publ-mit-podpubl-mit-podTykvová, TerezaReiff, Annika2024-04-082024-04-082023-02-152022https://hohpublica.uni-hohenheim.de/handle/123456789/6795This dissertation builds on and extends previous IPO literature by analyzing unresolved questions with regard to the phenomenon of IPO withdrawals and the effect of IPOs on industry rivals. After a short introductory chapter, chapter 2 contributes to the analysis of IPO withdrawal by taking a data-driven and forward-looking perspective. In particular, it applies two machine learning methods, namely lasso and random forest, to predict IPO withdrawal and compares the performance of both models to the performance of a logistic regression model. Results show that random forest predicts IPO withdrawal quite well and outperforms lasso and logit with regard to in-sample prediction and cross-sectional out-of-sample prediction. However, all models fail substantially when trying to predict future IPO withdrawal. One explanation for this puzzling finding is the presence of concept drift – a change in the relationship between the predictors and IPO withdrawal over time. Further, the study contributes to the clarification of the question of which variables are most important to predict IPO withdrawal by exploiting certain features of the machine learning methods and considering a vast selection of different predictors. Market characteristics at filing seem to be the most important variables for prediction in all models, while corporate governance and intermediary characteristics seem to be less important. Closely related to the second chapter, the third chapter takes a more theory-based perspective on IPO withdrawal. This chapter is co-authored with Tereza Tykvová and a reviewed version is published in the Journal of Corporate Finance. It addresses the question whether certain factors, particularly high-quality corporate governance and VC backing, may serve as signals for investors and can thus reduce the withdrawal probability, especially in risky market environments. The latter argument is based on the assumption that investors are especially careful in these situations and thus signals might be especially meaningful. Results from an interaction-term analysis suggest that corporate governance characteristics, like large and experienced boards, are indeed able to reduce the withdrawal probability in highly volatile markets. However, this finding does not hold true for VC backing per se. We therefore delve deeper into the effect of VCs by distinguishing three VC characteristics: syndicated vs. stand-alone VCs, domestic vs. foreign VCs, and VCs with high vs. low reputation. The analysis reveals that local VCs and VC syndication tend to reduce the withdrawal probability, particularly in highly volatile markets, which supports the signaling explanation. In contrast, the withdrawal probability for firms backed by reputable VCs tend to be lower only in less volatile and not in highly volatile markets. One explanation for this finding could be that these firms rather follow a dual-track strategy or postpone the IPO more likely in highly volatile markets than in less volatile markets. Chapter 4 moves away from IPO withdrawals towards the consideration of intra-industry effects of IPOs. Irrespective of the question of whether to withdraw or complete an IPO after filing, an IPO filing might influence its industry rivals. In order to analyze the mechanisms behind the effects of IPO filings on industry rivals more closely, I apply a new two-step-methodology which consists of an event study in the first step and a Difference-in-Difference analysis in the second step. This methodology allows to separately test for the existence of a competition and an information effect. The rationale of the competition effect is that by going public, firms gain some kind of competitive advantage over their industry rivals thereby increasing the competitive pressure in the industry and harm their rivals. The idea behind the information effect is that an IPO filing does not only deliver information about the IPO firm but about also about the industry in which it operates. In this connection, the information effect could either induce positive (by signaling good growth prospects) or negative (by foreshadowing future negative industry trends or revealing that the industry is overvalued) valuation effects on industry rivals. Results provide evidence for the existence of the competition effect, suggesting that IPO filings tend to harm industry rivals to a certain extent. In contrast, results do not provide sufficient evidence for the existence of the information effect. However, the lack of evidence for an aggregate information effect could also be the result of a positive effect on some but a negative effect on other rival firms which cancel each other out. Finally, chapter 5 concludes with a summary and provides and outlook for future research in the field of IPOs.Die vorliegende Dissertation beschäftigt sich mit dem Börsengang amerikanischer Unternehmen. Hierbei werden zwei Forschungsfelder in den Blick genommen. Erstens wird das Phänomen des vorzeitigen Rückzugs vom Börsengang untersucht. Aus methodischer Perspektive wird hierbei analysiert, in wie weit die Verwendung neuer Verfahren aus dem Bereich des Machine Learning einen Rückzug vom Börsengang präziser vorhersagen kann, als klassische, bisher genutzte statistische Verfahren. Aus inhaltlicher Perspektive wird die Kontextabhängigkeit bestimmter Einflussfaktoren hervorgehoben. Zweitens stehen die Auswirkungen des Börsengangs auf Konkurrenten im Fokus. Hierbei liegt ein besonderes Augenmerk auf der Frage, durch welche kausalen Mechanismen diese durch einen Börsengang konkurrierender Unternehmen beeinflusst werden. Diese Frage wird mit in diesem Forschungsfeld bisher nicht angewendeten Methoden aus dem Bereich der Kausalen Inferenz untersucht. Die Ergebnisse der durchgeführten Analysen liefern wesentliche neue Erkenntnisse zum Phänomen des Rückzugs vom Börsengang sowie zu den Auswirkungen eines Börsengangs auf Konkurrenten. Es stellt sich heraus, dass Machine Learning Verfahren den Rückzug tatsächlich präziser vorhersagen können als die bisher genutzten statistischen Verfahren. Jedoch zeigen sich gleichzeitig neue Schwierigkeiten bei der Vorhersage zukünftiger Rückzüge basierend auf historischen Daten. Des Weiteren deuten die Ergebnisse darauf hin, dass bestimmte Determinanten, insbesondere Variablen, die eine gute Corporate Governance signalisieren, besonders in unsicheren Marktbedingungen eine wichtige Rolle spielen. Ferner unterscheidet sich der Effekt von VC Backing hinsichtlich verschiedener VC Charakteristika. Hinsichtlich des Effekts von Börsengängen auf Konkurrenten bestätigt sich, dass von der Theorie bisher angenommene, aber nicht explizit getestete Kausalmechanismen in der Tat eine wichtige Rolle für die Auswirkungen auf Konkurrenten spielen können. Hierbei deuten die Ergebnisse insbesondere darauf hin, dass der Börsengang eines Konkurrenten die Konkurrenzsituation in der Industrie verschärft und deshalb mit negativen Effekten auf die Konkurrenten einhergeht. Die vorliegende Dissertation zeigt somit neue Erklärungsansätze auf, weist aber auch auf neue Fragen hin. Es deutet sich hierbei an, dass insbesondere das Zusammenspiel neue theoretischer Überlegungen mit innovativen methodischen Ansätzen zu neuen Erkenntnissen beitragen kann.engInitial public offeringsWithdrawalVolatile marketsIntra-industry effectsMachine learningIntra-industrielle EffekteVolatile MärkteRückzug330Going PublicMaschinelles LernenDoctoralThesis1835718620urn:nbn:de:bsz:100-opus-21150