nep-mst New Economics Papers
on Market Microstructure
Issue of 2019‒07‒29
six papers chosen by
Thanos Verousis

  1. Multi-Level Order-Flow Imbalance in a Limit Order Book By Ke Xu; Martin D. Gould; Sam D. Howison
  2. Who Sees the Trades? The Effect of Information on Liquidity in Inter-Dealer Markets By Garratt, Rod; Lee, Michael Junho; Martin, Antoine; Townsend, Robert M.
  3. Formal verification of trading in financial markets By Suneel Sarswat; Abhishek Kr Singh
  4. Deep Reinforcement Learning in Financial Markets By Souradeep Chakraborty
  5. European Gas Markets, Trading Hubs, and Price Formation: A Network Perspective By Woroniuk, D.; Karam, A.; Jamasb, T.
  6. The influence of Brazilian exports on price transmission processes in the coffee sector: A Markov-switching approach By Vollmer, Teresa; von Cramon-Taubadel, Stephan

  1. By: Ke Xu; Martin D. Gould; Sam D. Howison
    Abstract: We study the \emph{multi-level order-flow imbalance (MLOFI)}, which measures the net flow of buy and sell orders at different price levels in a limit order book (LOB). Using a recent, high-quality data set for 6 liquid stocks on Nasdaq, we use Ridge regression to fit a simple, linear relationship between MLOFI and the contemporaneous change in mid-price. For all 6 stocks that we study, we find that the goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector. Our results underline how the complex order-flow activity deep into the LOB can influence the price-formation process.
    Date: 2019–07
  2. By: Garratt, Rod (University of California at Santa Barbara); Lee, Michael Junho (Federal Reserve Bank of New York); Martin, Antoine (Federal Reserve Bank of New York); Townsend, Robert M. (MIT)
    Abstract: Dealers, who strategically supply liquidity to traders, are subject to both liquidity and adverse selection costs. While liquidity costs can be mitigated through inter-dealer trading, individual dealers’ private motives to acquire information compromise inter-dealer market liquidity. Post-trade information disclosure can improve market liquidity by counteracting dealers’ incentives to become better informed through their market-making activities. Asymmetric disclosure, however, exacerbates the adverse selection problem in inter-dealer markets, in turn decreasing equilibrium liquidity provision. A non-monotonic relationship may arise between the partial release of post-trade information and market liquidity. This points to a practical concern: a strategic post-trade platform has incentives to maximize adverse selection and may choose to release information in a way that minimizes equilibrium liquidity provision.
    Keywords: inter-dealer markets; liquidity; information design; platforms
    JEL: D62 D82 G14 G23
    Date: 2019–07–01
  3. By: Suneel Sarswat; Abhishek Kr Singh
    Abstract: We introduce a formal framework for analyzing trades in financial markets. An exchange is where multiple buyers and sellers participate to trade. These days, all big exchanges use computer algorithms that implement double sided auctions to match buy and sell requests and these algorithms must abide by certain regulatory guidelines. For example, market regulators enforce that a matching produced by exchanges should be \emph{fair}, \emph{uniform} and \emph{individual rational}. To verify these properties of trades, we first formally define these notions in a theorem prover and then give formal proofs of relevant results on matchings. Finally, we use this framework to verify properties of two important classes of double sided auctions. All the definitions and results presented in this paper are completely formalised in the Coq proof assistant without adding any additional axioms to it.
    Date: 2019–07
  4. By: Souradeep Chakraborty
    Abstract: In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision process (MDP) model to capture the financial trading markets. We review and propose various modifications to existing approaches and explore different techniques to succinctly capture the market dynamics to model the markets. We then go on to use deep reinforcement learning to enable the agent (the algorithm) to learn how to take profitable trades in any market on its own, while suggesting various methodology changes and leveraging the unique representation of the FMDP (financial MDP) to tackle the primary challenges faced in similar works. Through our experimentation results, we go on to show that our model could be easily extended to two very different financial markets and generates a positively robust performance in all conducted experiments.
    Date: 2019–07
  5. By: Woroniuk, D.; Karam, A.; Jamasb, T.
    Abstract: We apply network theory to analyse the interactions of trading hub prices, and to assess the harmonisation of the European gas market. We construct dynamic networks, where the nodes correspond to the twelve EU trading hubs, and where the edges weight the causality between the variations of the respective gas prices. Network density dynamically calculates the aggregate quantity of causal interactions recorded within the system, which provides information pertaining to the integration of the European gas network. We document a number of spikes in network density, suggesting short periods of improved connectivity of European gas markets. We argue that these results appear to be driven by exogenous factors, such as unseasonal weather patterns, seismic activity and pipeline capacity reductions or outages. The findings elucidate the time varying nature of European gas market dynamics, and the importance of continual monitoring of market evolution.
    Keywords: Market Integration, Information Transmissions, Natural Gas, Network Theory
    JEL: C32 F18 Q43 Q47 Q48
    Date: 2019–07–03
  6. By: Vollmer, Teresa; von Cramon-Taubadel, Stephan
    Abstract: Most analysis of agricultural commodity market integration is solely based on price information. However, adding trade data can improve the understanding of interactions between interrelated markets. We link the analysis of price transmission processes between spot and futures markets with trade information to study the influence of Brazilian coffee exports on global price interdependencies. Using a Markov-switching vector error correction model (MSVECM) we allow for structural changes over time. Our results reveal two regimes. One regime is characterized by periods of sideways or downward trending coffee prices with low price volatility, and the other one by phases of price spikes and high price volatility. Price information is transmitted through both the spot and the futures prices and the speed of the price transmission process is significantly affected by the total daily volume and value of Brazilian coffee exports.
    Keywords: price transmission,Markov-switching models,coffee,customs data,spot and futures markets
    Date: 2019

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