High‐Frequency Trading and Financial Time‐Series Prediction with Spiking Neural Networks

Wilmott ◽  
2021 ◽  
Vol 2021 (113) ◽  
pp. 18-33
Author(s):  
Kang Gao ◽  
Wayne Luk ◽  
Stephen Weston
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Spiking Neural Networks ◽  
High Frequency Trading ◽  
Financial Time

scholarly journals Financial Time Series Prediction Using Spiking Neural Networks

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e103656 ◽  
Author(s):  
David Reid ◽  
Abir Jaafar Hussain ◽  
Hissam Tawfik
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Spiking Neural Networks ◽  
Financial Time

scholarly journals Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

2020 ◽  
Vol 12 (6) ◽  
pp. 21-32
Author(s):  
Muhammad Zulqarnain ◽  
◽  
Rozaida Ghazali ◽  
Muhammad Ghulam Ghouse ◽  
Yana Mazwin Mohmad Hassim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time ◽  
Data Mining Approach ◽  
Learning Tasks ◽  
Financial Prices ◽  
Stock Indexes

Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.

Evolutionary-morphological learning machines for high-frequency financial time series prediction

2018 ◽  
Vol 42 ◽  
pp. 1-15 ◽  
Author(s):  
Ricardo de A. Araújo ◽  
Nadia Nedjah ◽  
José M. de Seixas ◽  
Adriano L.I. Oliveira ◽  
Silvio R. de L. Meira
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Learning Machines ◽  
Financial Time

Financial time series prediction using polynomial pipelined neural networks

2008 ◽  
Vol 35 (3) ◽  
pp. 1186-1199 ◽  
Author(s):  
Abir Jaafar Hussain ◽  
Adam Knowles ◽  
Paulo J.G. Lisboa ◽  
Wael El-Deredy
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Financial Time
Sign in / Sign up

Export Citation Format

Share Document