Ecer Fatih, Ardabili Sina, Band Shahab S, Mosavi Amir
Department of Business Administration, Afyon Kocatepe University, Afyonkarahisar 03030, Turkey.
Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran.
Entropy (Basel). 2020 Oct 31;22(11):1239. doi: 10.3390/e22111239.
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.
预测股票市场（SM）趋势是研究人员、投资者和交易员非常感兴趣的一个问题，因为成功预测股票市场的走势可能会带来各种好处。由于历史数据具有相当的非线性特征，准确估计股票市场走势是一个颇具挑战性的问题。本研究的目的是提出一种新颖的机器学习（ML）模型，用于预测伊斯坦布尔证券交易所（BIST）100指数的走势。在两种情况下，分别采用多层感知器 - 遗传算法（MLP - GA）和多层感知器 - 粒子群优化算法（MLP - PSO）进行建模，将双曲正切函数Tanh（x）和默认的高斯函数作为输出函数。本研究使用的历史金融时间序列数据来自1996年至2020年，包含九个技术指标。使用均方根误差（RMSE）、平均绝对百分比误差（MAPE）和相关系数值来评估结果，以比较所开发模型的准确性和性能。基于结果，与默认的高斯函数相比，将Tanh（x）作为输出函数显著提高了模型的准确性。种群大小为125的MLP - PSO，其次是种群大小为50的MLP - GA，在测试中提供了更高的准确性，其RMSE分别为0.732583和0.733063，MAPE分别为28.16%、29.09%，相关系数分别为0.694和0.695。根据结果，使用混合机器学习方法可以成功提高预测准确性。
