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...

This paper examines whether the SEC's mandate for central clearing of U.S. Treasury repo transactions could enable all-to-all trading and support the development of a standardized term repo market. By mitigating counterparty risk through central clearing, cash lenders may become more willing to...

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...

This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of...

One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading. At the same time, significant structural changes in the industry have occurred, with passive investing gaining momentum. The intersection of these two major trends...

We investigate high-frequency traders' behavior in the context of the fastest and most extreme price movements (EPMs) that can be observed in the market, specifically ultra-fast flash events, challenging the methodologies employed in the academic and practitioner literature for identifying...

Salience theory has been proposed as a new stock trading strategy. To assess the validity of this proposal, a complex decision trading system was constructed based on salience theory, a variational mode decomposition (VMD) model, a bidirectional gated recurrent unit (BiGRU) model, and...

We investigate the high-frequency dynamics of Bitcoin and Ethereum perpetual futures traded on Binance from January 2020 to December 2024. After a thorough discussion of the stylized facts and particularities of Bitcoin perpetual futures, based on previous research in futures markets, we...

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used; we deployed the...

This paper investigates the relationship among transaction taxes, high-frequency trading (HFT), and market quality. We use the agent-based modeling (ABM) approach to dynamically assess the impact of transaction taxes on market quality with and without high-frequency trading. Preliminary tests...

Chapter 1. Machine Learning in Finance: Transformation of Financial Markets (Musa Gün) -- Chapter 2. Digital Currencies and Financial Transformation (Bilal Bagis) -- Chapter 3. A Hybrid ARIMA-LSTM/GRU Model for Forecasting Monthly Trends in Turkey’s Gold and Currency Markets with a...

Among innovative technologies, Artificial Intelligence (AI) is often avouched as the game changerin the provision of financial services. In this regard, the algorithmic trading domain is no exception.The impact of AI in the industry is a catalyst for transformation in the operations and the...

As in many other sectors of EU economies, 'artificial intelligence' (AI) has entered the scene of the financial services industry as a game-changer. Trading on capital markets is undoubtedly one of the most promising AI application domains. A growing number of financial market players have in...

Algorithmic trading, so popular nowadays, uses many strategies that are algorithmizable and promise profitability. This research answers the question whether it is possible to successfully use a convexity arbitrage strategy in a bond portfolio in financial practice. It should provide a positive...

The present paper is focused on the solution of optimal control problems such as optimal acquisition, optimal liquidation, and market making in relation to the high-frequency trading market. We have modeled optimal control problems with the price approximated by the diffusion process for the...

This study addresses the economic rationale behind algorithmic trading in the Electric Vehicle (EV) sector, enhancing the interpretability of Q-learning agents. By integrating EV-specific data, such as Tesla's stock fundamentals and key supply chain players such as Albemarle and Panasonic...