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Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
Zur Reform der Einlagensicherung: Elemente einer anreizkompatiblen Europäischen Rückversicherung
(2020)
Bankeinlagen bis 100.000 Euro sind de jure überall im Euroraum gleichermaßen vor Verlusten geschützt. De facto hängt der Wert dieser gesetzlichen Haftungszusage unter anderem von der Ausstattung des nationalen Sicherungsfonds und der relativen Größe des Bankensektors in einer Volkswirtschaft ab. Um die Homogenität des Einlagenschutzes zu gewährleisten und die Bankenunion zu vollenden, bedarf es einer einheitlichen europäischen Einlagensicherung. Die bestehende implizite Risikoteilung im Euroraum ist ordnungspolitisch nicht wünschenswert. Ferner kann eine explizite und glaubwürdige Zweitsicherung Fehlanreize zur Übernahme exzessiver Risiken verhindern, bevor es zum Schadensfall kommt. Daher plädiert dieser Beitrag für ein zweistufiges, streng subsidiär organisiertes Rückversicherungsmodell: Nationale Erstversicherungen würden einen festgeschriebenen Teil, die europäische Rückversicherung nachrangig den Rest der Deckungssumme besichern. Die Rückversicherung gewährt diese Liquiditätshilfen in Form von Kassenkrediten. Weil die Haftung auf nationaler Ebene verbleibt, werden Risiken geteilt aber nicht vergemeinschaftet. Marktgerechte Prämien müssen nicht nur das individuelle Risikogewicht einer Bank sondern auch länderspezifische Risikofaktoren berücksichtigen. Zuletzt braucht der Rückversicherer umfangreiche Aufsichtsrechte, um die Zahlungsfähigkeit der Erstversicherer mit Hinblick auf die nationalen Haftungspflichten jederzeit sicherzustellen.
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.
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.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
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.
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.
This research examines the impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value. Using proprietary data on annualized advertising expenditures for 1651 firms spanning seven years, we document that both display advertising and paid search advertising exhibit positive effects on firm performance (measured by sales) and firm value (measured by Tobin's q). Paid search advertising has a more positive effect on sales than offline advertising, consistent with paid search being closest to the actual purchase decision and having enhanced targeting abilities. Display advertising exhibits a relatively more positive effect on Tobin's q than offline advertising, consistent with its long-term effects. The findings suggest heterogeneous economic benefits across different types of advertising, with direct implications for managers in analyzing advertising effectiveness and external stakeholders in assessing firm performance.
We show that High Frequency Traders (HFTs) are not beneficial to the stock market during flash crashes. They actually consume liquidity when it is most needed, even when they are rewarded by the exchange to provide immediacy. The behavior of HFTs exacerbate the transient price impact, unrelated to fundamentals, typically observed during a flash crash. Slow traders provide liquidity instead of HFTs, taking advantage of the discounted price. We thus uncover a trade-o↵ between the greater liquidity and efficiency provided by HFTs in normal times, and the disruptive consequences of their trading activity during distressed times.
This paper documents that resource reallocation across firms is an important mechanism through which creditor rights affect real outcomes. I exploit the staggered adoption of an international convention that provides globally consistent strong creditor protection for aircraft finance. After this reform, country-level productivity in the aviation sector increases by 12%, driven mostly by across-firm reallocation. Productive airlines borrow more, expand, and adopt new technology at the expense of unproductive ones. Such reallocation is facilitated by (i) easier and quicker asset redeployment; and (ii) the influx of foreign financiers offering innovative financial products to improve credit allocative efficiency. I further document an increase in competition and an improvement in the breadth and the quality of products available to consumers.
We present novel evidence on the value of cross-border political access. We analyze data on meetings of US multinational enterprises (MNEs) with European Commission (EC) policymakers. Meetings with Commissioners are associated with positive abnormal equity returns. We study channels of value creation through political access in the areas of regulation and taxation. US enterprises with EC meetings are more likely to receive favorable outcomes in their European merger decisions and have lower effective tax rates on foreign income than their peers without meetings. Our results suggest that access to foreign policymakers is of substantial value for MNEs.
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.
Accounting for financial stability: Bank disclosure and loss recognition in the financial crisis
(2020)
This paper examines banks’ disclosures and loss recognition in the financial crisis and identifies several core issues for the link between accounting and financial stability. Our analysis suggests that, going into the financial crisis, banks’ disclosures about relevant risk exposures were relatively sparse. Such disclosures came later after major concerns about banks’ exposures had arisen in markets. Similarly, the recognition of loan losses was relatively slow and delayed relative to prevailing market expectations. Among the possible explanations for this evidence, our analysis suggests that banks’ reporting incentives played a key role, which has important implications for bank supervision and the new expected loss model for loan accounting. We also provide evidence that shielding regulatory capital from accounting losses through prudential filters can dampen banks’ incentives for corrective actions. Overall, our analysis reveals several important challenges if accounting and financial reporting are to contribute to financial stability.
This paper provides an overview of how to use "big data" for economic research. We investigate the performance and ease of use of different Spark applications running on a distributed file system to enable the handling and analysis of data sets which were previously not usable due to their size. More specifically, we explain how to use Spark to (i) explore big data sets which exceed retail grade computers memory size and (ii) run typical econometric tasks including microeconometric, panel data and time series regression models which are prohibitively expensive to evaluate on stand-alone machines. By bridging the gap between the abstract concept of Spark and ready-to-use examples which can easily be altered to suite the researchers need, we provide economists and social scientists more generally with the theory and practice to handle the ever growing datasets available. The ease of reproducing the examples in this paper makes this guide a useful reference for researchers with a limited background in data handling and distributed computing.
The spreading of the Covid-19 virus causes a reduction in economic activity worldwide and may lead to new risks to financial stability. The authors draw attention to the urgency of the targeted mitigation strategies on the European level and suggest taking coordinated action on the fiscal side to provide liquidity to affected firms in the corporate sector. Otherwise, virus-related cashflow interruptions could lead to a new full-blown banking crisis. Monetary policy measures are unlikely to mitigate cash liquidity shortages at the level of individual firms. Coordinated action at European level is decisive to prevent markets from losing confidence in the resilience of banks, particularly in countries with limited fiscal capacity. In contrast to the euro crisis of 2011, the cause of the current crisis does not lie in the financial markets; therefore, the risk of moral hazard for banks or states is low.
This Policy Letter presents a proposal for designing a program of government assistance for firms hurt by the Coronavirus crisis in the European Union (EU). In our recent Policy Letter 81, we introduced a new, equity-type instrument, a cash-against-tax surcharge scheme, bundled across firms and countries in a European Pandemic Equity Fund (EPEF). The present Policy Letter 84 focuses on the principles and conditions relevant for the operationalization of a EPEF. Our proposal has several desirable features. It: a) offers better risk sharing opportunities, augmenting the resilience of businesses and EU economies; b) is need-based, thereby contributing to an effective use of resources; c) builds on conditions and credible controls, addressing adverse selection and moral hazard; d) is accessible to smaller and medium-sized firms, the backbone of Europe’s economy; e) applies Europe-wide uniform eligibility criteria, strengthening support among member states; f) is a scheme of limited duration, reducing (perceived) government interference in businesses; g) creates a template for a growth-oriented public policy, aligning public and private sector interests; and h) builds on the existing institutional infrastructure and requires minimal legislative adjustments.
This policy letter adds to the current discussion on how to design a program of government assistance for firms hurt by the Coronavirus crisis. While not pretending to provide a cure-all proposal, the advocated scheme could help to bring funding to firms, even small firms, quickly, without increasing their leverage and default risk. The plan combines outright cash transfers to firms with a temporary, elevated corporate profit tax at the firm level as a form of conditional payback. The implied equity-like payment structure has positive risk-sharing features for firms, without impinging on ownership structures. The proposal has to be implemented at the pan-European level to strengthen Euro area resilience.
Angesichts des kürzlich von der Bundesregierung verabschiedeten Konjunkturpakets, stellen sich die Autoren des Policy Letters die Frage, ob und inwieweit die angekündigte Mehrwertsteuersenkung sowie der Kinderbonus zur substantiellen Ankurbelung des Binnenkonsums führt. Aus den für das Haushaltskrisenbarometer erhobenen Daten zu Einkommensänderungen sowie Einkommens- und Kündigungserwartungen, können die Ökonomen keine zu erwartende Schwächung der Binnennachfrage ableiten. Der überwiegende Teil der deutschen Wohnbevölkerung scheint kurzfristig nicht davon auszugehen, finanzielle Einbußen aufgrund der Pandemie zu erleiden. Die Erwartungen hinsichtlich der künftigen Einkommensentwicklung haben sich gar über die letzten vier Umfragewellen graduell verbessert. Ferner kann dargelegt werden, dass weder die Konsum- noch die Sparneigung durch die Corona-Krise zum gegenwärtigen Zeitpunkt langfristig stark beeinflusst wird. So geben derzeit lediglich 10 Prozent der Befragten an, größere Anschaffungen angesichts der Pandemie vollständig gestrichen zu haben. Anfang April 2020 lag dieser Wert noch bei 16 Prozent. Die Befragten berichteten in 71 Prozent der Fälle ihre Konsumpläne und in 78 Prozent der Fälle ihre Sparverhalten nicht geändert zu haben. Im Lichte dieser Ergebnisse lassen sich Maßnahmen, die auf eine unspezifische Stimulierung der Binnennachfrage abzielen, nicht substantiell begründen und rechtfertigen.
We investigate the impact of reporting regulation on corporate innovation. Exploiting thresholds in Europe’s regulation and a major enforcement reform in Germany, we find that forcing firms to publicly disclose their financial statements discourages innovative activities. Our evidence suggests that reporting regulation has significant real effects by imposing proprietary costs on innovative firms, which in turn diminish their incentives to innovate. At the industry level, positive information spillovers (e.g., to competitors, suppliers, and customers) appear insufficient to compensate the negative direct effect on the prevalence of innovative activity. The spillovers instead appear to concentrate innovation among a few large firms in a given industry. Thus, financial reporting regulation has important aggregate and distributional effects on corporate innovation.
Consuming dividends
(2020)
This paper studies why investors buy dividend-paying assets and how they time their consumption accordingly. We combine administrative bank data linking customers’ consumption transactions and income to detailed portfolio data and survey responses on financial behavior. We find that private consumption is excessively sensitive to dividend income. Investors across wealth, income, and age distributions increase spending precisely around days of dividend receipt. Importantly, the consumption response is driven by financially prudent investors who select dividend portfolios, anticipate dividend income, and plan consumption accordingly. Our results contribute to the literature on a dividend clientele and provide evidence of ‘planned’ excess sensitivity.
Mehr Nachhaltigkeit im deutschen Leitindex DAX - Reformvorschläge im Lichte des Wirecard-Skandals
(2020)
Im Rahmen der Aufarbeitung des Wirecard-Skandals wird ebenfalls eine Änderung der Kriterien zur Aufnahme in den deutschen Leitindex DAX diskutiert. Die bislang von der Deutschen Börse vorgelegten Vorschläge zur Reformierung des DAX gehen in die richtige Richtung, sind aber nicht weitreichend genug. Es bedarf eines deutlichen Zeichens, dass sich künftig nur solche Unternehmen für den DAX qualifizieren können, die ein zumindest befriedigendes Maß an Nachhaltigkeit gemessen durch einen ESG (Environment, Social, Governance)-Risk-Score in ihrer Geschäftstätigkeit erreichen. Eine Simulation verdeutlicht, dass nach ESG-Kriterien seit langem kritisch betrachtete Unternehmen dem DAX nicht mehr angehören würden. Dies würde klare Anreize bei den Unternehmen setzen, Nachhaltigkeitsaspekte stärker als bisher in ihrer Strategie zu berücksichtigen. Letztlich kann eine Neugestaltung wichtiger Aktienindizes einen Beitrag dazu leisten, dass mehr Kapital in nachhaltig wirtschaftende Unternehmen und Sektoren fließt.
Die Wettbewerbsfähigkeit der deutschen Wirtschaft steht vor gewaltigen Herausforderungen. Traditionell starke Sektoren wie die Automobilindustrie oder der Maschinenbau befinden sich angesichts disruptiver Veränderungen durch neue Technologien, den Kampf gegen den Klimawandel und veränderte regulatorische Rahmenbedingungen in einer Umbruchphase. Zahlreiche Industriezweige wandeln sich durch den Einsatz von Künstlicher Intelligenz zu „Smart Industries“. Gleichzeitig gewinnt die Kompetenz in Querschnittstechnologien wie Cloud Computing oder Cyber Security an Bedeutung, da diese den effektiven Einsatz von Künstlicher Intelligenz erst ermöglichen. Eine Analyse der Wettbewerbsposition der deutschen Wirtschaft zeigt auf, dass in manchen Zukunftsfeldern ein erheblicher Nachholbedarf besteht.
We report the results of a longitudinal intervention with students across five universities in China designed to reduce online consumer debt. Our research design allocates individuals to either a financial literacy treatment, a self-control training program, or a zero-touch control group. Financial education interventions improve test scores on general financial literacy but only marginally affect future online borrowing. Our self-control treatment features detailed tracking of spending and borrowing activity with a third-party app and introspection about individuals' consumption with a counselor. These sessions reduce future online borrowing, delinquency charges, and borrowing for entertainment reasons - and are driven by the male subjects in the sample. Our results suggest that self-regulation can affect financial behavior in e-commerce platforms.