Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks

By Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis. 2017

329 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Omer Berat Sezer
2. A. Ozbayoglu
3. M. Ugur Gudelek
4. Weiwei Jiang
5. Dat Thanh Tran
6. Juho Kanniainen
7. Alexandros Iosifidis
8. Martin Magris

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Weiwei Jiang
3. Omer Berat Sezer
4. A. Ozbayoglu
5. Dat Thanh Tran
6. Juho Kanniainen
7. Alexandros Iosifidis
8. Martin Magris

In Signal Processing

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek
4. Weiwei Jiang
5. Juho Kanniainen
6. Alexandros Iosifidis
7. Martin Magris
8. Moncef Gabbouj

In Time-Series & Forecasting

1. Weiwei Jiang
2. M. Ugur Gudelek
3. Omer Berat Sezer
4. A. Ozbayoglu
5. Juho Kanniainen
6. Alexandros Iosifidis
7. Moncef Gabbouj
8. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In some studies [188, 189, 193, 199, 219], innovative transformations of financial time”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[60] proposed a novel method that used the last 100 entries from the limit order book to create a 2-dimensional image for the stock price prediction using CNN method.”
• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “For example, in [9] a CNN with both 2D and 1D convolution masks was trained to predict stock price movements.”
• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…GRU, DNN (Ding et al., 2014), DNN (Peng & Jiang, 2016) Li & Tam (2017) WT+LSTM LSTM Li et al. (2017a) LSTM SVM Nelson et al. (2017) LSTM MLP, RF Tsantekidis et al. (2017b) LSTM SVM, MLP Zhang et al. (2017a) GRU SVM Zhang et al. (2017b) SFM AR, LSTM Zhao et al. (2017) LSTM RNN, SVM, RF, AB…”