Entrepreneurship is recognized as an important discipline to achieve sustainable development and to address sustainability goals without losing sight of economic aspects. However, entrepreneurship rates are rather low in many industrialized countries with high income levels. Research clearly shows that there is a gap in the entrepreneurial process between intentions and subsequent actions. This means that not everyone with entrepreneurial ambitions also follows through and implements actions. This gap also exists for aspects of sustainability. As a result, there is a need to better understand the traditional and sustainability-focused entrepreneurial process in order to increase corresponding actions. This dissertation offers such a comprehensive perspective and sheds light on individual and contextual predictors for traditional and sustainability-focused behavior of entrepreneurs and self-employed across four studies. The first three studies focus on individual predictors. By providing a systematic literature review with 107 articles, Chapter 2 highlights the ambivalent role of religion for the entrepreneurial process. Relying on the theory of planned behavior (TPB) as theoretical basis, religion can have positive effects on entrepreneurial attitudes and behavioral control, but also negative consequences for other aspects of behavioral control and subjective norms due to religious restrictions. The quantitative empirical study in Chapter 3 similarly relies on the TPB and sheds light on individual perceptual factors influencing the sustainability-related intention-action gap in entrepreneurship. Using data from the 2021 Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) including 22,008 early-stage entrepreneurs from 44 countries worldwide, the results support our theoretical reasoning that sustainability-focused intentions are positively related to social entrepreneurial actions. In addition, it is demonstrated that positive perceptual moderators such as self-efficacy and knowing other entrepreneurs as role models strengthen this relationship while a negative perception such as fear of failure restricts social actions in early-stage entrepreneurship. The next quantitative empirical study in Chapter 4 examines the behavioral consequences of well-being at a sample of 6,955 German self-employed during COVID-19. This chapter builds on two complementary behavioral perspectives to predict how reductions in financial and non-financial well-being relate to investments in venture development. In this regard, reductions in financial well-being are positively related to time investments, supporting the performance feedback perspective in terms of higher search efforts under negative performance. In contrast, reductions in non-financial well-being are negatively related to time and monetary investments, yielding support for the broadening-and-build perspective indicating that negative psychological experiences narrow the thought-action repertoire and hinder resource deployment. The insights across these first three studies about individual predictors indicate that many different, subjective beliefs, perceptions and emotional states can influence the entrepreneurial process making entrepreneurship and self-employment highly individualized disciplines. The last quantitative empirical study provides an explorative view on a large number of contextual predictors for social and ecological considerations in entrepreneurial actions. Combining GEM data from 2021 on country level with further information from the World Bank and the OECD, a machine learning approach is employed on a sample of 84 countries worldwide. The results suggest that governmental and regulatory as well as cultural factors are relevant to predict social and ecological considerations. Moreover, market-related aspects are shown to be relevant predictors, especially socio-economic factors for social considerations and economic factors for ecological considerations. Overall, the four studies in this dissertation highlight the complexity of the entrepreneurial process being determined by many different individual and contextual factors. Due to the multitude of potential predictors, this dissertation can only give an initial overview of a selection of factors with many more aspects and interdependencies still to be examined by future research.

Small and medium-sized enterprises (SMEs) and mid-sized companies are vital contributors to the global economy, driving employment growth, fostering innovation, and enhancing international competiveness. However, in the aftermath of the Great Financial Crisis (GFC) and the collapse of the large finance company CIT Group, which provided 60% loans to US middle-market firms, banks reduced their lending activities. Thus, it became challenging for firms to obtain long-term loans. The financing gap has increased further due to high interest rates, the COVID-19 pandemic, the unstable situation in the real estate market as well as higher costs due to the adoption of digital infrastructure and sustainability goals. Therefore, the search for alternative financing solutions outside bank lending and public markets became unavoidable for SMEs and mid-sized companies. Private debt funds entered the market, and, since the GFC, they have played a crucial role in offering alternative financing for firms globally. Private debt fund managers raise capital commitments through closed-end funds (like private equity) and make senior loans (like banks) directly to, mostly, middlemarket firms. The private debt market has experienced rapid growth in recent decades. The private debt funds assets under management (AuM) increased by 380% from 2008 to 2022, reaching $1.5 trillion AuM in 2022 . The high growth of private debt shows great interest from investors in this alternative asset class and lucrative investment opportunities. Despite its substantial and growing size, the private debt market is relatively understudied. This dissertation introduces private debt as an important alternative financing source, provides an overview of private debt strategies, seniority, and structure, discusses the legal considerations concerning private debt, and briefly compares the two most mature private debt markets: Europe and the U.S. Moreover, it assesses the size of the European private debt market and compares its development in different European regions. Furthermore, it examines in detail the business model of private debt funds based on a survey of 191 European and U.S. private debt managers with private debt assets under management of over $390 billion. Finally, it delves deeper into the relationship between private debt and private equity funds and their role in buyouts. To sum up, this dissertation provides a basis and inspiration for future research to expand upon and dive deeper into the world of private debt funds, their business model, and their impact on portfolio companies and the economy as a whole.

This dissertation examines the relevance of regimes for stock markets. In three research articles, we cover the identification and predictability of regimes and their relationships to macroeconomic and financial variables in the United States. The initial two chapters contribute to the debate on the predictability of stock markets. While various approaches can demonstrate in-sample predictability, their predictive power diminishes substantially in out-of-sample studies. Parameter instability and model uncertainty are the primary challenges. However, certain methods have demonstrated efficacy in addressing these issues. In Chapter 1 and 2, we present frameworks that combine these methods meaningfully. Chapter 3 focuses on the role of regimes in explaining macro-financial relationships and examines the state-dependent effects of macroeconomic expectations on cross-sectional stock returns. Although it is common to capture the variation in stock returns using factor models, their macroeconomic risk sources are unclear. According to macro-financial asset pricing, expectations about state variables may be viable candidates to explain these sources. We examine their usefulness in explaining factor premia and assess their suitability for pricing stock portfolios. In summary, this dissertation improves our understanding of stock market regimes in three ways. First, we show that it is worthwhile to exploit the regime dependence of stock markets. Markov-switching models and their extensions are valuable tools for filtering the stock market dynamics and identifying and predicting regimes in real-time. Moreover, accounting for regime-dependent relationships helps to examine the dynamic impact of macroeconomic shocks on stock returns. Second, we emphasize the usefulness of macro-financial variables for the stock market. Regime identification and forecasting benefit from their inclusion. This is particularly true in periods of high uncertainty when information processing in financial markets is less efficient. Finally, we recommend to address parameter instability, estimation risk, and model uncertainty in empirical models. Because it is difficult to find a single approach that meets all of these challenges simultaneously, it is advisable to combine appropriate methods in a meaningful way. The framework should be as complex as necessary but as parsimonious as possible to mitigate additional estimation risk. This is especially recommended when working with financial market data with a typically low signal-to-noise ratio.

This thesis contains four parts that are all connected by their contributions to the Efficient Market Hypothesis and decision-making literature. Chapter two investigates how national stock market indices reacted to the news of national lockdown restrictions in the period from January to May 2020. The results show that lockdown restrictions led to different reactions in a sample of OECD and BRICS countries: there was a general negative effect resulting from the increase in lockdown restrictions, but the study finds strong evidence for underreaction during the lockdown announcement, followed by some overreaction that is corrected subsequently. This under-/overreaction pattern, however, is observed mostly during the first half of our time series, pointing to learning effects. Relaxation of the lockdown restrictions, on the other hand, had a positive effect on markets only during the second half of our sample, while for the first half of the sample, the effect was negative. The third chapter investigates the gender differences in stock selection preferences on the Taiwan Stock Exchange. By utilizing trading data from the Taiwan Stock Exchange over a span of six years, it becomes possible to analyze trading behavior while minimizing the self-selection bias that is typically present in brokerage data. To study gender differences, this study uses firm-level data. The percentage of male traders in a company is the dependent variable, while the company’s industry and fundamental/technical aspects serve as independent variables. The results show that the percentage of women trading a company rises with a company’s age, market capitalization, a company’s systematic risk, and return. Men trade more frequently and show a preference for dividend-paying stocks and for industries with which they are more familiar. The fourth chapter investigated the relationship between regret and malicious and benign envy. The relationship is analyzed in two different studies. In experiment 1, subjects had to fill out psychological scales that measured regret, the two types of envy, core self-evaluation and the big 5 personality traits. In experiment 2, felt regret is measured in a hypothetical scenario, and the subject’s felt regret was regressed on the other variables mentioned above. The two experiments revealed that there is a positive direct relationship between regret and benign envy. The relationship between regret and malicious envy, on the other hand, is mostly an artifact of core self-evaluation and personality influencing both malicious envy and regret. The relationship can be explained by the common action tendency of self-improvement for regret and benign envy. Chapter five discusses the differences in green finance regulation and implementation between the EU and China. China introduced the Green Silk Road, while the EU adopted the Green Deal and started working with its own green taxonomy. The first difference comes from the definition of green finance, particularly with regard to coal-fired power plants. Especially the responsibility of nation-states’ emissions abroad. China is promoting fossil fuel projects abroad through its Belt and Road Initiative, but the EU’s Green Deal does not permit such actions. Furthermore, there are policies in both the EU and China that create contradictory incentives for economic actors. On the one hand, the EU and China are improving the framework conditions for green financing while, on the other hand, still allowing the promotion of conventional fuels. The role of central banks is also different between the EU and China. China’s central bank is actively working towards aligning the financial sector with green finance. A possible new role of the EU central bank or the priority financing of green sectors through political decision-making is still being debated.

Survey data can be viewed as incomplete or partially missing from a variety of perspectives and there are different ways of dealing with this kind of data in the prediction and the estimation of economic quantities. In this thesis, we present two selected research contexts in which the prediction or estimation of economic quantities is examined under incomplete survey data. These contexts are first the investigation of composite estimators in the German Microcensus (Chapters 3 and 4) and second extensions of multivariate Fay-Herriot (MFH) models (Chapters 5 and 6), which are applied to small area problems. Composite estimators are estimation methods that take into account the sample overlap in rotating panel surveys such as the German Microcensus in order to stabilise the estimation of the statistics of interest (e.g. employment statistics). Due to the partial sample overlaps, information from previous samples is only available for some of the respondents, so the data are partially missing. MFH models are model-based estimation methods that work with aggregated survey data in order to obtain more precise estimation results for small area problems compared to classical estimation methods. In these models, several variables of interest are modelled simultaneously. The survey estimates of these variables, which are used as input in the MFH models, are often partially missing. If the domains of interest are not explicitly accounted for in a sampling design, the sizes of the samples allocated to them can, by chance, be small. As a result, it can happen that either no estimates can be calculated at all or that the estimated values are not published by statistical offices because their variances are too large.