Optimal execution in high-frequency trading with Bayesian learning

Du, Bian; Zhu, Huaiyu; Zhao, Jingdong

doi:10.1016/j.physa.2016.06.021

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…however, they were yet not applied much in a joint way with the concept of high-frequency data. therefore, for the future researches such a combination could be interesting (Du et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Volatility Patterns of the Largest Polish Companies: Some Evidence From High-Frequency Data

Drachal

2018

RIC

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Purpose. The article is focused on the empirical properties of the high-frequency data of 20 selected stocks from the Warsaw Stock Exchange (in particular the ones listed on WIG 20).The intraday data from at least more than 1 year were analysed. In particular, correlation between returns and durations were checked. Methodology. Also, the heterogeneous autoregressive model for realized volatility (HAR) was analysed and an attempt to construct the UHF-GARCH model was taken. The HAR model is a linear model and the UHF-GARCH is based on a certain adjustment of physical durations. Then, the standard ARMA-GARCH approach can be considered. Moreover, the hypothesis of Diamond and Verrecchia predicting a negative correlation between price changes and the time passed between transactions was checked. The analysis was done in R statistical software.

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Section: Discussionmentioning

confidence: 99%

Volatility Patterns of the Largest Polish Companies: Some Evidence From High-Frequency Data

Drachal

2018

RIC

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“…Du et al [8] used a Bayesian learning (BL) model to predict stock prices in the research. is model is actually similar to the autoregressive integrated moving average (ARIMA) model.…”

Section: Machine Learning-based Stock Price Predictionmentioning

confidence: 99%

Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

Song

Peng

Huang

2020

Scientific Programming

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The prediction of stock excess returns is an important research topic for quantitative trading, and stock price prediction based on machine learning is receiving more and more attention. This article takes the data of Chinese A-shares from July 2014 to September 2017 as the research object, and proposes a method of stock excess return forecasting that combines research reports and investor sentiment. The proposed method measures individual stocks released by analysts, separates the two indicators of research report attention and rating sentiment, calculates investor sentiment based on external market factors, and uses the LSTM model to represent the time series characteristics of stocks. The results show that (1) the accuracy and F1 evaluation indicators are used, and the proposed algorithm is better than the benchmark algorithm. (2) The performance of deep learning LSTM algorithm is better than traditional machine learning algorithm SVM. (3) Investor sentiment as the initial hidden state of the model can improve the accuracy of the algorithm. (4) The attention of the split research report takes the two indicators of investor sentiment and price as the input of the model, which can effectively improve the performance of the model.

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“…(Ho and Stoll 1980), consider the problem of dealers under competition (each dealer's pricing strategy depends not only on his own current and expected inventory position and his other characteristics, but also on the current and expected inventory and other characteristics of the competitor) and show that the bid and ask prices are shown to be related to the reservation (or indifference) prices of the agents. (Avellaneda & Stoikov 2008) combine the utility framework with the microstructure of actual limit order books, as described in the econo-physics literature, to infer reasonable arrival rates of buy and sell orders; (Du, Zhu & Zhao 2016) extend the price dynamics to follow a GBM in which the drift part is updated by Bayesian learning in the beginning of the transaction day to capture the trader's estimate of other traders' target sizes and directions. (Cont, Stoikov & Talreja 2010) describe a stylized model for the dynamics of a limit order book (which serves as a comprehensive introduction to limit order books), where the order flow is described by independent Poisson processes and estimate the model parameters from high-frequency order book time-series data from the Tokyo Stock Exchange.…”

Section: Significant Favorable (>+2%)mentioning

confidence: 99%

David vs Goliath (You against the Markets), A dynamic programming approach to separate the impact and timing of trading costs

Kashyap

2020

Physica A: Statistical Mechanics and its Applications

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Optimal execution in high-frequency trading with Bayesian learning

Cited by 5 publications

References 27 publications

Volatility Patterns of the Largest Polish Companies: Some Evidence From High-Frequency Data

Volatility Patterns of the Largest Polish Companies: Some Evidence From High-Frequency Data

Incorporating Research Reports and Market Sentiment for Stock Excess Return Prediction: A Case of Mainland China

David vs Goliath (You against the Markets), A dynamic programming approach to separate the impact and timing of trading costs

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