nep-mst New Economics Papers
on Market Microstructure
Issue of 2021‒02‒08
five papers chosen by
Thanos Verousis

  1. Market Making with Stochastic Liquidity Demand: Simultaneous Order Arrival and Price Change Forecasts By Agostino Capponi; Jos\'e E. Figueroa-L\'opez; Chuyi Yu
  2. Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism By Yong Shi; Wei Dai; Wen Long; Bo Li
  3. Deep Reinforcement Learning for Active High Frequency Trading By Antonio Briola; Jeremy Turiel; Riccardo Marcaccioli; Tomaso Aste
  4. High-Frequency Trading and Price Informativeness By Jasmin Gider; Simon N. M. Schmickler; Christian Westheide
  5. Exponential Kernels with Latency in Hawkes Processes: Applications in Finance By Marcos Costa Santos Carreira

  1. By: Agostino Capponi; Jos\'e E. Figueroa-L\'opez; Chuyi Yu
    Abstract: We provide an explicit characterization of the optimal market making strategy in a discrete-time Limit Order Book (LOB). In our model, the number of filled orders during each period depends linearly on the distance between the fundamental price and the market maker's limit order quotes, with random slope and intercept coefficients. The high-frequency market maker (HFM) incurs an end-of-the-day liquidation cost resulting from linear price impact. The optimal placement strategy incorporates in a novel and parsimonious way forecasts about future changes in the asset's fundamental price. We show that the randomness in the demand slope reduces the inventory management motive, and that a positive correlation between demand slope and investors' reservation prices leads to wider spreads. Our analysis reveals that the simultaneous arrival of buy and sell market orders (i) reduces the shadow cost of inventory, (ii) leads the HFM to reduce price pressures to execute larger flows, and (iii) introduces patterns of nonlinearity in the intraday dynamics of bid and ask spreads. Our empirical study shows that the market making strategy outperforms those which ignores randomness in demand, simultaneous arrival of buy and sell market orders, and local drift in the fundamental price.
    Date: 2021–01
  2. By: Yong Shi; Wei Dai; Wen Long; Bo Li
    Abstract: The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p({\Delta}t_(i+1) |G_i) where {\Delta}t_(i+1) represents the duration of the (i+1)-th transaction, G_i represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.
    Date: 2021–01
  3. By: Antonio Briola; Jeremy Turiel; Riccardo Marcaccioli; Tomaso Aste
    Abstract: We introduce the first end-to-end Deep Reinforcement Learning based framework for active high frequency trading. We train DRL agents to to trade one unit of Intel Corporation stocks by employing the Proximal Policy Optimization algorithm. The training is performed on three contiguous months of high frequency Limit Order Book data. In order to maximise the signal to noise ratio in the training data, we compose the latter by only selecting training samples with largest price changes. The test is then carried out on the following month of data. Hyperparameters are tuned using the Sequential Model Based Optimization technique. We consider three different state characterizations, which differ in the LOB-based meta-features they include. Agents learn trading strategies able to produce stable positive returns in spite of the highly stochastic and non-stationary environment, which is remarkable itself. Analysing the agents' performances on the test data, we argue that the agents are able to create a dynamic representation of the underlying environment highlighting the occasional regularities present in the data and exploiting them to create long-term profitable trading strategies.
    Date: 2021–01
  4. By: Jasmin Gider; Simon N. M. Schmickler; Christian Westheide
    Abstract: We study how stock price informativeness 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, idiosyncratic volatility decreases, mutual funds trade less actively and their holdings deviate less from the market-capitalization weighted portfolio. These findings suggest that price informativeness declines with HFT presence, consistent with theoretical models of HFTs' ability to anticipate informed order flow, reducing incentives to acquire fundamental information.
    Keywords: High-Frequency Trading, Price Efficiency, Information Acquisition, Information Production
    JEL: G10 G14
    Date: 2021–01
  5. By: Marcos Costa Santos Carreira
    Abstract: The Tick library allows researchers in market microstructure to simulate and learn Hawkes process in high-frequency data, with optimized parametric and non-parametric learners. But one challenge is to take into account the correct causality of order book events considering latency: the only way one order book event can influence another is if the time difference between them (by the central order book timestamps) is greater than the minimum amount of time for an event to be (i) published in the order book, (ii) reach the trader responsible for the second event, (iii) influence the decision (processing time at the trader) and (iv) the 2nd event reach the order book and be processed. For this we can use exponential kernels shifted to the right by the latency amount. We derive the expression for the log-likelihood to be minimized for the 1-D and the multidimensional cases, and test this method with simulated data and real data. On real data we find that, although not all decays are the same, the latency itself will determine most of the decays. We also show how the decays are related to the latency. Code is available on GitHub at -With-Latency.
    Date: 2021–01

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