Working Papers

Do Investors Use Sustainable Assets as Carbon Offsets? (2024)
With Jakob Famulok and Daniel Worring.
Formerly: Do Investors Compensate for Unsustainable Consumption Using Sustainable Assets?
Available at SSRN.




We present novel evidence that retail investors attempt offsetting their carbon footprints by investing sustainably. Analyzing 6,151 bank clients and conducting an experiment with 4,249 participants, we find higher footprints are linked to greener portfolios. In a randomized control trial, we show that the salience of investors’ carbon footprints compared to their peers causally shifts sustainable asset allocations, driven by participants with moderate environmental beliefs. We additionally identify a substitution effect between carbon offsetting through donations and sustainable assets. Our findings contribute to understanding behavioral drivers in sustainable investing, crucial for designing policies which align financial markets with environmental goals.

The President Reacts to News Channel of Government Communication (2023)
With Farshid Abdi, Loriana Pelizzon, Mila Getmansky Sherman, and Zorka Simon. 

SAFE Working Paper No. 314, available at SSRN.
Revise & Resubmit at Management Science. 

Presented at:

Studying about 1,200 economy-related tweets of President Trump, we establish the "President reacts to news" channel of stock returns. Using high-frequency identification of market movements and machine learning to classify the topics and textual sentiment of tweets, we address the observed heterogeneity in the aggregate stock market response to these messages. After controlling for market trends preceding tweets, we find that 80% of tweets are reactive and predictable rather than novel and informative. The exceptions are trade war tweets, where the President has direct policy authority, and his tweets can reveal investable private information or information about his policy function.

Informing climate risk analysis using textual information - A research agenda (2024)
With Malte Schierholz, Bolei Ma, Jacob Beck, Andreas Dimmelmeier, Hendrik Christian Doll, Maurice Fehr, Frauke Kreuter, and Alex Fraser. 

This project is part of the larger research agenda GIST - Greenhouse Gas Insights and Sustainability Tracking, a research collaboration between Deutsche Bundesbank and LMU Munich to generate high-quality, granular firm-level emissions and sustainability data. More information can be found here

We present a research agenda focused on efficiently extracting, assuring quality, and consolidating textual company sustainability information to address urgent climate change decision-making needs. Leveraging technological advancements, particularly in large language models (LLMs) and RAG (Retrieval-Augmented Generation) pipelines, we aim to unlock the potential of underutilized textual data, such as sustainability reports, alongside global administrative collections and proprietary sources. Key challenges include ensuring the quality of automatically extracted data, for which we propose leveraging domain knowledge and addressing human labeler variations. We discuss the complexities of evaluation due to loosely defined concepts and emphasize the importance of integrating extracted results with existing data to create gold standard datasets. By incorporating novel variables, such as firms' future commitments, we envision enabling new use-cases. Ultimately, our approach aims to provide unified, easily accessible, and FAIR (Findable, Accessible, Interoperable, Reusable) climate-related data to inform decision-making efficiently, thereby contributing to tackling climate change in the short term.

Do Gamblers Invest in Lottery Stocks? (2023)
With Tobin Hanspal and Andreas Hackethal. 

SAFE Working Paper No. 373, available at SSRN.

Presented at:


Previous studies document a relationship between gambling activity at the aggregate level and investments in securities with lottery-like features. We combine data on individual gambling consumption with portfolio holdings and trading records to examine whether gambling and trading act as substitutes or complements. We find that gamblers are more likely than the average investor to hold lottery stocks, but significantly less likely than active traders who do not gamble. Our results suggest that gambling behavior across domains is less relevant compared to other portfolio characteristics that predict investing in high-risk and high-skew securities, and that gambling on and off the stock market act as substitutes to satisfy the same need, e.g., sensation seeking.

Gray literature

Houston, we have a problem: Can satellite data fix the climate-related data gap? (2024)
With Andrés Alonso Robisco, José Manuel Carbó Martínez, and Elena Triebskorn.


Central banks and international supervisors have identified the difficulty of obtaining climate information as one of the key obstacles impeding the development of green financial products and markets. To bridge this data gap, the utilization of satellite imagery from Earth Observation (EO) systems may be necessary. In this paper, we analyze the potential of applying satellite data to green finance. To assess this, we first explore the policy debate from a central banking perspective. We then briefly describe the main challenges for economists in dealing with the EO data format and quantitative methodologies for measuring its economic materiality. Finally, using topic modeling, we perform a systematic literature review of recent academic studies to uncover in which topic areas satellite data is currently being used in green finance. We find the following research topics: physical risk materialization (including both acute and chronic risk), deforestation, energy and emissions, agricultural risk and land use and land cover. We conclude providing a comprehensive analysis on the financial materiality of this technology, mapping these research topics with new green financial instruments and markets under development, as well as some key considerations for policy discussion.

Extracting Data Citations with Large Language Models  (2024)
With Sebastian Seltmann and Hendrik Christian Doll.


Empirical researchers and research data centers (RDCs) face challenges in efficiently understanding and categorizing data sources and methodologies used in scholarly papers. This process currently relies on human readers and is time-consuming and prone to errors. To address this, we explore the potential of using Large Language Models (LLMs), specifically GPT-3.5, to automate the identification and categorization of research data sources. We analyze the accuracy of GPT-3.5 in detecting and summarizing data sources and methods in economics and finance papers. By employing web-scraping techniques, we collect a comprehensive sample of research papers and create human-labeled validation datasets. We evaluate the detection and prediction accuracy and address the issue of false answers provided by the model. Additionally, we assess the pre-processing requirements of GPT-3.5 for cost-effective implementation. Our paper also provides a guide for implementing our proposed solution at research institutions and RDCs worldwide, aiming to enhance data analysis and research data provision services.

Climate-related data needs for central banks and beyond - Leveraging novel textual and image data (2024)
With Susanne Walter and Hendrik Christian Doll.

Central banks and supervisors urgently need climate-related data for incorporating evidence-based climate change considerations. Besides limited structured administrative and proprietary data sources, a wealth of dispersed climate-related information is readily available in the form of unstructured texts (e.g., company sustainability reports) and images (e.g., satellite images). We describe how central banks may leverage this easily accessible information to provide structured climate-related data to an interested wider public. In doing so, central banks can enhance data usability, validate quality, and extend the scope of existing data sources. Furthermore, such approaches can help overcome remaining restrictions from licensing commercial vendors. We investigate the resulting benefits, challenges, and the potential for international collaboration. As data quality remains a challenge with automatic extraction from novel data sources, we describe evaluation attempts. In a case study for Deutsche Bundesbank, we outline early-stage results and lessons learned from ongoing innovation projects with collaborating academic institutions. We propose that the rapid speed in technological developments (such as large language models and multimodal learning) enables value-creating data generation and provides a unique opportunity to help close data gaps in the short and medium term.

Includes upcoming presentations. Unpublished papers available upon request.