Over recent decades, securities trading has changed fundamentally with the growing participation of retail investors. Digital platforms and mobile trading have substantially lowered entry barriers, leading to a surge in retail trading activity, particularly during periods of market stress. Retail orders are typically executed on dedicated retail venues, where a single market maker provides liquidity and bases quotes on prices from a reference market.

Retail venues also face situations in which the reference market becomes unexpectedly unavailable, for example due to volatility interruptions. In contrast to predictable after-hours trading, such interruptions abruptly deprive retail market makers of their pricing anchor. While prior research studies market maker behavior when reference prices are predictably absent, the effects of unexpected reference market disruptions on retail market making and retail trading activity remain largely unexplored.

The project contributes to the literature on market microstructure and retail trading by providing novel evidence on market maker behavior during unexpected reference market disruptions and by offering policy-relevant insights into the resilience and design of retail market structures.

Principal Investigator: Prof. Dr. Peter Gomber

Project Memebers: Florian Ewald, Niklas Trimpe, Julian Schmidt

Circuit breakers such as volatility interruptions are key market design tools used to curb excessive short-term price fluctuations and ensure orderly trading. A well-documented behavioral response is the magnet effect, whereby prices accelerate towards known thresholds as traders anticipate a trading halt.

While prior research documents the existence of the magnet effect, the role of information transparency, specifically the disclosure of triggering thresholds, remains underexplored. Theory suggests that disclosure may amplify the magnet effect by creating a focal point for trader behavior, whereas non-disclosure may mitigate such dynamics.

This project exploits a natural experiment stemming from a regulatory and technical change at a major European equity trading venue that introduces public disclosure of volatility interruption thresholds. Using high-frequency trade and quote data, we examine how increased transparency affects trading behavior around circuit breaker events and draw implications for market design and regulatory policy.

Principal Investigator: Prof. Dr. Peter Gomber

Project Memebers: Dr. Benjamin Clapham, Florian Ewald, Niklas Trimpe

The impact of fragmentation on price efficiency and discovery remains a central theme in market microstructure, particularly as cryptocurrency markets introduce fundamentally new trading architectures. While traditional equity markets balance the trade-offs between liquidity dispersion and competitive execution, the rise of decentralized exchanges (DEXs) and Automated Market Makers (AMMs) adds complexity through deterministic pricing functions and on-chain constraints such as block-time latency and gas fees. This research project investigates whether the increasing presence of AMMs improves price efficiency and quantifies their specific contribution to the price discovery process.

By exploiting the staggered introduction of Uniswap v2 and v3 as quasi-natural experiments, we implement a difference-in-differences framework to analyze how design innovations, such as concentrated liquidity, alter the role of DEXs. Our methodology integrates millisecond-level centralized exchange data with blockchain-resolved transactions to compute established efficiency measures and information share metrics. This unique analytical pipeline enables a rigorous cross-venue comparison between traditional limit-order books and decentralized protocols.

Ultimately, this project contributes empirical evidence on whether AMM adoption enhances or undermines price efficiency in fragmented crypto markets. It provides the first systematic venue-level map of DEXs’ contribution to price discovery relative to leading CEXs, yielding insights into how design innovations reshape their role. More broadly, the project advances market microstructure research by offering methodological tools for studying decentralized finance and informing debates over the design and regulation of the crypto market.

Principal Investigator: Prof. Dr. Peter Gomber, Prof. Dr. Oliver Hinz

Project Memebers: Dr. Benjamin Clapham, Björn Hanneke, Florian Ewald

Private households increasingly strive for financial health, understood as a high standard of living combined with strong resilience against financial risks. Financial stress, in contrast, entails severe psychological and economic consequences. Only recently have banks and asset managers begun to recognize the measurable financial health of their clients as a relevant business objective.

Existing approaches measure financial health either subjectively as “financial well-being” using survey data or objectively through financial indicators borrowed from institutional risk management. Prior studies typically focus on a limited set of risk measures or construct aggregate measures of financial health.

In this project, we aim to develop a consistent framework for measuring individual financial health, inspired by institutional risk management practices. We validate this framework using detailed retail customer data from a large bank, complemented by a field study conducted with its customers. This empirical setup allows us to rigorously assess the relevance and predictive power of different financial health dimensions.

Understanding and measuring financial health is crucial not only for academic research, but also for banks and asset managers seeking to leverage data, technology, and their core competencies in risk management to enhance customer centricity and long-term client value creation.

Principal Investigator: Prof. Dr. Andreas Hackethal, Dr. Philip Schnorpfeil

Sustainable investments have become immensely important for financial markets in recent years. From 2012 to 2019, ESG assets in European equity funds rose from 0.651 to 1.663 trillion euros and are expected to grow to around 5.5 to 7.6 trillion euros by 2025, making up around half of all European fund assets. This contrast with very little, evidence on private investors and their inclination towards sustainable investments.

In our envisioned project we cooperate with a large German retail bank and combine administrative bank data with survey data to elicit the motivations and obstacles of private investors to invest in ESG investments. Motivations and obstacles include return seeking, impact seeking, social norms, but also confusion and warm glow.

The online survey will allow us to confront the survey participants with one of three information treatments in order to be able to gain causal insights into the various motivations and obstacles vis-à-vis a non-treated control group. Post-treatment questions and the administrative data, which include investment and consumption records, allow us to control for individual characteristics and to observe any effects the information treatments might have on intended and actual investment and consumption choices. This careful empirical setup allows us to produce rigorous evidence on the motivations and obstacles of private investors to invest in ESG investments.

Understanding why people invest sustainably is important not only to academics, but also to institutional investors, who often invest on behalf of individuals.

Principal Investigator: Prof. Dr. Andreas Hackethal

We aim to explore how investors respond to changes in their inflation expectations using randomized control trials (RCT) with clients of a large German bank. We plan to run an online survey to elicit clients’ inflation expectations and beliefs about asset returns during periods of surging inflation. In multiple treatment arms, we then provide professional inflation forecasts and/or information about actual asset returns during past inflationary times to exogenously shift bank clients’ beliefs about inflation and/or asset returns. Post-treatment questions, administrative data, and follow-up surveys allow us to study the effects of the information treatments on intended and actual investment choices, as well as underlying mechanisms. This careful empirical setup allows us to produce rigorous evidence on how inflation expectations drive household financial-portfolio allocations.

These tests matter for asset managers and financial institutions, trying to understand consequences of heightened inflation expectations on portfolio reallocation and asset returns; policy makers, interested in how inflation affects capital markets and their stability; as well as central banks, which need to understand the effects of changes in inflation expectations. In congruence to the focus of efl, our project aims to utilize vast administrative bank data to study the effects of an RCT.

Principal Investigator: Prof. Dr. Andreas Hackethal

Project Members: Dr. Philip Schnorpfeil

Lead-lag effects in the context of financial markets describe price discovery situations, where some financial instruments are leading and providing price signals to other instruments lagging behind. Lead-lag correlations can arise as a consequence of cross-asset trading and of the mutual influence between price adjustment processes of different assets. Against this background, we aim at developing a price and liquidity discovery network at the most granular level of market and trading data possible. In the current research, either trading data of only few instruments are used, short time periods of observations are analyzed or coarse sampling frequencies are investigated. Therefore, the goal of this research project is to address these limitations by a deep and broad analysis of lead-lag structures across various assets and asset classes using ultra-high-frequency data on a nano-second time scale. Cross-asset effects, exploited by HFTs, are short-living. Therefore, the scrutinization of the sub-second trading area is crucial to measure the speed of price adjustments in a trading world exhibiting an ever increasing speed. Covering asset classes like stocks, futures and options could shed light on previously unknown and fundamentally unsuspected relationships between two assets. Relevant market variables for the analyses include midpoint, spread, depth and orderbook imbalance. Volatillity measures will also be assessed.

The main part of the analyses will be guided by cross-correlation estimators, such as HY-covariance estimator for handling non-synchronous data, and its extension allowing for leads and lags by introduction of the so-called contrast function. There is well-established evidence that both volatilities and correlations exhibit strong intraday variation. Therefore, the stableness as well as the strength of lead-lag structures within and across different time periods will be examined. Moreover, several studies have shown that the interdependence of stock markets increases during periods of market distress and high volatility such as with the global financial crisis. The research project shall also contribute to this knowledge and test lead-lag structures for their volatility-dependence. The temporal relationships of the instruments' returns will be of decisive importance to underscore the contribution of this thesis for academics and practitioners alike. Non-contemporaneous relationships, for instance, could open up opportunities for exploitation by, e.g., statistical and spot-futures arbitrage strategies for algorithmic and high-frequency traders.

Principal Investigator: Prof. Dr. Peter Gomber

Project Members: Micha Bender, Tino Cestonaro, Julian Schmidt

Volatility interruptions automatically interrupt continuous trading if pre-determined price thresholds are exceeded. While exchange operators and regulators advocate the use of volatility interruptions, the research community has a mixed perception with regard to their effectiveness and discussed potential negative side effects of these mechanisms. The underlying mechanism does not distinguish between plausible, meaningful price changes (in the following: fundamental price shocks) on the one hand and unsubstantiated price jumps on the other, e.g., due to mis-specified trades, erroneous trading algorithms, or fake news (in the following: error-induced price shocks). In order to mitigate this problem, the underlying mechanism would need to understand the particular market circumstances and trigger a volatility interruption only in those situations when it is actually needed.

Using a data science and machine learning approach to analyze and identify the causes of historical volatility interruptions, this project aims to lay the foundation for an important milestone regarding the safety and integrity of today’s financial markets. Based on high-resolution message data provided by Deutsche Börse, we plan to (1) employ a clustering approach to identify the circumstances under which volatility interruptions have been triggered historically on Xetra, and then (2) use those clusters as labels in a classification model that predicts those circumstances given pre-event order book information.

Such model could ultimately be implemented by exchange operators to discriminate between the above-mentioned cases and thereby restrict volatility interruptions to those instances in which they are actually needed and meaningful, that is, in case of error-induced price shocks but not in case of fundamental price shocks. This approach bears the potential to increase the effectiveness of market safeguards significantly and would serve to solve issues that researchers have criticized and markets have not solved yet.

Principal Investigator: Prof. Dr. Peter Gomber

Project Members: Dr. Benjamin Clapham, Florian Ewald, Niklas Trimpe

Digital currencies are on the rise. Once merely a vehicle for speculation, digital currencies such as Bitcoin, Litecoin, or Ethereum have reached mainstream consumers who use them for trading, payments, or private transactions. In response, centralized institutions such as central banks developed plans for their own digital currencies. One example of a digital currency of a central bank is the “Digital Euro”. The Digital Euro could offer consumers an additional choice for making transactions. It would offer a fast and easy means of sending and receiving money while protecting consumers’ privacy. Because the Digital Euro would be accessible to a broad audience, it contributes to the inclusion of the population in the digital currency space. The Digital Euro will likely also foster financial innovation and improve the efficiency of the payment system.

Despite its promises, the Digital Euro’s success hinges upon its adoption by consumers. Its issuers (i.e., central and national banks) must convince consumers to use it such that it can fully deliver on its promises to society. This project aims to identify consumers’ information needs related to the Digital Euro. By providing financial institutions (such as central or consumer banks) with knowledge about these information needs, we enable them to provide more targeted and consumer-centric information to consumers that defuse and ultimately overcome adoption barriers.

Monitoring how consumers search the web and analyzing their search queries reveals their information needs, which in turn can be satisfied through corresponding information campaigns. However, obtaining such data on how consumers search online is not easily possible: Only search engines themselves have detailed records of consumers’ searches. We overcame this challenge with the help of a novel Search Simulator that we developed. We then use modern Data Science tools to identify consumers’ information needs. Specifically, we rely on state-of-the-art natural language processing, such as transformer-based embedding models. Based on the identified similarities, we can leverage unsupervised machine learning to consolidate them into meaningful topics. Finally, these topics can inform advertising and information campaigns to overcome barriers to adopting the Digital Euro.

Principal Investigator: Prof. Dr. Bernd Skiera

Project Members: Maximillian Matthe, Daniel M. Ringel