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Deep Learning for Limit Order Books. (2016). Sirignano, Justin.
In: Papers.
RePEc:arx:papers:1601.01987.

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Cited: 15

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Cites: 32

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  1. DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift. (2022). Liu, Yan ; Vyetrenko, Svitlana ; Trinh, Loc ; El-Laham, Yousef ; Cao, Defu.
    In: Papers.
    RePEc:arx:papers:2211.11513.

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  2. The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning. (2020). Wu, JunJie ; Li, Jiahong ; Bu, Hui.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:36:y:2020:i:4:p:1541-1562.

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  3. Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. (2020). Kim, A ; Johnson, J. E. V., ; Sung, M.-C., ; Ma, T ; Lessmann, S ; Yang, Y.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:283:y:2020:i:1:p:217-234.

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  4. Deep Learning for Financial Applications : A Survey. (2020). Sezer, Omer Berat ; Gudelek, Mehmet Ugur ; Ozbayoglu, Ahmet Murat.
    In: Papers.
    RePEc:arx:papers:2002.05786.

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  5. Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators. (2019). Iosifidis, Alexandros ; Gabbouj, Moncef ; Kanniainen, Juho ; Ntakaris, Adamantios.
    In: Papers.
    RePEc:arx:papers:1907.09452.

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  6. Feature Engineering for Mid-Price Prediction with Deep Learning. (2019). Iosifidis, Alexandros ; Gabbouj, Moncef ; Kanniainen, Juho ; Mirone, Giorgio ; Ntakaris, Adamantios.
    In: Papers.
    RePEc:arx:papers:1904.05384.

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  7. Universal features of price formation in financial markets: perspectives from Deep Learning. (2018). Cont, Rama ; Sirignano, Justin.
    In: Working Papers.
    RePEc:hal:wpaper:hal-01754054.

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  8. Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics. (2018). Peters, Gareth W ; Toczydlowska, Dorota.
    In: Econometrics.
    RePEc:gam:jecnmx:v:6:y:2018:i:3:p:34-:d:158660.

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  9. Universal features of price formation in financial markets: perspectives from Deep Learning. (2018). Cont, Rama ; Sirignano, Justin.
    In: Papers.
    RePEc:arx:papers:1803.06917.

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  10. Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods. (2018). Iosifidis, Alexandros ; Gabbouj, Moncef ; Kanniainen, Juho ; Magris, Martin ; Ntakaris, Adamantios.
    In: Papers.
    RePEc:arx:papers:1705.03233.

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  11. Deep Learning for Mortgage Risk. (2018). Sadhwani, Apaar ; Giesecke, Kay ; Sirignano, Justin.
    In: Papers.
    RePEc:arx:papers:1607.02470.

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  12. Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis. (2017). Gabbouj, Moncef ; Kanniainen, Juho ; Iosifidis, Alexandros ; Tran, Dat Thanh.
    In: Papers.
    RePEc:arx:papers:1712.00975.

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  13. A High Frequency Trade Execution Model for Supervised Learning. (2017). Dixon, Matthew F.
    In: Papers.
    RePEc:arx:papers:1710.03870.

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  14. Tensor Representation in High-Frequency Financial Data for Price Change Prediction. (2017). Iosifidis, Alexandros ; Gabbouj, Moncef ; Kanniainen, Juho ; Thanh, Dat Tran .
    In: Papers.
    RePEc:arx:papers:1709.01268.

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  15. Sequence Classification of the Limit Order Book using Recurrent Neural Networks. (2017). Dixon, Matthew F.
    In: Papers.
    RePEc:arx:papers:1707.05642.

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References cited by this document

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Cocites

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  2. On Parametric Optimal Execution and Machine Learning Surrogates. (2022). Voss, Moritz ; Ludkovski, Mike ; Chen, Tao.
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  3. Rigorous multi-asset optimal execution with Bayesian learning of the drift. (2022). Drissi, Fayccal.
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  4. On effects of negative resilience on optimal trade execution in stochastic order books. (2021). Urusov, Mikhail ; Kruse, Thomas ; Ackermann, Julia.
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  5. Multi-asset optimal execution and statistical arbitrage strategies under Ornstein-Uhlenbeck dynamics. (2021). Gu, Olivier ; Drissi, Fayccal ; Bergault, Philippe.
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  6. Optimal liquidation trajectories for the Almgren-Chriss model. (2020). Lokka, A ; Xu, Junwei.
    In: LSE Research Online Documents on Economics.
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  7. Estimation of level-I hidden liquidity using the dynamics of limit order-book. (2020). Sim, Min Kyu ; Deng, Shijie.
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  8. On Detecting Spoofing Strategies in High Frequency Trading. (2020). Drapeau, Samuel ; Ling, Lan ; Day, Andrew ; Tao, Xuan.
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  9. Optimal trade execution in an order book model with stochastic liquidity parameters. (2020). Urusov, Mikhail ; Kruse, Thomas ; Ackermann, Julia.
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  10. Equilibrium Model of Limit Order Books: A Mean-field Game View. (2020). Noh, Eunjung ; Ma, Jin.
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  11. Optimal liquidation trajectories for the Almgren-Chriss model with Levy processes. (2020). Xu, Junwei ; Lokka, Arne.
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  12. Revisiting the stealth trading hypothesis: Does time-varying liquidity explain the size-effect?. (2019). Hautsch, Nikolaus ; Walsh, Christopher ; Cebiroglu, Gokhan .
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  13. How Option Hedging Shapes Market Impact. (2019). Abergel, Frederic ; Said, Emilio .
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