nep-mst New Economics Papers
on Market Microstructure
Issue of 2020‒11‒23
four papers chosen by
Thanos Verousis

  1. Comparing the market microstructure between two South African exchanges By Ivan Jericevich; Patrick Chang; Tim Gebbie
  2. High Frequency Fairness By Haeringer, Guillaume; Melton, Hayden
  3. Reinforced Deep Markov Models With Applications in Automatic Trading By Tadeu A. Ferreira
  4. Price discovery and gains from trade in asset markets with insider trading By Brünner, Tobias; Levinsky, Rene

  1. By: Ivan Jericevich; Patrick Chang; Tim Gebbie
    Abstract: We consider shared listings on two South African equity exchanges: the Johannesburg Stock Exchange (JSE) and the A2X Exchange. A2X is an alternative exchange that provides for both shared listings and new listings within the financial market ecosystem of South Africa. From a science perspective it provides the opportunity to compare markets trading similar shares, in a similar regulatory and economic environment, but with vastly different liquidity, costs and business models. A2X currently has competitive settlement and transaction pricing when compared to the JSE, but the JSE has deeper liquidity. In pursuit of an empirical understanding of how these differences relate to their respective price response dynamics, we compare the distributions and auto-correlations of returns on different time scales; we compare price impact and master curves; and we compare the cost of trading on each exchange. This allows us to empirically compare the two markets. We find that various stylised facts become similar as the measurement or sampling time scale increase. However, the same securities can have vastly different price responses irrespective of time scales. This is not surprising given the different liquidity and order-book resilience. Here we demonstrate that direct costs dominate the cost of trading, and the importance of competitively positioning cost ceilings. Universality is crucial for being able to meaningfully compare cross-exchange price responses, but in the case of A2X, it has yet to emerge in a meaningful way due to the infancy of the exchange -- making meaningful comparisons difficult.
    Date: 2020–11
  2. By: Haeringer, Guillaume; Melton, Hayden
    Abstract: The emergence of high frequency trading has resulted in ‘bursts’ of orders arriving at an exchange (nearly) simultaneously, yet most electronic financial exchanges im- plement the continuous limit order book which requires processing of orders serially. Contrary to an assumption that appears throughout the economics literature, the tech- nology that performs serialization provides only constrained random serial dictatorship (RSD) in the sense that not all priority orderings of agents are possible. We provide necessary and sufficient conditions for fairness under different market conditions on orders for constrained RSD mechanisms. Our results show that exchanges relying on the current serialization technology cannot ensure fairness, including exchanges using ‘speed bumps.’ We find that specific forms of constrained RSD ensure fairness under certain assumptions about the content of those orders but that the general case nev- ertheless requires unconstrained RSD. Our results have implications for the design of trading exchanges.
    Keywords: Electronic trading, limit order book, fairness, random serial dictatorship.
    JEL: D47 D71 G10
    Date: 2020–10–20
  3. By: Tadeu A. Ferreira
    Abstract: Inspired by the developments in deep generative models, we propose a model-based RL approach, coined Reinforced Deep Markov Model (RDMM), designed to integrate desirable properties of a reinforcement learning algorithm acting as an automatic trading system. The network architecture allows for the possibility that market dynamics are partially visible and are potentially modified by the agent's actions. The RDMM filters incomplete and noisy data, to create better-behaved input data for RL planning. The policy search optimisation also properly accounts for state uncertainty. Due to the complexity of the RKDF model architecture, we performed ablation studies to understand the contributions of individual components of the approach better. To test the financial performance of the RDMM we implement policies using variants of Q-Learning, DynaQ-ARIMA and DynaQ-LSTM algorithms. The experiments show that the RDMM is data-efficient and provides financial gains compared to the benchmarks in the optimal execution problem. The performance improvement becomes more pronounced when price dynamics are more complex, and this has been demonstrated using real data sets from the limit order book of Facebook, Intel, Vodafone and Microsoft.
    Date: 2020–11
  4. By: Brünner, Tobias; Levinsky, Rene
    Abstract: The present study contributes to the ongoing debate on possible costs and benefits of insider trading. We present a novel call auction model with insider information. Our model predicts that more insider information improves informational efficiency of prices, but this comes at the expense of reduced gains from trade. The model further implies that in the presence of insider information the call auction performs worse than continuous double auction. Testing these hypotheses in the lab we find that insider information increases informational efficiency of call auction prices but does not decrease the realized gains from trade. Contrary to the theoretical prediction, the call auction does not perform worse than the continuous double auction. In fact, when the probability of insider information is high, the call auction has the most informative prices and highest realized gains from trade. Our experiment provides new evidence, from markets with very asymmetrically dispersed information, that lends support to the decision by many stock exchanges to use call auctions when information asymmetries are severe and the need for accurate prices is large, e.g., at the open or close of the trading day.
    Keywords: call market,call auction,double auction,asymmetric information,experiment,informational efficiency
    JEL: D82 C92 G14
    Date: 2020

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