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Abstract：
In this study, it is aimed to compare the performances of the algorithms by predicting the movement directions of stock market indexes in developed countries by employing machine learning algorithms (MLMs) and determining the best estimation algorithm. For this purpose, the movement directions of indexes such as the NYSE 100 (the USA), NIKKEI 225 (Japan), FTSE 100 (the UK), CAC 40 (France), DAX 30 (Germany), FTSE MIB (Italy), and TSX (Canada) were estimated by employing the decision tree, random forest k-nearest neighbor, naive Bayes, logistic regression, support vector machines and artificial neural network algorithms. According to the results obtained, artificial neural networks were found to be the best algorithm for NYSE 100, FTSE 100, DAX 30 and FTSE MIB indices, while logistic regression was determined to be the best algorithm for the NIKKEI 225, CAC 40, and TSX indices. The artificial neural networks, which exhibited the highest average prediction performance, have been determined as the best prediction algorithm for the stock market indices of developed countries. It was also noted that artificial neural networks, logistic regression, and support vector machines algorithms were capable of predicting the directional movements of all indices with an accuracy rate of over 70 %.
摘要：
在这项研究中,它旨在通过使用机器学习算法（MLM）预测发达国家股市指数的运动方向并确定最佳估计算法来比较算法的性能。为此,纽约证券交易所100指数(美国)等指数的运动方向，NIKKEI225(日本),FTSE100（英国），CAC40(法国),DAX30(德国),FTSEMIB（意大利），和TSX(加拿大)通过使用决策树进行估计，随机森林k-近邻，天真的贝叶斯,逻辑回归，支持向量机和人工神经网络算法。根据获得的结果，人工神经网络被发现是纽约证券交易所100、FTSE100、DAX30和FTSEMIB指数的最佳算法，而逻辑回归被确定为NIKKEI225，CAC40和TSX指数的最佳算法。人工神经网络，表现出最高的平均预测性能，已被确定为发达国家股市指数的最佳预测算法。人们还指出，人工神经网络，逻辑回归，和支持向量机算法能够预测所有指标的方向运动，准确率超过70％。
