nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒04‒22
four papers chosen by
Thanos Verousis
University of Essex

  1. Universal features of price formation in financial markets: perspectives from Deep Learning By Justin Sirignano; Rama Cont
  2. Tick Size, Trading Strategies and Market Quality By Werner, Ingrid M.; Wen, Yuanji; Rindi, Barbara; Buti, Sabrina
  3. Information in Yield Spread Trades By Yang-Ho Park
  4. High-frequency trading and price informativeness By Gider, Jasmin; Schmickler, Simon; Westheide, Christian

  1. By: Justin Sirignano (UIUC - University of Illinois at Urbana Champaign - University of Illinois at Urbana-Champaign [Urbana]); Rama Cont (LPSM UMR 8001 - Laboratoire de Probabilités, Statistique et Modélisation - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model exhibits a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model — trained on data from all stocks — outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset-or sector-specific models as commonly done. Standard data normal-izations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecasting performance, showing evidence of path-dependence in price dynamics.
    Date: 2018–03–30
  2. By: Werner, Ingrid M. (The Ohio State University - Fisher College of Business); Wen, Yuanji (The University of Western Australia - Department of Accounting and Finance); Rindi, Barbara (Bocconi University and IGIER and Baffi Carefin); Buti, Sabrina (Université Paris Dauphine - Department of Finance)
    Abstract: We model a public limit order book (PLB) with rational investors choosing to supply or demand liquidity. Following a reduction in the tick size the effects on PLB’s market quality depend on the liquidity of the stocks. Spread improves for tick-constrained stocks and deteriorates for unconstrained stocks; inside depth decreases in particular for constrained stocks, and volume increases for unconstrained stocks. The model also shows how results change when competition from a crossing network generates order flow migration. We find empirical support for these predictions by exploiting the 2014 reduction of tick size at the Tokyo Stock Exchange.
    JEL: D40 G10 G20 G24
    Date: 2019–02
  3. By: Yang-Ho Park
    Abstract: Using positions data on bond futures, I document that speculators' spread trades contain private information about future economic activities and asset prices. Strong steepening trades are associated with negative payroll surprises in subsequent months and can predict asset markets' reaction to future payroll releases, suggesting that speculators hold superior information about future payrolls. Steepening trades can also predict the rise of stock prices within a few hours before subsequent FOMC announcements, implying that the pre-FOMC stock drift is driven by informed speculation. Overall, evidence highlights spread traders' superior information and its important role in explaining announcement returns and pre-announcement drifts.
    Keywords: Informed trading ; Term structure ; Business cycle ; Pre-FOMC ; Macroeconomic announcements
    JEL: E32 E43 G12 G14
    Date: 2019–04–12
  4. By: Gider, Jasmin; Schmickler, Simon; Westheide, Christian
    Abstract: We study how the informativeness of stock prices changes with the presence of high-frequency trading (HFT). Our estimate is based on the staggered start of HFT participation in a panel of international exchanges. With HFT presence, market prices are a less reliable predictor of future cash flows and investment, even more so for longer horizons. Further, firm-level idiosyncratic volatility decreases, and the holdings and trades by institutional investors deviate less from the market-capitalization weighted portfolio as a benchmark. Our results document that the informativeness of prices decreases subsequent to the start of HFT. These findings are consistent with theoretical models of HFTs' ability to anticipate informed order flow, resulting in decreased incentives to acquire fundamental information.
    Keywords: High-Frequency Trading,Price Efficiency,Information Acquisition,Information Production
    JEL: G10 G14
    Date: 2019

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