nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒10‒02
five papers chosen by
Thanos Verousis

  1. An Empirical Analysis on Financial Market: Insights from the Application of Statistical Physics By Haochen Li; Yi Cao; Maria Polukarov; Carmine Ventre
  2. Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market By Timothy DeLise
  3. ATMS: Algorithmic Trading-Guided Market Simulation By Song Wei; Andrea Coletta; Svitlana Vyetrenko; Tucker Balch
  4. Price Formation in the Foreign Exchange Market By Florent Gallien; Sergei Glebkin; Serge Kassibrakis; Semyon Malamud; Alberto Teguia
  5. New general dependence measures: construction, estimation and application to high-frequency stock returns By Aleksy Leeuwenkamp; Wentao Hu

  1. By: Haochen Li; Yi Cao; Maria Polukarov; Carmine Ventre
    Abstract: In this study, we introduce a physical model inspired by statistical physics for predicting price volatility and expected returns by leveraging Level 3 order book data. By drawing parallels between orders in the limit order book and particles in a physical system, we establish unique measures for the system's kinetic energy and momentum as a way to comprehend and evaluate the state of limit order book. Our model goes beyond examining merely the top layers of the order book by introducing the concept of 'active depth', a computationally-efficient approach for identifying order book levels that have impact on price dynamics. We empirically demonstrate that our model outperforms the benchmarks of traditional approaches and machine learning algorithm. Our model provides a nuanced comprehension of market microstructure and produces more accurate forecasts on volatility and expected returns. By incorporating principles of statistical physics, this research offers valuable insights on understanding the behaviours of market participants and order book dynamics.
    Date: 2023–08
  2. By: Timothy DeLise
    Abstract: Modern financial electronic exchanges are an exciting and fast-paced marketplace where billions of dollars change hands every day. They are also rife with manipulation and fraud. Detecting such activity is a major undertaking, which has historically been a job reserved exclusively for humans. Recently, more research and resources have been focused on automating these processes via machine learning and artificial intelligence. Fraud detection is overwhelmingly associated with the greater field of anomaly detection, which is usually performed via unsupervised learning techniques because of the lack of labeled data needed for supervised learning. However, a small quantity of labeled data does often exist. This research article aims to evaluate the efficacy of a deep semi-supervised anomaly detection technique, called Deep SAD, for detecting fraud in high-frequency financial data. We use exclusive proprietary limit order book data from the TMX exchange in Montr\'eal, with a small set of true labeled instances of fraud, to evaluate Deep SAD against its unsupervised predecessor. We show that incorporating a small amount of labeled data into an unsupervised anomaly detection framework can greatly improve its accuracy.
    Date: 2023–08
  3. By: Song Wei; Andrea Coletta; Svitlana Vyetrenko; Tucker Balch
    Abstract: The effective construction of an Algorithmic Trading (AT) strategy often relies on market simulators, which remains challenging due to existing methods' inability to adapt to the sequential and dynamic nature of trading activities. This work fills this gap by proposing a metric to quantify market discrepancy. This metric measures the difference between a causal effect from underlying market unique characteristics and it is evaluated through the interaction between the AT agent and the market. Most importantly, we introduce Algorithmic Trading-guided Market Simulation (ATMS) by optimizing our proposed metric. Inspired by SeqGAN, ATMS formulates the simulator as a stochastic policy in reinforcement learning (RL) to account for the sequential nature of trading. Moreover, ATMS utilizes the policy gradient update to bypass differentiating the proposed metric, which involves non-differentiable operations such as order deletion from the market. Through extensive experiments on semi-real market data, we demonstrate the effectiveness of our metric and show that ATMS generates market data with improved similarity to reality compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network (cWGAN) approach. Furthermore, ATMS produces market data with more balanced BUY and SELL volumes, mitigating the bias of the cWGAN baseline approach, where a simple strategy can exploit the BUY/SELL imbalance for profit.
    Date: 2023–09
  4. By: Florent Gallien (Swissquote); Sergei Glebkin (INSEAD); Serge Kassibrakis (Swissquote); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Alberto Teguia (UBC Sauder)
    Abstract: We study joint price formation in the dealer-to-dealer (D2D) and dealer-to-customer (D2C) segments of the foreign exchange (FX) market, both theoretically and empirically. Our theory accounts for dealer heterogeneity, market power, and non-exclusive customer-dealer relationship and shows that several statistics of the cross-section of D2C quotes help predict D2D prices and liquidity. In particular, D2D prices are negatively related to cross-sectional covariance between D2C mid-quotes and spreads, contrary to predictions of other theories of two-tiered markets. Our predictions are confirmed empirically using unique proprietary D2C data. Model calibration reveals and quantifies the FX market’s inelasticity and non-competitiveness.
    Keywords: Liquidity, Foreign Exchange, OTC markets, Price Impact, Market Power
    JEL: F31 G12 G14 G21
    Date: 2023–08
  5. By: Aleksy Leeuwenkamp; Wentao Hu
    Abstract: We propose a set of dependence measures that are non-linear, local, invariant to a wide range of transformations on the marginals, can show tail and risk asymmetries, are always well-defined, are easy to estimate and can be used on any dataset. We propose a nonparametric estimator and prove its consistency and asymptotic normality. Thereby we significantly improve on existing (extreme) dependence measures used in asset pricing and statistics. To show practical utility, we use these measures on high-frequency stock return data around market distress events such as the 2010 Flash Crash and during the GFC. Contrary to ubiquitously used correlations we find that our measures clearly show tail asymmetry, non-linearity, lack of diversification and endogenous buildup of risks present during these distress events. Additionally, our measures anticipate large (joint) losses during the Flash Crash while also anticipating the bounce back and flagging the subsequent market fragility. Our findings have implications for risk management, portfolio construction and hedging at any frequency.
    Date: 2023–08

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