TY - GEN
T1 - Financial Forecasting using Deep Learning with an Optimized Trading Strategy
AU - Maratkhan, Anuar
AU - Ilyassov, Ibrakhim
AU - Aitzhanov, Madiyar
AU - Demirci, M. Fatih
AU - Ozbayoglu, Murat
PY - 2019/6
Y1 - 2019/6
N2 - Financial forecasting using computational intelligence nowadays remains a hot topic. Recent improvements in deep neural networks allow us to predict financial market behavior. In our work we first implement a novel approach of [1], which converts financial time-series data to 2-D images and then feeds the generated images to a convolutional neural network as an input. We then hypothesize that the performance of the model can be improved using different techniques. Specifically, in our work, we improve the computational and financial performance of the previous approach by 1) fine-tuning the neural network hyperparameters, 2) creating images with 5 channels corresponding to indicator clusters, 3) improving financial evaluation using take profit and stop loss techniques, 4) evolutionary optimized parameters for trading strategy. The results of this study show that the above-mentioned strategies improve the model considerably. We conclude with future work that can be done in order to further improve the computational and financial performance of the model.
AB - Financial forecasting using computational intelligence nowadays remains a hot topic. Recent improvements in deep neural networks allow us to predict financial market behavior. In our work we first implement a novel approach of [1], which converts financial time-series data to 2-D images and then feeds the generated images to a convolutional neural network as an input. We then hypothesize that the performance of the model can be improved using different techniques. Specifically, in our work, we improve the computational and financial performance of the previous approach by 1) fine-tuning the neural network hyperparameters, 2) creating images with 5 channels corresponding to indicator clusters, 3) improving financial evaluation using take profit and stop loss techniques, 4) evolutionary optimized parameters for trading strategy. The results of this study show that the above-mentioned strategies improve the model considerably. We conclude with future work that can be done in order to further improve the computational and financial performance of the model.
KW - convolutional neural networks
KW - cuckoo search
KW - deep learning
KW - financial forecasting
KW - time-series classification
UR - http://www.scopus.com/inward/record.url?scp=85071294580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071294580&partnerID=8YFLogxK
U2 - 10.1109/CEC.2019.8789932
DO - 10.1109/CEC.2019.8789932
M3 - Conference contribution
AN - SCOPUS:85071294580
T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
SP - 838
EP - 844
BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019
Y2 - 10 June 2019 through 13 June 2019
ER -