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International Advanced Fellowship - UBB programme

under the coordination of Prof. Dr. Codruta Mare, Scientific Director of the Interdisciplinary Center for Data Science, UBB, which belongs to the Social Sciences Academic School of BBU, coordinated by the Big Data and Machine Learning STAR-UBB-N Center

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Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives

Prof. Dr. Joerg Osterrieder

Professor of Sustainable Finance

Associate Professor of Finance and Artificial Intelligence

University of Twente & Bern Business School

 

1. Research topic

 

Large fluctuations, instabilities, trends and uncertainty of financial markets constitute a substantial challenge for asset management companies, pension funds and regulators. Nowadays, most asset management companies and financial institutions follow a so-called systematic trading approach in their investment decisions. Systematic trading refers to applying predefined, rule-based trading strategies for buy- and sell orders.

However, automated or rules-based trading activities bring certain risks for market participants and the whole financial market. In times of increased market volatility, market turmoil or so-called market sell-offs, investors applying similar trading rules might undertake the same actions, escalating and increasing systemic market risk through such behavior. Such situations have been frequently observed on financial markets for instance, in March 2020 (sell-off related to the Covid pandemic), during the European Sovereign Debt crisis and the global financial crisis 2007-08.

Research in economics and management has begun to embrace the role that narratives play in guiding individual and collective decision- making. McCloskey (2011) describes unforeseen growth in economic development yet goes on to explain that no economic theory is able to capture this extent. She argues that a change in rhetoric had basically freed a social class (the bourgeoisie) and given it a sense of dignity and liberty. As such, economic change, she argues, depends to a great extent on social narratives that shape ideas and the beliefs of people. Yet, despite the notion that narratives, individual and collective actions, and market outcomes are inextricably linked, our knowledge about the mechanisms or processes through which they interact and how narratives can inform opinions or sway current thinking is still evolving. Entrepreneurs, for example, may use verbal communication to achieve plausibility (i.e., generate the sense that a given interpretation of events appears acceptable) or resonance (i.e., obtain alignment with the beliefs of the target audience; see van Werven et al., 2019). They may do so through rhetoric such as storytelling (Navis & Glynn, 2011) or crafting compelling arguments (van Werven et al., 2015) as well as employing combinations of figurative language and gesturing (Clarke et al., 2021) as they manage and conform with the expectation of their audience.

Outcomes of invoking narratives are consequential. The literature has indeed documented various forms of verbal communication– including written texts such as social media posts and blogs, or business plans or spoken text (Garud et al., 2014; Clarke et al., 2019, Clarke et al., 2021) – as a crucial means to secure support and investment. The narratives or rhetoric employed in these stories are used as vehicles for assembling and communicating details about ideas and future possibilities (Garud et al., 2014). In summary, narratives help audiences make sense of situations and situate the description into the audience's social and cultural framework (Lounsbury and Glynn, 2001).

In the following, we, therefore, explore computational techniques to predict financial market outcomes using text, speech, and video/picture data. Advances in data processing and machine learning allow new ways of analysing data and may have profound implications for empirical testing of lightly studied, yet complex, empirical financial relationships. This project therefore integrates various forms of narratives into the context of financial market analysis, leverages machine learning techniques, and aims to show how narratives are inextricably interwoven in the continuously unfolding financial market evolutions.

We will extend quantitative research through novel measurement techniques, the creation of new data sets, offering new solutions towards prediction problems, and the induction of new theories (Obschonka & Audretsch, 2020). We will also contribute to recent works that demonstrated the potential of theoretical and methodological advancements through the application of machine learning in the research practice (Mullainathan & Spiess, 2017; von Krogh, 2018). In pursuit of both practical 'relevance' of our research (Wiklund et al., 2019) and the contribution of "AI-integrated" research (Levesque et al. 2020), our approach will provide actionable insights.

2. Activities, Output and impact

The output and impact are in line with the STAR-UBB Academic Research Network of Excellence objectives and goals. Specifically, we contributed to academic progress as well as ensured substantial outreach and dissemination of the results and achievements. The following activities took place:

 

2.1. International Research Workshop on Digital Finance (May 22, 202): We organized a hybrid workshop on the topic of "Digital Finance", on the invitation of Endava, in Cluj-Napoca, Romania.

Endava (https://www.endava.com/) is an international company renowned for creating technological solutions that drive innovation and transform businesses.

The organizer from Endava was Stefana Belbe, senior data scientist at Endeva and researcher at Babes-Bolyai University, Cluj-Napoca, Romania.

This workshop was sSupported by the International Advanced Fellowship-UBB programme, generously funded by Babeș-Bolyai University under the contract nr.21PFE/30.12.2021, ID: PFE-550-UBB.

Furthermore, this workshop was part of the initiatives under the COST Action CA19130 on Fintech and AI in Finance, and the MSCA Industrial Doctoral Network on Digital Finance.

- Speakers:

- Prof. Dr. Codruta Mare - Professor of Statistics at Babes-Bolyai University, Grant Award Coordinator of COST Action CA19130, and Work Package Lead on European Financial Data Space in the MSCA Industrial Doctoral Network on Digital Finance.

  - Prof. Dr. Joerg Osterrieder - Professor of Finance at Bern Business School, Switzerland, and Associate Professor of Finance and Artificial Intelligence at the University of Twente, Netherlands. He is also the Action Chair of COST Action CA19130 and Coordinator of the MSCA Industrial Doctoral Network on Digital Finance.

- Participants: Over 120 researchers from the global AI community at Endava participated in this workshop.

  • Workshop Focus: The event provided an in-depth exploration of current topics and challenges in Digital Finance, creating a dialogue on recent advancements and strategic issues within the sector, from an international industry-academic point of view.

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The European COST Action CA19130 Fintech and AI in Finance at Endava

Thanks to Stefana Belbe and Codruta Mare

The MSCA Industrial Doctoral Network on Digital Finance at Endava

Thanks to Stefana Belbe and Codruta Mare

 

 

2.2. MSCA Industrial Doctoral Network on Digital Finance - Second Supervisory Board Meeting
 

Babes-Bolyai University (UBB), Cluj-Napoca, Romania, April 24, 2024 
 

Coordinator: Joerg Osterrieder
 

The second supervisory board meeting of the Marie Skłodowska-Curie Action on Digital Finance was convened at Babes-Bolyai University. This meeting was evaluating the progress of MSCA projects in each work package and setting future research directions.
 

The meeting aimed to review the status and outcomes of ongoing MSCA Digital Finance research and its progress so far.

 

Supported by: International Advanced Fellowship-UBB Programme, generously funded by Babeș-Bolyai University under the contract nr.21PFE/30.12.2021, ID: PFE-550-UBB. 

 

 

2.3. COST Action CA19130 Fintech and AI in Finance - Core Group Meeting

Babes-Bolyai University (UBB), Cluj-Napoca, Romania, April 24, 2024 

Chair: Joerg Osterrieder

The core group meeting of the COST Action CA19130 Fintech and AI in Finance discussed the current progress and was planning the period until September 2024, in particular the budget, future activities and meetings as well as the close cooperation with Babes-Bolyai University. 

Supported by: International Advanced Fellowship-UBB Programme, generously funded by Babeș-Bolyai University under the contract nr.21PFE/30.12.2021, ID: PFE-550-UBB. 

 

 

2.4. Generative AI - Workshop by IBM

Babes-Bolyai University (UBB), Cluj-Napoca, Romania, April 24, 2024 

IBM hosted a workshop explaining the details and background of AI. Prof. Mare was the main organizer and Prof. Osterrieder attended this workshop, creating new research cooperations with IBM.

 

 

2.5. COST Innovator Grant - A Visual Explainable Artificial Intelligence Tool

Babes-Bolyai University (UBB), Cluj-Napoca, Romania, April 26, 2024 

Discussion and strategic planning on the application for a COST Innovator Grant and preparation of the hearings for the second round in Brussels

2.6. Research Meeting for Future funding opportunities

Babes-Bolyai University (UBB), Cluj-Napoca, Romania, April 26, 2024 

Discussion and strategic planning on the application for a joint multilateral academid project between Switzerland (BFH), Romania (UBB) and Poland (Poznan University, Warsaw University)

3. Academic Paper

Several research activities have been carried out that resulted in various publications

3.1.  Enhancing Security in Blockchain Networks: Anomalies, Frauds, and Advanced Detection Techniques

J Osterrieder, S Chan, J Chu, Y Zhang, BH Misheva… - arXiv preprint arXiv:2402.11231, 2024

Blockchain technology, a foundational distributed ledger system, enables secure and transparent multi-party transactions. Despite its advantages, blockchain networks are susceptible to anomalies and frauds, posing significant risks to their integrity and security. This paper offers a detailed examination of blockchain's key definitions and properties, alongside a thorough analysis of the various anomalies and frauds that undermine these networks. It describes an array of detection and prevention strategies, encompassing statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and comprehensive risk assessment techniques. Through case studies, we explore practical applications of anomaly and fraud detection in blockchain networks, extracting valuable insights and implications for both current practice and future research. Moreover, we spotlight emerging trends and challenges within the field, proposing directions for future investigation and technological development. Aimed at both practitioners and researchers, this paper seeks to provide a technical, in-depth overview of anomaly and fraud detection within blockchain networks, marking a significant step forward in the search for enhanced network security and reliability.

 

3.2.  Hypothesizing Multimodal Influence: Assessing the Impact of Textual and Non-Textual Data on Financial Instrument Pricing Using NLP and Generative AI

K Bolesta, G Taibi, C Mare, B Hadji Misheva, C Hopp… - Available at SSRN 4698153, 2024

This paper presents an advanced conceptual framework for the analysis of textual data in the context of financial securities, hypothesizing that a comprehensive evaluation of events within the broader economic environment, particularly through their descriptions, significantly influences the pricing of financial instruments.

3.3. Metaverse Non Fungible Tokens

Osterrieder, Joerg and Chan, Stephen and Chu, Jeffrey and Zhang, Yuanyuan, Metaverse Non Fungible Tokens (February 20, 2024). Available at SSRN:  https://ssrn.com/abstract=4733153 or http://dx.doi.org/10.2139/ssrn.4733153

 

This paper examines the impact of the Metaverse and Non-Fungible Tokens (NFTs) on how we understand digital ownership and participate in online economies. It starts with a straightforward explanation of the Metaverse, focusing on its basic structure and the technologies that make it work, such as virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence (AI). The discussion then moves to NFTs, describing how they use blockchain technology to provide a secure way to show ownership of digital items. This part of the paper looks at how NFTs are changing the ways people create, collect, and sell digital content.

 

A key part of the analysis is how NFTs are used in the Metaverse. The paper explores how NFTs can help manage identities and ownership of digital assets, showing their potential to make digital interactions more meaningful. It also considers the broader effects of NFTs on the digital economy, including how they might change the way we think about art and ownership online, and their role in the development of decentralized finance (DeFi).

 

The structure of the paper includes a detailed review of related research, an exploration of NFTs' historical development, and an examination of the NFT ecosystem. It looks into various uses of NFTs, addresses the technical challenges and recent solutions, and discusses legal and ethical issues. The paper ends by considering what might come next for NFTs, suggesting areas for future exploration without making final judgments.

 

By focusing on the integration of NFTs within the Metaverse, this study aims to shed light on their growing significance for digital economies and cultures. It navigates through the complexities of digital asset management and interaction, contributing to the ongoing conversation on the future of online engagement and ownership.

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4. Team 

The fellowship was be conducted under the coordination of Prof. Dr. Codruta Mare, Scientific Director of the Interdisciplinary Center for Data Science, UBB, which belongs to the Social Sciences Academic School of BBU, coordinated by the Big Data and Machine Learning STAR-UBB-N Center.

The main team was composed of Prof. Dr. Codruta Mare, Scientific Director of ICDS and Prof. Dr. Joerg Osterrieder, Professor of Finance and Artificial Intelligence. Furthermore, Prof. Dr. Hadj Branka Misheva, Bern Business School, Prof. Dr. Marco Machado, University of Twente, as well as three PhD students of Prof. Osterrieder contributed to the research.

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5. Duration and Time-frame

Preliminary research was undertaken in 2023. The main research was done during the fellowship programme between January and August 2024. The research stay at UBB took place in April 2024.

A long-term continued cooperation will take place. Indeed, the two main research partners have been working together for the last three years and will continue to work together in a variety of research projects, notably the COST Action CA19130, a Marie-Sklowdowska Curie Action Doctoral Network as well as various other Horizon Europe and Erasmus+ programmes. Bilateral cooperations have been established via Erasmus+ and SEMP, the Swiss equivalent of Erasmus.

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