Market Microstructure

Learning Not to Spoof ArXiv ID: 2306.06087 “View on arXiv” Authors: Unknown Abstract As intelligent trading agents based on reinforcement learning (RL) gain prevalence, it becomes more important to ensure that RL agents obey laws, regulations, and human behavioral expectations. There is substantial literature concerning the aversion of obvious catastrophes like crashing a helicopter or bankrupting a trading account, but little around the avoidance of subtle non-normative behavior for which there are examples, but no programmable definition. Such behavior may violate legal or regulatory, rather than physical or monetary, constraints. In this article, I consider a series of experiments in which an intelligent stock trading agent maximizes profit but may also inadvertently learn to spoof the market in which it participates. I first inject a hand-coded spoofing agent to a multi-agent market simulation and learn to recognize spoofing activity sequences. Then I replace the hand-coded spoofing trader with a simple profit-maximizing RL agent and observe that it independently discovers spoofing as the optimal strategy. Finally, I introduce a method to incorporate the recognizer as normative guide, shaping the agent’s perceived rewards and altering its selected actions. The agent remains profitable while avoiding spoofing behaviors that would result in even higher profit. After presenting the empirical results, I conclude with some recommendations. The method should generalize to the reduction of any unwanted behavior for which a recognizer can be learned. ...

Liquidity takers behavior representation through a contrastive learning approach ArXiv ID: 2306.05987 “View on arXiv” Authors: Unknown Abstract Thanks to the access to the labeled orders on the CAC40 data from Euronext, we are able to analyze agents’ behaviors in the market based on their placed orders. In this study, we construct a self-supervised learning model using triplet loss to effectively learn the representation of agent market orders. By acquiring this learned representation, various downstream tasks become feasible. In this work, we utilize the K-means clustering algorithm on the learned representation vectors of agent orders to identify distinct behavior types within each cluster. ...

The Role of Twitter in Cryptocurrency Pump-and-Dumps ArXiv ID: 2306.02148 “View on arXiv” Authors: Unknown Abstract We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics. ...

Optimal execution and speculation with trade signals ArXiv ID: 2306.00621 “View on arXiv” Authors: Unknown Abstract We propose a price impact model where changes in prices are purely driven by the order flow in the market. The stochastic price impact of market orders and the arrival rates of limit and market orders are functions of the market liquidity process which reflects the balance of the demand and supply of liquidity. Limit and market orders mutually excite each other so that liquidity is mean reverting. We use the theory of Meyer-$σ$-fields to introduce a short-term signal process from which a trader learns about imminent changes in order flow. Her trades impact the market through the same mechanism as other orders. With a novel version of Marcus-type SDEs we efficiently describe the intricate timing of market dynamics at moments when her orders concur with that of others. In this setting, we examine an optimal execution problem and derive the Hamilton–Jacobi–Bellman (HJB) equation for the value function of the trader. The HJB equation is solved numerically and we illustrate how the trader uses the signals to enhance the performance of execution problems and to execute speculative strategies. ...

Discrete $q$-exponential limit order cancellation time distribution ArXiv ID: 2306.00093 “View on arXiv” Authors: Unknown Abstract Modeling financial markets based on empirical data poses challenges in selecting the most appropriate models. Despite the abundance of empirical data available, researchers often face difficulties in identifying the best-fitting model. Long-range memory and self-similarity estimators, commonly used for this purpose, can yield inconsistent parameter values, as they are tailored to specific time series models. In our previous work, we explored order disbalance time series from the broader perspective of fractional L’{“e”}vy stable motion, revealing a stable anti-correlation in the financial market order flow. However, a more detailed analysis of empirical data indicates the need for a more specific order flow model that incorporates the power-law distribution of limit order cancellation times. When considering a series in event time, the limit order cancellation times follow a discrete probability mass function derived from the Tsallis q-exponential distribution. The combination of power-law distributions for limit order volumes and cancellation times introduces a novel approach to modeling order disbalance in the financial markets. Moreover, this proposed model has the potential to serve as an example for modeling opinion dynamics in social systems. By tailoring the model to incorporate the unique statistical properties of financial market data, we can improve the accuracy of our predictions and gain deeper insights into the dynamics of these complex systems. ...

NYSE Price Correlations Are Abitrageable Over Hours and Predictable Over Years ArXiv ID: 2305.08241 “View on arXiv” Authors: Unknown Abstract Trade prices of about 1000 New York Stock Exchange-listed stocks are studied at one-minute time resolution over the continuous five year period 2018–2022. For each stock, in dollar-volume-weighted transaction time, the discrepancy from a Brownian-motion martingale is measured on timescales of minutes to several days. The result is well fit by a power-law shot-noise (or Gaussian) process with Hurst exponent 0.465, that is, slightly mean-reverting. As a check, we execute an arbitrage strategy on simulated Hurst-exponent data, and a comparable strategy in backtesting on the actual data, obtaining similar results (annualized returns $\sim 60$% if zero transaction costs). Next examining the cross-correlation structure of the $\sim 1000$ stocks, we find that, counterintuitively, correlations increase with time lag in the range studied. We show that this behavior that can be quantitatively explained if the mean-reverting Hurst component of each stock is uncorrelated, i.e., does not share that stock’s overall correlation with other stocks. Overall, we find that $\approx 45$% of a stock’s 1-hour returns variance is explained by its particular correlations to other stocks, but that most of this is simply explained by the movement of all stocks together. Unexpectedly, the fraction of variance explained is greatest when price volatility is high, for example during COVID-19 year 2020. An arbitrage strategy with cross-correlations does significantly better than without (annualized returns $\sim 100$% if zero transaction costs). Measured correlations from any single year in 2018–2022 are about equally good in predicting all the other years, indicating that an overall correlation structure is persistent over the whole period. ...

PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange ArXiv ID: 2305.07559 “View on arXiv” Authors: Unknown Abstract In a financial exchange, market impact is a measure of the price change of an asset following a transaction. This is an important element of market microstructure, which determines the behaviour of the market following a trade. In this paper, we first provide a discussion on the market impact observed in the BTC/USD Futures market, then we present a novel multi-agent market simulation that can follow an underlying price series, whilst maintaining the ability to reproduce the market impact observed in the market in an explainable manner. This simulation of the financial exchange allows the model to interact realistically with market participants, helping its users better estimate market slippage as well as the knock-on consequences of their market actions. In turn, it allows various stakeholders such as industrial practitioners, governments and regulators to test their market hypotheses, without deploying capital or destabilising the system. ...

Statistical Modeling of High Frequency Financial Data: Facts, Models and Challenges ArXiv ID: ssrn-1748022 “View on arXiv” Authors: Unknown Abstract The availability of high-frequency data on transactions, quotes and order flow in electronic order-driven markets has revolutionized data processing and statist Keywords: High-Frequency Trading, Market Microstructure, Electronization, Algorithmic Trading, Time-Series Analysis, Equity / Quantitative Finance Complexity vs Empirical Score Math Complexity: 7.5/10 Empirical Rigor: 6.0/10 Quadrant: Holy Grail Why: The paper involves advanced stochastic calculus and modeling of high-frequency data, indicating high mathematical complexity, while its focus on empirical high-frequency data and statistical methods suggests a strong, though not code-heavy, empirical backing. flowchart TD A["Research Goal: Model High-Frequency<br>Financial Data in Order-Driven Markets"] --> B["Data Collection:<br>Transactions, Quotes, Order Flow"] B --> C["Methodology:<br>Time-Series & Statistical Analysis"] C --> D["Computational Modeling:<br>Volatility Estimation & Microstructure"] D --> E["Key Finding 1:<br>Data Irregularities (Clock Effects)"] D --> F["Key Finding 2:<br>Microstructure Noise Bias"] D --> G["Key Finding 3:<br>Modeling Challenges & Solutions"]

All that Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors ArXiv ID: ssrn-1151595 “View on arXiv” Authors: Unknown Abstract We test and confirm the hypothesis that individual investors are net buyers of attention-grabbing stocks, e.g., stocks in the news, stocks experiencing high abn Keywords: Investor attention, Behavioral finance, Market microstructure, Trading behavior, Information asymmetry, Equities Complexity vs Empirical Score Math Complexity: 2.5/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper focuses on empirical testing of a behavioral hypothesis using event studies and regressions on large-scale trading datasets, requiring significant data processing and backtesting but relying on relatively straightforward statistical models. flowchart TD A["Research Goal<br/>Test if individual investors<br/>are net buyers of<br/>attention-grabbing stocks"] --> B["Methodology<br/>Event Study & Regression Analysis"] B --> C["Data Inputs<br/>Daily Trades (TAQ) &<br/>News Data (Reuters)"] C --> D["Computation<br/>Calculate Abnormal Attention<br/>(News/High Volume)<br/>and Net Buying Imbalance"] D --> E{"Key Findings"} E --> F["Individuals: Net Buyers<br/>of high-attention stocks"] E --> G["Institutions: Net Sellers<br/>or no consistent effect"] E --> H["Outcome: Attention-driven<br/>demand creates temporary<br/>price pressure"]

Discretionary Disclosure Strategies in Corporate Narratives: Incremental Information or Impression Management? ArXiv ID: ssrn-1089447 “View on arXiv” Authors: Unknown Abstract Prior research assumes that discretionary disclosures either (a) contribute to useful decision making by overcoming information asymmetries between managers and Keywords: Information Asymmetry, Voluntary Disclosure, Market Microstructure, Signaling Theory, Corporate Governance, Equity Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a literature review synthesizing prior accounting research, focusing on taxonomies and theoretical frameworks (low math complexity) without original data analysis, backtests, or implementation details (low empirical rigor). flowchart TD A["Research Goal: Do discretionary disclosures inform investors or manage impressions?"] --> B["Method: Content analysis of corporate narratives<br/>Quantifies information vs. sentiment scores"] B --> C["Data: 10-K filings / MD&A sections<br/>Market data for price impact"] C --> D["Computational Process: Textual analysis &<br/>Regression of scores on market microstructure metrics"] D --> E{"Outcomes"} E --> F["Information Effect: Reduced information asymmetry<br/>correlates with information scores"] E --> G["Impression Management Effect: Low-content, high-sentiment<br/>disclosures show limited price impact"]