Browsing by Subject "Maschinelles Lernen"

Now showing 1 - 4 of 4

Consumer prices : effects of learning algorithms and pandemic-related policy measures
(2023) Buchali, Katrin; Schwalbe, Ulrich
When it comes to product prices, two major topics have dominated the public debate in recent years: One is pricing with the help of artificial intelligence, and the other is the price level, which has risen more than usual with the onset of the COVID-19 pandemic. Higher prices create a loss of consumer surplus and possibly total welfare, which is the reason this topic has become ubiquitous in political discussions. This dissertation contributes to the debate by extending the existing literature on algorithmic pricing, which is said to facilitate personalized pricing, as well as collusive behavior and to enhance the general understanding of how government measures enforced during the COVID-19 pandemic contributed to (short-time) price developments. Thereby, the first part of the thesis addresses the concern that tacit collusion might occur if firms employ learning algorithms, as several simulation studies have demonstrated that algorithms using reinforcement learning are able to coordinate their pricing behavior and, as a result, achieve a collusive outcome without having been programmed for it. We discuss several conceptual challenges as well as challenges in the real-world application of algorithms and show by or own simulations that resulting market prices strongly depend on the type of algorithm or heuristic that is used by the firms to set prices. In the subsequent part of the thesis we examine how a self-learning pricing algorithm performs when faced with inequity-averse consumers. From our simulations we can conclude that consumers sense of fairness, which have prevented firms from engaging in price discrimination in the past years, can be incorporated into firms pricing decisions with the help of learning algorithms, making differential pricing strategies more feasible. The discussion surrounding the above-average price levels in many countries during the COVID-19 pandemic is extended in the third part of the thesis. We present empirical evidence for the impact of government-imposed restrictions and, as a consequence of their enforcement, reduced mobility on consumer prices during the COVID-19 pandemic. We show that the stringency of government measures had a positive and significant impact on consumer prices mainly in the food sector, which means that more stringent measures induced higher consumer prices in these categories.
Empirical essays on initial public offerings
(2022) Reiff, Annika; Tykvová, Tereza
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.
Screen for collusive behavior : a machine learning approach
(2024) Bantle, Melissa
The paper uses a machine learning technique to build up a screen for collusive behavior. Such tools can be applied by competition authorities but also by companies to screen the behavior of their suppliers. The method is applied to the German retail gasoline market to detect anomalous behavior in the price setting of the filling stations. Therefore, the algorithm identifies anomalies in the data-generating process. The results show that various anomalies can be detected with this method. These anomalies in the price setting behavior are then discussed with respect to their implications for the competitiveness of the market.
Sentiment analysis in electronic negotiations
(2017) Körner, Michael; Schoop, Mareike
The thesis analyzes the applicability of methods of Sentiment Analysis and Predictive Analytics on textual communication in electronic negotiation transcripts. In particular, the thesis focuses on examining whether an automatic classifier can predict the outcome of ongoing, asynchronous electronic negotiations with sufficient accuracy. When combined with influencing factors leading to the specific classification decision, such a classification model could be incorporated into a Negotiation Support System in order to proactively intervene in ongoing negotiations it judges as likely to fail and then to give advice to the negotiators to prevent negotiation failure. To achieve this goal, an existing data set of electronic negotiations was used in a first study to create a Sentiment Lexicon, which tracks verbal indicators for utterances of positive and, respectively, negative polarity. This lexicon was subsequently combined with a simplified, feature-based representation of electronic negotiation transcripts which was then used as training data for various machine learning classifiers in order to let them determine the outcome of the negotiations based on the transcripts in a second study. Here, complete negotiation transcripts were classified as well as partial transcrips in order to assess classification quality in ongoing negotiations. The third study of the thesis sought to refine the classification model with respect to sentence-based granularity. To this end, human coders were classifying negotiation sentences regarding their subjectivity and polarity. The results of this content analysis approach were then used to train sentence-level subjectivity and polarity classifiers. The fourth and final study analyzed different aggregation methods for these sentence-level classification results in order to support the classifiers on negotiation granularity. Different aggregation and classification models were discussed, applied to the negotiation data and subsequently evaluated. The results of the studies show that it is possible to a certain degree to use a sentiment-based representation of negotiation data to automatically determine negotiation outcomes. In combination with the sentence-based classification models, negotiation classification quality increased further. However, this improvement was only found to be significant for complete negotiation transcripts. If only partial transcripts are used – specifically to simulate an ongoing negotiation scenario – the models tend to behave more erratic and classifcation quality depletes. This result yields the assumption that polarized utterances (positive as well as negative) only carry unequivocal information (with respect to the outcome) towards the end of the negotiation. During the negotiation, the influence of these utterances becomes more ambiguous, hence decreasing classification accuracy on models using a representation based on sentiments. Regarding the original goal of the thesis, which is to provide a basic means to support ongoing negotiations, this means that supporting mechanisms employed by a Negotiation Support System should focus on moderation techniques and resolving of potentially conflicting situations. Approaches that could be used to employ further conflict diagnosis in interaction with the negotiators are given in the final chapter of the thesis, as well as a discussion of potential recommendations and advice the system could give and lastly, approaches to visualize the classification data to the negotiators.