nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒07‒19
four papers chosen by
Thanos Verousis

  1. Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field By Sebastian M. Krause; Edgar Jungblut; Thomas Guhr
  2. Price change prediction of ultra high frequency financial data based on temporal convolutional network By Wei Dai; Yuan An; Wen Long
  3. Exploring markets: Magic the gathering - a trading card game By Weber, Daniel
  4. The Limit Order Book Recreation Model (LOBRM): An Extended Analysis By Zijian Shi; John Cartlidge

  1. By: Sebastian M. Krause; Edgar Jungblut; Thomas Guhr
    Abstract: The distribution of liquidity within the limit order book is essential for the impact of market orders on the stock price and the emergence of price shocks. Hence it is of great interest to improve the understanding of the time-dependent dynamics of the limit order book. In our analysis we find a broad distribution of limit order lifetimes. Around the quotes we find a densely filled regime with mostly short living limit orders, far away from the quotes we find a sparse filling with mostly long living limit orders. We determine the characteristics of those two regimes and point out the main differences. Based on our research we propose a model for simulating the regime around the quotes.
    Date: 2021–06
  2. By: Wei Dai; Yuan An; Wen Long
    Abstract: Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.
    Date: 2021–07
  3. By: Weber, Daniel
    Abstract: Exploring Markets" is planned as a paper series discussing niche markets with interesting characteristics. The paper on hand focuses on the secondary market of the trading card game "Magic the Gathering", in which players play against each other with decks composed of cards they have collected. Recent high-volume trades raise the question if the investment in pop culture collectibles in general and in Magic the Gathering trading cards in particular can be considered as a legitimateand viable investment form. To answer this question, price developments and market characteristics are analyzed. The paper explicitly aims at people that have never heard of Magic the Gathering and are curious about its basic mechanisms and economics.
    Keywords: Market Study,Trading Card Games,Collectibles
    JEL: Z19
    Date: 2021
  4. By: Zijian Shi; John Cartlidge
    Abstract: The limit order book (LOB) depicts the fine-grained demand and supply relationship for financial assets and is widely used in market microstructure studies. Nevertheless, the availability and high cost of LOB data restrict its wider application. The LOB recreation model (LOBRM) was recently proposed to bridge this gap by synthesizing the LOB from trades and quotes (TAQ) data. However, in the original LOBRM study, there were two limitations: (1) experiments were conducted on a relatively small dataset containing only one day of LOB data; and (2) the training and testing were performed in a non-chronological fashion, which essentially re-frames the task as interpolation and potentially introduces lookahead bias. In this study, we extend the research on LOBRM and further validate its use in real-world application scenarios. We first advance the workflow of LOBRM by (1) adding a time-weighted z-score standardization for the LOB and (2) substituting the ordinary differential equation kernel with an exponential decay kernel to lower computation complexity. Experiments are conducted on the extended LOBSTER dataset in a chronological fashion, as it would be used in a real-world application. We find that (1) LOBRM with decay kernel is superior to traditional non-linear models, and module ensembling is effective; (2) prediction accuracy is negatively related to the volatility of order volumes resting in the LOB; (3) the proposed sparse encoding method for TAQ exhibits good generalization ability and can facilitate manifold tasks; and (4) the influence of stochastic drift on prediction accuracy can be alleviated by increasing historical samples.
    Date: 2021–07

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