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We empirically examine the Capital Purchase Program (CPP) used by the US gov- ernment to bail out distressed banks with equity infusions during the Great Recession. We find strong evidence that a feature of the CPP – the government’s ability to ap- point independent directors on the board of an assisted bank that missed six dividend payments to the Treasury – helped attenuate bailout-related moral hazard. Banks were averse to these appointments – the empirical distribution of missed payments exhibits a sharp discontinuity at five. Director appointments by the Treasury led to improved bank performance, lower CEO pay, and higher stock market valuations.
This paper explores the interplay of feature-based explainable AI (XAI) tech- niques, information processing, and human beliefs. Using a novel experimental protocol, we study the impact of providing users with explanations about how an AI system weighs inputted information to produce individual predictions (LIME) on users’ weighting of information and beliefs about the task-relevance of information. On the one hand, we find that feature-based explanations cause users to alter their mental weighting of available information according to observed explanations. On the other hand, explanations lead to asymmetric belief adjustments that we inter- pret as a manifestation of the confirmation bias. Trust in the prediction accuracy plays an important moderating role for XAI-enabled belief adjustments. Our results show that feature-based XAI does not only superficially influence decisions but re- ally change internal cognitive processes, bearing the potential to manipulate human beliefs and reinforce stereotypes. Hence, the current regulatory efforts that aim at enhancing algorithmic transparency may benefit from going hand in hand with measures ensuring the exclusion of sensitive personal information in XAI systems. Overall, our findings put assertions that XAI is the silver bullet solving all of AI systems’ (black box) problems into perspective.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
We define a sentiment indicator that exploits two contrasting views of return predictability, and study its properties. The indicator, which is based on option prices, valuation ratios and interest rates, was unusually high during the late 1990s, reflecting dividend growth expectations that in our view were unreasonably optimistic. We interpret it as helping to reveal irrational beliefs about fundamentals. We show that our measure is a leading indicator of detrended volume, and of various other measures associated with financial fragility. We also make two methodological contributions. First, we derive a new valuation-ratio decomposition that is related to the Campbell and Shiller (1988) loglinearization, but which resembles the traditional Gordon growth model more closely and has certain other advantages for our purposes. Second, we introduce a volatility index that provides a lower bound on the market's expected log return.
The pricing of an ambiguous asset, whose cash flow stream is uncertain, may be affected by three factors: the belief regarding the realization likelihood of cash flows, the subjective attitude towards risk, and the attitude towards ambiguity. While previous literature looks at the total price discount under ambiguity, this paper investigates with laboratory experiments how much effect each factor can induce. We apply both non-parametric and parametric methods to cleanly separate the belief effects, the risk premiums, and the ambiguity premiums from each other. Both methods lead to similar results: Overall, subjects have substantial ambiguity aversion, and ambiguity premiums account for the largest price deviation component when the degree of ambiguity is high. As information accumulates, ambiguity premiums decrease. We also find that beliefs do influence prices under ambiguity. This is not because beliefs are biased towards either good or bad scenarios per se, but because subjects display sticky belief updating as new information becomes available. The clear separation performed in this paper between belief and attitude also enables a more accurate estimation of the parameter of ambiguity aversion compared to previous studies, since the effect of beliefs is partialled out. Overall, we find empirically that both factors, belief and attitude towards ambiguity, are important factors in pricing under ambiguity.
The salience of ESG ratings for stock pricing: evidence from (potentially) confused investors
(2021)
We exploit the a modification to Sustainanlytics’ environmental, social, and governance (ESG) rating methodology, which is subsequently adopted by Morningstar, to study whether ESG ratings are salient for stock pricing. We show that the inversion of the rating scale but not new information leads some investors to make incorrect assessments about the meaning of the change in ESG ratings. They buy (sell) stocks they misconceive as ESG upgraded (downgraded) even when the opposite is true. This trading behavior exerts transitory price pressure on affected stocks. Our paper highlights the importance of ESG ratings for investors and consequently for asset prices.
Dieser Artikel behandelt das Zusammenspiel von staatlich organisierten sozialen Sicherungssystemen und der privaten Eigenvorsorge durch Vermögensbildung als Grundpfeiler der sozialen Marktwirtschaft in Deutschland. Die jährlichen Ausgaben der verschiedenen staatlichen Sicherungssysteme belaufen sich auf rund ein Drittel des erwirtschafteten Bruttosozialprodukts, wobei die umlagefinanzierten Alterssicherungssysteme für die Arbeitsnehmer den größten Anteil ausmachen. Sachvermögen in Form von selbst genutzten Wohnungen sowie Finanzvermögen in Form von Bankeinlagen und Ansprüche gegen private Versicherungen machen den größten Anteil der Eigenversorge aus. Aufgrund des niedrigen Zinsniveaus sowie des demografischen Wandels der Gesellschaft wird die Eigenvorsorge durch Anlagen an den internationalen Wertpapiermärkten sowohl für Selbständige als auch Arbeitsnehmer immer bedeutender.
We analyze the joint dynamics of prices, productivity, and employment across firms, building a dynamic equilibrium model of heterogeneous firms who compete for workers and customers in frictional labor and product markets. Using panel data on prices and output for German manufacturing firms, the model is calibrated to evaluate the quantitative contributions of productivity and demand for the labor market. Product market frictions decisively dampen the firms' employment adjustments to productivity shocks. We further analyze the impact of aggregate shocks to the first and second moments of productivity and demand and relate them to business-cycle features in our data.
Green finance upside down
(2021)
When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well-suited. Such tools aim to lower the burden to set privacy preferences according to owners’ privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations – such as the General Data Protection Regulation (GDPR) in Europe – in this paper an explainable model for default privacy setting prediction is introduced. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model’s design before it goes into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
More European, more uniform
(2021)
Die Distributed Ledger- bzw. Blockchain-Technologie führt zu einer zunehmenden Dezentralisierung von Finanzdienstleistungen („Decentralised Finance“), die weitgehend ohne die Einschaltung von Finanzintermediären angeboten werden können. Dazu trägt wesentlich die sog. „Tokenisierung“ von Vermögensgegenständen, Zahlungsmitteln und Rechten bei, die verschlüsselt als „Kryptowerte“ in verteilten Transaktionsregistern digital abgebildet werden können. Der vorliegende Beitrag erläutert die Grundlagen und Anwendungsfelder dezentraler Finanzdienstleistungen mit Kryptowerten, die mittelfristig die gesamte Architektur des Finanzsektors verändern könnten. Dieser Trend betrifft längst nicht nur die kontrovers diskutierten Zahlungsverkehrssysteme mit Kryptowährungen wie dem Bitcoin, sondern Handelsplattformen, Kapitalmärkte oder Unternehmensfinanzierungen. Es bildet sich ein rasch wachsendes Ökosystem aus Startups, Technologieunternehmen und etablierten Finanzdienstleistern, für das jedoch noch ein verlässlicher regulatorischer Rahmen fehlt. Die derzeit auf europäischer Ebene diskutierte Initiative „MiCA (Markets in Crypto Assets)“ geht in die richtige Richtung, sollte aber im Interesse der Wettbewerbsfähigkeit des europäischen Finanzsektors zeitnah umgesetzt werden.
WiWi news [1/2021]
(2021)
We show that financial advisors recommend more costly products to female clients, based on minutes from about 27,000 real-world advisory meetings and client portfolio data. Funds recommended to women have higher expense ratios controlling for risk, and women less often receive rebates on upfront fees for any given fund. We develop a model relating these findings to client stereotyping, and empirically verify an additional prediction: Women (but not men) with higher financial aptitude reject recommendations more frequently. Women state a preference for delegating financial decisions, but appear unaware of associated higher costs. Evidence of stereotyping is stronger for male advisors.
The recently observed disconnect between inflation and economic activity can be explained by the interplay between the zero lower bound (ZLB) and the costs of external financing. In normal times, credit spreads and the nominal interest rate balance out; factor costs dominate firms' marginal costs. When nominal rates are constrained, larger spreads can more than offset the effect of lower factor costs and induce only moderate inflation responses. The Phillips curve is hence flat at the ZLB, but features a positive slope in normal times and thus a hockey stick shape. Via this mechanism, forward guidance may induce deflationary effects.
We conducted a large-scale household survey in November 2020 to study how altering the time frame of a message (temporal framing) regarding an imminent positive income shock affects consumption plans. The income shock derives from the abolishment of the German solidarity surcharge on personal income taxes, effective in January 2021. We randomize across survey participants whether their extra disposable income is presented in Euros per month, Euros per year, or Euros per ten year-period. Our main findings are as follows: In General, we find our respondents’ intended Marginal Propensity to Consume (MPC) is 28.2%. Across all three treatments, the MPC is a positive function of age and being female while it is a negative function of the income increase’s size, self- control, and being unemployed. Temporal framing effects are statistically and economically highly significant as we find the monthly treatment groups’ average MPC 5.6 and 8.7 percentage points higher compared to the yearly and 10-yearly treatment groups. We will be able to analyze the real consumption behavior of households throughout 2021 based on re-surveying the participants as well as by using transaction-based bank data.
The authors examine the effectiveness of labor cost reductions as a means to stimulate economic activity and assesses the differences which may occur with the prevailing exchange rate regime. They develop a medium-scale three-region DSGE model and show that the impact of a cut in the employers’ social security contributions rate does not vary significantly under different exchange rate regimes. They find that both the interest rate and the exchange rate channel matters. Furthermore, the measure appears to be effective even if it comes along with a consumption tax increase to preserve long-term fiscal sustainability.
Finally, they assess whether obtained theoretical results hold up empirically by applying the local projection method. Regression results suggest that changes in employers’ social security contributions rates have statistically significant real effects – a one percentage point reduction leads to an average cumulative rise in output of around 1.3 percent in the medium term. Moreover, the outcome does not differ significantly across the different exchange rate regimes.
How people form beliefs is crucial for understanding decision-making un- der uncertainty. This is particularly true in a situation such as a pandemic, where beliefs will affect behaviors that impact public health as well as the aggregate economy. We conduct two survey experiments to shed light on potential biases in belief formation, focusing in particular on the tone of information people choose to consume and how they incorporate this information into their beliefs. In the first experiment, people express their preferences over pandemic-related articles with optimistic and pessimistic headlines, and are then randomly shown one of the articles. We find that respondents with more pessimistic prior beliefs about the pandemic are substantially more likely to prefer pessimistic articles, which we interpret as evidence of confirmation bias. In line with this, respondents assigned to the less preferred article rate it as less reliable and informative (relative to those who prefer it); they also discount information from the article when it is less preferred. We further find that these motivated beliefs end up impacting incentivized behavior. In a second experiment, we study how partisan views interact with information selection and processing. We find strong evidence of source dependence: revealing the news source further distorts information acquisition and processing, eliminating the role of prior beliefs in article choice.
We assess the effect and the timing of the corporate arm of the ECB quantitative easing (CSPP) on corporate bond issuance. Because of several contemporaneous measures, to isolate the programme effects we rely on one key eligibility feature: the euro denomination of newly issued bonds. We find that the significant increase in bonds issuance by eligible firms is due to the CSPP and that this effect took at least six months to unfold. This result holds even when comparing firms with similar ratings, thus providing evidence that unconventional monetary policy can foster a financing diversification regardless of firms’ risk profile. We also highlight the impact of the programme on the real economic activity. The evidence suggests that while all firms increased investment in capital expenditures and intangible assets, the CSPP induced eligible firms to invest in marketable and equity securities, to repurchase their own stocks, to hold cash and to carry out short-term investment.