A new auditory algorithm in stock market prediction on oil and gas sector in Nigerian stock exchange

Abstract

Stock market prediction is the process of forecasting future prices of stocks. Stock market prediction is a challenging process as a result of uncertainties that influence the market change of price. This paper proposes a nature-inspired algorithm, called Auditory Algorithm (AA), which follows the pathway of the auditory system like that of the human ear. The performance of AA is compared with that of high performance machine learning algorithms and continuous-time stochastic process. The machine learning algorithms used in this paper are Logistic Regression (LR), Support Vector Machine (SVM), Feed forward neural network (FFN) and Recurrent Neural Network (RNN) while continuous-time models such as Stochastic Differential Equation (SDE) and Geometric Brownian Motion (GBM) are also used. The results show that the overall performance of AA is superior to that of other algorithms compared in this paper, as it drastically reduced the forecast error to the barest minimum.