All investment firms in Europa must track the market quality of different exchanges and choose the best for their clients. Previous research has shown that a limit order book with additional market makers performs best in terms of market quality, followed by a pure limit order book and market maker. With the rise of cryptocurrencies, a new exchange called the automated market maker has appeared in the crypto markets. It is unclear how it compares to the limit order book regarding market quality. This thesis compares the automated market maker with the limit order book. We have built a simulation and tested seven different scenarios, varying in liquidity, information, price variability and fee. Six out of seven scenarios indicated that the automated market maker outperforms the limit order book.

The field of finance is an interesting field in which much research takes place. In particular, its sub-field of modeling the dynamics of order books is an interesting field, since it translates into modeling the behaviour of traders on the market. Most of the models proposed in this field can be divided into stochastic models or machine learning models. The problem with the former ones is that they often make assumptions that do not propagate to practical applications, while the problem with the latter is that the models are often incomprehensible, even though they yield good results. This work tries to combine both of those fields to maintain the transparency of stochastic models and the accuracy of machine learning models. We target the limit order book, which is a book used in financial markets to place orders for financial securities in. It is a challenge to both generate realistic order books and predict them as well, since the model should be able to understand the underlying dynamics of order books to do this. We present a deep-learning-based model that is able to generate order books, through the use of a generative adversarial network (GAN) like architecture, and is able to predict order books using a similar architecture for the generator. Our models are able to outperform traditional generative and predictive models in both instances, while it remains transparent in its calculation: it is still able to extract useful and interpretable features from the underlying data set. These features, like the probabilities for up or down movements, or stagnations, or the rates at which the variables within the order book move, can be used by traders or algorithms to make sense out of the predictions. Traders can, for example, ask themselves whether the extracted features are representative of the analyzed asset and can, through this question, build additional confidence in the model. Another use case could be to utilize those features as inputs to other models. To our knowledge, this is the first work that proposes a combination of a stochastic and machine learning model to learn the dynamics of the order book.

In this work we set out to determine the impact, if any, of the analysis of news on stock price prediction, that is, are we able to predict stock movements more accurately on a consistent basis than a proposed baseline or random guessing on the basis of news’ text analysis. We considered a methodology to be more accurate if its success rate is greater than that of a baseline or random guess. We considered a methodology to be consistently more accurate if the average of the success rates over a specified number of runs, say one hundred, is greater than that of a baseline or random guess. As a result, we discovered that the analysis of news, though readily available with modern day technological advancements, does come paired with some problems. 1. The widespread availability of news has made it more difficult to find that news which is of importance to us, news can cover anything and everything. 2. The content of news can discuss events happening anywhere from far past to the far future, making consistent analysis difficult. 3. Most financial news sources tend to block any mass datamining attempts. These problems can mostly be solved by making use of so-called 8-K reports. These reports only cover major events of companies sorted into nine different categories. The 8-K reports reduce the time interval the news impacts from the far past and far future to an interval of five business days, as the reports ought to be published within four business days. Finally, since companies are obligated to publish these reports by the U.S. securities and exchange commission, the reports are readily available and easily accessible through the U.S. securities and exchange commission website. We can then use these texts and analyze them using a rule-based or automatic text analysis approach. However, the rule-based text approach, using lists of positive and negative words for the analysis, tends to be unreliable as text contains a plethora of challenging cases. This problem is solved by using an automatic text analysis, using predetermined scores for texts. The form of automatic text analysis used, is a decision tree approach. Though single decision trees we construct have the characteristic to over-fit, we can construct random forests of decision trees on subsets of our input data to solve this problem. For our analysis we looked at the stocks prices of Tesla, Microsoft, EA and Amazon, due to their varying values. We gave scores to the texts of the 8-K reports using the stock price movement of the day of publishing. We did this for up to 4 business days prior to publishing as well. We also compensated for the market movements using the variable for days zero to four. We gave scores from -1, 0 or 1 dependent on the price movement. This generally resulted in success rates greater than our considered baseline of 33.33% of random guessing. The highest success rate for Tesla, Microsoft, EA and Amazon were in order: 73.09%, 100.00%, 88.64%, 84.95%.