DEEP CONTINUOUS-TIME MODELS IN NIGERIAN STOCK EXCHANGE SECTOR

David O. Oyewola, Aye Patrick Olabanji, Terrang.A.U and Jayeola Dare

Abstract

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

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