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We document the structure of firm-bank relationships across the eleven largest euro area countries and present new stylised facts using novel data from the recent credit registry of the Eurosystem - AnaCredit. We look at the number of banking relationships, reliance on the main bank, credit instruments, loan maturity and interest rates. The granularity of the data allows us to account for cross country differences in firm characteristics. Firms in Southern European countries borrow from a larger number of banks and obtain a lower share of credit from the main bank compared to those in Northern European countries. They also tend to borrow more on short term, more expensive instruments and to obtain loans with shorter maturity. This is consistent with the hypothesis that Southern European countries rely less on relationship banking and obtain credit less conducive to firm growth, in line with the smaller average size of Southern European firms. Instead, no clear pattern emerges in terms of interest rates, consistent with the idea that banks appropriate part of the surplus generated by relationship lending through higher rates.
Recent regulatory measures such as the European Union’s AI Act re-quire artificial intelligence (AI) systems to be explainable. As such, under-standing how explainability impacts human-AI interaction and pinpoint-ing the specific circumstances and groups affected, is imperative. In this study, we devise a formal framework and conduct an empirical investiga-tion involving real estate agents to explore the complex interplay between explainability of and delegation to AI systems. On an aggregate level, our findings indicate that real estate agents display a higher propensity to delegate apartment evaluations to an AI system when its workings are explainable, thereby surrendering control to the machine. However, at an individual level, we detect considerable heterogeneity. Agents possess-ing extensive domain knowledge are generally more inclined to delegate decisions to AI and minimize their effort when provided with explana-tions. Conversely, agents with limited domain knowledge only exhibit this behavior when explanations correspond with their preconceived no-tions regarding the relationship between apartment features and listing prices. Our results illustrate that the introduction of explainability in AI systems may transfer the decision-making control from humans to AI under the veil of transparency, which has notable implications for policy makers and practitioners that we discuss.
We analyze the repercussions of different kinds of uncertainty on cash demand, including uncertainty of the digital infrastructures, confidence crises of the financial system, natural disasters, political uncertainties, and inflationary crises. Based on a comprehensive literature survey, theoretical considerations and complemented by case studies, we derive a classification scheme how cash holdings typically evolve in each of these types of uncertainty by separating between demand for domestic and international cash as well as between transaction and store of value balances. Hereby, we focus on the stabilizing macroeconomic properties of cash and recommend guidelines for cash supply by central banks and the banking system. Finally, we exemplify our analysis with five case studies from the developing world, namely Venezuela, Zimbabwe, Afghanistan, Iraq, and Libya.
This literature survey explores the potential avenues for the design of a green auto asset-backed security by focusing on the European auto securitization market. In this context, we examine the entire value chain of the securitization process to understand the incentives and interests involved at various stages of the transaction. We review recent regulatory developments, feasibility concerns, and potential designs of a sustainable securitization framework. Our study suggests that a Green Auto ABS should be based on both a green use of proceeds and a green collateral-based methodology.
We provide evidence on the extent to which survey items in the Preference Survey Module and the resulting Global Preference Survey measuring social preferences − trust, altruism, positive and negative reciprocity − predict behavior in corresponding experimental games outside the original participant sample of Falk et al. (2022). Our results, which are based on a replication study with university students in Tehran, Iran, are mixed. While quantitative items considering hypothetical versions of the experimental games correlate significantly and economically meaningfully with individual behavior, none of the qualitative items show significant correlations. The only exception is altruism where results correspond more closely to the original findings.
We develop a quantity-driven general equilibrium model that integrates the term structure of interest rates with the repurchase agreements (repo) market to shed light on the com-bined effects of quantitative easing (QE) on the bond and money markets. We characterize in closed form the endogenous dynamic interaction between bond prices and repo rates, and show (i) that repo specialness dampens the impact of any given quantity of asset pur-chases due to QE on the slope of the term structure and (ii) that bond scarcity resulting from QE increases repo specialness, thus strengthening the local supply channel of QE.
In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries.
The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.
Climate change has become one of the most prominent concerns globally. In this paper, the authors study the transition risk of greenhouse gas emission reduction in structural environmental-macroeconomic DSGE models. First, they analyze the uncertainty in model prediction on the effect of unanticipated and pre-announced carbon price increases. Second, they conduct optimal model-robust policy in different settings. They find that reducing emissions by 40% causes 0.7% to 4% output loss with 2% on average. Pre-announcement of carbon prices affects the inflation dynamics significantly. The central bank should react slightly less to inflation and output growth during the transition risk. With optimal carbon price designs, it should react even less to inflation, and more to output growth.
We analyze the performance of marketplace lending using loan cash flow data from the largest platform, Lending Club. We find substantial risk-adjusted performance of about 40 basis points per month for the entire loan portfolio. Other loan portfolios grouped by risk category have similar risk-adjusted performance. We show that characteristics of the local bank sector for each loan, such as concentration of deposits and the presence of national banks, are related to the performance of loans. Thus, marketplace lending has the potential to finance a growing share of the consumer credit market in the absence of a competitive response from the traditional incumbents.
The discount control mechanisms that closed-end funds often choose to adopt before IPO are supposedly implemented to narrow the difference between share price and net asset value. We find evidence that non-discretionary discount control mechanisms such as mandatory continuation votes serve as costly signals of information to reveal higher fund quality to investors. Rents of the skill signaled through the announcement of such policies accrue to managers rather than investors as differences in skill are revealed through growing assets under management rather than risk- adjusted performance.
Armstrong et al. (2022) review the empirical methods used in the accounting literature to draw causal inferences. They document a growing number of studies using quasi-experimental methods and provide a critical perspective on this trend as well as the use of these methods in the accounting literature. In this discussion, I complement their review by broadening the perspective. I argue for a design-based approach to accounting research that shifts attention from methods to the entire research design. I also discuss why studies that aim to draw causal inferences are important, how these studies fit into the scientific process, and why assessing the strength of the research design is important when evaluating studies and aggregating research findings.
Retained earnings and foreign portfolio ownership: implications for the current account debate
(2023)
In some countries, a sizable fraction of savings is derived from corporate savings. Although larger, traded corporations are often co-owned by foreign portfolio investors, current international accounting standards allocate all corporate savings to the host country. This paper suggests a framework to correct for this misleading attribution and applies this concept to Germany. For the years 2012 to 2020, our corrections retrospectively reduce German savings and consequently the German current account surplus by, on average, €11.5bn annually. This amounts to approximately five percent of Germany’s average official current account surplus (€226.6bn) across these years.
We find that high macroeconomic uncertainty is associated with greater accumulation of physical capital, despite a reduction in investment and valuations. To reconcile this puzzling evidence, we show that uncertainty predicts lower depreciation and utilization of existing capital, which dominates the investment slowdown. Motivated by these dynamics, we develop a quantitative production-based model in which firms implement precautionary savings through reducing utilization rather than raising invest-ment. Through this novel intensive-margin mechanism, uncertainty shocks command a quarter of the equity premium in general equilibrium, while flexibility in utilization adjustments helps explain uncertainty risk exposures in the cross-section of industry returns.
We assemble a data set of more than eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. The approach also allows us to separate this index into uncertainty about sanctions against Russia, energy policy and other dimensions. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.
Flows of funds run by banks or by firms that belong to the same financial group as a bank are less volatile and less sensitive to bad past performance. This enables bank-affiliated funds to better weather distress and to hold lower precautionary cash buffers in comparison with their unaffiliated peers. Banks provide liquidity support to distressed affiliated funds by buying shares of those funds that are experiencing large outflows. This, in turn, diminishes the severity of strategic complementarities in investors’ redemptions. Liquidity support and other benefits of bank affiliation are conditional on the financial health of the parent company. Distress in the banking system spills over to the mutual fund sector via ownership links. Our research high-lights substantial dependencies between the banking system and the asset management industry, and identifies an important channel via which financial stability risks depend on the organisational structure of the financial sector.
Fund companies regularly send shareholder letters to their investors. We use textual analysis to investigate whether these letters’ writing style influences fund flows and whether it predicts performance and investment styles. Fund investors react to the tone and content of shareholder letters: A less negative tone leads to higher net flows. Thus, fund companies can use shareholder letters as a tactical instrument to influence flows. However, at the same time, a dishonest communication that is not consistent with the fund’s actual performance decreases flows. A positive writing style predicts higher idiosyncratic risk as well as more style bets, while there is no consistent predictive power for future performance.
Art-related non-fungible tokens (NFTs) took the digital art space by storm in 2021, generating massive amounts of volume and attracting a large number of users to a previously obscure part of blockchain technology. Still, very little is known about the attributes that influence the price of these digital assets. This paper attempts to evaluate the level of speculation associated with art NFTs, comprehend the characteristics that confer value on them and design a profitable trading strategy based on our findings. We analyze 860,067 art NFTs that have been deployed on the Ethereum blockchain and have been involved in 317,950 sales using machine learning methods to forecast the probability of sale, the trade frequency and the average price. We find that NFTs are highly speculative assets and that their price and recurrence of sale are heavily determined by the floor and the last sale prices, independent of any fundamental value.
Mamma mia! Revealing hidden heterogeneity by PCA-biplot : MPC puzzle for Italy's elderly poor
(2023)
I investigate consumption patterns in Italy and use a PCA-biplot to discover a consumption puzzle for the elderly poor. Data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) indicate that Italian poor old-aged households boast lower levels of the marginal propensity to consume (MPC) than suggested by the dominant consumption models. A customized regression analysis exhibits group differences with richer peers to be only half as large as prescribed by a traditional linear regression model. This analysis has benefited from a visualization technique for high-dimensional matrices related to the unsupervised machine learning literature. I demonstrate that PCA-biplots are a useful tool to reveal hidden relations and to help researchers to formulate simple research questions. The method is presented in detail and suggestions on incorporating it in the econometric modeling pipeline are given.
We investigate consumption patterns in Europe with supervised machine learning methods and reveal differences in age and wealth impact across countries. Using data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS), we assess how age and (liquid) wealth affect the marginal propensity to consume (MPC) in the Netherlands, Germany, France, and Italy. Our regression analysis takes the specification by Christelis et al. (2019) as a starting point. Decision trees are used to suggest alternative variable splits to create categorical variables for customized regression specifications. The results suggest an impact of differing wealth distributions and retirement systems across the studied Eurozone members and are relevant to European policy makers due to joint Eurozone monetary policy and increasing supranational fiscal authority of the EU. The analysis is further substantiated by a supervised machine learning analysis using a random forest and XGBoost algorithm.
Optimal monetary policy studies typically rely on a single structural model and identification of model-specific rules that minimize the unconditional volatilities of inflation and real activity. In their proposed approach, the authors take a large set of structural models and look for the model-robust rules that minimize the volatilities at those frequencies that policymakers are most interested in stabilizing. Compared to the status quo approach, their results suggest that policymakers should be more restrained in their inflation responses when their aim is to stabilize inflation and output growth at specific frequencies. Additional caution is called for due to model uncertainty.
This paper examines how the implementation of a new dark order - Midpoint Extended Life Order on NASDAQ - impacts financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high-frequency order book data and apply panel regression analysis to estimate the effect of M-ELO trading on market stability and liquidity provision. The results suggest a predominance of a speed bump effect of M-ELO rather than a darkness effect. We find that the introduction of M-ELO increases market stability by reducing the average number of mini-flash crashes, but its impact on market quality is mixed.
I have assessed changes in the monetary policy stance in the euro area since its inception by applying a Bayesian time-varying parameter framework in conjunction with the Hamiltonian Monte Carlo algorithm. I find that the estimated policy response has varied considerably over time. Most of the results suggest that the response weakened after the onset of the financial crisis and while quantitative measures were still in place, although there are also indications that the weakening of the response to the expected inflation gap may have been less pronounced. I also find that the policy response has become more forceful over the course of the recent sharp rise in inflation. Furthermore, it is essential to model the stochastic volatility relating to deviations from the policy rule as it materially influences the results.
This paper presents and compares Bernoulli iterative approaches for solving linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. I find that Bernoulli methods compare favorably in solving DSGE models to the QZ, providing similar accuracy as measured by the forward error of the solution at a comparable computation burden. The method can guarantee convergence to a particular, e.g., unique stable, solution and can be combined with other iterative methods, such as the Newton method, lending themselves especially to refining solutions.
Unconventional green
(2023)
We analyze the effects of the PEPP (Pandemic Emergency Purchase Programme), the temporary quantitative easing implemented by the ECB immediately after the burst of the Covid-19 pandemic. We show that the differences in aim, size and flexibility with respect to the traditional Corporate Sector Purchase Programme (CSPP) were able to significantly involve, in addition to the directly targeted bonds, also the green bond segment. Via a standard difference- in-differences model we estimate that the yield on green bonds declined by more than 20 basis points after the PEPP. In order to take into account also the differences attributable to the eligibility to the programme, we employ a triple difference estimator. Bonds that at the same time were green and eligible benefitted of an additional premium of 39 basis points.
Fabo, Janˇcokov ́a, Kempf, and P ́astor (2021) show that papers written by central bank researchers find quantitative easing (QE) to be more effective than papers written by academics. Weale and Wieladek (2022) show that a subset of these results lose statistical significance when OLS regressions are replaced by regressions that downweight outliers. We examine those outliers and find no reason to downweight them. Most of them represent estimates from influential central bank papers published in respectable academic journals. For example, among the five papers finding the largest peak effect of QE on output, all five are published in high-quality journals (Journal of Monetary Economics, Journal of Money, Credit and Banking, and Applied Economics Letters), and their average number of citations is well over 200. Moreover, we show that these papers have supported policy communication by the world’s leading central banks and shaped the public perception of the effectiveness of QE. New evidence based on quantile regressions further supports the results in Fabo et al. (2021).
Industry concentration and markups in the US have been rising over the last 3-4 decades. However, the causes remain largely unknown. This paper uses machine learning on regulatory documents to construct a novel dataset on compliance costs to examine the effect of regulations on market power. The dataset is comprehensive and consists of all significant regulations at the 6-digit NAICS level from 1970-2018. We find that regulatory costs have increased by $1 trillion during this period. We document that an increase in regulatory costs results in lower (higher) sales, employment, markups, and profitability for small (large) firms. Regulation driven increase in concentration is associated with lower elasticity of entry with respect to Tobin's Q, lower productivity and investment after the late 1990s. We estimate that increased regulations can explain 31-37% of the rise in market power. Finally, we uncover the political economy of rulemaking. While large firms are opposed to regulations in general, they push for the passage of regulations that have an adverse impact on small firms.
Output gap revisions can be large even after many years. Real-time reliability tests might therefore be sensitive to the choice of the final output gap vintage that the real-time estimates are compared to. This is the case for the Federal Reserve’s output gap. When accounting for revisions in response to the global financial crisis in the final output gap, the improvement in real-time reliability since the mid-1990s is much smaller than found by Edge and Rudd (Review of Economics and Statistics, 2016, 98(4), 785-791). The negative bias of real-time estimates from the 1980s has disappeared, but the size of revisions continues to be as large as the output gap itself.
The authors systematically analyse how the realtime reliability assessment is affected through varying the final output gap vintage. They find that the largest changes are caused by output gap revisions after recessions. Economists revise their models in response to such events, leading to economically important revisions not only for the most recent years, but reaching back up to two decades. This might improve the understanding of past business cycle dynamics, but decreases the reliability of real-time output gaps ex post.
We contribute to the debate about the future of capital markets and corporate finance, which has ensued against the background of a significant boom in private markets and a corresponding decline in the number of firms and the amount of capital raised in public markets in the US and Europe.
Our research sheds light on the fluctuating significance of public and private markets for corporate finance over time, and challenges the conventional view of a linear progression from one market to the other. We argue instead that a more complex pattern of interaction between public and private markets emerges, after taking a long-term perspective and examining historical developments more closely.
We claim that there is a dynamic divide between these markets, and identify certain factors that determine the degree to which investors, capital, and companies gravitate more towards one market than the other. However, in response to the status quo, other factors will gain momentum and favor the respective other market, leading to a new (unstable) equilibrium. Hence, we observe the oscillating domains of public and private markets over time. While these oscillations imply ‘competition’ between these markets, we unravel the complementarities between them, which also militate against a secular trend towards one market. Finally, we examine the role of regulation in this dynamic divide as well as some policy implications arising from our findings.
The European low-carbon transition began in the last few decades and is accelerating to achieve net-zero emissions by 2050. This paper examines how climate-related transition indicators of a large European corporate firm relate to its CDS-implied credit risk across various time horizons. Findings show that firms with higher GHG emissions have higher CDS spreads at all tenors, including the 30-year horizon, particularly after the 2015 Paris Agreement, and in prominent industries such as Electricity, Gas, and Mining. Results suggest that the European CDS market is currently pricing, to some extent, albeit small, the exposure to transition risk for a firm across different time horizons. However, it fails to account for a company’s efforts to manage transition risks and its exposure to the EU Emissions Trading Scheme. CDS market participants seem to find challenging to risk-differentiate ETS-participating firms from other firms.
An unfamiliar term in the not-too-distant past, “net zero” has become a headline-maker in the business and financial world with the growing importance of climate change. Succumbing to increasing pressure, companies and financial institutions around the world have come to adopt net-zero transition plans and targets, pledging to hit certain emission-reduction targets in a long-term period. Moreover, regulators around the world have started to require the disclosure or adoption of net-zero transition plans and targets.
However, an unintended consequence of net-zero transition commitments has been the increased popularity of divestments. That is, many firms seeking to fulfill a net-zero plan are passing on carbon-intensive assets (i.e., oil, gas, and coal assets) to other firms that are likely to be non-committal to environmental goals or that operate under less pressure from investors, stakeholders, and regulators. Such divestments, technically mergers and acquisitions (M&A) transactions, present an ideal opportunity to improve a divesting firm’s environmental record and reach ambitious net-zero goals, creating the impression that an emission reduction has occurred. However, the key is how acquiring firms handle these assets. If they continue operating as before, there will not be an overall improvement for the global climate. Worse, such assets can be operated by new owners in a way that causes more emissions. In any case, such divestments undermine the credibility and value of net-zero ambitions by allowing firms to reach targets by simply divesting assets.
This article explores the reasons and motivations for divestments or, more broadly M&As of carbon-intensive assets and explains why the increased role of net-zero commitments can be undermined by those transactions. We provide some evidence to illustrate the landscape of such transactions and the concerns they give rise to. Lastly, we explore several policy options to address the problem.
Using German and US brokerage data we find that investors are more likely to sell speculative stocks trading at a gain. Investors’ gain realizations are monotonically increasing in a stock’s speculativeness. This translates into a high disposition effect for speculative and a much lower disposition effect for non-speculative stocks. Our findings hold across asset classes (stocks, passive, and active funds) and explain cross-sectional differences in investor selling behavior which previous literature attributed primarily to investor demographics. Our results are robust to rank or attention effects and can be linked to realization utility and rolling mental account.
Who should hold bail-inable debt and how can regulators police holding restrictions effectively?
(2023)
This paper analyses the demand-side prerequisites for the efficient application of the bail-in tool in bank resolution, scrutinises whether the European bank crisis management and deposit insurance (CMDI) framework is apt to establish them, and proposes amendments to remedy identified shortcomings.
The first applications of the new European CMDI framework, particularly in Italy, have shown that a bail-in of debt holders is especially problematic if they are households or other types of retail investors. Such debt holders may be unable to bear losses, and the social implications of bailing them in may create incentives for decision makers to refrain from involving them in bank resolution. In turn, however, if investors can expect resolution authorities (RAs) to behave inconsistently over time and bail-out bank capital and debt holders despite earlier vows to involve them in bank rescues, the pricing and monitoring incentives that the crisis management framework seeks to invigorate would vanish. As a result, market discipline would be suboptimal and moral hazard would persist. Therefore, the policy objectives of the CMDI framework will only be achieved if critical bail-in capital is not held by retail investors without sufficient loss-bearing capacity. Currently, neither the CMDI framework nor capital market regulation suffice to assure that this precondition is met. Therefore, some amendments are necessary. In particular, debt instruments that are most likely to absorb losses in resolution should have a high minimum denomination and banks should not be allowed to self-place such securities.
Recent empirical evidence shows that most international prices are sticky in dollars. This paper studies the policy implications of this fact in the context of an open economy model, allowing for an arbitrary structure of asset markets, general preferences and technologies, time- or state-dependent price setting, and a rich set of shocks. We show that although monetary policy is less efficient and cannot implement the flexible-price allocation, inflation targeting remains robustly optimal in non-U.S. economies. The implementation of this non-cooperative policy results in a "global monetary cycle" with other countries importing the monetary stance of the U.S. The capital controls cannot unilaterally improve the allocation and are useful only when coordinated across countries. Thanks to the dominance of the dollar, the U.S. can extract rents in international goods and asset markets and enjoy a higher welfare than other economies. Although international cooperation benefits other countries by improving global demand for dollar-invoiced goods, it is not in the self-interest of the U.S. and may be hard to sustain.
This paper analyzes the current implementation status of sustainability and taxonomy-aligned disclosure under the Sustainable Finance Disclosure Regulation (SFDR) as well as the development of the SFDR categorization of funds offered via banks in Germany. Examining data provided by WM Group, which consists of more than 10,000 investment funds and 2,000 index funds between September 2022 and March 2023, we have observed a significant proportion of Article 9 (dark green) funds transitioning to Article 8 (light green) funds, particularly among index funds. As a consequence of this process, the profile of the SFDR classes has sharpened, which reflects an increased share of sustainable investments in the group of Article 9 funds. When differentiating between environmental and social investments, the share of environmental investments increased, but the share of social investments decreased in the group of Article 9 funds at the beginning of 2023. The share of taxonomy-aligned investments is very low, but slightly increasing for Article 9 funds. However, by March 2023 only around 1,000 funds have reported their sustainability proportions and this picture might change due to legal changes which require all funds in the scope of the SFDR to report these proportions in their annual reports being published after 1 January 2023.
Die Erklärung von Intelligenz fasziniert Menschen seit Jahrtausenden, scheint sich doch mit ihr die menschliche Singularität gegenüber Natur und Tier zu manifestieren. Zugleich betonen nicht nur philosophische Strömungen, sondern auch die Mathematik, die Neuro- und die Computerwissenschaften die Abhängigkeit menschlicher Intelligenz von mechanistischen Prozessen. Ob damit eine Verwandtschaft beider Formen der Informationsverarbeitung verbunden ist oder genau umgekehrt fundamentale Unterschiede bestehen, ist seit knapp hundert Jahren Gegenstand wissenschaftlicher Kontroversen. Fest steht allerdings, dass Maschinen jedenfalls in manchen Bereichen die menschliche Leistungsfähigkeit in Schnelligkeit und Präzision übertreffen können. Nähert man sich dieser Vorstellung, drängt sich die Frage auf, ob es sich empfiehlt, bestimmte Entscheidungen besser von Maschinen treffen, jedenfalls aber unterstützen zu lassen. Neben Ärzten, Rechtsanwälten und Börsenhändlern betrifft das auch Leitungsentscheidungen von Unternehmensführern.
Vor diesem Hintergrund wird im Folgenden ein Überblick über Formen künstlicher Intelligenz (KI) gegeben. Im Anschluss fokussiert der Beitrag auf die Rolle von KI im Kontext von Vorstandsentscheidungen. Dazu zählen allgemeine Sorgfaltspflichten, wenn über den Einsatz von KI im Unternehmen zu entscheiden ist. Geht es um die Unterstützung gerade von Vorstandsentscheidungen stellen sich zusätzlich Fragen der Kooperation von Mensch und Maschine, der Delegation des Kernbestands von Leitungsentscheidungen und der Einstandspflicht für KI.
Biodiversity loss poses a significant threat to the global economy and affects ecosystem services on which most large companies rely heavily. The severe financial implications of such a reduced species diversity have attracted the attention of companies and stakeholders, with numerous calls to increase corporate transparency. Using textual analysis, this study thus investigates the current state of voluntary biodiversity reporting of 359 European blue-chip companies and assesses the extent to which it aligns with the upcoming disclosure framework of the Task Force on Nature-related Financial Disclosures (TNFD). The descriptive results suggest a substantial gap between current reporting practices and the proposed TNFD framework, with disclosures largely lacking quantification, details and clear targets. In addition, the disclosures appear to be relatively unstandardized. Companies in sectors or regions exposed to higher nature-related risks as well as larger companies are more likely to report on aspects of biodiversity. This study contributes to the emerging literature on nature-related risks and provides detailed insights on the extent of the reporting gap in light of the upcoming standards.
We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG disparity have a higher option-implied cost of protection against downside tail risk. The impact of the misalignment across the different dimensions of the ESG score is distinct from that of ESG score level itself. Aggregate downside risk bears a negative price for firms with low ESG disparity.
This research focuses on the cost of financing green projects on the primary bond market and tests for a potential price differential between green bonds issued by government entities and those issued by supranational and private sector issuers. Our findings indicate that government entities benefit from more favorable pricing conditions worldwide. This advantage is growing over time and particularly pronounced for sovereigns and municipal authorities. Our analysis also reveals that country-specific factors, such as strong political commitment to address climate change, low income level and high degree of indebtedness are significant predictors of the pricing spread across bonds.
We explore how personality traits are related to household borrowing behavior. Using survey data representative for the Netherlands, we consider the Big Five personality traits (openness, conscientiousness, agreeableness, extraversion and neuroticism), as well as the belief that one is master of one’s fate (locus of control). We hypothesize that personality traits can complement as well as substitute financial knowledge of a household. We present three sets of results. First, we find that personality traits are positively correlated with borrowing expectations. Locus of control, extraversion and agreeableness are correlated with informal borrowing expectations, which is the expectation that one can borrow from family and friends. With respect to expectations on the approval of a formal loan application, it is locus of control and conscientiousness that are positively associated. Effect sizes are large and economically meaningful. Second, we find that personality traits are important for borrowing constraints. A more internal locus of control and higher neuroticism are correlated with being denied for credit, as well as discouraged borrowing. Our third set of results reports findings on personality traits and loan regret, and how traits are correlated with dealing with loan troubles. Many households in our sample express regret (21%), but more open, more agreeable and more neurotic individuals are more likely to express regret. Our results are not driven by financial knowledge, time preferences or risk attitudes. Overall these findings imply that non-cognitive traits are important for borrowing behavior of households.
Contagious stablecoins?
(2023)
Can competing stablecoins produce efficient and stable outcomes? We study competition among stablecoins pegged to a stable currency. They are backed by interest-bearing safe assets and can be redeemed with the issuer or traded in a secondary market. If an issuer sticks to an appropriate investment and redemption rule, its stablecoin is invulnerable to runs. Since an issuer must pay interest on its stablecoin if other issuers also pay interest, competing interest-bearing stablecoins, however, are contagious and can render the economy inefficient and unstable. The efficient allocation is uniquely implemented when regulation prevents interest payments on stablecoins.
The right to ask questions and voice their opinions at annual general meetings (AGMs) represents one of the few avenues for shareholders to communicate directly and publicly with the firm’s management. Examining AGM transcripts of U.S. companies between 2007 and 2021, we find that shareholders actively express their concerns about environmental, social and governance (ESG) issues in accordance with their specific relationship with the company. Further, they are also demonstrably more vocal about ESG issues at AGMs of firms with poor sustainability performance. What is more, we show that this soft engagement translates into a more negative tone which, in turn, results in lower approval rates for management proposals. Shareholders' soft engagement at AGMs is hence an effective way to "walk the talk".
The issuance of sustainability-linked loans (SLLs) has grown exponentially in recent years. Using a scoring methodology, we examine the underlying key performance indicators of a large sample of SLLs and analyze whether their design creates effective incentives for improving corporate sustainability performance. We demonstrate that the majority of loans fails to meet key requirements that would make them credible instruments for generating effective sustainability incentives. These findings call into question the actual sustainability impact that may be achieved through the issuance of ESG-linked debt.
This study looks at potential windfall profits for the four banking acquisitions in 2023. Based on accounting figures, an FT article states that a total of USD 44bn was left on the table. We see accounting figures as a misleading analysis. By estimating marked-based cumulative abnormal returns (CAR), we find positive abnormal returns in all four cases which when made quantifiable, are around half of the FT’s accounting figures. Furthermore, we argue that transparent auctions with enough bidders should be preferred to negotiated bank sales.
This document was provided/prepared by the Economic Governance and EMU Scrutiny Unit at the request of the ECON Committee.
Shallow meritocracy
(2023)
Meritocracies aspire to reward hard work and promise not to judge individuals by the circumstances into which they were born. However, circumstances often shape the choice to work hard. I show that people's merit judgments are "shallow" and insensitive to this effect. They hold others responsible for their choices, even if these choices have been shaped by unequal circumstances. In an experiment, US participants judge how much money workers deserve for the effort they exert. Unequal circumstances disadvantage some workers and discourage them from working hard. Nonetheless, participants reward the effort of disadvantaged and advantaged workers identically, regardless of the circumstances under which choices are made. For some participants, this reflects their fundamental view regarding fair rewards. For others, the neglect results from the uncertain counterfactual. They understand that circumstances shape choices but do not correct for this because the counterfactual—what would have happened under equal circumstances—remains uncertain.
Investors' return expectations are pivotal in stock markets, but the reasoning behind these expectations remains a black box for economists. This paper sheds light on economic agents' mental models -- their subjective understanding -- of the stock market, drawing on surveys with the US general population, US retail investors, US financial professionals, and academic experts. Respondents make return forecasts in scenarios describing stale news about the future earnings streams of companies, and we collect rich data on respondents' reasoning. We document three main results. First, inference from stale news is rare among academic experts but common among households and financial professionals, who believe that stale good news lead to persistently higher expected returns in the future. Second, while experts refer to the notion of market efficiency to explain their forecasts, households and financial professionals reveal a neglect of equilibrium forces. They naively equate higher future earnings with higher future returns, neglecting the offsetting effect of endogenous price adjustments. Third, a series of experimental interventions demonstrate that these naive forecasts do not result from inattention to trading or price responses but reflect a gap in respondents' mental models -- a fundamental unfamiliarity with the concept of equilibrium.
Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.
The financial sector plays an important role in financing the green transformation. Various regulatory initiatives in the EU aim to improve transparency in relation to the sustainability of financial products and the sustainability of economic activities of non-financial and financial undertakings. For credit institutions, the Green Asset Ratio (GAR) has been established by the European regulatory authorities as a key performance indicator (KPI) for measuring the proportion of Taxonomy-aligned on-balance-sheet exposure in relation to the total assets. The breakdown of the total GAR by type of counterparty, environmental objective and type of asset provides in-depth information about the sustainability profile of a credit institution. This information, which has not been available to date, may also initiate discussions between management and shareholders or other stakeholders regarding the future sustainability strategy of credit institutions. This paper provides an overview of the regulatory background and the method of calculating the GAR along different dimensions. Finally, the potential benefits and limitations of the GAR are discussed.
The Federal Reserve has been publishing federal funds rate prescriptions from Taylor rules in its Monetary Policy Report since 2017. The signals from the rules aligned with Fed action on many occasions, but in some cases the Fed opted for a different route. This paper reviews the implications of the rules during the coronavirus pandemic and the subsequent inflation surge and derives projections for the future.
In 2020, the Fed took the negative prescribed rates, which were far below the effective lower bound on the nominal interest rate, as support for extensive and long-lasting quantitative easing. Yet, the calculations overstate the extent of the constraint, because they neglect the supply side effects of the pandemic.
The paper proposes a simple model-based adjustment to the resource gap used by the rules for 2020. In 2021, the rules clearly signaled the need for tightening because of the rise of inflation, yet the Fed waited until spring 2022 to raise the federal funds rate. With the decline of inflation over the course of 2023, the rules’ prescriptions have also come down. They fall below the actual federal funds rate target range in 2024. Several caveats concerning the projections of the interest rate prescriptions are discussed.
We document the individual willingness to act against climate change and study the role of social norms in a large sample of US adults. Individual beliefs about social norms positively predict pro-climate donations, comparable in strength to universal moral values and economic preferences such as patience and reciprocity. However, we document systematic misperceptions of social norms. Respondents vastly underestimate the prevalence of climate-friendly behaviors and norms. Correcting these misperceptions in an experiment causally raises individual willingness to act against climate change as well as individual support for climate policies. The effects are strongest for individuals who are skeptical about the existence and threat of global warming.
Looking beyond ESG preferences: The role of sustainable finance literacy in sustainable investing
(2024)
We assess how sustainable finance literacy affects people’s sustainable investment behavior, using a pre-registered experiment. We find that an increase in sustainable finance literacy leads to a 4 to 5% increase in the probability of investing sustainably. This effect is moderated by sustainability preferences. In the absence of moderate sustainability preferences, any additional increase in sustainable finance literacy is at minimum irrelevant, and we find some evidence that it might even reduce sustainable investments. Our findings underscore the role of knowledge in shaping sustainable investment decisions, highlighting the importance of factors beyond sustainability preferences.