PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• Anthony, M., Bartlett, P. L., & Bartlett, P. L. (1999). Neural network learning: Theoretical Foundations. Cambridge University Press.

• Azulay, A., & Weiss, Y. (2018). Why do deep convolutional networks generalize so poorly to small image transformations? Journal of Machine Learning, 20(184), 1-25. https://doi.org/10.48550/arXiv.1805.12177

• Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). On the importance of the Pearson correlation coefficient in noise reduction. IEEE Transactions on Audio, Speech, and Language Processing, 16(4), 757-765. https://doi.org/10.1109/tasl.2008.919072

• Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.

• Cartwright, T. J. (1991). Planning and chaos theory. Journal of the American Planning Association, 57(1), 44-56. https://doi.org/10.1080/01944369108975471

• Chollet, F. (2017). Deep learning with Python. Simon and Schuster.

• Cowpertwait, P. S. P., & Metcalfe, A. V. (2009) Time series data. In Introductory time series with R (pp. 1-25). Springer. https://doi.org/10.1007/978-0-387-88698-5_1

• Dempster, M. A. H., & Romahi, Y. S. (2002). Intraday FX trading: An evolutionary reinforcement learning approach. In H. Yin, N. Allinson, R. Freeman, J. Keane & S. Hubbard (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2002 (pp. 347-358). Springer. https://doi.org/10.1007/3-540-45675-9_52

• Dempster, M. A., & Leemans, V. (2006). An automated FX trading system using adaptive reinforcement learning. Expert Systems with Applications, 30(3), 543-552. https://doi.org/10.1016/j.eswa.2005.10.012

• Fang, F., Ventre, C., Basios, M., Kong, H., Kanthan, L., Li, L., Martinez-Regoband, D., & Wu, F. (2022). Cryptocurrency trading: A comprehensive survey. Financial Innovation, 8(13). https://doi.org/10.1186/s40854-021-00321-6

• Ganesh, S., Vadori, N., Xu, M., Zheng, H., Reddy, P., & Veloso, M. (2019). Reinforcement learning for market making in a multi-agent dealer market. arXiv Preprint. https://doi.org/10.48550/arXiv.1911.05892

• Graves, A. (2012). Long short-term memory. In Supervised sequence labelling with recurrent neural networks (pp. 37-45). Springer. https://doi.org/10.1007/978-3-642-24797-2_4

• Grondman, I., Busoniu, L., Lopes, G. A., & Babuska, R. (2012). A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 1291-1307. https://doi.org/10.1109/tsmcc.2012.2218595

• Gronwald, M. (2014). The economics of bitcoins - Market characteristics and price jumps. (Working Paper No. 5121). https://www.cesifo.org/DocDL/cesifo1_wp5121.pdf

• Haferkorn, M., & Diaz, J. M. Q. (2014). Seasonality and interconnectivity within cryptocurrencies - An analysis on the basis of bitcoin, litecoin and namecoin. In A. Lugmayr (Ed). International Workshop on Enterprise Applications and Services in the Finance Industry (pp. 106-120). Springer. https://doi.org/10.1007/978-3-319-28151-3_8

• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735

• Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2). https://doi.org/10.5121/ijdkp.2015.5201

• Huang, B., Huan, Y., Xu, L. D., Zheng, L., & Zou, Z. (2019). Automated trading systems statistical and machine learning methods and hardware implementation: A survey. Enterprise Information Systems, 13(1), 132-144. https://doi.org/10.1080/17517575.2018.1493145

• Huang, C. Y. (2018). Financial trading as a game: A deep reinforcement learning approach. arXiv Preprint. https://doi.org/10.48550/arXiv.1807.02787

• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.

• Jeong, G., & Kim, H. Y. (2019). 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. https://doi.org/10.1016/j.eswa.2018.09.036

• Kolm, P. N., & Ritter, G. (2019). Modern perspectives on reinforcement learning in finance. Journal of Machine Learning in Finance, 1(1).

• Konda, V. R., & Tsitsiklis, J. N. (1999). Actor-critic algorithms. In S. Solla, T. Leen & K. Müller (Eds.), NIPS’99: Proceedings of the 12th International Conference on Neural Information Processing Systems (pp. 1008-1014). MIT Press.

• Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data preprocessing for supervised leaning. International Journal of Computer and Information Engineering, 1(12), 4104-4109. https://doi.org/10.5281/zenodo.1082415

• Li, Y. (2017). Deep reinforcement learning: An overview. arXiv Preprint. https://doi.org/10.48550/arXiv.1701.07274

• Liao, S., Wang, J., Yu, R., Sato, K., & Cheng, Z. (2017). CNN for situations understanding based on sentiment analysis of twitter data. Procedia Computer Science, 111, 376-381. https://doi.org/10.1016/j.procs.2017.06.037

• Liu, X. Y., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., & Wang, C. (2020). FinRL: A deep reinforcement learning library for automated stock trading in quantitative finance. arXiv Preprint. https://doi.org/10.48550/arXiv.2011.09607

• Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN-LSTM model for gold price time-series forecasting. Neural Computing and Applications, 32, 17351-17360. https://doi.org/10.1007/s00521-020-04867-x

• Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Artificial Intelligence for Smart System Simulation, 2020, Article 6622927 https://doi.org/10.1155/2020/6622927

• Lucarelli, G., & Borrotti, M. (2019). A deep reinforcement learning approach for automated cryptocurrency trading. In J. MacIntyre, I. Maglogiannis, L. Iliadis & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations (pp. 247-258). Springer. https://doi.org/10.1007/978-3-030-19823-7_20

• Moody, J., Saffell, M., Andrew, W. L., Abu-Mostafa, Y. S., LeBaraon, B., & Weigend, A. S. (1999). Minimizing downside risk via stochastic dynamic programming. Computational Finance, 403-415.

• Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). Forecasting the equity risk premium: The role of technical indicators. Management Science, 60(7), 1772-1791. https://doi.org/10.1287/mnsc.2013.1838

• Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/tkde.2009.191

• Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. https://doi.org/10.1037/h0042519

• Sattarov, O., Muminov, A., Lee, C. W., Kang, H. K., Oh, R., Ahn, J., Oh, H. J., & Jeon, H. S. (2020). Recommending cryptocurrency trading points with deep reinforcement learning approach. Applied Sciences, 10(4), Article 1506. https://doi.org/10.3390/app10041506

• Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015). High-dimensional continuous control using generalized advantage estimation. arXiv Preprint. https://doi.org/10.48550/arXiv.1506.02438

• Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv Preprint. https://doi.org/10.48550/arXiv.1707.06347

• Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065. https://doi.org/10.1109/access.2019.2912200

• Sola, J., & Sevilla, J. (1997). Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Transactions on Nuclear Science, 44(3), 1464-1468. https://doi.org/10.1109/23.589532

• Tucnik, P. (2010). Optimization of automated trading system’s interaction with market environment. In P. Forbrig & H. Günther (Eds.), Perspectives in Business Informatics Research (pp. 55-61). Springer. https://doi.org/10.1007/978-3-642-16101-8_5

• Van Otterlo, M., & Wiering, M. (2012). Reinforcement learning and Markov decision processes. In M. Wiering & M. Van Otterlo (Eds.), Reinforcement Learning. Adaptation, Learning, and Optimization (pp. 3-42). Springer. https://doi.org/10.1007/978-3-642-27645-3_1

• Vrigazova, B. (2021). The proportion for splitting data into training and test set for the bootstrap in classification problems. Business Systems Research Journal, 12(1) 228-242. https://doi.org/10.2478/bsrj-2021-0015

• Wu, C. H., Lu, C. C., Ma, Y. F., & Lu, R. S. (2018). A new forecasting framework for bitcoin price with LSTM. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 168-175). IEEE Publishing. https://doi.org/10.1109/icdmw.2018.00032

• Xiong, Z., Liu, X. Y., Zhong, S., Yang, H., & Walid, A. (2018). Practical deep reinforcement learning approach for stock trading. arXiv Preprint. https://doi.org/10.48550/arXiv.1811.07522

• Yang, H., Liu, X. Y., Zhong, S., & Walid, A. (2020, October 15-16). Deep reinforcement learning for automated stock trading: An ensemble strategy. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1-8). ACM Publishing. https://doi.org/10.1145/3383455.3422540

• Zhang, W., Yang, Z., Shen, J., Liu, M., Huang, Y., Zhang, X., Tang, R., & Li, Z. (2021). Learning to build high-fidelity and robust environment models. In N. Oliver, F. Pérez-Cruz, S. Kramer, J. Read & J. A. Lozano (Eds.), Machine Learning and Knowledge Discovery in Databases (pp. 104-121). Springer. https://doi:10.1007/978-3-030-86486-6_7

• Zhang, Z., Zhang, Y., & Li, Z. (2018). Removing the feature correlation effect of multiplicative noise. arXiv Preprint. https://doi.org/10.48550/arXiv.1809.07023