2. Cohort

The focus project Theorizing Digital Innovation in Finance addresses the digital transformation of the financial sector. Technologies such as blockchain, applications of artificial intelligence, and the widespread use of alternative forms of financing go hand in hand with new financial services, business models, organizational practices, and new consumer behavior. Traditional and new market participants face the following challenges: Which financial services should be developed in the future? Which services are to be bought from the market and which are offered by the company itself? Which services should the organization offer in the B2B and B2C business? The focus project is devoted to these questions and will answer them in specific research projects. The conceptual framework is the concept of digital innovation, which states that digital technologies change organizations through and through (Barrett et al. 2015; Nambisan et al. 2017; Yoo et al. 2010), which is why new theoretical frameworks are also necessary to understand the effects of digitalization of financial services on the organizational and customer side (Wessel et al. 2020; Yoo 2013). Such frameworks have so far hardly been developed, which is why the focus project will use the context of digital financial innovations to develop and test theoretical frameworks by using qualitative and quantitative methods.

2021

Finance Seminar (Skema Business School Paris) • Sustainable Finance Research Seminar (University of Zurich) • FinTech and cryptofinance Webinar (Sorbonne Université) • Ringvorlesung Universelle Eigenschaften des Entscheidens (Univesität Bremen) • ITM (Information Technology Management) Distinguished Speaker Series (Scheller College of Business - Georgia Tech) • Data Science Forum (Univesität Bremen) • Field Day in Field Experients (Univesität Bremen) • Volkswirtschaftliches Forschungskolloquium (Universität Regensburg) • Finance Research Seminar (TU München) • IFABS Conference (Oxford University) • European Alternative Finance Research Conference (Utrecht University)

2020

Herbsttagung Frankfurter Institut für Risikomanagement und Regulierung (FIRM) (House of Finance, Goethe Universität) • Danish Finance Institute Seminar (University of Southern Denmark) • Digitalisierung der Finanzdienstleistung (Verbraucherzentrale Bundesverband) • AI in Finance: Privacy and Data Protection Sectoral Dialogue (Singapore Management Univesity) • Determinants of Non-cash Transactions Conference (Hochschule Bremen)

• Cumming, Douglas, Lars Hornuf, Moein Karami and Denis Schweizer (2021): Disentangling Crowdfunding from Fraudfunding, Journal of Business Ethics, im Erscheinen. SSRN.
• Cumming, Douglas und Lars Hornuf (2021): Marketplace Lending of SMEs, Strategic Entrepreneurship Journal, im Erscheinen.  SSRN.
• Hornuf, Lars, Eliza Stenzhorn und Tim Vintis (2021): Are Sustainability-Oriented Investors Different? Evidence from Equity Crowdfunding, im Erscheinen. Journal of Technology Transfer. SSRN.
• Hornuf, Lars, Tobias Schilling und Armin Schwienbacher (2021): The Relevance of Investor Rights in Crowdinvesting, Journal of Corporate Finance, im Erscheinen. Download..
• Göthner, Maximilian, Lars Hornuf und Tobias Regner (2021): Protecting Investors in Equity Crowdfunding: An Empirical Analysis of the Small Investor Protection Act, Technological Forecasting and Social Change, im Erscheinen. Download.
• Brandl, Barbara and Lars Hornuf (2020): Where Did FinTechs Come from, and Where Do They Go? The Transformation of the Financial Industry in Germany after Digitalization, Frontiers in Artificial Intelligence (Artificial Intelligence in Finance). Download. 

Smart, resilient, technologically enhanced logistics systems are widely regarded as the future for organisations. Together Big Data, Artificial Intelligence (AI) and Machine Learning present many opportunities for enhancing decision making. This project looks at the design, usage and adoption of unstructured big data-based decision support in global supply chain management and operations. Amongst others we raise questions related to how automated decision support approaches aid in global supply chain decision-making, and can be designed in a user-friendly manner? Which contextual factors play a role in and influence the development of big data and AI capabilities and outcomes related to its adoption for global supply chain operations? Etc.

"Self-quantification" or "self-measurement" (also known as "personal analytics", "quantified self" or "self tracking") has been an ongoing trend for several years now. Those terms describe the activity in which people voluntarily and autonomously monitor themselves and record certain events in their lives using digital technologies. Thereby, data is generated and analyzed in order to, for example, create various statistics for the user.

Research has already dealt with various aspects of self-measurement, e.g., with the question which factors make users accept and use "wearables". Research assumes that mainly positive effects on user behavior occur, e.g., more exercise to achieve certain goals or the identification of bad habits and unhealthy diets. Nevertheless, these positive effects come along with many concerns and potentially negative effects: data protection advocates warn against permanent surveillance and the disclosure of sensitive data that could be made available to third parties such as health insurance companies and companies such as pharmaceutical manufacturers through sharing via social networks. Negative effects can also arise directly at the user’s side: The constant tracking of activities or one's own eating behavior, for example, can become an obsession.

The project investigates, among others, the underlying motivations for the use of “consumer-technology interfaces.” It also aims to identify the short and long-term effects of the use of “consumer-technology interfaces”. Furthermore, data protection concerns of users of “consumer-technology interfaces” will also be looked at in this project.

This project investigates the suitability of machine learning approaches at the intersection of accounting and finance. Our main interests lie within „Macro-Accounting“ and „Accounting for the Anomaly Zoo“. „Macro-Accounting“ describes the association between accounting variables (e.g., firm performance) and macro-economic outcomes, such as economic growth or overall stock market performance. „Accounting for the Anomaly Zoo“ studies the explanatory power of accounting and finance characteristics for the cross-section of stock returns. „Anomaly Zoo“ is the collective term for the myriad of value relevant accounting and finance variables discussed by prior literature. Given the vast amount of influential variables and supposed non-linearities, the lack of application of machine learning approaches is quite surprising. In this project, we aim to close this gap in the literature. Recent works by Kalay et al. (2018) (for „Macro-Accounting“) and Gu et al. (2020) (for the „Accounting for the Anomaly Zoo“) serve as the starting point of our analyses.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Forthcoming in the Review of Financial Studies.

Kalay, A., Nallareddy, S., & Sadka, G. (2018). Uncertainty and sectoral shifts: The interaction between firm-level and aggregate-level shocks, and macroeconomic activity. Management Science, 64(1), 198-214.

• Fieberg, Christian, Kerstin Lopatta , Thomas Tammen und Sebastian A. Tideman (forthcoming): Political affinity and investors’ response to the acquisition premium in cross-border M&A transactions – A moderation analysis. Strategic Management Journal. Download.
• Pakhchanyan, Suren, Christian Fieberg ,  Daniel Metkound Thomas Kaspereit (forthcoming): Machine learning for categorization of operational risk events using textual description. Journal of Operational Risk.
• Bassen, Alexander, Christian Fieberg, Othar Kordsachia , Kerstin Lopatta  und Bastian Nendza (2021):Sustainable Development Investing: Creating a scalable SDI-aligned index. Financing for Sustainable Development Office (FSDO) of the United Nations, in its capacity as Secretariat of the Global Investors for Sustainable Development (GISD) Alliance. Download.
• Tubbenhauer, Tobias,Christian Fieberg  und Thorsten Poddig (2021): Multi-Agent-Based VaR Forecasting. Journal of Economic Dynamics and Control, Vol. 131, 2021. Download.