David O. Oyewola, Aye Patrick Olabanji, Terrang.A.U and Jayeola Dare
An ensemble continuous time model for predicting Nigerian stock market is proposed in this paper. The proposed technique is the integration of different continuous time models such as Stochastic Differential Equation (SDE), Geometric Brownian Motion (GBM) and Constant Elastic Variance (CEV) with Recurrent Neural Network (RNN). To validate the effectiveness and robustness of the method, it was tested using data from eleven sectors of Nigerian stock exchange which comprises of closing price stock from January 2017 to December 2019. The past returns were also tested using the aforementioned datasets, and the results show that past returns have predictive power in predicting future performance using Welch and F-statistics at 95% confidence intervals. The results indicated that Deep Continuous-time model is a promising algorithm that have the capacity to effectively predict the Nigerian stock market. Read full PDF
Keywords: stochastic differential equation, geometric Brownian motion, constant elastic variance, recurrent neural network, stock
 Rodriguez, J. STAR (2005): Forecasting performance on Spanish Ibex-35stock index, Journal of Empirical Finance, 12(2005), 490-509
 Sabaithip Boonpeng, Piyasak Jeatrakul,(2016). Decision Support System for Investing in Stock Market by using OAANeural Network. 8th International Conference on Advanced Computational Intelligence Chiang Mai, Thailand; February 14-16.
 David Oyewola, Emmanuel Gbenga Dada, Ezekiel Olaoluwa Omole and K. A. Al-Mustapha, (2019). Predicting Nigerian Stock Returns using Technical Analysis and Machine Learning, European Journal of Electrical and Computer Engineering, Vol. 3, 1-8.
 Oussama Lachiheb, Mohamed Salah Gouider (2018). A hierarchical Deep neural network Design for stock returns prediction, International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2018, 3-5 September 2018, Belgrade, Serbia, Procedia Computer Science 126 (2018)264-272.
 Osei Antwi (2017). Stochastic Modeling of Stock Price Behavior on Ghana Stock Exchange, International Journal of System Science and Applied Mathematics, 2, 116-125.
 Read, J., Bifet, A., Pfahringer, B., and Holmes, G (2012). Batch-incremental versus instance- incremental learning in dynamic and evolving data Advances in Intelligent Data Analysis XI pp. 313-323 (2012): Springer.
 Scott M, Niranjan M, Melvin D, Prager R (1998). Maximum realisable performance: a principled method for enhancing performance by using multiple classifiers. In: Proceedings of the British MachineVision Conference. David O.Oyewola et al. / Journal of Science and Technology Research 2(1) 2020 pp. 106-120 120
 S. M. Shamsuddin, S. H. Jaaman, and M. Darus (2009).Neuro-Rough trading rules for Mining Kuala Lumpur composite index,”European Journal of Scientific Research, vol. 28, no. 2, pp. 278–286.
 Yan-Mi, David C. Yen, Yuah-Chiao Lin, and Chih-Fong Tsai (2011). Predicting stock returns by classifier ensembles, Applied Soft Computing, vol. 11, no. 2,pp. 2452-2459.
 Yuqing Dai, Yuning Zhang (2013). Machine Learning in Stock Price Trend Forecasting. Stanford University (2013).
 Zhang, J. and Xiao, X (2000). Predicting chaotic time-series using recurrent neural network, Chinese Physics Letter, 17(2000), 88-90.
 Zliobaite, I., Bifet, A., Pfahringer, B., and Holmes, G (2014). Active learning with drifting Streaming data. IEEE transactions on neural networks and learning systems, 25(2014), 27-39.
 Avci, E (2007). Forecasting daily and seasonal returns of the ISE-100 Index with neural network models, Dogus University Journal, 8(2007), 128-42.
 Antwi O. (2017). Stochastic Modeling of Stock Price Behavior on Ghana Stock Exchange, International Journal of System Science and Applied Mathematics, 2, 116-125.