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Deep Learning for Market by Order Data
13 references·2 citing·51 second hop
Deep Learning for Market by Order Data (2021)Deep Learning for Market by Order DataDeep Reinforcement Learning for Trading (2020)Deep Reinforcement Learning f…Deep Learning for Portfolio Optimization (2020)Deep Learning for Portfolio O…Comparison of Financial Models for Stock Price Prediction (2020)Comparison of Financial Model…ABIDES (2020)ABIDESUniversal features of price formation in financial markets: perspectives from deep learning (2019)Universal features of price f…Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019)Enhancing Time-Series Momentu…DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2019)DeepLOB: Deep Convolutional N…Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods (2018)Benchmark dataset for mid‐pri…Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017)Forecasting Stock Prices from…Limit order books (2013)Limit order booksTrading and Exchanges: Market Microstructure for Practitioners (2002)Trading and Exchanges: Market…Market microstructure theory (1995)Market microstructure theoryCommon risk factors in the returns on stocks and bonds (1993)Common risk factors in the re…Lob-based deep learning models for stock price trend prediction: a benchmark study (2024)Lob-based deep learning model…Predicting Stock Price Changes Based on the Limit Order Book: A Survey (2022)Predicting Stock Price Change…Information Effects on the Bid-Ask Spread (1983)Network externalities, competition, and compatibility (1985)Liquidity and Market Structure (1988)Secondary Trading Costs in the Municipal Bond Market (2006)Liquidity in Asset Markets With Search Frictions (2009)The Online Advertising Industry: Economics, Evolution, and Privacy (2009)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)Risk, Return, and Equilibrium: Empirical Tests (1973)The Cross-Section of Expected Stock Returns (1992)On Persistence in Mutual Fund Performance (1997)Illiquidity and stock returns: cross-section and time-series effects (2002)A five-factor asset pricing model (2014)A Coefficient of Agreement for Nominal Scales (1960)Drift Independent Volatility Estimation Based on High, Low, Open, and Close Prices (2000)Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis (2018)Stock Market Prediction Based on Generative Adversarial Network (2019)Comparing Predictive Accuracy (1994)Does Algorithmic Trading Improve Liquidity? (2011)Deep learning with long short-term memory networks for financial market predictions (2017)A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM (2020)A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions (2021)A hybrid ARIMA and support vector machines model in stock price forecasting (2004)Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data (2009)High-Frequency Trading and Price Discovery (2014)Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data (2019)Using Deep Learning for price prediction by exploiting stationary limit order book features (2020)Forecasting mid-price movement of Bitcoin futures using machine learning (2021)Empirical properties of asset returns: stylized facts and statistical issues (2001)Empirical Market Microstructure (2007)A Stochastic Model for Order Book Dynamics (2010)Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators (2020)Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods (2020)A comprehensive review on multiple hybrid deep learning approaches for stock prediction (2022)Lifetime Portfolio Selection under Uncertainty: The Continuous-Time Case (1969)Risk Aversion in the Small and in the Large (1976)Deep learning in the stock market—a systematic survey of practice, backtesting, and applications (2022)An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges (2023)Performance Measurement in a Downside Risk Framework (1994)The Sharpe Ratio (1994)Time Series Momentum (2011)Machine Learning for Active Portfolio Management (2021)Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing (2022)End-to-end risk budgeting portfolio optimization with neural networks (2023)Continuous Auctions and Insider Trading (1985)Power-Law Distributions in Empirical Data (2009)Deep learning for limit order books (2018)Power-law distributions in empirical data (2018)Multifractal analysis of financial markets: a review (2019)Market microstructure: A survey (2000)A Tale of Two Time Scales: Determining Integrated Volatility with Noisy High Frequency Data (2003)Realized Variance and Market Microstructure Noise (2006)
this work references cited by second hop

References

  • 1993Common risk factors in the returns on stocks and bonds26,615 cited
  • 1995Market microstructure theory1,305 cited
  • 2002Trading and Exchanges: Market Microstructure for Practitioners517 cited
  • 2017Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks394 cited
  • 2013Limit order books310 cited
  • 2019DeepLOB: Deep Convolutional Neural Networks for Limit Order Books288 cited
  • 2019Universal features of price formation in financial markets: perspectives from deep learning267 cited
  • 2020Deep Reinforcement Learning for Trading226 cited
  • 2020Deep Learning for Portfolio Optimization150 cited
  • 2018Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods117 cited
  • 2019Enhancing Time-Series Momentum Strategies Using Deep Neural Networks96 cited
  • 2020ABIDES64 cited
  • 2020Comparison of Financial Models for Stock Price Prediction55 cited

Cited by

  • 2022Predicting Stock Price Changes Based on the Limit Order Book: A Survey29 cited
  • 2024Lob-based deep learning models for stock price trend prediction: a benchmark study29 cited

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