Peter Schwendner

@zhaw.ch

Head of Institute for Wealth and Asset Management
Zurich University of Applied Sciences

Peter Schwendner


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https://researchid.co/pschwen

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Finance, Computers in Earth Sciences, Economics, Econometrics and Finance
20

Scopus Publications

637

Scholar Citations

15

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • Digital assets: risks, regulations, mitigation
    Huei-Wen Teng, Wolfgang Karl Härdle, Joerg Osterrieder, Daniel Traian Pele, Lennart John Baals, Vassilios Papavassiliou, Karolina Bolesta, Audrius Kabašinskas, Olivija Filipovska, Nikolaos S. Thomaidis, Alexios-Ioannis Moukas, Sam Goundar, Jamal Abdul Nasir, Abraham Itzhak Weinberg, Veni Arakelian, Ciprian-Octavian Truică, Mutlu Akar, Esra Kabaklarlı, Elena-Simona Apostol, Maria Iannario, Barbara Bȩdowska-Sójka, Hanna Kristín Skaftadóttir, Ozgur Yildirim, Albulena Shala, Galena Pisoni, Ioana Florina Coita, Szabolcs Korba, Christian M. Hafner, Peter Schwendner, Bálint Molnár, Elda Xhumari
    Financial Innovation, 2026
    Digital assets (DAs) such as cryptocurrencies, tokenized securities, stablecoins, non-fungible tokens (NFTs), and central bank digital currencies, are transforming financial markets with new business models, investment opportunities, and transaction efficiencies. Underpinned by blockchain, distributed ledger technology, and smart contracts, digital innovations are reshaping the financial ecosystem. However, their rapid growth introduces substantial risks, including fraud, market manipulation, cybersecurity threats, and regulatory uncertainty. This position paper offers an interdisciplinary and empirically grounded analysis of the DA landscape. We define and classify major asset types, trace their evolution from speculative instruments to functional tools, and assess current adoption trends. Additional technological developments (e.g., decentralized finance and NFT expansion) are examined for their role in accelerating this transformation. We also analyze the global regulatory landscape, highlighting jurisdictional differences, classification challenges, and emerging governance frameworks. To address key risks, we derive mitigation strategies via quantitative analysis and case-based evidence. The risks include balancing innovation with investor protection through adaptive regulatory design, promoting cross-border regulatory harmonization to prevent arbitrage and fragmentation, and supporting experimentation through regulatory sandboxes and innovation hubs. By adopting a forward-looking, evidence-based, and collaborative regulatory approaches, stakeholders can harness the benefits of DAs while managing systemic risks and maintaining market integrity.
  • The Stock and Option Market Response to Negative ESG News
    Tomasz Orpiszewski, Mark James Thompson, Peter Schwendner
    International Journal of Accounting, 2025
    Synopsis The research problem This study investigates the response of stock prices and equity options to negative environmental, social, and governmental (ESG) incidents. Motivation Despite the growing significance of ESG news and the prominent role of equity options in portfolio management, empirical research on the relationship between options and ESG news remains notably scarce. Yet, the bulk of active asset managers, hedge funds, and investment banks continuously analyze stock prices and option prices simultaneously, to improve strategy performance and risk management and to search for information signals. The test hypotheses We hypothesize that negative ESG news events tend to negatively impact stock prices and trigger an increase in implied volatility (IV). We also postulate that the magnitude of market response is driven by the event characteristics, that is, “severity,” “novelty,” and “reach,” as well as the financial materiality. Target population Active asset managers, investment banks, regulators. Adopted methodology We employed a two-stage approach to analyze the effect of ESG events on stocks and option IVs. We estimated expected returns and expected change in IV using regression techniques. First, we used these to calculate abnormal returns (AR) and abnormal implied volatility (AIV). We then summed up the ARs and AIVs over several days to obtain cumulative abnormal returns (CAR) and cumulative abnormal changes in volatility (CAIV). Second, we used the CARs and CAIVs as dependent variables for fixed effect panel regressions. Analyses We created a large dataset of daily stock and options data on the companies from the S&P 500 index between 2006 and 2021. Most importantly, we use two unique datasets — one on negative ESG news from RepRisk and the other on options prices from OptionMetrics. Out of nearly 3 billion option prices, we extracted option implied volatilities for each stock, applied Bayesian imputation, and obtained daily average IV per stock. Findings Results of event studies indicate that, on average, option IV exhibits a minimal day-to-day reaction to ESG incidents. However, more severe incidents and those reported by major business sources result in a more pronounced increase in IV within 10 days following the event. Additionally, our empirical analysis highlights the influence of financial materiality on asset prices, as financially material ESG incidents lead to significant declines in stock prices and an upward surge in IV. Notably, this effect is particularly prominent for incidents related to natural capital. In conclusion, the options market incorporates information regarding ESG events, while stock prices exhibit comparatively lower and less systematic reactivity.
  • Explaining New Issuance Premiums in the US Corporate Bond Market (2016–2020)
    Andrea Günster, Ramon Jud, Peter Schwendner
    Journal of Financial Data Science, 2025
    The authors estimate the size of the new issuance premiums on the primary US corporate bond market from 2016 to 2020 to be about +1.5 bps. Linear regression of the NIP against new issue spread, market spread level and momentum, issue size, and tenor difference show only small predictive power. Therefore, the authors employ random forest models and use 17 explanatory features. The main determinants shown by the random forests are the number of syndicate banks, features capturing valuation uncertainty, the tenor of the bond, various market features, and most importantly the indicative premium at the initial pricing thoughts. The feature selection is largely confirmed by least absolute shrinkage and select operator (LASSO) regressions as a robustness check with a significantly lower model fit.
  • Editorial: Trends in AI4ESG: AI for sustainable finance and ESG technology
    Peter Schwendner, Jan-Alexander Posth
    Frontiers in Artificial Intelligence, 2024
    Editorial on the Research Topic "Trends in AI4ESG: AI for sustainable finance and ESG technology".
  • Enhancing portfolio management using artificial intelligence: literature review
    Kristina Sutiene, Peter Schwendner, Ciprian Sipos, Luis Lorenzo, Miroslav Mirchev, Petre Lameski, Audrius Kabasinskas, Chemseddine Tidjani, Belma Ozturkkal, Jurgita Cerneviciene
    Frontiers in Artificial Intelligence, 2024
    Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.
  • Investor demand in syndicated EFSF/ESM bond issuances
    Martin Hillebrand, Marko Mravlak, Peter Schwendner
    Open Research Europe, 2023
    European Financial Stability Facility (EFSF) and European Stability Mechanism (ESM) were set up at the peak of the European sovereign debt crisis to issue bonds and lend to countries under current funding stress. This study analyses investor demand in syndicated bond issuances of EFSF and ESM from 2014 to 2020 on an unprecedented granularity level using a dataset of individual orders with statistical inference. Particularly, we investigate orderbook dynamics for three main aspects: first, we determine the main factors segmenting investor demand. Second, we analyse price dynamics in the transactions and their relation to investor demand. Third, we investigate whether any indications of orderbook inflation might explain the increased volatility in orderbook volume. We identify issuance tranche and tenor as the main determinants of investor demand that are largely anticipated in the notional. Further, we note that ESM is doing economical pricing, where the new issue premium tends to be lower in a market context with larger demand. Lastly, we find a mixture of an increasing number and an increasing volume of orders as drivers of large order books. This confirms that there are no indications of orderbook inflation tendencies in the analysed time period.
  • Editorial: Artificial Intelligence in Finance and Industry: volume II—highlights from the 7th European conference
    Andreas Henrici, Rudolf M. Füchslin, Peter Schwendner
    Frontiers in Artificial Intelligence, 2023
    COPYRIGHT © 2023 Henrici, Füchslin and Schwendner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Artificial Intelligence in Finance and Industry: volume II—highlights from the 7th European conference
  • Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation
    Fernando de Meer Pardo, Peter Schwendner, Marcus Wunsch
    Journal of Financial Data Science, 2022
    Generative adversarial networks (GANs) have been shown to be able to generate samples of complex financial time series, particularly by employing the concept of path signatures, a universal description of the geometric properties of a data stream whose expected value uniquely characterizes the time series. Specifically, the SigCWGAN model (Ni et al. 2020) can generate time series of arbitrary length; however, the parameters of the neural network employed grow exponentially with the dimension of the underlying time series, which makes the model intractable when seeking to generate large financial market scenarios. To overcome this problem of dimensionality, the authors propose an iterative generation procedure relying on the concept of hierarchies in financial markets. The authors construct an ensemble of GANs that they call the Hierarchical-SigCWGAN, which is based on hierarchical clustering that approximates signatures in the spirit of the original model. The Hierarchical-SigCWGAN can scale to higher dimensions and generate large-dimensional scenarios in which the joint behavior of all the assets in the market is replicated. The model is validated by comparing its performance on a series of similarity metrics with respect to the original SigCWGAN on a dataset in which it is still tractable and by showing its scalability on a larger dataset.
  • Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory
    Peter Schwendner, Jochen Papenbrock, Markus Jaeger, Stephan Krügel
    Journal of Financial Data Science, 2021
    In this article, the authors present a conceptual framework named adaptive seriational risk parity (ASRP) to extend hierarchical risk parity (HRP) as an asset allocation heuristic. The first step of HRP (quasi-diagonalization), determining the hierarchy of assets, is required for the actual allocation done in the second step (recursive bisectioning). In the original HRP scheme, this hierarchy is found using single-linkage hierarchical clustering of the correlation matrix, which is a static tree-based method. The authors compare the performance of the standard HRP with other static and adaptive tree-based methods, as well as seriation-based methods that do not rely on trees. Seriation is a broader concept allowing reordering of the rows or columns of a matrix to best express similarities between the elements. Each discussed variation leads to a different time series reflecting portfolio performance using a 20-year backtest of a multi-asset futures universe. Unsupervised learningbased on these time-series creates a taxonomy that groups the strategies in high correspondence to the construction hierarchy of the various types of ASRP. Performance analysis of the variations shows that most of the static tree-based alternatives to HRP outperform the single-linkage clustering used in HRP on a risk-adjusted basis. Adaptive tree methods show mixed results, and most generic seriation-based approaches underperform.
  • Interpretable Machine Learning for Diversified Portfolio Construction
    Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, Peter Schwendner
    Journal of Financial Data Science, 2021
    In this article, the authors construct a pipeline to benchmark hierarchical risk parity (HRP) relative to equal risk contribution (ERC) as examples of diversification strategies allocating to liquid multi-asset futures markets with dynamic leverage (volatility target). The authors use interpretable machine learning concepts (explainable AI) to compare the robustness of the strategies and to back out implicit rules for decision-making. The empirical dataset consists of 17 equity index, government bond, and commodity futures markets across 20 years. The two strategies are back tested for the empirical dataset and for about 100,000 bootstrapped datasets. XGBoost is used to regress the Calmar ratio spread between the two strategies against features of the bootstrapped datasets. Compared to ERC, HRP shows higher Calmar ratios and better matches the volatility target. Using Shapley values, the Calmar ratio spread can be attributed especially to univariate drawdown measures of the asset classes. <b>TOPICS:</b>Quantitative methods, statistical methods, big data/machine learning, portfolio construction, performance measurement <b>Key Findings</b> ▪ The authors introduce a procedure to benchmark rule-based investment strategies and to explain the differences in path-dependent risk-adjusted performance measures using interpretable machine learning. ▪ They apply the procedure to the Calmar ratio spread between hierarchical risk parity (HRP) and equal risk contribution (ERC) allocations of a multi-asset futures portfolio and find HRP to have superior risk-adjusted performance. ▪ The authors regress the Calmar ratio spread against statistical features of bootstrapped futures return datasets using XGBoost and apply the SHAP framework by Lundberg and Lee (2017) to discuss the local and global feature importance.
  • The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets
    Jan-Alexander Posth, Piotr Kotlarz, Branka Hadji Misheva, Joerg Osterrieder, Peter Schwendner
    Frontiers in Artificial Intelligence, 2021
  • Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
    Jochen Papenbrock, Peter Schwendner, Markus Jaeger, Stephan Krügel
    Journal of Financial Data Science, 2021
  • Editorial: AI and Financial Technology
    Paolo Giudici, Ronald Hochreiter, Jörg Osterrieder, Jochen Papenbrock, Peter Schwendner
    Frontiers in Artificial Intelligence, 2019
  • Sentiment Analysis of European Bonds 2016–2018
    Peter Schwendner, Martin Schüle, Martin Hillebrand
    Frontiers in Artificial Intelligence, 2019
  • Tail-risk protection trading strategies
    N. Packham, J. Papenbrock, P. Schwendner, F. Woebbeking
    Quantitative Finance, 2017
  • Handling risk-on/risk-off dynamics with correlation regimes and correlation networks
    Jochen Papenbrock, Peter Schwendner
    Financial Markets and Portfolio Management, 2015
  • Static versus dynamic hedges: An empirical comparison for barrier options
    Bernd Engelmann, Matthias R. Fengler, Morten Nalholm, Peter Schwendner
    Review of Derivatives Research, 2006
  • Quoting multiasset equity options in the presence of errors from estimating correlations
    Matthias R. Fengler, Peter Schwendner
    Journal of Derivatives, 2004
  • Ladder climbing and multiphoton dissociation of polyatomic molecules excited with short pulses: Basic theory and applications to HCO
    Peter Schwendner, Christian Beck, Reinhard Schinke
    Physical Review A Atomic Molecular and Optical Physics, 1998
  • Photodissociation of Ar+2 in strong laser fields
    Peter Schwendner, Frank Seyl, Reinhard Schinke
    Chemical Physics, 1997

RECENT SCHOLAR PUBLICATIONS

  • EUPopLink Country report-Switzerland
    D Ursprung, P Schwendner
    Open Research Europe 6 (173), 173 , 2026
    2026
  • Digital assets: risks, regulations, mitigation
    HW Teng, WK Härdle, J Osterrieder, DT Pele, LJ Baals, V Papavassiliou, ...
    Financial Innovation 12 (1), 65 , 2026
    2026
    Citations: 19
  • The stock and option market response to negative ESG news
    T Orpiszewski, MJ Thompson, P Schwendner
    The International Journal of Accounting 60 (04), 2440002 , 2025
    2025
    Citations: 16
  • Explaining New Issuance Premiums in the US Corporate Bond Market (2016–2020)
    A Günster, R Jud, P Schwendner
    The Journal of Financial Data Science 7 (3) , 2025
    2025
    Citations: 2
  • A method for uncovering tokenisation archetypes and their effects: thus spoke Switzerland
    A Plepi, P Schwendner
    2024
    Citations: 1
  • Towards Tokenised Bond Markets? Lessons from Switzerland
    A Plepi, P Schwendner
    SUERF Policy Brief, No 1058 , 2024
    2024
  • Network analysis of the global trade of hot air: key lessons for the Paris Agreement
    R Kotsch, R Betz, P Schwendner, J Abrell
    SSRN , 2024
    2024
    Citations: 1
  • AI-Driven Failed Trials in Investment Strategies: A Network Data Analysis Approach
    K Bolesta, M Akar, I Coita, C Tarantola, M Iannario, J Osterrieder, C Sipos, ...
    2024
  • Investor demand in syndicated EFSF/ESM bond issuances
    M Hillebrand, M Mravlak, P Schwendner
    Open Research Europe 3, 96 , 2024
    2024
    Citations: 3
  • Investor Activity in EFSF/ESM secondary bond markets
    M Hillebrand, M Mravlak, L Breitsamter, P Schwendner
    European Stability Mechanism Working Paper , 2024
    2024
  • A discussion paper for possible approaches to building a statistically valid backtesting framework
    V Arakelian, K Bolesta, S Vlah Jeric, Y Liu, J Osterrieder, V Potì, ...
    Available at SSRN 4893677 , 2024
    2024
    Citations: 4
  • Bio-value-at-risk: A Concept to assessing the implications of biodiversity risks on portfolio management using geospatial analysis
    JA Posth, P Schwendner, P Laube, T Orpiszewski
    Available at SSRN 4784271 , 2024
    2024
    Citations: 6
  • Trends in AI4ESG: AI for sustainable finance and ESG technology
    P Schwendner, JA Posth
    Frontiers in artificial intelligence 7, 1448045 , 2024
    2024
    Citations: 8
  • Enhancing portfolio management using artificial intelligence: literature review
    K Sutiene, P Schwendner, C Sipos, L Lorenzo, M Mirchev, P Lameski, ...
    Frontiers in artificial intelligence 7, 1371502 , 2024
    2024
    Citations: 84
  • Case studies of primary and secondary market dynamics
    P Schwendner
    16th International Conference of the ERCIM WG on Computational and … , 2023
    2023
  • Artificial Intelligence in Finance and Industry: volume II—highlights from the 7th European conference
    A Henrici, RM Füchslin, P Schwendner
    Frontiers in Artificial Intelligence 6, 1267377 , 2023
    2023
  • The role of derivatives in sustainable investing: a practical guide to addressing sustainability-related challenges linked to the use of derivatives in sustainable portfolios
    H Kimmerle, P Schwendner, S Döbeli
    Swiss Sustainable Finance , 2023
    2023
  • Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation
    F de Meer Pardo, P Schwendner, M Wunsch
    The Journal of Financial Data Science 4 (4), 110-132 , 2022
    2022
    Citations: 9
  • Accelerated Data Science, AI and GeoAI for Sustainable Finance in Central Banking and Supervision
    J Papenbrock, J Ashley, P Schwendner
    BIS Irving Fisher Bulletin 56 , 2022
    2022
    Citations: 10
  • Artificial Intelligence in Finance and Industry: highlights from 6 European COST conferences
    P Deflorin, RM Füchslin, A Henrici, J Osterrieder, P Schwendner, ...
    5th European COST Conference on Artificial Intelligence in Industry and … , 2022
    2022

MOST CITED SCHOLAR PUBLICATIONS

  • Enhancing portfolio management using artificial intelligence: literature review
    K Sutiene, P Schwendner, C Sipos, L Lorenzo, M Mirchev, P Lameski, ...
    Frontiers in artificial intelligence 7, 1371502 , 2024
    2024
    Citations: 84
  • Interpretable Machine Learning for Diversified Portfolio Construction
    M Jaeger, S Krügel, D Marinelli, J Papenbrock, P Schwendner
    The Journal of Financial Data Science 3 (3) , 2021
    2021
    Citations: 62
  • System and method for risk management and portfolio optimization
    J Papenbrock, P Schwendner
    US Patent App. 14/213,986 , 2014
    2014
    Citations: 53
  • Handling risk-on/risk-off dynamics with correlation regimes and correlation networks
    J Papenbrock, P Schwendner
    Financial Markets and Portfolio Management 29 (2), 125-147 , 2015
    2015
    Citations: 52
  • Static versus dynamic hedges: an empirical comparison for barrier options
    B Engelmann, MR Fengler, M Nalholm, P Schwendner
    Review of Derivatives Research 9 (3), 239-264 , 2006
    2006
    Citations: 48
  • Photodissociation of Ar2+ in strong laser fields
    P Schwendner, F Seyl, R Schinke
    Chemical physics 217 (2-3), 233-247 , 1997
    1997
    Citations: 41
  • Hedging under alternative stickiness assumptions: an empirical analysis for barrier options
    B Engelmann, MR Fengler, P Schwendner
    Journal of Risk 12 (1), 53-77 , 2009
    2009
    Citations: 32
  • Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios
    J Papenbrock, P Schwendner, M Jaeger, S Krügel
    The Journal of Financial Data Science 3 (2), 51-69 , 2021
    2021
    Citations: 30
  • Quoting multiasset equity options in the presence of errors from estimating correlations
    MR Fengler, P Schwendner
    Journal of Derivatives 11 (4), 43 , 2004
    2004
    Citations: 25
  • European government bond dynamics and stability policies: taming contagion risks
    P Schwendner, M Schuele, T Ott, M Hillebrand
    Journal of Network Theory in Finance 1 (4), 1-24 , 2015
    2015
    Citations: 23
  • Digital assets: risks, regulations, mitigation
    HW Teng, WK Härdle, J Osterrieder, DT Pele, LJ Baals, V Papavassiliou, ...
    Financial Innovation 12 (1), 65 , 2026
    2026
    Citations: 19
  • AI and financial technology
    P Giudici, R Hochreiter, J Osterrieder, J Papenbrock, P Schwendner
    Frontiers in Artificial Intelligence 2, 25 , 2019
    2019
    Citations: 18
  • Adaptive Seriational Risk Parity and Other Extensions for Heuristic Portfolio Construction Using Machine Learning and Graph Theory
    P Schwendner, J Papenbrock, M Jaeger, S Krügel
    The Journal of Financial Data Science 3 (4), 65-83 , 2021
    2021
    Citations: 17
  • The stock and option market response to negative ESG news
    T Orpiszewski, MJ Thompson, P Schwendner
    The International Journal of Accounting 60 (04), 2440002 , 2025
    2025
    Citations: 16
  • Tail-risk protection trading strategies
    N Packham, J Papenbrock, P Schwendner, F Woebbeking
    Quantitative Finance 17 (5), 729-744 , 2017
    2017
    Citations: 16
  • Accelerated Data Science, AI and GeoAI for Sustainable Finance in Central Banking and Supervision
    J Papenbrock, J Ashley, P Schwendner
    BIS Irving Fisher Bulletin 56 , 2022
    2022
    Citations: 10
  • The pricing of multi-asset options using a Fourier grid method
    B Engelmann, P Schwendner
    Journal of Computational Finance , 1998
    1998
    Citations: 10
  • Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation
    F de Meer Pardo, P Schwendner, M Wunsch
    The Journal of Financial Data Science 4 (4), 110-132 , 2022
    2022
    Citations: 9
  • Trends in AI4ESG: AI for sustainable finance and ESG technology
    P Schwendner, JA Posth
    Frontiers in artificial intelligence 7, 1448045 , 2024
    2024
    Citations: 8
  • The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets
    JA Posth, PK Kotlarz, B Hadji-Misheva, J Osterrieder, P Schwendner
    Frontiers in Artificial Intelligence 4, 668465 , 2021
    2021
    Citations: 7