Martina Björkman Nyqvist, Jakob Svensson, David Yanagizawa-Drott, Can good products drive out bad? A randomized intervention in the antimalarial medicine market in Uganda, Journal of the European Economic Association, Vol. 20 (3), 2022. (Journal Article)
 
How can quality be improved in markets in developing countries, which are known to be plagued by substandard and counterfeit (“fake”, in short) products? We study the market for antimalarial drugs in Uganda, where we randomly assign entry of a retailer (non-governmental organization (NGO)) providing a superior product - an authentic drug priced below the market - and investigate how incumbent firms and consumers respond. We find that the presence of the NGO had economically important effects. Approximately one year after the new market actor entered, the share of incumbent firms selling fake drugs dropped by more than 50% in the intervention villages, with higher quality drugs sold at significantly lower prices. Household survey evidence further shows that the quality improvements were accompanied by consumers expecting fewer fake drugs sold by drug stores. The intervention increased use of the antimalarial drugs overall. The results are consistent with a simple model where the presence of a seller committed to high quality, as opposed to an average firm, strengthens reputational incentives for competing firms to improve quality in order to not be forced out of the market, leading to “good driving out bad”. |
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Erich Walter Farkas, Francesco Ferrari, Urban Ulrych, Pricing autocallables in a Heston-like local-stochastic volatility model, In: SFI Research Days 2022. 2022. (Conference Presentation)

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Urban Ulrych, Antonello Cirulli, Michal Kobak, Portfolio Construction with Hierarchical Momentum, In: SFI Research Days 2022. 2022. (Conference Presentation)

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Steven Ongena, Steven Ongena, "Too-Big-To-Strand?" Bond Versus Bank Financing in the Transition to a Low-Carbon Economy, In: FRIC'22 Conference on Financial Frictions. 2022. (Conference Presentation)

What is the role market- and bank-based debt play in the climate transition process? We present evidence that bond markets price the risk that assets held by fossil fuel firms strand, while banks in the syndicated loan market seemingly do not price this risk much. Consequently, to fulfill their financing needs fossil fuel firms increasingly rely less on bonds and more on loans. We can interpret the within-firm bond-to-loan substitution along stranding risk as a contraction in the supply of bond versus bank funding. Within the banking sector especially the big banks are willing to provide cheaper and more financing to fossil fuel firms. |
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Steven Ongena, "Too-Big-To-Strand?" Bond Versus Bank Financing in the Transition to a Low-Carbon Economy, In: ESM Finance Seminar. 2022. (Conference Presentation)

What is the role market- and bank-based debt play in the climate transition process? We present evidence that bond markets price the risk that assets held by fossil fuel firms strand, while banks in the syndicated loan market seemingly do not price this risk much. Consequently, to fulfill their financing needs fossil fuel firms increasingly rely less on bonds and more on loans. We can interpret the within-firm bond-to-loan substitution along stranding risk as a contraction in the supply of bond versus bank funding. Within the banking sector especially the big banks are willing to provide cheaper and more financing to fossil fuel firms. |
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Aleksandra Urman, Mykola Makhortykh, “Foreign beauties want to meet you”: The sexualization of women in Google’s organic and sponsored text search results, New Media & Society, 2022. (Journal Article)
 
Search engines serve as information gatekeepers on a multitude of topics dealing with different aspects of society. However, the ways search engines filter and rank information are prone to biases related to gender, ethnicity, and race. In this article, we conduct a systematic algorithm audit to examine how one specific form of bias, namely, sexualization, is manifested in Google’s text search results about different national and gender groups. We find evidence of the sexualization of women, particularly those from the Global South and East, in search outputs in both organic and sponsored search results. Our findings contribute to research on the sexualization of people in different forms of media, bias in web search, and algorithm auditing as well as have important implications for the ongoing debates about the responsibility of transnational tech companies for preventing systems they design from amplifying discrimination. |
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Urban Ulrych, Antonello Cirulli, Michal Kobak, Portfolio Construction with Hierarchical Momentum, In: 4th International Conference on Computational Finance 2022. 2022. (Conference Presentation)

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Urban Ulrych, David Anderson, Accelerated American Option Pricing with Deep Neural Networks, In: 4th International Conference on Computational Finance 2022. 2022. (Conference Presentation)

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Gernot Wagner, Markus Leippold, Alexander Wagner, What a Stock-Price Divergence Reveals About Climate Policy and Risk, In: Bloomberg Green, 3 June 2022. (Media Coverage)

U.S. and European companies vulnerable to the clean energy transition have seen their stock prices go very different ways since Russia invaded Ukraine. |
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Luca Gabriel Müller, Rendite von unterschiedlichen Typen von Strukturierten Produkten im Backtest, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Bachelor's Thesis)
 
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Maurus Dora, Driving Factors in Financial Crises and their Influence on Money Laundering, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

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Stefano Nicoli, Deep Learning for Portfolio Optimization, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)

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Lorenz Lees, Sorting out the Factor Zoo, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
In this master’s thesis, I examined 50 key performance indicators to establish,
whether those are valuable independent factors, and to which group they belong to.
Therefore, I formulated the following research questions: Are the key performance
indicators factors? To which factor group do they belong, or are they independent
factors? |
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Jirong Liu, Two-Stage Portfolio Optimization via Sentiment Analysis and Deep Learning, University of Zurich, Faculty of Business, Economics and Informatics, 2022. (Master's Thesis)
 
The significance of the pre-selection of high-quality assets before the official
formation cannot be ignored during the portfolio optimization process. Therefore,
with the top 30 most discussed Chinese A-share stocks in GUBA, this
thesis has tried to combine deep learning techniques for predicting the stock
price to select high-quality stocks, with different portfolio allocation strategies
to see the portfolio performance of the combinations. In the first place, LSTM
with benchmark inputs, LSTM with subjective sentiment extracted from comments
data of GUBA, and LSTM with objective sentiment extracted from
market-based indexes were separately constructed to predict the stock price.
The results have shown that adding investors’ sentiment in a subjective way or
in an objective way can significantly elevate the prediction performance. In
the second stage, high-quality stocks screened from those three models in the
first stage and randomly-chosen stocks were respectively combined with three
portfolio allocation strategies, including "Equal Weights", "Global Minimum
Variance Optimization(GMV) without short-selling", and "Global Minimum
CVaR(GMC) without short-selling". The results have validated the importance
of the pre-selection of high-quality assets before the official formation of a
portfolio. The combination of LSTM and GMC has achieved the highest return.
The combination of LSTM considering investors’ sentiment extracted from
comments data of GUBA and equal weights strategy has shown the highest
Sharpe ratio.
Keywords: Stock Price Prediction, Deep Learning, LSTM, Portfolio Optimization,
Sentiment Analysis, Markowitz Mean-Variance Optimization, CVaR |
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Julia Anna Bingler, Mathias Kraus, Markus Leippold, Nicolas Webersinke, Cheap talk and cherry-picking: What climatebert has to say on corporate climate risk disclosures, Finance Research Letters, Vol. 47, 2022. (Journal Article)
 
Disclosure of climate-related financial risks greatly helps investors assess companies’ preparedness for climate change. Voluntary disclosures such as those based on the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD) are being hailed as an effective measure for better climate risk management. We ask whether this expectation is justified. We do so by training ClimateBERT, a deep neural language model fine-tuned based on the language model BERT. In analyzing the disclosures of TCFD-supporting firms, ClimateBERT comes to the sobering conclusion that the firms’ TCFD support is mostly cheap talk and that firms cherry-pick to report primarily non-material climate risk information. |
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Simon Hediger, Loris Michel, Jeffrey Näf, On the use of random forest for two-sample testing, Computational Statistics & Data Analysis, Vol. 170, 2022. (Journal Article)
 
Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on R^d. Furthermore, the built-in variable importance measure of the Random Forest gives potential insights into which variables make out the difference in distribution. An asymptotic power analysis for the proposed tests is conducted. Finally, two real-world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, the R-package “hypoRF” is provided. |
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Anna Czyżkowska, Jan Cieciuch, Marital goals: Circular value-based model and measurement, Current Psychology, Vol. 41 (6), 2022. (Journal Article)
 
The aim of the research presented in this article was to develop a comprehensive model and measurement of marital goals. The aim of Study 1 was to validate the initial model of marital goals according to Schwartz’s model of values (defined as general transsituational goals). The sample consisted of 684 participants (50% female), all of whom were either married or cohabiting couples aged between 25 and 60 years (M = 37.2; SD = 5.3). The obtained results led to a modification of our initial theoretical model. In the final version of the model, we distinguished eight types of marital goals located in a circular way around two basic dimensions that were similar but not identical to those of Schwartz’s model: (1) oneself versus other focus and (2) relationship commitment versus avoidance. We validated the model in Study 2 in another sample of 1268 participants (50% female) with married couples aged between 18 and 86 years (M = 47.2; SD = 16.1). The measurement model was confirmed through a confirmatory factor analysis; the circular structure was confirmed through multidimensional scaling; the validity of the distinguished goals was confirmed through correlational analyses with value priorities, and marital satisfaction. |
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Florian Spychiger, Claudio Tessone, Liudmila Zavolokina, Gerhard Schwabe, Incentivizing Data Quality in Blockchain-Based Systems – The Case of the Digital Cardossier, Distributed Ledger Technologies: Research and Practice, 2022. (Journal Article)
 
Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This paper uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive design in permissioned blockchains and discuss its latest implications. |
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Robin Wigglesworth, Julian Kölbel, Aggregate ESG confusion, In: Financial Times, 31 May 2022. (Media Coverage)

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David Anderson, Urban Ulrych, Accelerated American Option Pricing with Deep Neural Networks, In: The XIX International Conference on Finance and Banking FI BA 2022. 2022. (Conference Presentation)

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