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...

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...

This conceptual article introduces Perceived Cognitive Assistance (PCA)-a novel psychological construct capturing how interactive support from Large Language Models (LLMs) alters investors' perception of their cognitive capacity to execute complex trading strategies. PCA formalizes a behavioral...

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...

In this paper, we seek to ascertain whether the recently developed ordered fuzzy number (OFN) representation of a limit order book (LOB) by Marszałek and Burczyński (2024) may serve as a measure of stock liquidity. In particular, we aim to test whether this measure contains similar or distinct...