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Deep learning for limit order books
Justin A. Sirignano
Industrial and Enterprise Systems Engineering
Coordinated Science Lab
Research output
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Contribution to journal
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Article
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peer-review
Overview
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Dive into the research topics of 'Deep learning for limit order books'. Together they form a unique fingerprint.
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Engineering
Deep Learning
100%
Neural Network Architecture
100%
Graphics Processing Unit
50%
Spatial Distribution
50%
Network Model
50%
Dimensional Model
50%
Data Model
50%
Empirical Model
50%
Feedforward
50%
Nonlinear Feature
50%
Joint Distribution
50%
Keyphrases
Deep Learning
100%
Limit Order Book
100%
Neural Network
57%
Neural Network Architecture
28%
Standard Neural Network
28%
Performance Improvement
14%
Computationally Efficient
14%
Large Amount of Data
14%
Computational Challenges
14%
Logistic Regression Model
14%
Dropout
14%
Tail Distribution
14%
Joint Distribution
14%
Neural Network Model
14%
Logistic Regression
14%
Nonlinear Features
14%
Specific Structure
14%
Low-dimensional Models
14%
Risk Management
14%
New Architecture
14%
Price Movement
14%
Management Application
14%
Fully Connected
14%
U.S. Stocks
14%
Modeling Spatial Distribution
14%
Feedforward Architecture
14%
Computer Science
Neural Network
100%
Deep Learning
100%
Neural Network Architecture
40%
Logistic Regression Model
40%
Joint Distribution
20%
Data Model
20%
Neural Network Model
20%
Risk Management
20%
Spatial Distribution
20%
Dimensional Model
20%
Chemical Engineering
Neural Network
100%
Deep Learning
100%
Economics, Econometrics and Finance
Logit Model
100%
Risk Management
50%