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The long-term distributional and welfare effects of Covid-19 school closures (2020)

Nicola Fuchs-Schündeln Dirk Krueger Alexander Ludwig Irina Popova

Using a structural life-cycle model, we quantify the long-term impact of school closures during the Corona crisis on children affected at different ages and coming from households with different parental characteristics. In the model, public investment through schooling is combined with parental time and resource investments in the production of child human capital at different stages in the children's development process. We quantitatively characterize both the long-term earnings consequences on children from a Covid-19 induced loss of schooling, as well as the associated welfare losses. Due to self-productivity in the human capital production function, skill attainment at a younger stage of the life cycle raises skill attainment at later stages, and thus younger children are hurt more by the school closures than older children. We find that parental reactions reduce the negative impact of the school closures, but do not fully offset it. The negative impact of the crisis on children's welfare is especially severe for those with parents with low educational attainment and low assets. The school closures themselves are primarily responsible for the negative impact of the Covid-19 shock on the long-run welfare of the children, with the pandemic-induced income shock to parents playing a secondary role.

Monetary policy surprises and exchange rate behavior (2020)

Refet S. Gürkaynak Hakan Kara Burçin Kısacıkoğlu Sang Seok Lee

Central banks unexpectedly tightening policy rates often observe the exchange value of their currency depreciate, rather than appreciate as predicted by standard models. We document this for Fed and ECB policy days using event studies and ask whether an information effect, where the public attributes the policy surprise to an unobserved state of the economy that the central bank is signaling by its policy may explain the abnormality. It turns out that many informational assumptions make a standard two- country New Keynesian model match this behavior. To identify the particular mechanism, we condition on multiple asset prices in the event study and model implications for these. We find that there is heterogeneity in this dimension in the event study and no model with a single regime can match the evidence. Further, even after conditioning on possible information effects driving longer term interest rates, there appear to be other drivers of exchange rates. Our results show that existing models have a long way to go in reconciling event study analysis with model-based mechanisms of asset pricing.

Predictability and the cross-section of expected returns: a challenge for asset pricing models (2020)

Christian Schlag Michael Semenischev Julian Thimme

Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria models have to satisfy to produce expected return patterns in line with the data and discuss various examples.

Machine learning sentiment analysis, COVID-19 news and stock market reactions (2020)

Michele Costola Michael Nofer Oliver Hinz Loriana Pelizzon

The possibility to investigate the impact of news on stock prices has observed a strong evolution thanks to the recent use of natural language processing (NLP) in finance and economics. In this paper, we investigate COVID-19 news, elaborated with the ”Natural Language Toolkit” that uses machine learning models to extract the news’ sentiment. We consider the period from January till June 2020 and analyze 203,886 online articles that deal with the pandemic and that were published on three platforms: MarketWatch.com, Reuters.com and NYtimes.com. Our findings show that there is a significant and positive relationship between sentiment score and market returns. This result indicates that an increase (decrease) in the sentiment score implies a rise in positive (negative) news and corresponds to positive (negative) market returns. We also find that the variance of the sentiments and the volume of the news sources for Reuters and MarketWatch, respectively, are negatively associated to market returns indicating that an increase of the uncertainty of the sentiment and an increase in the arrival of news have an adverse impact on the stock market.

The terminator of social welfare? : The economic consequences of algorithmic discrimination (2020)

Kevin Bauer Nicolas Pfeuffer Benjamin M. Abdel-Karim Oliver Hinz Michael Kosfeld

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.

On the (ir)relevance of monetary incentives in risk preference elicitation experiments (2020)

Andreas Hackethal Michael Kirchler Christine Laudenbach Michael Razen Annika Weber

Incentivized experiments in which individuals receive monetary rewards according to the outcomes of their decisions are regarded as the gold standard for preference elicitation in experimental economics. These task-related real payments are considered necessary to reveal subjects' "true preferences". Using a systematic, large-sample approach with three subject pools of private investors, professional investors, and students, we test the effect of task-related monetary incentives on risk preferences elicited in four standard experimental tasks. We find no systematic differences in behavior between subjects in the incentivized and non-incentivized regimes. We discuss implications for academic research and for applications in the field.

Beyond moral hazard arguments: the role of national deposit insurance schemes for member states’ preferences on EDIS (2020)

Mario Tümmler Matthias Thiemann

Discussions regarding the planned European Deposit Insurance Scheme (EDIS), the missing third pillar of the European Banking Union, have been ongoing since the Commission published its initial legisla-tive proposal in 2015. A breakthrough in negotiations has yet to be achieved. The gridlock on EDIS is most commonly attributed to moral hazard concerns over insufficient risk reduction harboured on the side of northern member states, particularly Germany, due to the weak state of some other member states’ banking sectors. While moral hazard based on uneven risk reduction is helpful for explaining divergent member-state preferences on the scope of necessary risk reduction, this does not explain preferences on the institutional design of EDIS. In this paper, we argue that contrary to persistent differences on necessary risk reduction, preferences regarding the institutional design of EDIS have become more closely aligned. We analyse how preferences on EDIS developed in the key member states of Germany, France, and Italy. In all sampled countries, we find path-dependent benefits con-nected to the current design of national Deposit Guarantee Schemes (DGS) that shifted preferences of the banking sector or significant subsectors in favour of retaining national DGSs. Overall, given that a compromise on risk-reduction can be accomplished, we argue that current preferences in these key member states provide an opportunity to implement EDIS in the form of a reinsurance system that maintains national DGSs in combination with a supranational fund.

The COVID-19 shock and equity shortfall: firm-level evidence from Italy (2020)

Elena Carletti Tommaso Oliviero Marco Pagano Loriana Pelizzon Marti Subrahmanyam

We employ a representative sample of 80,972 Italian firms to forecast the drop in profits and the equity shortfall triggered by the COVID-19 lockdown. A 3-month lockdown generates an aggregate yearly drop in profits of about 10% of GDP, and 17% of sample firms, which employ 8.8% of the sample’s employees, become financially distressed. Distress is more frequent for small and medium-sized enterprises, for firms with high pre-COVID-19 leverage, and for firms belonging to the Manufacturing and Wholesale Trading sectors. Listed companies are less likely to enter distress, whereas the correlation between distress rates and family firm ownership is unclear. (JEL G01, G32, G33)

Inside the ESG ratings: (dis)agreement and performance (2020)

Monica Billio Michele Costola Iva Hristova Carmelo Latino Loriana Pelizzon

We analyze the ESG rating criteria used by prominent agencies and show that there is a lack of a commonality in the definition of ESG (i) characteristics, (ii) attributes and (iii) standards in defining E, S and G components. We provide evidence that heterogeneity in rating criteria can lead agencies to have opposite opinions on the same evaluated companies and that agreement across those providers is substantially low. Those alternative definitions of ESG also a↵ect sustainable investments leading to the identification of di↵erent investment universes and consequently to the creation of di↵erent benchmarks. This implies that in the asset management industry it is extremely dicult to measure the ability of a fund manager if financial performances are strongly conditioned by the chosen ESG benchmark. Finally, we find that the disagreement in the scores provided by the rating agencies disperses the e↵ect of preferences of ESG investors on asset prices, to the point that even when there is agreement, it has no impact on financial performances.

Advertising arbitrage (2020)

Sergey Kovbasyuk Marco Pagano

Arbitrageurs with a short investment horizon gain from accelerating price discovery by advertising their private information. However, advertising many assets may overload investors' attention, reducing the number of informed traders per asset and slowing price discovery. So arbitrageurs optimally concentrate advertising on just a few assets, which they overweight in their portfolios. Unlike classic insiders, advertisers prefer assets with the least noise trading. If several arbitrageurs share information about the same assets, inefficient equilibria can arise, where investors' attention is overloaded and substantial mispricing persists. When they do not share, the overloading of investors' attention is maximal.

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