Almahdi, S., & Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. *Expert Systems with Applications, 87*, 267–279.

Article
Google Scholar

Baek, Y., & Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. *Expert Systems with Applications, 113*, 457–480.

Article
Google Scholar

Bao, W., Yue, J., & Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short-term memory. *PLoS One, 12*(7), e0180944.

Article
Google Scholar

Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and risk management in the credit card industry. *Journal of Banking & Finance, 72*, 218–239.

Article
Google Scholar

Cavalcante, R. C., Brasileiro, R. C., Souza, V. L. F., Nobrega, J. P., & Oliveira, A. L. I. (2016). Computational intelligence and financial markets: A survey and future directions. *Expert System with Application, 55*, 194–211.

Article
Google Scholar

Chai, J. Y., & Li, A. M. (2019). Deep learning in natural language processing: A state-of-the-art survey. In *The proceeding of the 2019 international conference on machine learning and cybernetics* (pp. 535–540). Japan: Kobe.

Google Scholar

Chai, J. Y., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. *Expert Systems with Applications, 40*(10), 3872–3885.

Article
Google Scholar

Chai, J. Y., & Ngai, E. W. T. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. *Expert Systems with Applications, 140*, 112903. https://doi.org/10.1016/j.eswa.2019.112903.

Article
Google Scholar

Chakraborty, S. (2019). *Deep reinforcement learning in financial markets* Retrieved from https://arxiv.org/pdf/1907.04373.pdf. Accessed 04 Apr 2020.

Google Scholar

Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., & Vlachogiannakis, E. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. *Expert Systems with Applications, 112*, 353–371.

Article
Google Scholar

Chen, C. T., Chen, A. P., & Huang, S. H. (2018a). Cloning strategies from trading records using agent-based reinforcement learning algorithm. In *The proceeding of IEEE international conference on agents* (pp. 34–37).

Google Scholar

Chen, H., Xiao, K., Sun, J., & Wu, S. (2017). A double-layer neural network framework for high-frequency forecasting. *ACM Transactions on Management Information Systems, 7*(4), 11.

Article
Google Scholar

Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., & Stanley, H. E. (2018b). Which artificial intelligence algorithm better predicts the Chinese stock market? *IEEE Access, 6*, 48625–48633.

Article
Google Scholar

Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. *Expert Systems with Applications, 83*, 187–205.

Article
Google Scholar

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. (2011). Natural language processing (almost) from scratch. *Journal of Machine Learning Research, 12*, 2493–2537.

Google Scholar

Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep direct reinforcement learning for financial signal representation and trading. *IEEE Transactions on Neural Networks and Learning Systems, 28*(3), 653–664.

Article
Google Scholar

Dingli, A., & Fournier, K. S. (2017). Financial time series forecasting—A machine learning approach. *International Journal of Machine Learning and Computing, 4*, 11–27.

Google Scholar

Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. *IEEE Transactions on Image Processing, 15*(12), 3736–3745.

Article
Google Scholar

Feuerriegel, S., & Prendinger, H. (2016). News-based trading strategies. *Decision Support Systems, 90*, 65–74.

Article
Google Scholar

Fischer, T., & Krauss, C. (2017). Deep learning with long short-term memory networks for financial market predictions. *European Journal of Operational Research, 270*(2), 654–669.

Article
Google Scholar

Galeshchuk, S., & Mukherjee, S. (2017). Deep networks for predicting the direction of change in foreign exchange rates. *Intelligent Systems in Accounting, Finance and Maangement, 24*(4), 100–110.

Article
Google Scholar

Gunduz, H., Yaslan, Y., & Cataltepe, Z. (2017). Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations. *Knowledge-Based Systems, 137*, 138–148.

Article
Google Scholar

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. *Neurocomputing, 187*, 27–48.

Article
Google Scholar

Han, J., Jentzen, A., & Weinan, E. (2018). Solving high-dimensional partial differential equations using deep learning. *The proceedings of the National Academy of Sciences of the United States of America (PNAS)*; 8505–10).

Hernandez, J., & Abad, A. G. (2018). Learning from multivariate discrete sequential data using a restricted Boltzmann machine model. In *The proceeding of IEEE 1st Colombian conference on applications in computational intelligence (ColCACI)* (pp. 1–6).

Google Scholar

Hsu, P. Y., Chou, C., Huang, S. H., & Chen, A. P. (2018). A market making quotation strategy based on dual deep learning agents for option pricing and bid-ask spread estimation. *The proceeding of IEEE international conference on agents* (pp. 99–104).

Jeong, G., & Kim, H. Y. (2018). Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies and transfer learning. *Expert Systems with Applications, 117*, 125–138.

Article
Google Scholar

Jiang, X., Pan, S., Jiang, J., & Long, G. (2018). Cross-domain deep learning approach for multiple financial market predictions. *The proceeding of international joint conference on neural networks* (pp. 1–8).

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P. E., Guelton, L. H., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. *Expert Systems with Applications, 100*, 234–245.

Article
Google Scholar

Kim, H. Y., & Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. *Expert Systems with Applications, 103*, 25–37.

Article
Google Scholar

Krausa, M., & Feuerriegel, S. (2017). *Decision support from financial disclosures with deep neural networks and transfer learning* Retrieved from https://arxiv.org/pdf/1710.03954.pdf Accessed 04 Apr 2020.

Book
Google Scholar

Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P500. *European Journal of Operational Research, 259*(2), 689–702.

Article
Google Scholar

Martinez-Miranda, E., McBurney, P., & Howard, M. J. W. (2016). Learning unfair trading: A market manipulation analysis from the reinforcement learning perspective. In *The proceeding of 2016 IEEE conference on evolving and adaptive intelligent systems (EAIS)* (pp. 103–109).

Chapter
Google Scholar

Matsubara, T., Akita, R., & Uehara, K. (2018). Stock price prediction by deep neural generative model of news articles. *IEICE Transactions on Information and Systems, 4*, 901–908.

Article
Google Scholar

Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2017). Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. *IEEE Access, 6*, 55392–55404.

Article
Google Scholar

Ravi, V., Pradeepkumar, D., & Deb, K. (2017). Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. *Swarm and Evolutionary Computation, 36*, 136–149.

Article
Google Scholar

Rönnqvist, S., & Sarlin, P. (2017). Bank distress in the news describing events through deep learning. *Neurocomputing, 264*(15), 57–70.

Article
Google Scholar

Sehgal, N., & Pandey, K. K. (2015). Artificial intelligence methods for oil price forecasting: A review and evaluation. *Energy System, 6*, 479–506.

Article
Google Scholar

Sevim, C., Oztekin, A., Bali, O., Gumus, S., & Guresen, E. (2014). Developing an early warning system to predict currency crises. *European Journal of Operational Research, 237*(3), 1095–1104.

Article
Google Scholar

Sezer, O. B., Ozbayoglu, M., & Gogdu, E. (2017). A deep neural-network-based stock trading system based on evolutionary optimized technical analysis parameters. *Procedia Computer Science, 114*, 473–480.

Article
Google Scholar

Shen, F., Chao, J., & Zhao, J. (2015). Forecasting exchange rate using deep belief networks and conjugate gradient method. *Neurocomputing, 167*, 243–253.

Article
Google Scholar

Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. *Multimedia Tools Application, 76*, 18569–18584.

Article
Google Scholar

Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018). Big data: Deep learning for financial sentiment analysis. *Journal of Big Data, 5*(3), 1–25.

Google Scholar

Song, Q., Liu, A., & Yang, S. Y. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. *Neurocomputing, 264*, 20–28.

Article
Google Scholar

Tadaaki, H. (2018). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. *Expert Systems with Applications, 117*, 287–299.

Google Scholar

Wang, C., Han, D., Liu, Q., & Luo, S. (2019). A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM. *IEEE Access, 7*, 2161–2167.

Article
Google Scholar

Yan, H., & Ouyang, H. (2017). Financial time series prediction based on deep learning. *Wireless Personal Communications, 102*, 683–700.

Article
Google Scholar

Zhang, J., & Maringer, D. (2015). Using a genetic algorithm to improve recurrent reinforcement learning for equity trading. *Computational Economics, 47*, 551–567.

Article
Google Scholar

Zheng, J., Fu, X., & Zhang, G. (2017). Research on exchange rate forecasting based on a deep belief network. *Neural Computing and Application, 31*, 573–582.

Article
Google Scholar

Zhu, B., Yang, W., Wang, H., & Yuan, Y. (2018). A hybrid deep learning model for consumer credit scoring. In *The proceeding of international conference on artificial intelligence and big data* (pp. 205–208).

Google Scholar