nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒06‒10
four papers chosen by
Thanos Verousis

  1. Trading and arbitrage in cryptocurrency markets By Makarov, Igor; Schoar, Antoinette
  2. Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection By Ishita Chakraborty; Minkyung Kim; K. Sudhir
  3. Trade Clustering and Power Laws in Financial Markets By Makoto Nirei; John Stachurski; Tsutomu Watanabe
  4. The cost of clearing fragmentation By Benos, Evangelos; Huang, Wenqian; Menkveld, Albert; Vasios, Michalis

  1. By: Makarov, Igor; Schoar, Antoinette
    Abstract: Cryptocurrency markets exhibit periods of large, recurrent arbitrage opportunities across exchanges. These price deviations are much larger across than within countries, and smaller between cryptocurrencies, highlighting the importance of capital controls for the movement of arbitrage capital. Price deviations across countries co-move and open up in times of large bitcoin appreciation. Countries with higher bitcoin premia over the US bitcoin price see widening arbitrage deviations when bitcoin appreciates. Finally, we decompose signed volume on each exchange into a common and an id- iosyncratic component. The common component explains 80% of bitcoin returns. The idiosyncratic components help explain arbitrage spreads between exchanges.
    Keywords: cryptocurrencies; bitcoin; arbitrage; price impact; capital controls
    JEL: F3 G3
    Date: 2019
  2. By: Ishita Chakraborty (School of Management, Yale University); Minkyung Kim (School of Management, Yale University); K. Sudhir (Cowles Foundation & School of Management, Yale University; School of Management, Yale University)
    Abstract: The authors address two novel and signi?cant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level restaurant performance. Using reviews for empirical illustration, they ?nd that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the “hard” sentiment classi?cation problems. Further, accounting for attribute self-selection signi?cantly impacts sentiment scores, especially on attributes that are frequently missing.
    Keywords: Text mining, Natural language processing (NLP), Convolutional neural networks (CNN), Long-short term memory (LSTM) Networks, Deep learning, Lexicons, Endogeneity, Self-selection, Online reviews, Online ratings, Customer satisfaction
    JEL: M1 M3 C8 C5
    Date: 2019–05
  3. By: Makoto Nirei (University of Tokyo); John Stachurski (Australian National University); Tsutomu Watanabe (University of Tokyo)
    Abstract: This study provides an explanation of the emergence of power laws in trading volume and asset returns. In the model, traders infer other traders' private signals regarding the value of an asset from their actions and adjust their own behavior accordingly. When the number of traders is large and the signals for asset value are noisy, this leads to power laws for equilibrium volume and returns. We also provide numerical results showing that the model reproduces observed distributions of daily stock volume and returns.
    Date: 2018–11
  4. By: Benos, Evangelos (Bank of England); Huang, Wenqian (Bank for International Settlements); Menkveld, Albert (VU Amsterdam); Vasios, Michalis (Norges Bank Investment Management)
    Abstract: Fragmenting clearing across multiple central counter-parties (CCPs) is costly. This is because dealers providing liquidity globally, cannot net trades cleared in different CCPs and this increases their collateral costs. These costs are then passed on to their clients through price distortions which take the form of a price differential (basis) when the same products are cleared in different CCPs. Using proprietary data, we document an economically significant CCP basis for dollar swap contracts cleared both at the Chicago Mercantile Exchange (CME) and the London Clearing House (LCH) and provide empirical evidence consistent with a collateral cost explanation of this basis.
    Keywords: Central clearing; CCP basis; collateral; fragmentation
    JEL: G10 G12 G14
    Date: 2019–05–31

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