In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of...

Deep learning has transformed numerous areas of data science by achieving outstanding performance in tasks such as image recognition, speech processing, and natural language understanding. Recently, the challenges of financial forecasting-marked by nonlinear dynamics, volatility, and regime...

This thesis contains three studies on the impact of new technologies on financial markets. The first study investigates in an experiment whether algorithmic trading has an impact on the formation of asset price bubbles. It finds that especially market-maker algorithms lead to traded prices being...

Robo-advisors are novel tools in financial markets that provide investors with low-cost financial advice, usually based on individual characteristics like risk attitudes. In a portfolio choice experiment running over 10 weeks, we study how much investors benefit from robo advice. We also study...