nep-mst New Economics Papers
on Market Microstructure
Issue of 2018‒09‒10
six papers chosen by
Thanos Verousis

  1. Inventory Management, Dealers' Connections, and Prices in OTC Markets By Colliard, Jean-Edouard; Foucault, Thierry; Hoffmann, Peter
  2. Monetary Policy Announcement and Algorithmic News Trading in the Foreign Exchange Market By Keiichi Goshima; Yusuke Kumano
  3. Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO By Bartosz Uniejewski; Grzegorz Marcjasz; Rafal Weron
  4. DeepLOB: Deep Convolutional Neural Networks for Limit Order Books By Zihao Zhang; Stefan Zohren; Stephen Roberts
  5. Disagreement after News: Gradual Information Diffusion or Differences of Opinion? By Anastassia Fedyk
  6. Over-the-counter market liquidity and securities lending By Nathan Foley-Fisher; Stefan Gissler; Stephane Verani

  1. By: Colliard, Jean-Edouard; Foucault, Thierry; Hoffmann, Peter
    Abstract: We propose a new model of interdealer trading. Dealers trade together to reduce their inventory holding costs. Core dealers share these costs efficiently and provide liquidity to peripheral dealers, who have heterogeneous access to core dealers. We derive predictions about the effects of peripheral dealers' connectedness to core dealers and the allocation of aggregate inventories between core and peripheral dealers on the distribution of interdealer prices, the efficiency of interdealer trades, and trading costs for the dealers' clients. For instance, the dispersion of interdealer prices is higher when fewer peripheral dealers are connected to core dealers or when their aggregate inventory is higher.
    Keywords: interdealer trading; Inventory management; OTC markets
    Date: 2018–07
  2. By: Keiichi Goshima (Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Yusuke Kumano (Deputy Director and Economist, Institute for Monetary and Economic Studies (currently, Research and Statistics Department), Bank of Japan (E-mail:
    Abstract: We analyze the effects of algorithmic news trading (ANT) in the foreign exchange market around the time that the Bank of Japan makes public announcements of its policy decisions. To observe the activity level of ANT, we propose a novel measure based on a web access record to a central bank fs webpage. We find that our proposed measure appropriately captures the activity level of ANT. Employing an event study analysis and a VAR analysis, we find that ANT increases market volatility immediately after the monetary policy announcements, and that ANT activity indirectly decreases market liquidity through increasing volatility. In addition, we suggest that ANT trades based on changes of texts on monetary policy announcements.
    Keywords: Algorithmic trading, Monetary policy, High frequency data, Foreign exchange market, News trading, Market microstructure, Web access record
    JEL: E58 F31 G14
    Date: 2018–08
  3. By: Bartosz Uniejewski; Grzegorz Marcjasz; Rafal Weron
    Abstract: Using a unique set of prices from the German EPEX market we take a closer look at the fine structure of intraday markets for electricity with its continuous trading for individual load periods up to 30 minutes before delivery. We apply the least absolute shrinkage and selection operator (LASSO) to gain statistically sound insights on variable selection and provide recommendations for very short-term electricity price forecasting.
    Keywords: Intraday electricity market; Variable selection; Price forecasting; LASSO
    JEL: C14 C22 C51 C53 Q47
    Date: 2018–08–31
  4. By: Zihao Zhang; Stefan Zohren; Stephen Roberts
    Abstract: We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The model is trained using electronic market quotes from the London Stock Exchange. Our model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments and outperforms existing methods such as Support Vector Machines, standard Multilayer Perceptrons, as well as other previously proposed convolutional neural network (CNN) architectures. The results obtained lead to good profits in a simple trading simulation, especially when compared with the baseline models. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.
    Date: 2018–08
  5. By: Anastassia Fedyk (Harvard Business School)
    Abstract: This paper explores the long-standing empirical fact of increased trading volume around news releases through the lens of canonical models of gradual information diffusion and differences of opinion. I use a unique dataset of clicks on news by key finance professionals to distinguish between trading among investors who see the news at different times and trading among investors who see the same news but disagree regarding its interpretation. Consistent with gradual information diffusion, dispersion in the timing of investors' attention is strongly predictive of daily volume around earnings announcements and volume within minutes of individual news articles. Furthermore, delayed attention is predictive of minute-level return continuation, daily-level post-earnings-announcement drift, and monthly-level return momentum. Differences of opinion, measured as heterogeneity in the investors clicking on the news, are generally weaker in explaining trading volume around news, but plays a larger role when the news is more textually ambiguous.
    Date: 2018
  6. By: Nathan Foley-Fisher (Federal Reserve Board); Stefan Gissler (Federal Reserve Board); Stephane Verani (Federal Reserve Board)
    Abstract: This paper studies how over-the-counter market liquidity is affected by securities lending. We combine micro-data on corporate bond market trades with securities lending transactions, in which U.S. life insurance companies are major counterparties. Applying a difference-in-difference empirical strategy, we show that the shutdown of AIG’s securities lending programs in 2008 caused a statistically and economically significant reduction in the market liquidity of corporate bonds held by AIG. We also show that an important mechanism behind the decrease in liquidity was a shift towards relatively small trades among a greater number of dealers in the interdealer market.
    Date: 2018

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