nep-mst New Economics Papers
on Market Microstructure
Issue of 2015‒10‒25
five papers chosen by
Thanos Verousis

  1. The Effects of Make and Take Fees in Experimental Markets By Vince Bourke; David Porter
  2. Mathematical Foundations of Realtime Equity Trading. Liquidity Deficit and Market Dynamics. Automated Trading Machines By Vladislav Gennadievich Malyshkin; Ray Bakhramov
  3. Detrended cross-correlations between returns, volatility, trading activity, and volume traded for the stock market companies By Rafal Rak; Stanislaw Drozdz; Jaroslaw Kwapien; Pawel Oswiecimka
  4. Advertised Prices in Decentralized Markets By Derek Stacey
  5. Real-Time Forecasting with a Large, Mixed Frequency, Bayesian VAR By McCracken, Michael W.; Owyang, Michael T.; Sekhposyan, Tatevik

  1. By: Vince Bourke (Economic Science Institute, Chapman University); David Porter (Economic Science Institute, Chapman University)
    Abstract: We conduct a series of experiments to examine the effects of the make and take fee structure currently used by equity exchanges in the U.S. We examine the effects of these fees on measures of market quality (allocative efficiency, trading volume, book depth, and the bid-ask spread). With the exception of increased book depth, we document no significant effects of make and take fees relative to a baseline case in which trading fees are assessed on both sides of a transaction.
    Keywords: make and take fees, double auction, experimental economics
    JEL: G2 G12 G14
    Date: 2015
  2. By: Vladislav Gennadievich Malyshkin; Ray Bakhramov
    Abstract: We postulates, and then show experimentally, that liquidity deficit is the driving force of the markets. In the first part of the paper a kinematic of liquidity deficit is developed. The calculus-like approach, which is based on Radon--Nikodym derivatives and their generalization, allows us to calculate important characteristics of observable market dynamics. In the second part of the paper this calculus is used in an attempt to build a dynamic equation in the form: future price tend to the value maximizing the number of shares traded per unit time. To build a practical automated trading machine P&L dynamics instead of price dynamics is considered. This allows a trading automate resilient to catastrophic P&L drains to be build. The results are very promising, yet when all the fees and trading commissions are taken into account, are close to breakeven. In the end of the paper important criteria for automated trading systems are presented. We list the system types that can and cannot make money on the market. These criteria can be successfully applied not only by automated trading machines, but also by a human trader.
    Date: 2015–10
  3. By: Rafal Rak; Stanislaw Drozdz; Jaroslaw Kwapien; Pawel Oswiecimka
    Abstract: We consider a few quantities that characterize trading on a stock market in a fixed time interval: logarithmic returns, volatility, trading activity (i.e., the number of transactions), and volume traded. We search for the power-law cross-correlations among these quantities aggregated over different time units from 1 min to 10 min. Our study is based on empirical data from the American stock market consisting of tick-by-tick recordings of 31 stocks listed in Dow Jones Industrial Average during the years 2008-2011. Since all the considered quantities except the returns show strong daily patterns related to the variable trading activity in different parts of a day, which are the best evident in the autocorrelation function, we remove these patterns by detrending before we proceed further with our study. We apply the multifractal detrended cross-correlation analysis with sign preserving (MFCCA) and show that the strongest power-law cross-correlations exist between trading activity and volume traded, while the weakest ones exist (or even do not exist) between the returns and the remaining quantities. We also show that the strongest cross-correlations are carried by those parts of the signals that are characterized by large and medium variance. Our observation that the most convincing power-law cross-correlations occur between trading activity and volume traded reveals the existence of strong fractal-like coupling between these quantities.
    Date: 2015–10
  4. By: Derek Stacey (Ryerson University)
    Abstract: A model of a decentralized market is developed that features search frictions, advertised prices and bargaining. Sellers can post ask prices to attract buyers through a process of directed search, but ex post there is the possibility of renegotiation. Similarly, buyers can advertise negotiable bid prices to attract sellers. Even though transaction prices often differ from quoted prices, advertised bid and ask prices play a crucial role in directing search and reducing trading frictions. The features and predictions of the model align well with aspects of the secondary market for transferable taxicab license plates in Toronto. This provides a useful and unique context for studying the relationships between advertised and actual prices in a decentralized market.
    Date: 2015
  5. By: McCracken, Michael W. (Federal Reserve Bank of St. Louis); Owyang, Michael T. (Federal Reserve Bank of St. Louis); Sekhposyan, Tatevik (Texas A&M University)
    Abstract: We assess point and density forecasts from a mixed-frequency vector autoregression (VAR) to obtain intra-quarter forecasts of output growth as new information becomes available. The econometric model is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. We impose restrictions on the VAR to account explicitly for the temporal ordering of the data releases. Because this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. The relative performance of the model is compared to forecasts from various time-series models and the Survey of Professional Forecaster's. We further illustrate the possible usefulness of our proposed VAR for causal analysis.
    Keywords: Vector autoregression; Blocking model; Stacked vector autoregression; Mixed-frequency estimation; Bayesian methods; Nowcasting; Forecasting
    JEL: C22 C52 C53
    Date: 2015–10–08

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