nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒03‒22
six papers chosen by
Thanos Verousis

  1. High-Frequency Trading and Price Informativeness By Jasmin Gider; Simon N. M. Schmickler; Christian Westheide
  2. Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets By Yusen Lin; Jinming Xue; Louiqa Raschid
  3. The Adoption of Blockchain-based Decentralized Exchanges: A Market Microstructure Analysis of the Automated Market Maker By Agostino Capponi; Ruizhe Jia
  4. Phase Transitions in Kyle's Model with Market Maker Profit Incentives By Charles-Albert Lehalle; Eyal Neuman; Segev Shlomov
  5. Forecasting high-frequency financial time series: an adaptive learning approach with the order book data By Parley Ruogu Yang
  6. Price and volume dynamics in bubbles By Liao, Jingchi; Peng, Cheng; Zhu, Ning

  1. By: Jasmin Gider; Simon N. M. Schmickler; Christian Westheide
    Abstract: We study how stock price informativeness changes with the presence of high-frequency trading (HFT). Our estimate is based on the staggered start of HFT participation in a panel of international exchanges. With HFT presence market prices are a less reliable predictor of future cash flows and investment, even more so for longer horizons. Further, idiosyncratic volatility decreases, mutual funds trade less actively and their holdings deviate less from the market-capitalization weighted portfolio. These findings suggest that price informativeness declines with HFT presence, consistent with theoretical models of HFTs' ability to anticipate informed order flow, reducing incentives to acquire fundamental information.
    Keywords: High-Frequency Trading, Price Efficiency, Information Acquisition, Information Production
    JEL: G10 G14
    Date: 2021–01
  2. By: Yusen Lin; Jinming Xue; Louiqa Raschid
    Abstract: Trading in Over-The-Counter (OTC) markets is facilitated by broker-dealers, in comparison to public exchanges, e.g., the New York Stock Exchange (NYSE). Dealers play an important role in stabilizing prices and providing liquidity in OTC markets. We apply machine learning methods to model and predict the trading behavior of OTC dealers for US corporate bonds. We create sequences of daily historical transaction reports for each dealer over a vocabulary of US corporate bonds. Using this history of dealer activity, we predict the future trading decisions of the dealer. We consider a range of neural network-based prediction models. We propose an extension, the Pointwise-Product ReZero (PPRZ) Transformer model, and demonstrate the improved performance of our model. We show that individual history provides the best predictive model for the most active dealers. For less active dealers, a collective model provides improved performance. Further, clustering dealers based on their similarity can improve performance. Finally, prediction accuracy varies based on the activity level of both the bond and the dealer.
    Date: 2021–03
  3. By: Agostino Capponi; Ruizhe Jia
    Abstract: We analyze the market microstructure of Automated Market Maker (AMM) with constant product function, the most prominent type of blockchain-based decentralized crypto exchange. We show that, even without information asymmetries, the order execution mechanism of the blockchain-based exchange induces adverse selection problems for liquidity providers if token prices are volatile. AMM is more likely to be adopted for pairs of coins which are stable or of high personal use for investors. For high volatility tokens, there exists a market breakdown such that rational liquidity providers do not deposit their tokens in the first place. The adoption of AMM leads to a surge of transaction fees on the underlying blockchain if token prices are subject to high fluctuations.
    Date: 2021–03
  4. By: Charles-Albert Lehalle; Eyal Neuman; Segev Shlomov
    Abstract: We consider a stochastic game between three types of players: an inside trader, noise traders and a market maker. In a similar fashion to Kyle's model, we assume that the insider first chooses the size of her market-order and then the market maker determines the price by observing the total order-flow resulting from the insider and the noise traders transactions. In addition to the classical framework, a revenue term is added to the market maker's performance function, which is proportional to the order flow and to the size of the bid-ask spread. We derive the maximizer for the insider's revenue function and prove sufficient conditions for an equilibrium in the game. Then, we use neural networks methods to verify that this equilibrium holds. We show that the equilibrium state in this model experience interesting phase transitions, as the weight of the revenue term in the market maker's performance function changes. Specifically, the asset price in equilibrium experience three different phases: a linear pricing rule without a spread, a pricing rule that includes a linear mid-price and a bid-ask spread, and a metastable state with a zero mid-price and a large spread.
    Date: 2021–03
  5. By: Parley Ruogu Yang
    Abstract: This paper proposes a forecast-centric adaptive learning model that engages with the past studies on the order book and high-frequency data, with applications to hypothesis testing. In line with the past literature, we produce brackets of summaries of statistics from the high-frequency bid and ask data in the CSI 300 Index Futures market and aim to forecast the one-step-ahead prices. Traditional time series issues, e.g. ARIMA order selection, stationarity, together with potential financial applications are covered in the exploratory data analysis, which pave paths to the adaptive learning model. By designing and running the learning model, we found it to perform well compared to the top fixed models, and some could improve the forecasting accuracy by being more stable and resilient to non-stationarity. Applications to hypothesis testing are shown with a rolling window, and further potential applications to finance and statistics are outlined.
    Date: 2021–02
  6. By: Liao, Jingchi; Peng, Cheng; Zhu, Ning
    Abstract: We propose a framework based on two ingredients—extrapolative beliefs and the disposition effect—and show that it can generate the sharp rise in both prices and volume observed in many bubbles. We test this framework using novel, account-level data on the 2014–2015 Chinese stock market bubble. The interaction of extrapolative beliefs and the disposition effect explains 30% of the rise in volume. Investors who are both extrapolative and prone to the disposition effect are quick to buy a stock with positive past returns, but also quick to sell it if good returns continue.
    Keywords: bubbles; the disposition effect; extrapolation; volume
    JEL: G11 G12
    Date: 2019–04–02

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