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Regulatory impact analysis (RIA) serves to evaluate whether regulatory actions fulfill the desired goals. Although there are different frameworks for conducting RIA, they are only applicable to regulations whose impact can be measured with structured data. Yet, a significant and increasing number of regulations require firms to comply by communicating textual data to consumers and supervisors. Therefore, we develop a methodological framework for RIA in case of unstructured data based on textual analysis and apply it to a recent financial market regulation: MiFID II.
Firms, researchers, and policy makers often want to measure consumption and especially how events, promotions, or policies affect it. Measuring consumption reactions is often hard. Firms lack access to competitors’ sales data and regularly do not share their own with outsiders. Large samples of smartphone location data could solve this problem. This article describes a research project using smartphone location data to estimate consumption reactions to political conflict during the Trump presidency.
What does your personality reveal about your financial behavior? Evidence from a FinTech experiment
(2022)
We co-operate with a German financial account aggregator (FAA) and conduct a personality survey with 1,700 app users. We combine the survey results with their anonymized transaction data and investigate links between personality traits and spending behavior. Observing many lottery windfalls in our dataset and treating these incidents as real-life experiments, we ask: what do individuals do with unexpected income changes? Our findings suggest that highly extraverted individuals tend to overspend in response to lottery windfalls.
Business practitioners increasingly use Artificial Intelligence (AI) applications to assist customers in making decisions due to their higher prediction quality. Yet, customers are frequently reluctant to rely on advice generated from machines, especially when their decision is at stake. Our study proposes a solution, which is to bring a human expert in the loop of machine advice. We empirically test whether customers are more accepting expert-AI collaborative advice than expert or AI advice.
ETFs Prove Their Worth in Turbulent Times / Eric Leupold, Managing Director / Head of Cash Market, Deutsche Börse AG
Is Human-AI Advice Better than Human or AI Advice? / Cathy Liu Yang, Kevin Bauer, Xitong Li, Oliver Hinz
What Does Your Personality Reveal about Your Financial Behavior? Evidence from a FinTech Experiment / Andreas Hackethal, Fabian Nemeczek, Jan Radermacher
“MiCA” – Regulating the European Markets in Crypto-Assets / Dr. Stefan Berger, Member of the European Parliament, Committee on Economic and Monetary Affairs
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