nep-mst New Economics Papers
on Market Microstructure
Issue of 2022‒09‒05
six papers chosen by
Thanos Verousis

  1. Learning Financial Networks with High-frequency Trade Data By Kara Karpman; Sumanta Basu; David Easley
  2. The Impact of Retail Investors Sentiment on Conditional Volatility of Stocks and Bonds By Elroi Hadad; Haim Kedar-Levy
  3. Quantum Quantitative Trading: High-Frequency Statistical Arbitrage Algorithm By Xi-Ning Zhuang; Zhao-Yun Chen; Yu-Chun Wu; Guo-Ping Guo
  4. Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures By Kyungsub Lee
  5. Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification By Mostafa Shabani; Dat Thanh Tran; Juho Kanniainen; Alexandros Iosifidis
  6. Delta Hedging Liquidity Positions on Automated Market Makers By Akhilesh; Khakhar; Xi Chen

  1. By: Kara Karpman; Sumanta Basu; David Easley
    Abstract: Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, nonlinear dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure (either realized volatility or returns kurtosis) of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.
    Date: 2022–08
  2. By: Elroi Hadad; Haim Kedar-Levy
    Abstract: We measure bond and stock conditional return volatility as a function of changes in sentiment, proxied by six indicators from the Tel Aviv Stock Exchange. We find that changes in sentiment affect conditional volatilities at different magnitudes and often in an opposite manner in the two markets, subject to market states. We are the first to measure bonds conditional volatility of retail investors sentiment thanks to a unique dataset of corporate bond returns from a limit-order-book with highly active retail traders. This market structure differs from the prevalent OTC platforms, where institutional investors are active yet less prone to sentiment.
    Date: 2022–08
  3. By: Xi-Ning Zhuang; Zhao-Yun Chen; Yu-Chun Wu; Guo-Ping Guo
    Abstract: Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading in this work by utilizing variable time condition number estimation and quantum linear regression.The algorithm complexity has been reduced from the classical benchmark O(N^2d) to O(sqrt(d)(kappa)^2(log(1/epsilon))^2 )). It shows quantum advantage, where N is the length of trading data, and d is the number of stocks, kappa is the condition number and epsilon is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.
    Date: 2021–04
  4. By: Kyungsub Lee
    Abstract: The Hawkes model is suitable for describing self and mutually exciting random events. In addition, the exponential decay in the Hawkes process allows us to calculate the moment properties in the model. However, due to the complexity of the model and formula, few studies have been conducted on the performance of Hawkes volatility. In this study, we derived a variance formula that is directly applicable under the general settings of both unmarked and marked Hawkes models for tick-level price dynamics. In the marked model, the linear impact function and possible dependency between the marks and underlying processes are considered. The Hawkes volatility is applied to the mid-price process filtered at 0.1-second intervals to show reliable results; furthermore, intraday estimation is expected to have high utilization in real-time risk management. We also note the increasing predictive power of intraday Hawkes volatility over time and examine the relationship between futures and stock volatilities.
    Date: 2022–07
  5. By: Mostafa Shabani; Dat Thanh Tran; Juho Kanniainen; Alexandros Iosifidis
    Abstract: Deep Learning models have become dominant in tackling financial time-series analysis problems, overturning conventional machine learning and statistical methods. Most often, a model trained for one market or security cannot be directly applied to another market or security due to differences inherent in the market conditions. In addition, as the market evolves through time, it is necessary to update the existing models or train new ones when new data is made available. This scenario, which is inherent in most financial forecasting applications, naturally raises the following research question: How to efficiently adapt a pre-trained model to a new set of data while retaining performance on the old data, especially when the old data is not accessible? In this paper, we propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities and adapt it to achieve high performance in new ones. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed, and this knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data. The auxiliary connections are constrained to be of low rank. This not only allows us to rapidly optimize for the new task but also reduces the storage and run-time complexity during the deployment phase. The efficiency of our approach is empirically validated in the stock mid-price movement prediction problem using a large-scale limit order book dataset. Experimental results show that our approach enhances prediction performance as well as reduces the overall number of network parameters.
    Date: 2022–07
  6. By: Akhilesh (Adam); Khakhar; Xi Chen
    Abstract: Liquidity Providers on Automated Market Makers generate millions of USD in transaction fees daily. However, the net value of a Liquidity Position is vulnerable to price changes in the underlying assets in the pool. The dominant measure of loss in a Liquidity Position is Impermanent Loss. Impermanent Loss for Constant Function Market Makers has been widely studied. We propose a new metric to measure Liquidity Position PNL based on price movement from the underlying assets. We show how this new metric more appropriately measures the change in the net value of a Liquidity Position as a function of price movement in the underlying assets. Our second contribution is an algorithm to delta hedge arbitrary Liquidity Positions on both uniform liquidity Automated Market Makers (such as Uniswap v2) and concentrated liquidity Automated Market Makers (such as Uniswap v3) via a combination of derivatives.
    Date: 2022–08

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