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Deep learning-based investment strategy: technical indicator clustering and residual blocks

2021    Maratkhan, Anuar; Ilyassov, Ibrakhim; Aitzhanov, Madiyar; Demirci, M. Fatih; Ozbayoglu, A. Murat

Financial forecasting using computational intelligence nowadays remains a hot topic. Recent improvements in deep neural networks allow us to predict financial market behavior better than traditional machine learning approaches. In this paper, we propose three novel deep learning-based financial forecasting frameworks, all of which considerably outperform existing approaches, yielding a much better annual financial return on DOW-30 stocks and Exchange-Traded Funds (ETFs) tested between January 1, 2007, and December 31, 2016. The first framework Convolutional Neural Networks with Technical Indicator Clustering (CNN-TIC) creates images with multiple channels corresponding to the technical indicator clusters and employs the take profit and stop loss techniques to obtain a superior annual financial return. The second model Evolutionary Optimized CNN-TIC (EO-CNN-TIC) computes the optimal values in the take profit and stop loss techniques using one of the recently created evolutionary optimization algorithms, Cuckoo Search. Finally, the third model Residual Network with Technical Analysis (ResNet-TA) applies residual blocks to the convolutional part of the neural network architecture to extract more useful features from deeper layers. Both CNN-TIC and EO-CNN-TIC are based on clustering the technical indicators by their similarity in behavior and creating separate five distinct images based on the five clusters, while ResNet-TA takes advantage of going deeper in the network with residual blocks. All three models further improve their performances by hyperparameter tuning. On DOW-30 stocks, we were able to achieve annual returns of 20.45% , 29.54% , and 36.70% for CNN-TIC, EO-CNN-TIC, and ResNet-TA, whereas for ETFs, 16.56% , 19.20% , and 32.09% annual returns were observed, respectively. We conclude with future work that can be done in order to further improve the computational and financial performances of the models.