nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒05‒29
five papers chosen by
Thanos Verousis

  1. High-frequency Anticipatory Trading and Its Influences: Small Informed Trader vs. Front-runner By Ziyi Xu; Xue Cheng
  2. Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting By Adamantios Ntakaris; Moncef Gabbouj; Juho Kanniainen
  3. The foreign exchange market By Alain Chaboud; Dagfinn Rime; Vladyslav Sushko
  4. Deep Stock: training and trading scheme using deep learning By Sungwoo Kang
  5. Effects of Information Overload on Financial Markets: How Much Is Too Much? By Alejandro Bernales; Marcela Valenzuela; Ilknur Zer

  1. By: Ziyi Xu; Xue Cheng
    Abstract: In this paper, the interactions between a large informed trader (IT, for short) and a high-frequency trader (HFT, for short) who can anticipate the former's incoming order are studied in an extended Kyle's (1985) model. Equilibria under various specific situations are discussed. Relying on the speed advantage, HFT always trades in the same direction as the large order in advance. However, whether or not she provides liquidity depends on her inventory aversion, the prediction accuracy, and the market activeness. She may supply liquidity back (act as a front-runner) or continue to take it away (in this case we call her a small IT). Small IT always harms the large trader while front-runner may benefit her. Besides, we find surprisingly that (1) increasing the noise in HFT's signal may in fact decrease IT's profit; (2) although providing liquidity, a front-runner may harm IT more than a small IT.
    Date: 2023–04
  2. By: Adamantios Ntakaris; Moncef Gabbouj; Juho Kanniainen
    Abstract: High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading. A well-documented and tested method that considers these time-irregularities is a type of recurrent neural network, named long short-term memory neural network. This type of neural network is formed based on cells that perform sequential and stale calculations via gates and states without knowing whether their order, within the cell, is optimal. In this paper, we propose a revised and real-time adjusted long short-term memory cell that selects the best gate or state as its final output. Our cell is running under a shallow topology, has a minimal look-back period, and is trained online. This revised cell achieves lower forecasting error compared to other recurrent neural networks for online high-frequency trading forecasting tasks such as the limit order book mid-price prediction as it has been tested on two high-liquid US and two less-liquid Nordic stocks.
    Date: 2023–04
  3. By: Alain Chaboud; Dagfinn Rime; Vladyslav Sushko
    Abstract: This chapter discusses the structure and functioning of the spot foreign exchange (FX) market. The market structure, which has become far more complex over the past three decades, has mostly evolved endogenously as the global FX market is subject to notably less regulatory oversight than equity and bond markets in most countries. Major banks used to dominate liquidity provision, but they have found their role challenged by high frequency trading firms in an increasingly fragmented electronic market. The information structure of the market has also changed. As such, high-frequency cross-asset correlations, especially with the futures market, have become more important. The chapter also discusses the important role of the official sector in the FX market, and it highlights a few special topics such as flash events and the FX fixing scandal. We conclude with some suggestions for future research.
    Keywords: financial markets, foreign exchange, market microstructure, dealer intermediation, electronic trading, algorithmic trading
    JEL: F31 G15
    Date: 2023–04
  4. By: Sungwoo Kang
    Abstract: Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market, leading to the development of techniques to gain above-market returns, known as alpha. Systematic trading has undergone significant advances in recent decades, with deep learning emerging as a powerful tool for analyzing and predicting market behavior. In this paper, we propose a model inspired by professional traders that look at stock prices of the previous 600 days and predicts whether the stock price rises or falls by a certain percentage within the next D days. Our model, called DeepStock, uses Resnet's skip connections and logits to increase the probability of a model in a trading scheme. We test our model on both the Korean and US stock markets and achieve a profit of N\% on Korea market, which is M\% above the market return, and profit of A\% on US market, which is B\% above the market return.
    Date: 2023–04
  5. By: Alejandro Bernales; Marcela Valenzuela; Ilknur Zer
    Abstract: Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 months, even after controlling for standard predictors and other news-based measures. Information overload also affects the cross-section of stock returns: Investors require higher risk premia to hold small, high beta, high volatile, and unprofitable stocks. Such findings are consistent with theories emphasizing that information overload increases information and estimation risk and deteriorates investors' decision accuracy amid their limited attention.
    Keywords: Limited attention; Dispersion; Sentiment; Predicting returns; Behavioral biases
    JEL: G40 G41 G12 G14
    Date: 2023–03–09

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