Deep learning for limit order books
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Publication:5234311
DOI10.1080/14697688.2018.1546053zbMath1420.91555arXiv1601.01987OpenAlexW3121451803WikidataQ128880029 ScholiaQ128880029MaRDI QIDQ5234311
Publication date: 26 September 2019
Published in: Quantitative Finance (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1601.01987
Learning and adaptive systems in artificial intelligence (68T05) Actuarial science and mathematical finance (91G99)
Related Items (20)
A deep learning approach to estimating fill probabilities in a limit order book ⋮ A reinforcement learning approach to optimal execution ⋮ On detecting spoofing strategies in high-frequency trading ⋮ Double Deep Q-Learning for Optimal Execution ⋮ Data-driven hedging of stock index options via deep learning ⋮ Cross-impact of order flow imbalance in equity markets ⋮ A two-step framework for arbitrage-free prediction of the implied volatility surface ⋮ Optimal liquidation through a limit order book: a neural network and simulation approach ⋮ A generative model of a limit order book using recurrent neural networks ⋮ Deep order flow imbalance: Extracting alpha at multiple horizons from the limit order book ⋮ Deep-Learning Solution to Portfolio Selection with Serially Dependent Returns ⋮ Deep reinforcement learning for the optimal placement of cryptocurrency limit orders ⋮ Optimal market-making strategies under synchronised order arrivals with deep neural networks ⋮ Universal features of price formation in financial markets: perspectives from deep learning ⋮ Encoding of high-frequency order information and prediction of short-term stock price by deep learning ⋮ Learning multi-market microstructure from order book data ⋮ Forecasting financial time series with Boltzmann entropy through neural networks ⋮ Quantum blind signature scheme for supply chain financial ⋮ Order scoring, bandit learning and order cancellations ⋮ Optimal Execution: A Review
Uses Software
Cites Work
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