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...

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...

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...

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 econometrics, Autoregressive Conditional Duration (ACD) models use high-frequency economic or financial duration data, which mostly exhibit irregular time intervals. The ACD model is widely used to examine the duration of transaction volume and duration of price variations in stock markets....

We use a laboratory experiment to explore the effect of a change in pre-trade anonymity in a quote-driven dealer-to-customer market, organised as a request for quote (RFQ). We consider two treatments in which dealers interact with two types of customers (informed or uninformed). In the first...

In this research, we attempted to fit a trading system based on polynomial moving regression bands (MRB) to Nasdaq100 stocks from 2017 till the end of March 2024. Since stocks movement does not follow a linear behavior, we used multiple degree polynomial regression models to identify the stocks'...

We examine the impact of increasing competition among the fastest traders by analyzing a new low-latency microwave network connecting exchanges trading the same stocks. Using a difference-in-differences approach comparing German stocks with similar French stocks, we find improved market...

In today's complex financial markets, "Algorithmic Trading" has become very important. The study delves into the amalgamation of four pivotal indicators - Relative Strength Index (RSI), Exponential Moving Average (EMA), Volume-Weighted Average Price (VWAP), and Moving Average...

The transaction-level analysis of security price changes by Madhavan, Richardson, and Roomans (1997, hereafter MRR) is a useful framework for financial analysis. The first-order Markov property of trading indicator variables is a critical assumption in the MRR model, which contradicts the...