Contributions published at Financial Engineering (Markus Leippold)

This investor analysis in the cryptocurrency market provides evidence that the composition of investors contains valuable information. The Ethereum blockchain addresses of 122 successful ICOs were analysed for the 2016 to 2019 period. The resulting investor base was categorized into four groups and used to estimate token return. The results indicate that a sophisticated investor base generates statistically significant alpha at the 10% level for the 365-days horizon, but not for the shorter horizons.

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This thesis developed an independent derivation of the functional strategy of Swiss Re Legal & Compliance (L&C), complementary to the one derived department-internally. Structur-ally, the approach of this thesis builds on applied frameworks of L&C, but further critically evaluates and enhances these frameworks by taking academic and established best practice sources into consideration. This makes for an objective and evidence-based procedure, mit-igating personal biases. The result of this thesis deviates from the internal derivation by en-visioning a fully tech-enabled L&C department as a lean service deliverer, deploying an increasingly versatile workforce that resides and acts closer to the firm-internal client.

There is ample evidence that factor momentum exists in the standard long--short mixed approach to factor investing. However, the excess returns are put under scrutiny due to the high implementation costs. We present a novel real-life approach that relies on the long-only integrated approach to factor investing. Instead of exploiting the potential momentum in factor portfolios, our strategy builds on the momentum of the optimal factor score weights in the integrated approach, which allows us to additionally profit from the serial dependence in the factors' interaction effects. One limitation of short-term timing strategies is their high turnover. By including the information of the covariance matrix and minimizing the strategy’s risk to the market portfolio, we can substantially reduce turnover. The resulting timing alpha remains significant even after transaction costs in a robust statistical test framework across the major stock markets.

Measuring congruence between two texts has several useful applications, such as detecting the prevalent deceptive and misleading news headlines on the web. Many works have proposed machine learning based solutions such as text similarity between the headline and body text to detect the incongruence. Text similarity based methods fail to perform well due to different inherent challenges such as relative length mismatch between the news headline and its body content and non-overlapping vocabulary. On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness. This paper proposes a method that uses inter-mutual attention-based semantic matching between the original and synthetically generated headlines, which utilizes the difference between all pairs of word embeddings of words involved. The paper also investigates two more variations of our method, which use concatenation and dot-products of word embeddings of the words of original and synthetic headlines. We observe that the proposed method outperforms prior-arts significantly for two publicly available datasets.

Our goal is to introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to help and encourage work on improving algorithms for retrieving climate-specific information and detecting fake news in social and mass media to reduce the impact of misinformation on the formation of public opinion on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. Although during this process, we could count on the support of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the FEVER framework, which provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of an exciting long-term joint effort by the climate science and AI community to develop robust algorithms to verify the facts for climate-related claims.

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fastmoving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce CLIMATEXT, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT [1] can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.