Contributions in Merlin

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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.

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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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

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.

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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