Wirtschaftswissenschaftliche Fakultät
Refine
Year of publication
Document Type
- Doctoral Thesis (75)
- Article (37)
Language
- English (100)
- German (10)
- Multiple languages (2)
Has Fulltext
- yes (112)
Is part of the Bibliography
- no (112)
Keywords
- Armut (3)
- Climate change (3)
- Digitalisierung (3)
- Management (3)
- Marketing (3)
- Robust optimization (3)
- - (2)
- Classification (2)
- Combinatorial optimization (2)
- Digitalization (2)
Institute
Tech titans and crypto giants : mutual returns predictability and trading strategy implications
(2024)
This study examines the directional return predictability between the technology sector of U.S. stock market and three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin). Using daily data from August 7, 2015, to February 8, 2024, and the cross-quantilogram approach in both static and dynamic settings, the results reveal significant positive predictability in the stock market–cryptocurrency nexus. The technology sector, semiconductors subsector, and Nvidia Corporation exert predictive power over cryptocurrency returns and vice versa across several quantiles and lags. When controlling for the impact of other financial variables, namely, U.S. dollar and U.S. treasury markets, the return predictability holds, especially for the two largest cryptocurrencies, Bitcoin and Ethereum, which reflects their importance and tighter connections with the U.S. technology sector. A trading strategy based on the results of the cross-quantilograms outperforms a benchmark strategy (i.e., always long position in either stocks or cryptocurrency), which underlines the practical implications of our main findings, particularly in terms of the significant return interactions between U.S. technology/semiconductors stocks and large cryptocurrencies.
We study the effects of temperature on occupational health using administrative data on Swiss occupational accidents from 1996 to 2019. Our results imply that on hot days (T𝑚𝑎𝑥 ≥ 30 ◦C) the number of occupational accidents increases by 7.4% and on ice days (T𝑚𝑎𝑥 < 0 ◦C) by 6.3%, relative to mild days. We find that extreme temperatures cause an average of 2600 workplace accidents each year, costing CHF 91 million annually. We provide suggestive evidence for insufficient sleep on hot days as a mechanism. While extreme temperatures worsen occupational health, we observe limited labor supply adaption for most workers.
We study iterative constraint and variable generation methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. The goal of this work is to find a set of starting scenarios that provides strong lower bounds early in the process. To this end we define the Relevant Scenario Recognition Problem (RSRP) which finds the optimal choice of scenarios which maximizes the corresponding objective value. We show for classical and two-stage robust optimization that this problem can be solved in polynomial time if the number of selected scenarios is constant and NP-hard if it is part of the input. Furthermore, we derive a linear mixed-integer programming formulation for the problem in both cases.
Since solving the RSRP is not possible in reasonable time, we propose a machine-learning-based heuristic to determine a good set of starting scenarios. To this end, we design a set of dimension-independent features, and train a Random Forest Classifier on already solved small-dimensional instances of the problem. Our experiments show that our method is able to improve the solution process even for larger instances than contained in the training set, and that predicting even a small number of good starting scenarios can considerably reduce the optimality gap. Additionally, our method provides a feature importance score which can give new insights into the role of scenario properties in robust optimization.
This study advances the field of Computationally Intensive Theory Development (CTD) by examining the capabilities of Explainable Artificial Intelligence (XAI), in particular SHapley Additive exPlanations (SHAP), for theory development, while providing guidelines for this process. We evaluate SHAP’s methodological abilities and develop a structured approach for using SHAP to harness insights from black-box predictive models. For this purpose, we leverage a dual-methodological approach. First, to assess SHAP’s capabilities in uncovering patterns that shape a phenomenon, we conduct a Monte-Carlo simulation study. Second, to illustrate and guide the theory development process with SHAP for CTD, we apply SHAP in a use-case using real-world data. Based on these analyses, we propose a stepwise uniform and replicable approach giving guidance that can benefit rigorous theory development and increase the traceability of the theorizing process. With our structured approach, we contribute to the use of XAI approaches in research and, by uncovering patterns in black-box prediction models, add to the ongoing search for next-generation theorizing methods in the field of Information Systems (IS).
There is empirical evidence that decision makers show negative behaviours towards algorithmic advice compared to human advice, termed as algorithm aversion. Taking a trust theoretical perspective, this study broadens the quite monolithic view on behaviour to its cognitive antecedent: cognitive trust, i.e. trusting beliefs and trusting intentions. We examine initial trust (cognitive trust and behaviour) as well as its development after performance feedback by conducting an online experiment that asked participants to forecast the expected demand for a product. Advice accuracy was manipulated by ± 5 % relative to the participant’s initial forecasting accuracy determined in a pre-test. Results show that initial behaviour towards algorithmic advice is not influenced by cognitive trust. Furthermore, the decision maker’s initial forecasting accuracy indicates a threshold between near-perfect and bad advice. When advice accuracy is at this threshold, we observe behavioural algorithm appreciation, particularly due to higher trusting integrity beliefs in algorithmic advice.
In robust combinatorial optimization, we would like to find a solution that performs well under all realizations of an uncertainty set of possible parameter values. How we model this uncertainty set has a decisive influence on the complexity of the corresponding robust problem. For this reason, budgeted uncertainty sets are often studied, as they enable us to decompose the robust problem into easier subproblems. We propose a variant of discrete budgeted uncertainty for cardinality-based constraints or objectives, where a weight vector is applied to the budget constraint. We show that while the adversarial problem can be solved in linear time, the robust problem becomes NP-hard and not approximable. We discuss different possibilities to model the robust problem and show experimentally that despite the hardness result, some models scale relatively well in the problem size.
A growing number of multinational companies (MNCs) report on their progress toward contributing to the Sustainable Development Goals (SDGs) in their annual reports, yet the amount and quality of the information they disclose varies significantly. The aim of this study is twofold: First, we investigate how transparent MNCs report on their SDG engagement and second, we study how the reported SDG engagement changed over time due to major shifts in sustainability reporting requirements. Using a dataset of the largest German MNCs, we analyze their disclosure of SDG contribution reporting practices. Our results show that, overall, between 2016 and 2021 all German MNCs increased their SDG reporting activity and that the MNCs adopted four different practices to report their SDG engagement. We also found that the largest MNCs were more transparent and systematic in their SDG reporting than smaller MNCs, whose reporting was more heterogeneous. Our study contributes to legitimacy research and the role of transparency in corporate reporting as well as the debates on the efficacy of sustainability reporting legislation in the context of SDG engagement. Our findings imply that regulations on environmental and social regulation increase the transparency of sustainability reporting.
Corporate Sustainability Performance (CSP) reporting is becoming increasingly important to investors who seek to identify and invest in companies that are managing their Environmental, Social and Governance (ESG) risks effectively. The European Union's Non‐Financial Reporting Directive (NFRD), which was implemented in 2017, mandates that certain large companies must disclose their sustainability performance. This study examines the impact of the EU NFRD on the firm value of listed European firms using a difference‐in‐differences regression model. We find that the mandatory disclosure of corporate sustainability performance does not significantly affect firm value at an aggregate level. However, the results suggest minor inter‐industry differences, which can be attributed to varying sustainability performance metrics across industries. These findings contribute not only to the nascent literature on mandatory sustainability disclosures but also to the deliberations of policymakers and regulators across the world who are devising and implementing mandatory corporate sustainability performance disclosure regulations.
Algorithms are capable of advising human decision‐makers in an increasing number of management accounting tasks such as business forecasts. Due to expected potential of these (intelligent) algorithms, there are growing research efforts to explore ways how to boost algorithmic advice usage in forecasting tasks. However, algorithmic advice can also be erroneous. Yet, the risk of using relatively bad advice is largely ignored in this research stream. Therefore, we conduct two online experiments to examine this risk of using relatively bad advice in a forecasting task. In Experiment 1, we examine the influence of performance feedback (revealing previous relative advice quality) and source of advice on advice usage in business forecasts. The results indicate that the provision of performance feedback increases subsequent advice usage but also the usage of subsequent relatively bad advice. In Experiment 2, we investigate whether advice representation, that is, displaying forecast intervals instead of a point estimate, helps to calibrate advice usage towards relative advice quality. The results suggest that advice representation might be a potential countermeasure to the usage of relatively bad advice. However, the effect of this antidote weakens when forecast intervals become less informative.
Climate change is leading to an increased scarcity of resources such as freshwater, energy, arable land and wildlife. This is perceived as a major security threat. However, the literature remains unclear on whether scarcity mitigates or exacerbates conflict. We design a novel laboratory experiment to investigate hostile behavior under scarcity. Participants interact repeatedly in a dynamic common-pool resource (CPR) and a joy-of-destruction game. The experiment distinguishes between two types of scarcity: Endogenous scarcity in the form of a deliberate human choice to overexploit resources and exogenous scarcity in the form of adverse environmental conditions. Our results show that endogenous scarcity exacerbates hostility. We trace this to participants being guided by negative reciprocity while finding no support for inequality aversion. The results indicate that to avoid hostility, policy makers will have to reduce human-induced scarcity. However, our results also show that exogenous scarcity mitigates hostility. This is in line with a vast body of literature from psychology, anthropology and biology finding increased levels of cooperation for all forms of life under environmental distress. It suggests that managing perceptions around increasing scarcities could be a second potential avenue for policy action.