nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒09‒06
three papers chosen by
Thanos Verousis

  1. Call of duty: Designated market maker participation in call auctions By Theissen, Erik; Westheide, Christian
  2. Bilinear Input Normalization for Neural Networks in Financial Forecasting By Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
  3. How does standardization affect OTC markets? Evidence from the Small Bang reform in the CDS market By Manac, Radu-Dragomir; Banti, Chiara; Kellard, Neil

  1. By: Theissen, Erik; Westheide, Christian
    Abstract: Many equity markets combine continuous trading and call auctions. Oftentimes designated market makers (DMMs) supply additional liquidity. Whereas prior research has focused on their role in continuous trading, we provide a detailed analysis of their activity in call auctions. Using data from Germany's Xetra system, we find that DMMs are most active when they can provide the greatest benefits to the market, i.e., in relatively illiquid stocks and at times of elevated volatility. Their trades stabilize prices and they trade profitably.
    Keywords: Designated market makers,Call auctions
    JEL: G10
    Date: 2021
  2. By: Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis
    Abstract: Data normalization is one of the most important preprocessing steps when building a machine learning model, especially when the model of interest is a deep neural network. This is because deep neural network optimized with stochastic gradient descent is sensitive to the input variable range and prone to numerical issues. Different than other types of signals, financial time-series often exhibit unique characteristics such as high volatility, non-stationarity and multi-modality that make them challenging to work with, often requiring expert domain knowledge for devising a suitable processing pipeline. In this paper, we propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme, which takes into account the bimodal characteristic of financial multivariate time-series, requires no expert knowledge to preprocess a financial time-series since this step is formulated as part of the end-to-end optimization process. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data from two large-scale limit order books coming from the Nordic and US markets, show significant improvements over other normalization techniques in forecasting future stock price dynamics.
    Date: 2021–09
  3. By: Manac, Radu-Dragomir; Banti, Chiara; Kellard, Neil
    Abstract: Focusing on the most liquid segment of the European CDS market, this paper studies the impact of key standardization reforms. We document that the introduction of an upfront fee to standardize the cash flow of CDS contracts created an initial capital cost for traders, leading to higher CDS prices. This relation holds after accounting for well-known determinants of spreads, suggesting a separate funding channel driven by the greater capital intensity of trading. This effect is stronger when dealers are likely to bear the initial capital cost and is present across all industries, except for swaps written on financials.
    Date: 2021–08–23

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