Financiera desde una perspectiva histórica, el que los derechos humanos desempeñen este papel es más la excepción que la regla. El discurso en materia de derechos humanos en el contexto de la austeridad inducida por la deuda soberana ha variado mucho con el tiempo. Lejos de mostrar progreso, su historia revela los cambios del paradigma del derecho de los derechos humanos. Las páginas que siguen hacen foco en uno de esos cambios, ocurrido en la transición entre las décadas de los setenta y ochenta. En la década de los setenta, los Estados recientemente independizados invocaban los derechos humanos en especial para afirmar su soberanía y alejar la interferencia internacional. El paradigma estructural sobre derechos humanos desapareció abruptamente de los debates sobre austeridad en la década de los ochenta, cuando la crisis de deuda soberana golpeó al Sur Global y creó la necesidad de asistencias multilaterales para obtener liquidez. Frente a la presión de reconsiderar el impacto social de los programas de ajuste estructural que promovía, el Fondo Monetario Internacional desplazó los términos del debate y en lugar de hablar de “necesidades humanas”, un término relacionado con los derechos humanos, pasó a hablar de “capital humano”. En consecuencia, en el momento en que los derechos humanos adquirían el estatus de “última utopía”, dejaron de tener relevancia para la austeridad. Por lo tanto, que el discurso sobre los derechos humanos promueva o no los objetivos sociales dependerá del contexto y del momento histórico. El artículo culmina con una propuesta de paradigma político del derecho de los derechos humanos que refleja estas perspectivas.
Historically Central Bank Independence (CBI) was anything but the norm. CBI seems to contradict core principles of democracy. Most economists were also against CBI. After the Great Inflation of the 1970ies many empirical studies demonstrated that there is a strong negative correlation between the degree of CBI and the rate of inflation. In 1990 most major countries had endowed their central bank with the status of independence. Overburdening with elevated expectations and additional competences are threatening the reputation of central banks and undermining the case for CBI.
SAFE Update October 2021
(2021)
We identify strong cross-border institutions as a driver for the globalization of in-novation. Using 67 million patents from over 100 patent offices, we introduce novel measures of innovation diffusion and collaboration. Exploiting staggered bilateral in-vestment treaties as shocks to cross-border property rights and contract enforcement, we show that signatory countries increase technology adoption and sourcing from each other. They also increase R&D collaborations. These interactions result in techno-logical convergence. The effects are particularly strong for process innovation, and for countries that are technological laggards or have weak domestic institutions. Increased inter-firm rather than intra-firm foreign investment is the key channel.
SAFE Update December 2021
(2021)
SAFE Update August 2021
(2021)