Using deep learning to detect price change indications in financial markets

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

145 citations recorded by the source corpus.

Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The introduction of electronic trading and the availability of large amounts of data allow for developing novel machine learning techniques that address some of the difficulties faced by the aforementioned methods. In this work we propose a deep learning methodology, based on recurrent neural networks, that can be used for predicting future price movements from large-scale high-frequency time-series data on Limit Order Books. The proposed method is evaluated using a large-scale dataset of limit order book events.

Subfields: Finance, 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 Finance

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

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 [216], the authors used the limit order book time series data and LSTM method for trend prediction.”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[56] also used limit order book time series data and LSTM method for the trend prediction.”
• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “In [12], a RNN architecture with LSTM units that also operates on a similar raw data representation was proposed with separate normalization schemes for order prices and volumes.”
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, unknown): “markets, the LSTM has been popular in recent years, [20], [28], [42]–[47] all utilising LSTMs to analyse financial data.”
• 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…”