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 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'...

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 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...

We propose a high-frequency rebalancing algorithm (HFRA) and compare its performance with periodic rebalancing (PR) and threshold rebalancing (TR) strategies. PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals, whereas TR is a process...

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...

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...

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...

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...

Inspired by recent advances in the deep learning literature, this article introduces a novel hybrid anomaly detection framework specifically designed for limit order book (LOB) data. A modified Transformer autoencoder architecture is proposed to learn rich temporal LOB subsequence...

We propose a class of execution algorithms that consists of a strategic layer and a speculative layer. The strategic layer is an optimal trading schedule that encodes the trader's objective, her tolerance to risk, and the impact of her own trades in the market. The schedule of the strategic...

This paper examines the short-run weak-form efficiency of equities, using a proprietary data set permitting analysis of orderbook characteristics to measure short-term price predictability. The results show that high levels of algorithmic trader activity in a stock lowers the level of short-run...

Betas from return regressions are commonly used to measure systematic financial market risks. "Good" beta measurements are essential for a range of empirical inquiries in finance and macroeconomics. We introduce a novel econometric framework for the nonparametric estimation of time-varying betas...

This paper develops the necessary methodology for high frequency ANOVA, which includes the estimations of idiosyncratic volatility and realized R-Squared. Because the residual process is latent in the high frequency regression, the estimation of idiosyncratic volatility is notoriously difficult...

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in...

We argue that owing to traders' inability to fully express their preferences over the execution times of their orders, contemporary stock market designs are prone to latency arbitrage. In turn, we propose a new order type, which allows traders to specify the time at which their orders are...

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 reports results and analysis from simulation experiments in which a population of adaptive automated traders compete with one another and co-evolve their trading behaviors on a high-fidelity model of a contemporary electronic financial exchange. The automated traders are all using the...

In recent years major financial exchanges have introduced Frequent Batch Auctions (FBAs) as a novel automated auction mechanism for matching buyers and sellers of various types of asset, in contrast to the traditional Continuous Double Auction (CDA) that has been the basis of such exchanges...