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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 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.
The ECB’s Outright Monetary Transactions (OMT) program, launched in summer 2012, indirectly recapitalized periphery country banks through its positive impact on the value of sovereign bonds. However, the regained stability of the European banking sector has not fully transferred into economic growth. We show that zombie lending behavior of banks that still remained undercapitalized after the OMT announcement is an important reason for this development. As a result, there was no positive impact on real economic activity like employment or investment. Instead, firms mainly used the newly acquired funds to build up cash reserves. Finally, we document that creditworthy firms in industries with a high prevalence of zombie firms suffered significantly from the credit misallocation, which slowed down the economic recovery.
We investigate the transmission of central bank liquidity to bank deposits and loan spreads in Europe over the January 2006 to June 2010 period. We find evidence consistent with an impaired transmission channel due to bank risk. Central bank liquidity does not translate into lower loan spreads for high-risk banks, even as it lowers deposit rates for both high-risk and low-risk banks. This adversely affects the balance sheets of high-risk bank borrowers, leading to lower payouts, lower capital expenditures, and lower employment. Overall, our results suggest that banks’ capital constraints at the time of an easing of monetary policy pose a challenge to the effectiveness of the bank lending channel and the effectiveness of the central bank as a lender of last resort.
The European Central Bank (ECB) has finalized its comprehensive assessment of the solvency of the largest banks in the euro area and on October 26 disclosed the results of this assessment. In the present paper, Acharya and Steffen compare the outcomes of the ECB's assessment to their own benchmark stress tests conducted for 39 publically listed financial institutions that are also included in the ECB's regulatory review. The authors identify a negative correlation between their benchmark estimates for capital shortfalls and the regulatory capital shortfall, but a positive correlation between their benchmark estimates for losses under stress both in the banking book and in the trading book. They conclude that the regulatory stress test outcomes are potentially heavily affected by discretion of national regulators in measuring what is capital, and especially the use of risk-weighted assets in calculating the prudential capital requirement.
We develop a dynamic recursive model where political and economic decisions interact, to study how excessive debt-GDP ratios affect political sustainability of prudent fiscal policies. Rent seeking groups make political decisions – to cooperate (or not) – on the allocation of fiscal budgets (including rents) and issuance of sovereign debt. A classic commons problem triggers collective fiscal impatience and excessive debt issuing, leading to a vicious circle of high borrowing costs and sovereign default. We analytically characterize debt-GDP thresholds that foster cooperation among rent seeking groups and avoid default. Our analysis and application helps in understanding the politico-economic sustainability of sovereign rescues, emphasizing the need for fiscal targets and possible debt haircuts. We provide a calibrated example that quantifies the threshold debt-GDP ratio at 137%, remarkably close to the target set for private sector involvement in the case of Greece.
Motivated by the observation that survey expectations of stock returns are inconsistent with rational return expectations under real-world probabilities, we investigate whether alternative expectations hypotheses entertained in the asset pricing literature are consistent with the survey evidence. We empirically test (1) the notion that survey forecasts constitute rational but risk-neutral forecasts of future returns, and (2) the notion that survey fore- casts are ambiguity averse/robust forecasts of future returns. We find that these alternative hypotheses are also strongly rejected by the data, albeit for different reasons. Hypothesis (1) is rejected because survey return forecasts are not in line with risk-free interest rates and because survey expected excess returns are predictable. Hypothesis (2) is rejected because agents are not al- ways pessimistic about future returns, instead often display overly optimistic return expectations. We speculate as to what kind of expectations theories might be consistent with the available survey evidence.
Optimal trend inflation
(2017)
We present a sticky-price model incorporating heterogeneous Firms and systematic firm-level productivity trends. Aggregating the model in closed form, we show that it delivers radically different predictions for the optimal inflation rate than canonical sticky price models featuring homogenous Firms:
(1) the optimal steady-state inflation rate generically differs from zero and,
(2) inflation optimally responds to productivity disturbances.
Using micro data from the US Census Bureau to estimate the inflation-relevant productivity trends at the firm level, we find that the optimal US inflation rate is positive. It was slightly above 2 percent in the year 1986, but continuously declined thereafter, reaching about 1 percent in the year 2013.
We analytically characterize optimal monetary policy for an augmented New Keynesian model with a housing sector. In a setting where the private sector has rational expectations about future housing prices and inflation, optimal monetary policy can be characterized without making reference to housing price developments: commitment to a 'target criterion' that refers to inflation and the output gap only is optimal, as in the standard model without a housing sector. When the policymaker is concerned with potential departures of private sector expectations from rational ones and seeks to choose a policy that is robust against such possible departures, then the optimal target criterion must also depend on housing prices. In the empirically realistic case where housing is subsidized and where monopoly power causes output to fall short of its optimal level, the robustly optimal target criterion requires the central bank to 'lean against' housing prices: following unexpected housing price increases, policy should adopt a stance that is projected to undershoot its normal targets for inflation and the output gap, and similarly aim to overshoot those targets in the case of unexpected declines in housing prices. The robustly optimal target criterion does not require that policy distinguish between 'fundamental' and 'non-fundamental' movements in housing prices.
In the secondary art market, artists play no active role. This allows us to isolate cultural influences on the demand for female artists’ work from supply-side factors. Using 1.5 million auction transactions in 45 countries, we document a 47.6% gender discount in auction prices for paintings. The discount is higher in countries with greater gender inequality. In experiments, participants are unable to guess the gender of an artist simply by looking at a painting and they vary in their preferences for paintings associated with female artists. Women's art appears to sell for less because it is made by women.
In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days.
Credit boom detection methodologies (such as threshold method) lack robustness as they are based on univariate detrending analysis and resort to ratios of credit to real activity. I propose a quantitative indicator to detect atypical behavior of credit from a multivariate system - a monetary VAR. This methodology explicitly accounts for endogenous interactions between credit, asset prices and real activity and detects atypical credit expansions and contractions in the Euro Area, Japan and the U.S. robustly and timely. The analysis also proves useful in real time.
This paper investigates the risk channel of monetary policy on the asset side of banks’ balance sheets. We use a factoraugmented vector autoregression (FAVAR) model to show that aggregate lending standards of U.S. banks, such as their collateral requirements for firms, are significantly loosened in response to an unexpected decrease in the Federal Funds rate. Based on this evidence, we reformulate the costly state verification (CSV) contract to allow for an active financial intermediary, embed it in a New Keynesian dynamic stochastic general equilibrium (DSGE) model, and show that – consistent with our empirical findings – an expansionary monetary policy shock implies a temporary increase in bank lending relative to borrower collateral. In the model, this is accompanied by a higher default rate of borrowers.
A common prediction of macroeconomic models of credit market frictions is that the tightness of financial constraints is countercyclical. As a result, theory implies a negative collateralizability premium; that is, capital that can be used as collateral to relax financial constraints provides insurance against aggregate shocks and commands a lower risk compensation compared with non-collateralizable assets. We show that a longshort portfolio constructed using a novel measure of asset collateralizability generates an average excess return of around 8% per year. We develop a general equilibrium model with heterogeneous firms and financial constraints to quantitatively account for the collateralizability premium.
Most insurers in the European Union determine their regulatory capital requirements based on the standard formula of Solvency II. However, there is evidence that the standard formula inaccurately reflects insurers’ risk situation and may provide misleading steering incentives. In the second pillar, Solvency II requires insurers to perform a so-called “Own Risk and Solvency Assessment” (ORSA). In their ORSA, insurers must establish their own risk measurement approaches, including those based on scenarios, in order to derive suitable risk assessments and address shortcomings of the standard formula. The idea of this paper is to identify scenarios in such a way that the standard formula in connection with the ORSA provides a reliable basis for risk management decisions. Using an innovative method for scenario identification, our approach allows for a simple but relatively precise assessment of marginal and even non-marginal portfolio changes. We numerically evaluate the proposed approach in the context of market risk employing an internal model from the academic literature and the Solvency Capital Requirement (SCR) calculation under Solvency II.
Gradient capital allocation, also known as Euler allocation, is a technique used to redistribute diversified capital requirements among different segments of a portfolio. The method is commonly employed to identify dominant risks, assessing the risk-adjusted profitability of segments, and installing limit systems. However, capital allocation can be misleading in all these applications because it only accounts for the current portfolio composition and ignores how diversification effects may change with a portfolio restructuring. This paper proposes enhancing the gradient capital allocation by adding “orthogonal convexity scenarios” (OCS). OCS identify risk concentrations that potentially drive portfolio risk and become relevant after restructuring. OCS have strong ties with principal component analysis (PCA), but they are a more general concept and compatible with common empirical patterns of risk drivers being fat-tailed and increasingly dependent in market downturns. We illustrate possible applications of OCS in terms of risk communication and risk limits.
Research on interbank networks and systemic importance is starting to recognise that the web of exposures linking banks balance sheets is more complex than the single-layer-of-exposure paradigm. We use data on exposures between large European banks broken down by both maturity and instrument type to characterise the main features of the multiplex structure of the network of large European banks. This multiplex network presents positive correlated multiplexity and a high similarity between layers, stemming both from standard similarity analyses as well as a core-periphery analyses of the different layers. We propose measures of systemic importance that fit the case in which banks are connected through an arbitrary number of layers (be it by instrument, maturity or a combination of both). Such measures allow for a decomposition of the global systemic importance index for any bank into the contributions of each of the sub-networks, providing a useful tool for banking regulators and supervisors. We use the dataset of exposures between large European banks to illustrate the proposed measures.
The analyses of intersectoral linkages of Leontief (1941) and Hirschman (1958) provide a natural way to study the transmission of risk among interconnected banks and to measure their systemic importance. In this paper we show how classic input-output analysis can be applied to banking and how to derive six indicators that capture different aspects of systemic importance, using a simple numerical example for illustration. We also discuss the relationship with other approaches, most notably network centrality measures, both formally and by means of a simulated network.
We uncover a new channel for spillovers of funding dry-ups. The 2016 US money market fund (MMF) reform exogenously reduced unsecured MMF funding for some banks. We use novel data to trace those banks to a platform for corporate deposit funding. We show that intensified competition for corporate deposits spilled the funding squeeze over to other banks with no MMF exposure. These banks paid more for deposits, and their pool of funding providers deteriorated. Moreover, their lending volumes and margins declined, and their stocks underperformed. Our results suggest that banks' competitiveness in funding markets affect their competitiveness in lending markets.
We present a network model of the interbank market in which optimizing risk averse banks lend to each other and invest in non-liquid assets. Market clearing takes place through a tâtonnement process which yields the equilibrium price, while traded quantities are determined by means of a matching algorithm. We compare three alternative matching algorithms: maximum entropy, closest matching and random matching. Contagion occurs through liquidity hoarding, interbank interlinkages and fire sale externalities. The resulting network configurations exhibits a core-periphery structure, dis-assortative behavior and low clustering coefficient. We measure systemic importance by means of network centrality and input-output metrics and the contribution of systemic risk by means of Shapley values. Within this framework we analyze the effects of prudential policies on the stability/efficiency trade-off. Liquidity requirements unequivocally decrease systemic risk but at the cost of lower efficiency (measured by aggregate investment in non-liquid assets); equity requirements tend to reduce risk (hence increase stability) without reducing significantly overall investment.
In many cases, the dire situation of public finances calls into question the very soundness of sovereigns and prompts corrective actions with far-reaching consequences. In this context, European authorities responded with several measures on different fronts, for instance by passing the "Fiscal Compact", which entered into force on January 1, 2013. Of critical importance in this framework is the assessment of a country’s situation by way of statistical measures, in order to take corrective actions when called for according to the letter of the law. If these statistics are not correct, there is a risk of imposing draconian measures on countries that do not really need it.
The implications of delegating fiscal decision making power to sub-national governments has become an area of significant interest over the past two decades, in the expectation that these reforms will lead to better and more efficient provision of public goods and services. The move towards decentralization has, however, not been homogeneously implemented on the revenue and expenditure side: decentralization has materialized more substantially on the latter than on the former, creating "vertical fiscal imbalances". These imbalances measure the extent to which sub-national governments’ expenditures are financed through their own revenues. This mismatch between own revenues and expenditures may have negative consequences for public finances performance, for example by softening the budget constraint of sub-national governments. Using a large sample of countries covering a long time period from the IMF’s Government Finance Statistics Yearbook, this paper is the first to examine the effects of vertical fiscal imbalances on fiscal performance through the accumulation of government debt. Our findings suggest that vertical fiscal imbalances are indeed relevant in explaining government debt accumulation, and call for a degree of caution when promoting fiscal decentralization.
Since the outbreak of the financial crisis, the macro-prudential policy paradigm has gained increasing prominence (Bank of England, 2009; Bernanke, 2011). The dynamics of this shift in the economic discourse, and the reasons this shift has not taken place prior to the crisis have not been addressed systemically. This paper investigates the evolution of the economic discourse on systemic risk and banking regulation to better understand these changes and their timing. Further, we use our sample to inquire whether, and if so, why the economic regulatory studies failed to recommend a reliable banking regulation prior to the crisis. By following a discourse analysis, we establish that the economic discourse on banking regulation has not been suitable for providing the knowledge basis required for a dynamically reliable banking regulation, and we identify the underlying reasons for such failure. These reasons include the obsession of economic discourse with optimization and particular forms of formalism, particularly, partial equilibrium analysis. Further, the economic discourse on banking regulation excludes historical and practitioners’ discourses and ignores weak signals. We point out that post-crisis, these epistemological failures of the economic discourse on banking regulation were not sufficiently recognized and that recent attempts to conceptualize systemic risk as a negative externality and to thus price it point to the persistence of formalism, equilibrium thinking and optimization, with their attending dangers.
We develop a novel empirical approach to identify the effectiveness of policies against a pandemic. The essence of our approach is the insight that epidemic dynamics are best tracked over stages, rather than over time. We use a normalization procedure that makes the pre-policy paths of the epidemic identical across regions. The procedure uncovers regional variation in the stage of the epidemic at the time of policy implementation. This variation delivers clean identification of the policy effect based on the epidemic path of a leading region that serves as a counterfactual for other regions. We apply our method to evaluate the effectiveness of the nationwide stay-home policy enacted in Spain against the Covid-19 pandemic. We find that the policy saved 15.9% of lives relative to the number of deaths that would have occurred had it not been for the policy intervention. Its effectiveness evolves with the epidemic and is larger when implemented at earlier stages.
We show that the correct experiment to evaluate the effects of a fiscal adjustment is the simulation of a multi year fiscal plan rather than of individual fiscal shocks. Simulation of fiscal plans adopted by 16 OECD countries over a 30-year period supports the hypothesis that the effects of consolidations depend on their design. Fiscal adjustments based upon spending cuts are much less costly, in terms of output losses, than tax-based ones and have especially low output costs when they consist of permanent rather than stop and go changes in taxes and spending. The difference between tax-based and spending-based adjustments appears not to be explained by accompanying policies, including monetary policy. It is mainly due to the different response of business confidence and private investment.
We develop a methodology to identify and rank “systemically important financial institutions” (SIFIs). Our approach is consistent with that followed by the Financial Stability Board (FSB) but, unlike the latter, it is free of judgment and it is based entirely on publicly available data, thus filling the gap between the official views of the regulator and those that market participants can form with their own information set. We apply the methodology to annual data on three samples of banks (global, EU and euro area) for the years 2007-2012. We examine the evolution of the SIFIs over time and document the shifs in the relative weights of the major geographic areas. We also discuss the implication of the 2013 update of the identification methodology proposed by the FSB.
Banking and markets
(2001)
This paper integrates a number of recent themes in the literature in banking and asset markets–optimal risk sharing, limited market participation, asset-price volatility, market liquidity, and financial crises–in a general-equilibrium theory of the financial system. A complex financial system comprises both financial markets financial institutions. Financial institutions can take the form of intermediaries or banks. Banks, inlike intermediaries, are subject to runs, but crises do not imply market failure. We show that a sophisticated financiel system–a system with complete markets for aggregate risk and limited market participation–is incentive-efficient, if the institutions take the form of intermediaries, or else constrained-efficient, of they take the form of banks. We also consider an economy in which the markets for aggregate risks are incomplete. In this context, there is a rolefpr prudential regulation: regulating liquidity can improve welfare.
In this paper we propose a way forward towards increased financial resilience in times of growing disagreement concerning open borders, free trade and global regulatory standards. In light of these concerns, financial resilience remains a highly valued policy objective. We wish to contribute by suggesting an agenda of concrete, do-able steps supporting an enhanced level of resilience, combined with a deeper understanding of its relevance in the public domain.
First, remove inconsistencies across regulatory rules and territorial regimes, and ensure their credibility concerning implementation. Second, discourage the use of financial regulatory standards as means of international competition. Third, give more weight to pedagogically explaining the established regulatory standards in public, to strengthen their societal backing.
We study the information flow from the ECB on policy dates since its inception, using tick data. We show that three factors capture about all of the variation in the yield curve but that these are different factors with different variance shares in the window that contains the policy decision announcement and the window that contains the press conference. We also show that the QE-related policy factor has been dominant in the recent period and that Forward Guidance and QE effects have been very persistent on the longer-end of the yield curve. We further show that broad and banking stock indices' responses to monetary policy surprises depended on the perceived nature of the surprises. We find no evidence of asymmetric responses of financial markets to positive and negative surprises, in contrast to the literature on asymmetric real effects of monetary policy. Lastly, we show how to implement our methodology for any policy-related news release, such as policymaker speeches. To carry out the analysis, we construct the Euro Area Monetary Policy Event- Study Database (EA-MPD). This database, which contains intraday asset price changes around the policy decision announcement as well as around the press conference, is a contribution on its own right and we expect it to be the standard in monetary policy research for the euro area.
We investigate whether government credit guarantee schemes, extensively used at the onset of the Covid-19 pandemic, led to substitution of non-guaranteed with guaranteed credit rather than fully adding to the supply of lending. We study this issue using a unique euro-area credit register data, matched with supervisory bank data, and establish two main findings. First, guaranteed loans were mostly extended to small but comparatively creditworthy firms in sectors severely affected by the pandemic, borrowing from large, liquid and well-capitalized banks. Second, guaranteed loans partially substitute pre-existing non-guaranteed debt. For firms borrowing from multiple banks, the substitution mainly arises from the lending behavior of the bank extending guaranteed loans. Substitution was highest for funding granted to riskier and smaller firms in sectors more affected by the pandemic, and borrowing from larger and stronger banks. Overall, the evidence indicates that government guarantees contributed to the continued extension of credit to relatively creditworthy firms hit by the pandemic, but also benefited banks’ balance sheets to some extent.
Using novel monthly data for 226 euro-area banks from 2007 to 2015, we investigate the determinants of changes in banks’ sovereign exposures and their effects during and after the crisis. First, public, bailed out and poorly capitalized banks responded to sovereign stress by purchasing domestic public debt more than other banks, with public banks’ purchases growing especially in coincidence with the largest ECB liquidity injections. Second, bank exposures significantly amplified the transmission of risk from the sovereign and its impact on lending. This amplification of the impact on lending does not appear to arise from spurious correlation or reverse causality.
We extend the classical ”martingale-plus-noise” model for high-frequency prices by an error correction mechanism originating from prevailing mispricing. The speed of price reversal is a natural measure for informational efficiency. The strength of the price reversal relative to the signal-to-noise ratio determines the signs of the return serial correlation and the bias in standard realized variance estimates. We derive the model’s properties and locally estimate it based on mid-quote returns of the NASDAQ 100 constituents. There is evidence of mildly persistent local regimes of positive and negative serial correlation, arising from lagged feedback effects and sluggish price adjustments. The model performance is decidedly superior to existing stylized microstructure models. Finally, we document intraday periodicities in the speed of price reversion and noise-to-signal ratios.
We investigate the characteristics of infrastructure as an asset class from an investment perspective of a limited partner. While non U.S. institutional investors gain exposure to infrastructure assets through a mix of direct investments and private fund vehicles, U.S. investors predominantly invest in infrastructure through private funds. We find that the stream of cash flows delivered by private infrastructure funds to institutional investors is very similar to that delivered by other types of private equity, as reflected by the frequency and amounts of net cash flows. U.S. public pension funds perform worse than other institutional investors in their infrastructure fund investments, although they are exposed to underlying deals with very similar project stage, concession terms, ownership structure, industry, and geographical location. By selecting funds that invest in projects with poor financial performance, U.S. public pension funds have created an implicit subsidy to infrastructure as an asset class, which we estimate within the range of $730 million to $3.16 billion per year depending on the benchmark.
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.
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.
This paper shows that support for climate action is high across survey participants from all EU countries in three dimensions: (1) Participants are willing to contribute personally to combating climate change, (2) they approve of pro-climate social norms, and (3) they demand government action. In addition, there is a significant perception gap where individuals underestimate others' willingness to contribute to climate action by over 10 percentage points, influencing their own willingness to act. Policymakers should recognize the broad support for climate action among European citizens and communicate this effectively to counteract the vocal minority opposed to it.
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.
Speculative news on corporate takeovers may hurt productivity because uncertainty and threat of job loss cause anxiety, distraction, and reduced collaboration and morale among employees and managers. Using a panel of OECD-headquartered firms, we show that firm productivity temporarily declines upon announcements of speculative takeover rumors that do not materialize. This productivity dip is more pronounced for targets and for firms in countries with weaker employee rights and less long-term orientation. Abnormal stock returns mirror these results. The evidence fosters our understanding of potential real effects of speculative financial news and the costs of takeover threats.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
Discussions about the banking union have restarted. Its success so far is limited: national banking sectors are still overwhelmingly exposed to their own countries’ economies, cross border banking has not increased and capital and liquidity remain locked within national boundaries. The policy letter highlights that the current debate, centered on sovereign exposures and deposit insurance, misses critical underlying problems in the supervision and resolution frameworks. The ECB supervisors’ efforts to facilitate cross-border banking have been hampered by national ringfencing. The resolution framework is not up to its task: limited powers of the SRB, prohibitive access conditions and limited size of the Single Resolution Fund limit its effectiveness. A lack of a coherent European framework for insolvency unlevels the regulatory field and creates incentives to bypass European rules. The new Commission and European Parliament, with the new ECB leadership, provide a unique opportunity to address these shortcomings and make the banking union work.
There is much discussion today about a possible digital euro (PDE). Is this attention exaggerated? Are “central bank digital currencies” (CBDCs) “a solution in search of a problem”, as some have argued? This article summarizes the main facts about the PDE and concludes that, if the decision on adoption had to be taken today, the arguments against would outweigh those in favor. However, there may be future circumstances in which having a CBDC ready for use can indeed be useful. Therefore, preparing is a good thing, even if the odds of its usefulness in normal conditions are slim.
In its first ten years (2014-2023), the banking union was successful in its prudential agenda but failed spectacularly in its underlying objective: establishing a single banking market in the euro area. This goal is now more important than ever, and easier to attain than at any time in the last decade. To make progress, cross-border banks should receive a specific treatment within general banking union legislation. Suggestions are made on how to make such regulatory carve-out effective and legally sound.
This policy note summarizes our assessment of financial sanctions against Russia. We see an increase in sanctions severity starting from (1) the widely discussed SWIFT exclusions, followed by (2) blocking of correspondent banking relationships with Russian banks, including the Central Bank, alongside secondary sanctions, and (3) a full blacklisting of the ‘real’ export-import flows underlying the financial transactions. We assess option (1) as being less impactful than often believed yet sending a strong signal of EU unity; option (2) as an effective way to isolate the Russian banking system, particularly if secondary sanctions are in place, to avoid workarounds. Option (3) represents possibly the most effective way to apply economic and financial pressure, interrupting trade relationships.
We assess, through VAR evidence, the effects of monetary policy on banks’ risk exposure and find the presence of a risk-taking channel. A model combining fragile banks prone to risk mis-incentives and credit constrained firms, whose collateral fluctuations generate a balance sheet channel, is used to rationalize the evidence. A monetary expansion increases bank leverage. With two consequences: on the one side this exacerbates risk exposure; on the other, the risk spiral depresses output, therefore dampening the conventional amplification effect of the financial accelerator.
We assess the effects of monetary policy on bank risk to verify the existence of a risk-taking channel - monetary expansions inducing banks to assume more risk. We first present VAR evidence confirming that this channel exists and tends to concentrate on the bank funding side. Then, to rationalize this evidence we build a macro model where banks subject to runs endogenously choose their funding structure (deposits vs. capital) and risk level. A monetary expansion increases bank leverage and risk. In turn, higher bank risk in steady state increases asset price volatility and reduces equilibrium output.
Exit strategies
(2014)
We study alternative scenarios for exiting the post-crisis fiscal and monetary accommodation using a macromodel where banks choose their capital structure and are subject to runs. Under a Taylor rule, the post-crisis interest rate hits the zero lower bound (ZLB) and remains there for several years. In that condition, pre-announced and fast fiscal consolidations dominate - based on output and inflation performance and bank stability - alternative strategies incorporating various degrees of gradualism and surprise. We also examine an alternative monetary strategy in which the interest rate does not reach the ZLB; the benefits from fiscal consolidation persist, but are more nuanced.
We present new statistical indicators of the structure and performance of US banks from 1990 to today, geographically disaggregated at the level of individual counties. The constructed data set (20 indicators for some 3150 counties over 31 years, for a total of about 2 million data points) conveys a detailed picture of how the geography of US banking has evolved in the last three decades. We consider the data as a stepping stone to understand the role banks and banking policies may have played in mitigating, or exacerbating, the rise of poverty and inequality in certain US regions.
We examine the impact of so-called "Crisis Contracts" on bank managers' risk-taking incentives and on the probability of banking crises. Under a Crisis Contract, managers are required to contribute a pre-specified share of their past earnings to finance public rescue funds when a crisis occurs. This can be viewed as a retroactive tax that is levied only when a crisis occurs and that leads to a form of collective liability for bank managers. We develop a game-theoretic model of a banking sector whose shareholders have limited liability, so that society at large will suffer losses if a crisis occurs. Without Crisis Contracts, the managers' and shareholders' interests are aligned, and managers take more than the socially optimal level of risk. We investigate how the introduction of Crisis Contracts changes the equilibrium level of risk-taking and the remuneration of bank managers. We establish conditions under which the introduction of Crisis Contracts will reduce the probability of a banking crisis and improve social welfare. We explore how Crisis Contracts and capital requirements can supplement each other and we show that the efficacy of Crisis Contracts is not undermined by attempts to hedge.
Executive Stock Option Programs (SOPs) have become the dominant compensation instrument for top-management in recent years. The incentive effects of an SOP both with respect to corporate investment and financing decisions critically depend on the design of the SOP. A specific problem in designing SOPs concerns dividend protection. Usually, SOPs are not dividend protected, i.e. any dividend payout decreases the value of a manager’s options. Empirical evidence shows that this results in a significant decrease in the level of corporate dividends and, at the same time, into an increase in share repurchases. Yet, few suggestions have been made on how to account for dividends in SOPs. This paper applies arguments from principal-agent-theory and from the theory of finance to analyze different forms of dividend protection, and to address the relevance of dividend protection in SOPs. Finally, the paper relates the theoretical analysis to empirical work on the link between share repurchases and SOPs.
We design, field and exploit survey data from a representative sample of the French population to examine whether informative social interactions enter householdsístockholding decisions. Respondents report perceptions about their circle of peers with whom they interact about Önancial matters, their social circle and the population. We provide evidence for the presence of an information channel through which social interactions ináuence perceptions and expectations about stock returns, and financial behavior. We also find evidence of mindless imitation of peers in the outer social circle, but this does not permeate as many layers of financial behavior as informative social interactions do.
We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on results from the true model. We apply our method to a calibrated affine model and fine that relative wealth equivalent losses are below 1.16% if the jump size is stochastic and below 1% if the jump size is constant and γ ≥ 5. We perform robustness checks for various levels of risk-aversion, expected jump size, and jump intensity.
We consider the continuous-time portfolio optimization problem of an investor with constant relative risk aversion who maximizes expected utility of terminal wealth. The risky asset follows a jump-diffusion model with a diffusion state variable. We propose an approximation method that replaces the jumps by a diffusion and solve the resulting problem analytically. Furthermore, we provide explicit bounds on the true optimal strategy and the relative wealth equivalent loss that do not rely on quantities known only in the true model. We apply our method to a calibrated affine model. Our findings are threefold: Jumps matter more, i.e. our approximation is less accurate, if (i) the expected jump size or (ii) the jump intensity is large. Fixing the average impact of jumps, we find that (iii) rare, but severe jumps matter more than frequent, but small jumps.
Extending the data set used in Beyer (2009) to 2017, we estimate I(1) and I(2) money demand models for euro area M3. After including two broken trends and a few dummies to account for shifts in the variables following the global financial crisis and the ECB's non-standard monetary policy measures, we find that the money demand and the real wealth relations identified in Beyer (2009) have remained remarkably stable throughout the extended sample period. Testing for price homogeneity in the I(2) model we find that the nominal-to-real transformation is not rejected for the money relation whereas the wealth relation cannot be expressed in real terms.
The complexities of geopolitical events, financial and fiscal crises, and the ebb and flow of personal life circumstances can weigh heavily on individuals’ minds as they make critical economic decisions. To investigate the impact of cognitive load on such decisions, the authors conducted an incentivized online experiment involving a representative sample of 2,000 French households. The results revealed that exposure to a taxing and persistent cognitive load significantly reduced consumption, particularly for individuals under the threat of furlough, while simultaneously increasing their account balances, particularly for those not facing such employment uncertainty. These effects were not driven by supply constraints or a worsening of credit constraints. Instead, cognitive load primarily affected the optimality of the chosen policy rules and impaired the ability of the standard economic model to accurately predict consumption patterns, although this effect was less pronounced among college-educated subjects
Abundant studies show that individuals often struggle and frequently fail to form a correct perception of how much they are worth in terms of income or net wealth, both in absolute terms and relative to others. The authors find that wealth misperception arises even in a frictionless environment. They show that this wealth misperception is related to low cognitive abilities and inattention, and that subjects who misperceive wealth have a greater tendency to borrow and spend out of gains. A standard optimal consumption choice model, enriched with a rational but inattentive agent à la Gabaix aligns the key experimental findings.
We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique—neural networks—to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers’ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts’ systematic biases in expectations formation—and identify ex ante situations in which such biases are likely to arise.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
How do insiders trade?
(2016)
We characterize how informed investors trade in the options market ahead of corporate news when they receive private, but noisy, information about (i) the timing of the announcement and (ii) its impact on stock prices. Our theoretical framework generates a rich set of predictions about the insiders’ behavior and their maximum expected returns. Three different analyses offer empirical support for our approach. First, predicted trades resemble illegal insider trades documented in SEC litigation cases with insiders being more likely to trade in options that offer higher expected returns. Second, pre-announcement patterns in unusual activity in the options market ahead of significant corporate news are consistent with the predictions of our framework. We employ our approach to characterize informed trading ahead of twelve different types of news including the announcement of earnings, corporate guidance, M&As, product innovations, management changes, and analyst recommendations. Third, to address concerns that pre-announcement patterns are driven by speculation, we show that measures capturing trading activity in call (put) options with high expected returns predict significant positive (negative) corporate news in the aggregate cross-section.
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.
We provide a comprehensive analysis of the determinants of trading in the sovereign credit default swaps (CDS) market, using weekly data for single-name sovereign CDS from October 2008 to September 2015. We describe the anatomy of the sovereign CDS market, derive a law of motion for gross positions and their components, and identify the key factors that drive the cross-sectional and time-series properties of trading volume and net notional amounts outstanding. While a single principal component accounts for 54 percent of the variation in sovereign CDS spreads, the largest common factor explains only 7 percent of the variation in sovereign CDS net notional amounts outstanding. Moreover, unlike for CDS spreads, common global factors explain very little of the variation in sovereign CDS trading and net notional amounts outstanding, suggesting that it is driven primarily by idiosyncratic country risk. We analyze several local and regional channels that may explain the trading in sovereign CDS: (a) country-specific credit risk shocks, including changes in a country's credit rating and related outlook changes, (b) the announcement and issuance of domestic and international debt, (c) macroeconomic sentiment derived from conventional and unconventional monetary policy, macro-economic news and shocks, and (d) regulatory channels, such as changes in bank capital adequacy requirements. All our findings suggest that sovereign CDS are more likely used for hedging than for speculative purposes.
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.
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.
The case for corona bonds
(2020)
Corona bonds are feasible and important to preserve the European project. We set out a number of principles that might serve as a blueprint for the European institutions. Importantly, Corona bonds could be issued through a new public law entity and include all the safeguards required for the protection of the fundamental values of the EU. This proposal is pragmatic in the sense that it facilitates the choice European leaders have to make now; necessary to secure the resilience of the European Union. The political risks are significantly higher now than in 2010. The gargantuan challenge of tackling the combined impact of climate change, migration, digitalization, geopolitical shifts, and the spread of autocracy, requires leadership and joint action by the Council and the Eurogroup.
The paper compares provision of public infrastructure via public-private partnerships (PPPs) with provision under government management. Due to soft budget constraints of government management, PPPs exert more effort and therefore have a cost advantage in building infrastructure. At the same time, hard budget constraints for PPPs introduce a bankruptcy risk and bankruptcy costs. Consequently, if bankruptcy costs are high, PPPs may be less efficient than public management, although this does not result from PPPs’ higher interest costs.
We study self- and cross-excitation of shocks in the Eurozone sovereign CDS market. We adopt a multivariate setting with credit default intensities driven by mutually exciting jump processes, to capture the salient features observed in the data, in particular, the clustering of high default probabilities both in time (over days) and in space (across countries). The feedback between jump events and the intensity of these jumps is the key element of the model. We derive closed-form formulae for CDS prices, and estimate the model by matching theoretical prices to their empirical counterparts. We find evidence of self-excitation and asymmetric cross-excitation. Using impulse-response analysis, we assess the impact of shocks and a potential policy intervention not just on a single country under scrutiny but also, through the effect on cross-excitation risk which generates systemic sovereign risk, on other interconnected countries.
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.
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.
In an experimental setting in which investors can entrust their money to traders, we investigate how compensation schemes affect liquidity provision and asset prices. Investors face a trade-off between risk and return. At the benefit of a potentially higher return, they can entrust their money to a trader. However this investment is risky, as the trader might not be trustworthy. Alternatively, they can opt for a safe but low return. We study how subjects solve this trade-off when traders are either liable for losses or not, and when their bonuses are either capped or not. Limited liability introduces a conflict of interest because it makes traders value the asset more than investors. To limit losses, investors should thus restrict liquidity provision to force traders to trade at a lower price. By contrast, bonus caps make traders value the asset less than investors. This should encourage liquidity provision and decrease prices. In contrast to these predictions, we find that under limited liability investors contribute to asset price bubbles by increasing liquidity provision and that caps fail to tame bubbles. Overall, giving investors skin in the game fosters financial stability.
Previous research has documented strong peer effects in risk taking, but little is known about how such social influences affect market outcomes. The consequences of social interactions are hard to isolate in financial data, and theoretically it is not clear whether peer effects should increase or decrease risk sharing. We design an experimental asset market with multiple risky assets and study how exogenous variation in real-time information about the portfolios of peer group members affects aggregate and individual risk taking. We find that peer information ameliorates under-diversification that occurs in a market without such information. One reason is that peer information increases risk aversion and induces a concern for relative income position that may reduce or amplify risk taking, depending on whether the context highlights the most or least successful trader. Thus, contrary to conventional wisdom, we show that social interactions may help to reduce earnings volatility in financial markets, and we discuss implications for institutional design.
Do markets correct individual behavioral biases? In an experimental asset market, we compare the outcomes of a standard market economy to those of a an island economy that removed market interactions. We observe asset price bubbles in the market economy while prices are stable in the island economy. We also find that subjects took more risk following larger losses, resulting in larger prices and consistent with a gambling for resurrection motive. This motive can translate into bubbles in the market economy because higher prices increase average losses and thus reinforce the desire to resurrect. By contrast, the absence of such a strategic complementarity in island economies can explain the more stable outcome. These results suggest that markets do not correct behavioral biases, rather the contrary.
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
We investigate the relationship between anchoring and the emergence of bubbles in experimental asset markets. We show that setting a visual anchor at the fundamental value (FV) in the first period only is sufficient to eliminate or to significantly reduce bubbles in laboratory asset markets. If no FV-anchor is set, bubble-crash patterns emerge. Our results indicate that bubbles in laboratory environments are primarily sparked in the first period. If prices are initiated around the FV, they stay close to the FV over the entire trading horizon. Our insights can be related to initial public offerings and the interaction between prices set on pre-opening markets and subsequent intra-day price dynamics.
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.
Liquidity derivatives
(2022)
It is well established that investors price market liquidity risk. Yet, there exists no financial claim contingent on liquidity. We propose a contract to hedge uncertainty over future transaction costs, detailing potential buyers and sellers. Introducing liquidity derivatives in Brunnermeier and Pedersen (2009) improves financial stability by mitigating liquidity spirals. We simulate liquidity option prices for a panel of NYSE stocks spanning 2000 to 2020 by fitting a stochastic process to their bid-ask spreads. These contracts reduce the exposure to liquidity factors. Their prices provide a novel illiquidity measure refllecting cross-sectional commonalities. Finally, stock returns significantly spread along simulated prices.
Peer effects can lead to better financial outcomes or help propagate financial mistakes across social networks. Using unique data on peer relationships and portfolio composition, we show considerable overlap in investment portfolios when an investor recommends their brokerage to a peer. We argue that this is strong evidence of peer effects and show that peer effects lead to better portfolio quality. Peers become more likely to invest in funds when their recommenders also invest, improving portfolio diversification compared to the average investor and various placebo counterfactuals. Our evidence suggests that social networks can provide good advice in settings where individuals are personally connected.
What are the aggregate and distributional consequences of the relationship be-tween an individual’s social network and financial decisions? Motivated by several well-documented facts about the influence of social connections on financial decisions, we build and calibrate a model of stock market participation with a social network that emphasizes the interplay between connectivity and network structure. Since connections to informed agents help spread information, there is a pivotal role for factors that determine sorting among agents. An increase in the average number of connections raises the average participation rate, mostly due to richer agents. A higher degree of sorting benefits richer agents by creating clusters where information spreads more efficiently. We show empirical evidence consistent with the importance of connectivity and sorting. We discuss several new avenues for future research into the aggregate impact of peer effects in finance.
Small and medium-sized firms typically obtain capital via bank financing. They often rely on a mixture of relationship and arm’s-length banking. This paper explores the reasons for the dominance of heterogeneous multiple banking systems. We show that the incidence of inefficient credit termination and subsequent firm liquidation is contingent on the borrower’s quality and on the relationship bank’s information precision. Generally, heterogeneous multiple banking leads to fewer inefficient credit decisions than monopoly relationship lending or homogeneous multiple banking, provided that the relationship bank’s fraction of total firm debt is not too large.
Small and medium-sized firms typically obtain capital via bank financing. They often rely on a mixture of relationship and arm’s-length banking. This paper explores the reasons for the dominance of heterogeneous multiple banking systems. We show that the incidence of inefficient credit termination and subsequent firm liquidation is contingent on the borrower’s quality and on the relationship bank’s information precision. Generally, heterogeneous multiple banking leads to fewer inefficient credit decisions than monopoly relationship lending or homogeneous multiple banking, provided that the relationship bank’s fraction of total firm debt is not too large.
Doing safe by doing good : ESG investing and corporate social responsibility in the U.S. and Europe
(2019)
This paper examines the profitability of investing according to environmental, social and governance (ESG) criteria in the U.S. and Europe. Based on data from 2003 to 2017, we show that a portfolio long in stocks with the highest ESG scores and short in those with the lowest scores yields a significantly negative abnormal return. Interestingly, this is caused by the strong positive return of firms with the lowest ESG activity. As we find that increasing ESG scores reduce firm risk (particularly downside risk), this hints at an insurance-like character of corporate social responsibility: Firms with low ESG activity need to offer a corresponding risk premium. The perception of ESG as an insurance can be shown to be stronger in more volatile capital markets for U.S. firms, but not for European firms. Socially responsible investment may therefore be of varying attractiveness in different market phases.
Open-end real estate funds are of particular importance in the German bankdominated financial system. However, recently the German open-end fund industry came under severe distress which triggered a broad discussion of required regulatory interventions. This paper gives a detailed description of the institutional structure of these funds and of the events that led to the crisis. Furthermore, it applies recent banking theory to open-end real estate funds in order to understand why the open-end fund structure was so prevalent in Germany. Based on these theoretical insights we evaluate the various policy recommendation that have been raised.
This paper examines the effect of imperfect labor market competition on the efficiency of compensation schemes in a setting with moral hazard, private information and risk-averse agents. Two vertically differentiated firrms compete for agents by offering contracts with fixed and variable payments. Vertical differentiation between firms leads to endogenous, type-dependent exit options for agents. In contrast to screening models with perfect competition, we find that existence of equilibria does not depend on whether the least-cost separating allocation is interim efficient. Rather, vertical differentiation allows the inferior firm to offer (cross-)subsidizing fixed payments even above the interim efficient level. We further show that the efficiency of variable pay depends on the degree of competition for agents: For small degrees of competition, low-ability agents are under-incentivized and exert too little effort. For large degrees of competition, high-ability agents are over-incentivized and bear too much risk. For intermediate degrees of competition, however, contracts are second-best despite private information.
This study examines the role of actual and perceived financial sophistication (i.e., financial literacy and confidence) for individuals' wealth accumulation. Using survey data from the German SAVE initiative, we find strong gender- and education-related differences in the distribution of the two variables and their effects on wealth: As financial literacy rises in formal education, whereas confidence increases in education for men but decreases for women, we observe that women become strongly underconfident with higher education, while men remain overconfident.Regarding wealth accumulation, we show that financial literacy has a positive effect that is stronger for women than for men and that is increasing (decreasing) in education for women (men). Confidence, however, supports only highly-educated men's wealth. When considering different channels for wealth accumulation, we observe that financial literacy is more important for current financial market participation, whereas confidence is more strongly associated with future-oriented financial planning. Overall, we demonstrate that highly-educated men's wealth levels benefit from their overconfidence via all financial decisions considered, but highly-educated women's financial planning suffers from their underconfidence. This may impair their wealth levels in old age.
We analyze the market reaction to the sentiment of the CEO speech at the Annual General Meeting (AGM). As the AGM is typically preceded by several information disclosures, the CEO speech may be expected to contribute only marginally to investors’ decision-making. Surprisingly, however, we observe from the transcripts of 338 CEO speeches of German corporates between 2008 and 2016 that their sentiment is significantly related to abnormal stock returns and trading volumes following the AGM. Using a novel business-specific German dictionary based on Loughran and McDonald (2011), we find a negative association of the post-AGM returns with the speeches’ negativity and a positive association with the speeches’ relative positivity (i.e. positivity relative to negativity). Relative positivity moreover corresponds with a lower trading volume in a short time window surrounding the AGM. Investors hence seem to perceive the sentiment of CEO speeches at AGMs as a valuable indicator of future firm performance.
We examine firms’ simultaneous choice of investment, debt financing and liquidity in a large sample of US corporates between 1980 and 2014. We partition the sample according to the firms’ financial constraints and their needs to hedge against future shortfalls in operating income. In contrast to earlier work, our joint estimation approach shows that cash flows affect the corporate decisions of unconstrained firms more strongly than those of constrained firms. Investment-cash flow sensitivities are particularly intense for unconstrained firms with high hedging needs. Investment opportunities (as proxied by Q), however, play a larger role for constrained firms with the effects being strongest in case of low hedging needs. Interestingly, constrained firms with low hedging needs are found to employ more debt to finance their investment opportunities and build up significant cash holdings at the same time. Our results hence indicate overinvestment behavior for unconstrained firms but no underinvestment for constrained firms if they have low hedging needs.
In this paper, we propose a model of credit rating agencies using the global games framework to incorporate information and coordination problems. We introduce a refined utility function of a credit rating agency that, additional to reputation maximization, also embeds aspects of competition and feedback effects of the rating on the rated firms. Apart from hinting at explanations for several hypotheses with regard to agencies' optimal rating assessments, our model suggests that the existence of rating agencies may decrease the incidence of multiple equilibria. If investors have discretionary power over the precision of their private information, we can prove that public rating announcements and private information collection are complements rather than substitutes in order to secure uniqueness of equilibrium. In this respect, rating agencies may spark off a virtuous circle that increases the efficiency of the market outcome.
This paper studies the use of performance pricing (PP) provisions in debt contracts and compares accounting-based with rating-based pricing designs. We find that rating-based provisions are used by volatile-growth borrowers and allow for stronger spread increases over the credit period. Accounting-based provisions are employed by opaque-growth borrowers and stipulate stronger spread reductions. Further, a higher spread-increase potential in rating-based contracts lowers the spread at the loan’s inception and improves the borrower’s performance later on. In contrast, a higher spread-decrease potential in accounting-based contracts lowers the initial spread and raises the borrower’s leverage afterwards. The evidence indicates that rating-based contracts are indeed employed for different reasons than accounting-based contracts: the former to signal a borrower’s quality, the latter to mitigate investment inefficiencies.
We examine how a firms' investment behavior affects the investment of a neighboring firm. Economic theory yields ambiguous predictions regarding the direction of firm peer effects and consistent with earlier work, we find that firms display similar investment behavior within an area using OLS analysis. Exploiting time-variation in the rise of U.S. states' corporate income taxes and utilizing heterogeneity in firms' exposure to increases in corporate income tax rates, we identify the causal impact of local firms' investments. Using this as an instrumental variable in a 2SLS estimation, we find that an increases in local firms' investment reduces the investment of a local peer firm. This effect is more pronounced if local competition among firms is stronger and supports theories that firm investments are strategic substitutes due to competition.
Public employees in many developing economies earn much higher wages than similar privatesector workers. These wage premia may reflect an efficient return to effort or unobserved skills, or an inefficient rent causing labor misallocation. To distinguish these explanations, we exploit the Kenyan government’s algorithm for hiring eighteen-thousand new teachers in 2010 in a regression discontinuity design. Fuzzy regression discontinuity estimates yield a civil-service wage premium of over 100 percent (not attributable to observed or unobserved skills), but no effect on motivation, suggesting rent-sharing as the most plausible explanation for the wage premium.
From 1963 through 2015, idiosyncratic risk (IR) is high when market risk (MR) is high. We show that the positive relation between IR and MR is highly stable through time and is robust across exchanges, firm size, liquidity, and market-to-book groupings. Though stock liquidity affects the strength of the relation, the relation is strong for the most liquid stocks. The relation has roots in fundamentals as higher market risk predicts greater idiosyncratic earnings volatility and as firm characteristics related to the ability of firms to adjust to higher uncertainty help explain the strength of the relation. Consistent with the view that growth options provide a hedge against macroeconomic uncertainty, we find evidence that the relation is weaker for firms with more growth options.
This paper aims at an improved understanding of the relationship between monetary policy and racial inequality. We investigate the distributional effects of monetary policy in a unified framework, linking monetary policy shocks both to earnings and wealth differentials between black and white households. Specifically, we show that, although a more accommodative monetary policy increases employment of black households more than white households, the overall effects are small. At the same time, an accommodative monetary policy shock exacerbates the wealth difference between black and white households, because black households own less financial assets that appreciate in value. Over multi-year time horizons, the employment effects are substantially smaller than the countervailing portfolio effects. We conclude that there is little reason to think that accommodative monetary policy plays a significant role in reducing racial inequities in the way often discussed. On the contrary, it may well accentuate inequalities for extended periods.
Using a novel experimental design, I test how the exposure to information about a group’s relative performance causally affects the members’ level of identification and thereby their propensity to harm affiliates of comparison groups. I find that both, being informed about a high and poor relative performance of the ingroup similarly fosters identification. Stronger ingroup identification creates increased hostility against the group of comparison. In cases where participants learn about poor relative performance, there appears to be a direct level effect additionally elevating hostile discrimination. My findings shed light on a specific channel through which social media may contribute to intergroup fragmentation and polarization.
How does group identity affect belief formation? To address this question, we conduct a series of online experiments with a representative sample of individuals in the US. Using the setting of the 2020 US presidential election, we find evidence of intergroup preference across three distinct components of the belief formation cycle: a biased prior belief, avoid-ance of outgroup information sources, and a belief-updating process that places greater (less) weight on prior (new) information. We further find that an intervention reducing the salience of information sources decreases outgroup information avoidance by 50%. In a social learn-ing context in wave 2, we find participants place 33% more weight on ingroup than outgroup guesses. Through two waves of interventions, we identify source utility as the mechanism driving group effects in belief formation. Our analyses indicate that our observed effects are driven by groupy participants who exhibit stable and consistent intergroup preferences in both allocation decisions and belief formation across all three waves. These results suggest that policymakers could reduce the salience of group and partisan identity associated with a policy to decrease outgroup information avoidance and increase policy uptake.
Incentives, self-selection, and coordination of motivated agents for the production of social goods
(2021)
We study, theoretically and empirically, the effects of incentives on the self-selection and coordination of motivated agents to produce a social good. Agents join teams where they allocate effort to either generate individual monetary rewards (selfish effort) or contribute to the production of a social good with positive effort complementarities (social effort). Agents differ in their motivation to exert social effort. Our model predicts that lowering incentives for selfish effort in one team increases social good production by selectively attracting and coordinating motivated agents. We test this prediction in a lab experiment allowing us to cleanly separate the selection effect from other effects of low incentives. Results show that social good production more than doubles in the low- incentive team, but only if self-selection is possible. Our analysis highlights the important role of incentives in the matching of motivated agents engaged in social good production.
In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.
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.
Using experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.
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.
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.
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.