Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Abstract

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites

“… Optimized ANN model with crow search algorithm The MLP has many drawbacks such as slow convergence rate and stuck into the local minima. The training process and the convergence rate of MLP become more accurate when it is trained using a metaheuristic approaches like PSO 38 , 46 – 48 and GA 33 , 49 . However, CSA has been never investigated in training ANN.…”

Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

“…Deep learning scientists have often sought to improve the model's efficiency and loss function value by the model's training epochs. Stochastic gradient descent (SGD) is one of these approaches that give a single learning rate for all weight updates and does not modify the learning rate throughout the training 35 . Nevertheless, this method is not effective for the training model due to frequent fluctuations; it will keep overshooting near to the desired exact minima and very time‐consuming to converge to the correct network weights, which is inapplicable for online battery RUL prediction.…”

A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators

“…The use of ensemble models reported by some researchers found that ensemble models provide better performance than a single predictive model [40,67,68]. According to Fatih et al [69], there still is little research that has been done to predict the stock market using ensemble models.…”

“…They used random forest and XGBoost classifier, and their study revealed ensemble models perform better if the proper combination of technical indicators is used as input features for a model. According to Ernest et al [69], the ensemble machine learning models provide superior results in comparison with any individual machine learning model. In their study, they focused on the tree-based ensemble models, and their models were trained with three different stock exchange datasets.…”