nep-mst New Economics Papers
on Market Microstructure
Issue of 2023‒03‒27
four papers chosen by
Thanos Verousis

  1. Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets By Yutong Lu; Gesine Reinert; Mihai Cucuringu
  2. Price Discovery for Derivatives By Christian Keller; Michael Tseng
  3. Exploring the Advantages of Transformers for High-Frequency Trading By Fazl Barez; Paul Bilokon; Arthur Gervais; Nikita Lisitsyn
  4. Sorting Versus Screening in Decentralized Markets With Adverse Selection By Sarah Auster; Piero Gottardi

  1. By: Yutong Lu; Gesine Reinert; Mihai Cucuringu
    Abstract: The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic networks of stocks. Our empirical studies employ high-frequency limit order book data from 2017-01-03 to 2019-12-09. By applying spectral clustering on co-trading networks, we uncover economically meaningful clusters of stocks. Beyond the static Global Industry Classification Standard (GICS) sectors, our data-driven clusters capture the time evolution of the dependency among stocks. Furthermore, we demonstrate statistically significant positive relations between low-latency co-trading and return covariance. With the aid of co-trading networks, we develop a robust estimator for high-dimensional covariance matrix, which yields superior economic value on portfolio allocation. The mean-variance portfolios based on our covariance estimates achieve both lower volatility and higher Sharpe ratios than standard benchmarks.
    Date: 2023–02
  2. By: Christian Keller; Michael Tseng
    Abstract: A theory of price discovery across derivative markets with respect to higher-order information is obtained, via a model where an informed agent trades a complete set of state-contingent claims under general information asymmetry. In an equivalent options formulation, the informed agent has private information regarding arbitrary aspects of the payoff distribution of an underlying asset and trades a complete menu of options, with no assumption on the possible payoff distributions or the nature of private information. We characterize, in closed form, the informed demand, price impact, and information efficiency of prices. Our results contain a theory of insider trading on higher-order moments of the underlying payoff as a special case. The informed demand formula prescribes options strategies for trading on any given moment and extends those already used in practice for, e.g.~volatility trading. The volatility smile is explained by an "insider smile" of implied volatilities.
    Date: 2023–02
  3. By: Fazl Barez; Paul Bilokon; Arthur Gervais; Nikita Lisitsyn
    Abstract: This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model, called \textbf{HFformer}, is then introduced for time series forecasting which incorporates a Transformer encoder, linear decoder, spiking activations, and quantile loss function, and does not use position encoding. Furthermore, possible high-frequency trading strategies for use with the HFformer model are discussed, including trade sizing, trading signal aggregation, and minimal trading threshold. Ultimately, the performance of the HFformer and Long Short-Term Memory models are assessed and results indicate that the HFformer achieves a higher cumulative PnL than the LSTM when trading with multiple signals during backtesting.
    Date: 2023–02
  4. By: Sarah Auster; Piero Gottardi
    Abstract: We study the role of traders' meeting capacities in decentralized markets with adverse selection. Uninformed customers choose trading mechanisms in order to find a provider for a service. Providers are privately informed about their quality and aim to match with one of the customers. We consider a rich set of meeting technologies and characterize the properties of the equilibrium allocations for each of them. In equilibrium, different provider types can be separated either via sorting---they self-select into different submarkets---or screening within the trading mechanism, or a combination of the two. We show that, as the meeting technology improves, the equilibrium features more screening and less sorting. Interestingly, this reduces both the average quality of trade as well as the total level of trade in the economy. The trading losses are, however, compensated by savings in entry costs, so that welfare increases.
    Keywords: Competitive Search, Adverse Selection, Market Segmentation
    JEL: C78 D44 D83
    Date: 2022–08

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