Textual Information and IPO Underpricing: A Machine Learning Approach

This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates

The Role of Reviews in Decision-Making

With the rise of social media such as blogs and social networks, these interpersonal communication expressed by online reviews has become more and more important as an influential source of information both for the managers and for the consumers. In-depth purchasing-related information is made available to markers. Now we can utilize this new source of information to understand how consumers evaluate products and make decision in relation with it. Since reviews are text data, new ways to analyze the data is needed and text-mining plays the role here together with the help of traditional statistical methods. With these methods, we can examine the contents of reviews and identify the key areas that impact consumers’ decision-making

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions

Big data techniques in auditing research and practice: current trends and future opportunities

This paper analyzes the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces

Empirical essays on initial public offerings

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.This 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

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco