I have been working in the area of financial statistics, quantitative finance, algorithmic trading, and digitisation of the finance industry for more than 15 years.
Joerg is the Action Chair of the European COST Action 19130 Fintech and Artificial Intelligence in Finance, an interdisciplinary research network combining 200+ researchers and 49 countries world-wide.
He was the director of studies for an executive education course on "Big Data Analytics, Blockchain and Distributed Ledger" for six consecutive courses, co-director of studies for "Machine Learning and Deep Learning in Finance" and has been the main organizer of an annual research conference series on Artificial Intelligence in Industry and Finance since 2016.
He is a founding associate editor of Digital Finance, an editor of Frontiers Artificial Intelligence in Finance and frequent reviewer for academic journals, among them the European Journal of Finance and the Journal of Investment Strategies.
In addition, he serves as an expert reviewer for the European Commission on the "Executive Agency for Small & Medium-sized Enterprises" and the "European Innovation Council Accelerator Pilot" programmes.
Previously he worked as an executive director at Goldman Sachs and Merrill Lynch, as quantitative analyst at AHL as well as a member of the senior management at Credit Suisse Group. Joerg is now also active at the intersection of academia and industry, focusing on the transfer of research results to the financial services sector in order to implement practical solutions.
My education:
- Diploma in Business Mathematics, University of Ulm, Germany, 2002
- Master of Science Mathematics, Syracuse University, USA, 2002
- PhD in Financial Mathematics, ETH Zurich, Switzerland, 2007
Expertise
Economics, Econometrics and Finance
- Volatility
- Finance
- Learning
- Cryptocurrency
- Machine Learning
- Bitcoin
Computer Science
- Artificial Intelligence
Social Sciences
- Markets
Organisations
European COST (Cooperation in Science and Technology) Action 19130 Fintech and Artificial Intelligence in Finance
I am the Action Chair of the COST Action Fintech and AI in Finance. With a network of 49 countries and 200+ researchers, we are working on a substantial number of research topics, including, but not limited to: Reinforcement learning for trading, Sentiment analysis for Finance, Machine learning for Finance, Fintech applications, Blockchain and Cryptocurrencies.
Global reseearch cooperations
I have close research cooperations with academics from around the globe
- Professor Ali Hirsa, Columbia University, US, jointly working on synthetic data generation, reinforcement learning for finance, explainable artificial intelligence, co-supervising MSc and PhD students
- Professor Stephan Sturm, Worcester Polytechnic Institute, US, working on financial mathematics, including reinforcement learning for Finance, co-supervising MSc and PhD students
- Dr. Alex Posth, Zurich University of Applied Sciences, Switzerland, working on self-play algorithms for Finance
- Professor Stephen Chan, American University of Sharjah, UAE, working on blockchain and cryptocurrencies
- Professor Saralees Nadarajah, Manchester University, UK, working on statistical properties of cryptocurrencies
- Professor Codruta Mare, Babes-Bolyai University, Romania, working on sentiment analysis for Finance
- Professor Ioana-Florina Coita, University of Oradea, Romania, working on sentiment analysis for Finance
- Professor Branka Hadji Misheva, Bern University of Applied Sciences, Switzerland, working on reinforcement learning for finance and explainable AI for Finance
- Professor Ronald Hochreiter, Vienna University of Business and Economics, Austria, working on AI and financial technology
PhD Co-supervision and PhD committees
I am involved in the PhD Co-Supervision and PhD committees of several universities in Europe and the US.
- Patchara Santawisook, August 2022, "Price Impact of VIX Futures and Two Order Book Mean-Field Games", member of the PhD Committee, main supervisor: Prof. Dr. Stephan Sturm, Worcester Polytechnic University (WPI), US. Dissertation Committee: Dr. Stephan Sturm, WPI (Advisor), Dr. Marcel Y. Blais, WPI, Dr. Jörg Osterrieder, University of Twente, Dr. Andrew Papanicolaou, North Carolina State University, Dr. Qingshuo Song, WPI Dr. Frank Zou, WPI
- Sebastian Singer, 2021 - 2025, co-advisor and member of the PhD Committee, main supervisor: Prof. Dr. Ronald Hochreiter, WU Vienna, Austria
- Dr. Piotr Kotlarz, 2019 - 2023, local advisor, PhD at University of Liechtenstein
- Dr. Branka Hadji Misheva, 2019 - 2023, local advisor, PhD at University of Pavia, Italy
- Dr. Rui Li, 2020, PhD examiner, main supervisor: Saralees Nadarajah, University of Manchester, UK
- Dr. Idika Okorie, 2019, PhD examiner, main supervisor: Saralees Nadarajah, University of Manchester, UK
- Dr. M. Weibel, 2019, PhD examiner, main supervisor: Juri Hinz, University of Technology, Sydney, Australia
ING Group - University of Twente Cooperation - Associate Professorship Finance and Artificial Intelligence
I am working closing with ING Group, the Global Analytics team, on advanced, quantitative, data-driven research projects, relevant both for academia and industry.
1. Applications of synthetic data generation for Finance
•Testing trading strategies robustness, comparing portfolio construction methods, estimating the risk of a portfolio or a strategy, alternative pricing and hedging of options and other derivatives, generating trading signals, detecting anomalies in fundamental data, with a particular focus on using generative adversarial networks.
•Synthetic generator for (arbitrage-free) volatility surfaces
•Synthetic data generators that are differentially private, i.e. do not leak information about the original data, and still have enough features
2. Research on risk management related topics
3. Privacy-enhancing techniques for storing and analysing confidential data
4. Federated Learning. This is a machine learning technique that trains an algorithm across multiple servers holding local data samples, without exchanging them. Research is needed into how this can be used in Finance applications, especially those that use confidential data.
5. Applications of Reinforcement learning in Finance. Existing applications include portfolio optimization and optimal trade execution. Further research is needed to extend this technique to other areas in finance.
6. The value of innovation projects in Finance. Innovative projects have a high-risk of failure and are often also focused on cost reduction and loss-avoidance topics. Therefore the impact on the P&L of the company is not immediately clear. The project is supposed to find ways of measuring the cost/benefit ratio and provide a conceptual approach.
7. The use of "meta labeling" technique (tailored to non-HFT strategies). The approach consist in building a secondary ML model that learns how to use a primary exogenous model. It can help build an ML system on top of a white box (like a fundamental model founded on economic theory). The advantages of the approach is that it uses a way higher signal to noise ratio than when applying ML directly to (very noisy) traditional financial data.
8. Early warning systems for credit risk. Despite many years of research into credit risk, large and unexpected losses still happen frequently. Research on the causal relationships between market prices and external ratings as well as applying machine learning techniques and using new datasets for predicting downgrading and default of loans is beneficial to reduce credit losses.
Publications
Research profiles
- Reinforcement Learning for Finance (MSc)
- Information Systems for the Financial Services Industry (MSc)
- Applications of Artificial Intelligence in Business (MSc)
For students:
- I have several graduation internships available (MSc and BSc), in cooperation with ING Group, Amsterdam. Programming experience needed.
- If you are interested in writing your BSc or MSc thesis with me, please contact me directly. I have a substantial number of topics that would qualify for a thesis.
General topics for MSc and BSc thesis:
- Quantitative Finance
- Artificial Intelligence and Machine Learning
- Cryptocurrencies
- Reinforcement Learning
- Other topics possible on request
Affiliated study programs
Courses academic year 2023/2024
Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.
Courses academic year 2022/2023
Since 2015, I have worked on more than 30 research projects, mainly as project lead or principal investigator, funded by Europe Horizon 2020, Horizon Europe, Swiss National Science Foundation, Innosuisse and the Finance industry. The topics cover many aspects of quantitative, data-driven topics for Finance, ranging from trading strategies, efficient markets to machine learning and artificial intelligence in Finance, including latest developments such as blockchain, virtual currencies, Fintech and sustainable Finance.
Most notable international projects:
- MSCA Industrial Doctoral Network on Digital Finance, Coordinator, 2024 - 2027, 4.5 Mio EUR
- Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives / Project leader / Swiss National Science Foundation / 236'118 CHF / August 2023 - August 2026
- Cooperation ING Group - University of Twente
- Action Chair COST Action 19130 Fintech and Artificial Intelligence, Horizon Europe
- FIN-TECH – Financial Supervision and Technology Compliance Training Programme, EU Horizon 2020
- Network-based credit risk models in P2P lending markets / Project leader / Swiss National Science Foundation / 347'836 CHF / August 2022 - August 2025
More details:
- MSCA Industrial Doctoral Network on Digital Finance, Coordinator, 2024 - 2027, 4.5 Mio EUR
- Strategic Research fund within the BMS Research Theme Emerging Technologies & Societal Transformations “Digital Transformation of Finance and Society / PI / 20'000 EUR / January 2023 - December 2023
- Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives / Project leader / Swiss National Science Foundation / 236'118 CHF / August 2023 - August 2026
- Network-based credit risk models in P2P lending markets / Project leader / Swiss National Science Foundation / 347'836 CHF / August 2022 - August 2025
- Anomaly and fraud detection in blockchain networks / Project leader / Swiss National Science Foundation / 6'700 CHF / August 2022 - August 2023
- Conferences on Artificial Intelligence in Finance / Innosuisse / Project leader / 80'000 CHF / Januar 2021 - July 2022
- Strengthening Swiss Financial SMEs through Applicable Reinforcement Learning / Deputy project leader / Innosuisse / 312'315 CHF / April 2021 - July 2022
- COST Action Fintech and Artificial Intelligence in Finance - Grant Holder / Project leader / Horizon Europe / 800'000 EUR / April 2020 - April 2025
- Human-machine centered collaboration to crowdsource insights / Project leader / Innosuisse / 15'000 CHF / June 2021 - December 2021
- Towards Explainable Artificial Intelligence and Machine Learning in Credit Risk Management / Project co-leader / Innosuisse / 282'969 CHF / Sept 2020 - Sept 2022
- Decentralized financing of Fairtrade producers using a blockchain-based solution / Deputy project leader / Innosuisse / 250'539 CHF / August 2020 - January 2023
- Advanced/AI-supported Rating Models for P2P systems / Project co-leader / Innosuisse / 15'000 CHF / July 2020 - July 2021
- Currency hedging for SMEs and pension funds / Project leader / Innosuisse / 439'610 CHF / Oct 2018 - Oct 2021
- Hybrid Approach for Robust Identification and Measurement of Investors Driving Corporate Sustainability and Innovation. Design of Policy Tools for Evaluating the Impact of Specific Investors and Assessing the Quality of Companies’ Investor Bases. / Project leader / Swiss National Science Foundation / 150'000 CHF / February 2020 - August 2021
- Digitalisation non-bankable assets (specifically: art) / Deputy project leader / Innosuisse / 300k CHF / January 2020 - June 2020
- Deep Learning & Neuronal Networks: Selbstständige KI-Agenten zur Entwicklung von neuartigen Handelsstrategien im Asset Management auf Basis von Self-Play / Deputy project leader / Innosuisse / 15'000 CHF / July 2019 - January 2020
- Assessment of derivatives-based hedging solutions / Project co-leader / Swiss Asset Manager / 15'000 CHF / June 2021 - November 2021
- Enhancing the Financing of Fairtrade Producers using Blockchain Technology / Innosuisse / Team member / 250'539 CHF/ August 2020 - January 2023
- 6th European Conference on Artificial Intelligence in Finance and Industry 2021 / Project leader / 20'000 CHF / Industry funding / January 2021 - September 2021
- 5th European Conference on Artificial Intelligence in Finance and Industry 2020 / Project leader / 20'000 CHF / Industry funding / January 2020 - September 2020
- 4th European Conference on Artificial Intelligence in Finance and Industry 2019 / Project leader / 20'000 CHF / Industry funding / January 2019 - September 2019
- 3th European Conference on Artificial Intelligence in Finance and Industry 2018 / Project leader / 20'000 CHF / Industry funding / January 2018 - September 2018
- 2nd European Conference on Artificial Intelligence in Finance and Industry 2017 / Project leader / 20'000 CHF / Industry funding / January 2017 - September 2017
- 1st European Conference on Artificial Intelligence in Finance and Industry 2016 / Project leader / 20'000 CHF / Industry funding /January 2016 - September 2016
- FIN-TECH – Financial Supervision and Technology Compliance Training Programme / Project leader / 200'000 EUR / Europe Horizon 2020 / April 2018 - April 2021
- Digitales Immobilien Dossier (DIGIM) / Project co-leader / Innosuisse / 204'012 CHF / November 2018 - April 2020
- Swisscom E-Signatur TP Technik / Project leader / Swisscom / 80k CHF / January 2018 - December 2019
- Blockchain and Virtual Currencies / Project co-leader / Swiss National Science Foundation / 100k CHF / January 2018 - December 2018
- Large Scale Data-Driven Financial Risk Modelling / Team member / Innosuisse / 309'000 CHF / January 2017 - July 2019 /
- Mathematics and Fintech: The next revolution in the digital transformation of the finance industry / Project leader / Swiss National Science Foundation / 300k CHF / January 2017 - December 2019 /
- Swissnex Research Stay New York / Project leader / Swissnex / 10k CHF / July 2018
- Quantitative trading strategies / Project leader / Industry funding / 80k CHF / April 2016 - December 2017
- Long historical data for futures / Project leader / Industry funding / 20k CHF / April 2016 - December 2016
- Automation and industrialization of quantitative research / Project leader / University funding / 10k CHF / April 2015 - December 2016
- RENERG2 - RENewable enERGies in future energy supply / Innosuisse / Team member / 48'000 CHF / July 2013 - December 2016
Current projects
Strategic Research fund within the BMS Research Theme Emerging Technologies & Societal Transformations “Digital Transformation of Finance and Society"
European Innovation Council Accelerator Pilot
Reviewer for this Horizon Europe programme
Executive Agency for Small & Medium-sized Enterprises
Reviewer for this H2020 programme from the European Commission
Artificial Intelligence in Finance
ING Group - University of Twente Cooperation
Joint cooperation of ING Group and University of Twente, advancing Artificial Intelligence in Finance
COST Action Fintech and Artificial Intelligence in Finance
European Cooperation in Science and Technology
Action Chair of the COST Action 19130 Leading a European Research Project with 200+ researchers from 49 countries globally
MSCA Industrial Doctoral Network on Digital Finance
Summary: DIGITAL is an initiative focused on transforming the European financial sector through digitalization and innovation, aligning with the EU's strategic priorities and contributing to the UN Sustainable Development Goals. This initiative recognizes the need for Europe to make significant investments in the next five years in areas such as a European financial data space, artificial intelligence (AI) for financial markets, explainable and fair AI decisions, blockchain applications, and the sustainability of digital finance to maintain global competitiveness. The initiative involves a network of eight top-ranking European universities, four international corporations, two SMEs, three research centers, and the European Central Bank, aiming to address the industry's research gaps and train new PhD graduates equipped with digital finance skills. DIGITAL proposes five interconnected research objectives to advance methodologies and business models in digital finance, with 17 Early Stage Researchers (ESRs) from both academia and industry participating. Training will cover a wide range of topics including data quality, AI and machine learning, explainability of AI, blockchain applications, and sustainable finance. Finally, the need for an Industrial Doctoral Network is highlighted to help the European finance industry compete globally by addressing issues such as data quality, complex model deployment, AI trust and adoption, algorithmic bias, and a shortage of skilled labor.
Narrative Digital Finance: a tale of structural breaks, bubbles & market narratives
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.
Anomaly and fraud detection in blockchain networks
Background: Blockchain networks are increasingly being implemented into healthcare, supply chain, and retail systems, through smart contracts, smart devices, smart identity management. Although the use of this technology brings with it benefits, it can also still cause problems. A particular problem is derived from the immutability property, which means that fraudulent transactions or transfers of information cannot be reversed. Rationale: Blockchains can be attacked via a deluge of requests or transactions within a short time span, resulting in the loss of connectivity to the blockchain for users and businesses, or even financial institutions. Therefore, the rapid detection of anomalies from such activities is critical in order to prevent damage from occurring, or correct any damage as soon as possible to reduce the severity of its impact.Overall objectives: This project will study the problem of anomaly and fraud detection from the perspective of blockchain-based networks. Anomaly and fraud detection in blockchain-based networks is more complex due to their unique properties such as decentralisation, global reach, anonymity, etc., which make them different from traditional networks.Specific aims: To further the understanding of the sources and behaviours of anomalies and fraud in blockchain-based networks, and develop new improved methods for both static and dynamic anomaly detection that can be used alongside blockchain-based systems for real-time fraud detection.Methods: Developing and implementing static anomaly detection methods via a hybrid approach and developing dynamic anomaly detection methods using extreme value theory.Expected results: This research work will be able to contribute to improving the security relating to blockchain-based networks by providing more accurate and efficient methods for detecting anomalies and fraud and reducing the impact of losses resulting from these anomalies.Impact for the field: The project will be particularly beneficial alongside real world blockchain-based networks to allow for the fast detection of anomalous or fraudulent data, preventing damage or allowing for damage to be corrected as soon as possible. For cryptocurrency networks, this will reduce the impact of market manipulation, fraud, and more widely on global financial markets, currencies, and trade. In addition, the project will be of interest to a broad range of cryptocurrency and blockchain stakeholders including (but not limited to) academics, financial institutions, policymakers, regulators, and cybercrime agencies.
Network-based credit risk models in P2P lending markets
P2P (peer-to-peer) lending today consists of the lending of money to individuals and businesses through online services without bank intermediation (Thakor, 2020). P2P platforms offer a secure cyberspace (Niu et al., 2020) where borrowers are linked to investors who engage (usually) in a buyout auction, where the bidding process ends when the loan has been fully funded (Xia et al., 2017). Bank lending is backed by deposits, uninsured debt and equity; thus, banks have skin in the game, unlike P2P lending platforms, where loans are funded by investors directly, i.e., through investors’ equity. Higher interest rates and diversification potential incentivize lenders, represented by individuals and recently also by banks, hedge funds, venture capital firms and private equity firms (Giudici et al. 2019a), to participate in P2P lending. Traditional banks receive loan repayments that are used to pay out depositors, subordinated debt holders and potentially shareholders, while P2P platforms receive fees from loan origination (paid by the borrower) and transaction fees. Administration of lending tends to be cheaper for P2P platforms, which provide an online marketplace and initial risk classification, while banks are subject to much tighter regulation and thus have higher costs (Thakor, 2020). However, banks have much richer data at their disposal (e.g., through long-term relational banking), which makes their task of identifying potential nonperforming loans easier. One would therefore expect P2P platforms to attract borrowers who would otherwise not be eligible for bank loans. This effect is amplified during recessions, as reduced access to bank credit directs riskier borrowers towards the P2P markets. This phenomenon has been observed empirically, as several studies have found that after the 2008 recession, the growth of P2P markets accelerated (e.g., Jin and Zhu, 2015). Similar growth is likely to unfold during and after the current worldwide economic crisis induced by the COVID-19 pandemic.Given the nature of P2P markets, they are characterized as immature industries with loose regulation, greater information asymmetry and increased credit risk, which all lead to higher default rates. This leaves the door open to considerable risks. To mitigate adverse selection and moral hazard problems, one needs to build trust. In traditional bank-lending markets, trust is constructed via relational banking, using collateral, certified accounts, risk monitoring, the presence of a board of directors, tighter regulation, etc. (Emekter, 2015). Voluntary implementation of these mechanisms would incur significant costs and thus marginalize the competitive edge of P2P lending markets. Several recent studies have found that the failure of P2P platforms in China is related to general market conditions (bond yields), ownership, information disclosure, and popularity, while political ties were found to also play an important role (e.g., Gao et al., 2021, He and Li, 2021). A hands-on approach to establishing trust between investors and P2P markets is to use accurate credit risk models. The main objective of the proposed research project is to design a state-of-the art and interpretable credit risk models for P2P lending markets.
Hybrid Approach for Robust Identification and Measurement of Investors Driving Corporate Sustainability and Innovation. Design of Policy Tools for Evaluating the Impact of Specific Investors and Assessing the Quality of Companies’ Investor Bases
Following several decades of profit-oriented research in finance and economics, we have recently been observing a profound transformation in investor perception and a visible shift towards a sustainable financial system. The list of UN PRI signatories includes already over 2,000 large asset owners and keeps growing every year. Several reports (US SIF 2018) indicate that over 20% of professionally managed assets in the U.S. is already being invested according to the principles of socially responsible investing (SRI). While an increasing number of institutional investors are indicating their commitment to social and environmental sustainability, it remains unclear which investors have the most substantial and lasting effect on the sustainability of companies.In this project, we intend to measure the extent to which specific investors influence the sustainability of companies they invest in. We focus on the measurable side of sustainability, in particular the environmental impact of the activity of companies, and the level of corporate innovation, measured using patents data. By combining a previously untested dataset with a novel, hybrid methodology, we seek to answer deep-rooted scientific and practical questions such as whether the investor base affects the sustainability and innovation potential of a company. If so, can we identify and highlight investors who are effectively driving the future development of companies across the globe?By answering these questions, we will provide clear guidance on the design of policy tools to support investors and monitor the investor bases of companies. To maximize the societal and scientific impact of this project, we will use our findings to design two practical tools for policymakers and investors, which will empower them to make more viable and future-oriented decisions concerning sustainability and innovation. The first tool will enable an evaluation of the impact of a specific investor on sustainability, based on their historical behavior. The second tool will provide information whether company’s investor base is likely to promote its sustainable development.Our approach differs sharply from other projects in this field:- While the bulk of existing studies either use investor groups, we focus on the impact of individual investors on the evolution of sustainability of the companies they invest in. This will involve building and using untested dataset on investors and company data.- We extend the scope of the analysis to include crucial investors such as insurers, banks, pension funds, hedge funds as well as sovereign wealth funds.- We focus simultaneously on sustainability and innovation, which has not been investigated in a comprehensive analysis so far.- We include European and U.S. companies and investors, in contrast to U.S.-focused studies.- The proposed hybrid methodology combines linear and non-linear approaches as well as machine learning. It provides a variety of novelties over conventional approaches such as: adaptivity and time variation, addresses the spurious regression problem, and combines linear and non-linear dynamics. We also include a multi-tier data aggregation technique.- We provide user-friendly assessment tools and seek to maximize usability of results.Through contacts with national and international organizations as well as public and private sector investors, we will disseminate the research and tools to an academic and non-academic audience.
Mathematics and Fintech - the next revolution in the digital transformation of the finance industry
Our focus will be on the digitization and transformation of the finance industry. In recent years, Fintech companies, defined as organizations that combine innovative business models and technology to enable, enhance and disrupt financial services, have gained substantial funding and are the main drivers of innovation and digitalisation. Projections show that Fintech companies are expected to take away up to 60% of the revenues of the traditional banking sector within the next ten years.This topic is particularly relevant for Switzerland as one of the main global financial centers. Worldwide Venture Capital (VC) investment in Fintech ventures tripled in 2014 to more than $12 billion, while the Swiss banking industry is substantially lacking behind compared to other world financial centres. As a reaction, the Swiss government has now set Fintech as a top priority on their agenda. This research project will help in the transformation of the Swiss finance industry by laying the academic and mathematical foundations for the use of Fintech in the area of algorithmic strategies, risk management and investment banking. Its academic concepts and conclusions can also be used in a more general context and applied to a larger range of industries.In particular, the methods developed can also be applied in the context of Industry 4.0.This research is directly related to the goals and deliverables of the COST Action TD1409 (Mathematics for Industry Network - MI-NET).
In the press
- September 2023, Are companies being ripped off by big banks over share buybacks?, The Sunday Times, September 10, 2023
- August 2023, SocietyBytes Science Magazine, Wie Geschlechtervielfalt und KI die Fintech-Branche verändern
- July 2023, Financial Times Weekend Edition, Opinion On Wall Street. If companies are going to buy back shares, they should pay a fair price by Brooke Masters. Some are entering into oblique contracts with brokers that can cost far more than purchasing on the open market. Referencing our work on share buybacks.
- February 2022, Künstliche Intelligenz im Staat, Governance aus Sicht der Wissenschaft, Verwaltung und Industrie
- February 2022, Experteninterview Pionierblog-Beitrag für PostFinance, Künstliche Intelligenz in der Finanzindustrie
- October 2021, Greater Zurich boosts AI in Finance
- April 2021: Article on our Innosuisse Fairtrade Capital together with the School of Management and Law, ZHAW; Trying to bring producers and end-consumers together via digital tokens and the blockchain
- November 2021: Jörg Osterrieder von der ZHAW über Fintech, Openbanking und Blockchain
- September 2020, Fintech interview: “Artificial intelligence must not be a black box”
News on utwente.nl
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