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In a series of experiments, we provide evidence that people pay special attention to the probability of losing. We first analyze this behavior in the typically used one-shot choice tasks. We then extend our analysis to repeated decisions in choice tasks, as well as allocation and investment tasks. Additionally, we test both decision making under risk and under gradually removed uncertainty, as with decisions from experience. Our findings of explicit attention to loss probabilities contradict the predictions of normative and descriptive decision theories, such as Expected Utility Theory and (Cumulative) Prospect Theory. We suggest a value function with a jump rather than a kink at the reference point, which separates gains and losses.

Conditional heteroskedasticity of the error terms is a common occurrence in financial factor models, such as the CAPM and Fama-French factor models. This feature necessitates the use of heteroskedasticity consistent (HC) standard errors to make valid inference for regression coefficients. In this paper, we show that using weighted least squares (WLS) or adaptive least squares (ALS) to estimate model parameters generally leads to smaller HC standard errors compared to ordinary least squares (OLS), which translates into improved inference in the form of shorter confidence intervals and more powerful hypothesis tests. In an extensive empirical analysis based on historical stock returns and commonly used factors, we find that conditional heteroskedasticity is pronounced and that WLS and ALS can dramatically shorten confidence intervals compared to OLS, especially during times of financial turmoil.

This article demonstrates that low bank capital carries a negative externality because it amplifies local shock spillovers. We exploit a natural disaster that is transmitted to firms in nondisaster areas via their banks. Firms connected to a strongly disaster-exposed bank with lowest-quartile capitalization significantly reduce their total borrowing by 6.6% and tangible assets by 6.9% compared to similar firms connected to a well-capitalized bank. These findings translate to negative regional effects on GDP and unemployment. Additionally, following a disaster event, banks reduce their exposure to currently unaffected but generally disaster-prone areas.

Recent literature has documented substantial arbitrage opportunities in markets for cryptocurrencies. Given these rather surprising findings, we re-examine the exchange rates of four cryptocurrencies on 21 exchanges with data from October 2018 to June 2019. Our results first provide evidence that price differences exist based on our created arbitrage indices, however, at only a fraction of values reported in previous literature. We then analyze the drivers of these price differences, identifying exchange-specific factors to have the most important role. Based on this, we test actual arbitrage strategies for specific cryptocurrency and fiat currency pairs, finding, in contrast to previous literature, that arbitrage opportunities hardly exist.

Fintech and big tech companies are making rapid inroads into credit markets. We hand construct a global database of fintech and big tech lending volumes for 79 countries over 2013-2018. Using a panel regression analysis, we find these new forms of digital lending are larger in countries with higher GDP per capita (albeit at a declining rate), where banking sector mark-ups are higher, and where banking regulation is less stringent. We also find that these alternative forms of credit are more developed where the ease of doing business is greater, investor protection disclosure and the efficiency of the judicial system are more advanced, and where bond and equity markets are more developed. Overall, fintech and big tech credit seem to complement other forms of credit, rather than substitute for them.

Using a difference-in-differences approach and relying on conftdential supervisory data and an unique proprietary data set available at the European Central Bank related to the 2016 EU-wide stress test, this paper presents novel empirical evidence that supervisory scrutiny associated to stress testing has a disciplining effect on bank risk. We ftnd that banks that participated in the 2016 EU-wide stress test subsequently reduced their credit risk relative to banks that were not part of this exercise. Relying on new metrics for supervisory scrutiny that measure the quantity, potential impact, and duration of interactions between banks and supervisors during the stress test, we find that the disciplining effect is stronger for banks subject to more intrusive supervisory scrutiny during the exercise.

https://ssrn.com/abstract=3202916; We show that banker bonuses cannot be understood exclusively as incentive contracts, but also incorporate a significant risk sharing dimension between bank shareholders and bank employees. This contrasts with the conventional view whereby diversified shareholders fully insure risk averse employees. However, financial frictions imply that shareholder value is concave in a bank's cash reserves---making shareholders effectively risk averse. The optimal contract between shareholders and employees then involves some degree of risk sharing. Using extensive payroll data on 1.26 million bank employee years in the Austrian, German, and Swiss banking sectors, we show that the structure of bonus pay within and across banks is compatible with an economically significant risk sharing motive, but difficult to rationalize based on incentive theories of bonus pay only.

Theories suggest that asset encumbrance, the ring-fencing of certain assets for protected debtholders, can affect banks’ risk taking and lead to funding instability. We test these hypotheses using a unique, hand-collected dataset on outstanding covered bonds issued by a sample of listed European banks. Our results suggest that the effect of asset encumbrance on risk depends on the proportion of debtholders exerting market discipline and on the bank’s liquidity buffers. We deal with concerns regarding omitted variables and reverse causality using several fixed effects estimations and an instrumental variables approach.

We adapt the design of four experimental studies on retirement decision-making and conduct replications with a larger online sample from the broader population. We replicate most of the main effects of the original studies. In particular, we confirm that consumption decisions are less efficient when subjects need to borrow from the future than when they need to save from the present. When subjects collect retirement benefits as lump sum instead of annuities, they choose to retire later, as suggested by the original study. We also confirm that savings are higher when they are incentivized with matching contributions than when incentivized with tax rebates. However, when faced with varying survival risks, subjects in our replication make only partial adjustments to spending paths when ambiguity is reduced. We also propose a further experimental research agenda in related topics and discuss practical issues on subject recruitment, attrition, and redesign of complex tasks.

Purpose This paper aims to empirically describe the general characteristics and the investment behavior of high-net-worth individuals (HNWIs) who pursue impact investing. Design/methodology/approach Data was collected from members of a global impact investor network, using an online questionnaire, a portfolio-data collection tool and semi-structured interviews. Findings Wealthy private impact investors are largely similar in terms of their general characteristics and investment behavior, but they diverge in their interest in specific Sustainable Development Goals (SDGs). They tend to be strongly values-driven and to adopt an investment time horizon of 7+ years for their impact investments, which they expect to yield financial returns that are no different from those of traditional investments. Interestingly, these investors perceive the well-established sustainable investing strategies of exclusion, environmental, social and governance (ESG) integration and best-in-class as not having high impact-generating potential. Practical implications Suggestions are provided about how wealthy private investors could use the findings to improve their impact investment decisions. Advice is offered to investment professionals on how to optimize impact investment products and services for this economically and societally highly relevant target group. Originality/value To the best of the authors’ knowledge, this is the first scientific study to investigate the general characteristics and investment behavior of HNWIs who pursue impact investing. HNWIs have great relevance for financial markets yet they are out of reach for most researchers. As a result, they are poorly understood, and apparently also often misunderstood, which has substantial economic and social implications that this paper helps mitigate.

Economic theories posit conflicting hypotheses on how wealth inequality affects entrepre-neurial dynamism. We investigate the impact of wealth inequality on business dynamics by constructing local measures of household wealth inequality based on financial rents, home equity, and 1880 farmland. We then identify the effect of wealth inequality on entrepre-neurship by instrumenting it with land distribution under the 1862 Homestead Act. Wealth inequality decreases firm entry and exit, and the proportion of high-tech businesses across metropolitan statistical areas. Wealth inequality also lowers the supply of public goods, such as education. Growth in income per capita consequently lags.

Disclosure of climate-related financial risks greatly helps investors assess companies’ preparedness for climate change. Voluntary disclosures such as those based on the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD) are being hailed as an effective measure for better climate risk management. We ask whether this expectation is justified. We do so by training ClimateBERT, a deep neural language model fine-tuned based on the language model BERT. In analyzing the disclosures of TCFD-supporting firms, ClimateBERT comes to the sobering conclusion that the firms’ TCFD support is mostly cheap talk and that firms cherry-pick to report primarily non-material climate risk information., Finance Research Letters, 47 (Part B), ISSN:1544-6123

Do credit ratings help enforce market discipline on banks? Analyzing a uniquely comprehensive data set consisting of 1,081 rating change announcements for 154 international financial institutions between January 2004 and December 2015, we find that rating downgrades for internal reasons, such as adverse changes in the operating performance or capital structure of banks, are associated with a significant credit default swap spread widening. However, this widening only occurs for banks that are not perceived as to be Too-Big-to-Fail (TBTF). Our findings question the reliability of credit ratings as a tool to discipline TBTF banks and suggest that regulatory monitoring should remain the main mechanism for disciplining these banks.

Small and micro-enterprises are usually majority-owned by entrepreneurs. Using a unique sample of loan applications from such firms, we study the role of owners’ gender in bank credit decisions and post-credit-decision firm outcomes. We find that, ceteris paribus, female entrepreneurs are more prudent loan applicants than are males because they are less likely to apply for credit or to default after loan origination. The relatively more aggressive behavior of male applicants pays off, however, in terms of higher average firm performance after loan origination.

Market reactions to the 2019 novel coronavirus disease (COVID-19) provide new insights into how real shocks and financial policies drive firm value. Initially, internationally oriented firms, especially those more exposed to trade with China, underperformed. As the virus spread to Europe and the United States, corporate debt and cash holdings emerged as important value drivers, relevant even after the Fed intervened in the bond market. The content and tone of conference calls mirror this development over time. Overall, the results illustrate how anticipated real effects from the health crisis, a rare disaster, were amplified through financial channels. (JEL G01, G12, G14, G32, F14) Received: May 27, 2020; editorial decision June 16, 2020 by Editor Andrew Ellul. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Strong regulatory actions are needed to combat climate change, but climate policy uncertainty makes it difficult for investors to quantify the impact of future climate regulation. We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger for firms with more carbon-intense business models. For carbon-intense firms, the cost of protection against downside tail risk is magnified at times when the public’s attention to climate change spikes, and it decreased after the election of climate change skeptic President Trump.

The purpose of this work is to develop an evolutionary finance model with a risk-free asset playing the role of a numeraire. The model describes a market where one risk-free and several “short-lived” risky assets (securities) are traded in discrete time. The risky securities live one period, yield random payoffs at the end of it, and then are re-born at the beginning of the next period. The main goal of the study is to identify investment strategies that make it possible for an investor to “survive” in the market selection process. It is shown that a strategy of this kind exists, is in a sense asymptotically unique and can be described by a simple explicit formula amenable for quantitative investment analysis.

International audience; Between 2010 and 2012 and with bank stability as the ultimate target, five European countries implemented a tax levy on banks’ liabilities thereby decreasing the cost of equity relative to the cost of debt. Using a difference-in-differences approach we assess the impact of this tax levy on banks’ participation in the syndicated loan market. We further investigate the impact of the tax levy along bank size and capital structure. We find that banks located in countries where the tax levy was implemented supply more credit. This increase is more significant for larger lenders and banks that are more capital constrained.

We develop the first top-down method to estimate the greenness of financial portfolios, in terms of alignment to the EU Taxonomy for sustainable activities. We also develop a method to estimate, at the same time, the portfolio exposure to climate transition risk. We provide sector-level, standardized and transparent coefficients for both estimates, based on definitions of greenness and transition risk that are applicable across countries. We analyse the portfolios of Euro Area investors in 2020, based on the confidential Securities Holdings Statistics of the European Central Bank. We find that, overall, the greenness of Euro Area investors' portfolios is lower than their exposure to transition risk (1.3% vs. 5.5%). Across financial institutions, we estimate greenness and exposure to transition risk, respectively, at 1.4% and 6.1% for investment funds, at 0.3% and 1.7% for banks and at 1.2% and 5.0% for insurers. Our analysis also shows that investors with large amounts invested in green activities can have at the same time large exposures to transition risk.

We establish general “collapse to the mean” principles that provide conditions under which a law-invariant functional reduces to an expectation. In the convex setting, we retrieve and sharpen known results from the literature. However, our results also apply beyond the convex setting. We illustrate this by providing a complete account of the “collapse to the mean” for quasiconvex functionals. In the special cases of consistent risk measures and Choquet integrals, we can even dispense with quasiconvexity. In addition, we relate the “collapse to the mean” to the study of solutions of a broad class of optimisation problems with law-invariant objectives that appear in mathematical finance, insurance, and economics. We show that the corresponding quantile formulations studied in the literature are sometimes illegitimate and require further analysis.

Governments around the world are tackling the COVID-19 pandemic with a mix of public health, fiscal, macroprudential, monetary, and/or market-based policies. We assess the impact of the pandemic in Europe on sovereign CDS spreads using an event study methodology. We find that a higher number of cases and deaths and public health containment responses significantly increase the uncertainty among investors in European government bonds. Other governmental policies magnify the effect in the short run as supply chains are disrupted. Moreover, an increased debt-to-GDP ratio significantly boosts the cumulative abnormal change of CDS spreads, which indicates that investors are concerned about countries that are too indebted and thus have a limited capacity to intervene and provide fiscal stimuli and emergency fiscal packages to businesses and households.

It is estimated that hundreds of thousands of tons of solar panel waste are going to be produced yearly just in the United States from the year 2035 on, most of which could be recycled. This paper estimates the amount of scrap material to be produced from solar photovoltaic panels decommissioning and determines the optimal date and location to establish centralized or regional recycling centers to better deal with this issue on its early stages, between the years 2024 and 2042. Solar panel recycling could become a multi-billion USD industry over that time, however the main challenge today is to keep its overall costs down while allowing for the majority of panels to be recycled. Real Options Analysis is deployed to assess the optimal solution to face this challenge. This approach allows determining the optimal time and location to invest in recycling centers and the best strategy to undertake among different alternatives. The goal of this paper is to set a cornerstone for dealing with solar panel decommissioning and recycling at the end of their useful life in the United States, and we also determine a model that accounts for optimal location of the recycling facilities, which is a novel approach. This paper also offers a new application of the ROA modeling for estimating the optimal investment date for solar panel recycling plants from the investor perspective of the U.S. government in Washington D.C. Further applications of the model proposed in this work could allow for a similar analysis at an international level.

We consider a stochastic model of a financial market with one-period assets and endogenous asset prices. The model was initially developed and analyzed in the context of Evolutionary Finance with the main focus on questions of “survival and extinction” of investment strategies (portfolio rules). In this paper we view the model from a different, game-theoretic, perspective and analyze Nash equilibrium properties of survival portfolio rules.

In a capital adequacy framework, risk measures are used to determine the minimal amount of capital that a financial institution has to raise and invest in a portfolio of prespecified eligible assets in order to pass a given capital adequacy test. From a capital efficiency perspective, it is important to be able to do so at the lowest possible cost and to identify the corresponding portfolios, or, equivalently, their payoffs. We study the existence and uniqueness of such optimal payoffs as well as their behavior under a perturbation or an approximation of the underlying capital position. This behavior is naturally linked to the continuity properties of the set‐valued map that associates to each capital position the corresponding set of optimal eligible payoffs. Upper continuity can be ensured under fairly natural assumptions. Lower continuity is typically less easy to establish. While it is always satisfied in a polyhedral setting, it generally fails otherwise, even when the reference risk measure is convex. However, lower continuity can often be established for eligible payoffs that are close to being optimal. Besides capital adequacy, our results have a variety of natural applications to pricing, hedging, and capital allocation problems.

We study local loan conditions when, under external pressure, banks close branches. After the closure of nearby branches of their credit granting banks, firms that locally and hurriedly transfer to other banks receive an equivalent interest rate. However, and in stark contrast, where branch closures do not take place firms that purposely switch banks receive a 63 basis points discount. At the same time, the loan default rate for the (more expensive) transfer loans is on average a full percentage point lower than that for the (cheaper) switching loans. This suggests that firms that establish new relationships after their bank branch closes are “better” than regular switchers in terms of unobservable characteristics. Taken together, these findings provide evidence of losses for firms when banks close branches, even if local markets remain competitive.

Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new robust orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional return distribution is multivariate generalized hyperbolic and the dispersion matrix dynamics are driven by the leading factors in a principle component decomposition. Each of these leading factors is endowed with a univariate GARCH structure, while the remaining eigenvalues are kept constant over time. Joint maximum likelihood estimation of all model parameters is performed via an expectation maximization algorithm, and is applicable in high dimensions. The new model generates realistic correlation forecasts even for large asset universes and captures rising pairwise correlations in periods of market distress better than numerous competing models. Moreover, it leads to improved forecasts of an eigenvalue-based financial systemic risk indicator. Crucially, it generates portfolios with much lower turnover and superior risk-adjusted returns net of transaction costs, outperforming the equally weighted strategy even under high transaction fees.

We study the impact of monetary policy on the supply of bank credit when bank lending is denominated in foreign currencies. Accessing a comprehensive supervisory dataset from Hungary, we find that the supply of bank credit in a foreign currency is less sensitive to changes in domestic monetary conditions than the equivalent supply in the domestic currency. Changes in foreign monetary conditions similarly affect bank lending more in the foreign than in the domestic currency. Hence when banks lend in multiple currencies the domestic bank lending channel is weakened and international bank lending channels become operational.

In this short paper we briefly introduce some of the works presented during the conference “Network models and stress testing for financial stability” which was held in Mexico City hosted by Banco de Mexico on September 26-27, 2017. The papers ranged from new applications of network models for financial stability analysis to stress testing at central banks and common assets contagion. The novelty and originality of most of the works make this special issue a good read for the specialists in the Financial Stability field, including academics as well as practitioners and obviously financial authorities. We hope that you find this short introduction to the special issue and the special issue itself, interesting and useful for your everyday work.

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic covariances, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return.