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A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown.<\/jats:p>","DOI":"10.1155\/2020\/8285149","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T23:32:07Z","timestamp":1593473527000},"page":"1-16","source":"Crossref","is-referenced-by-count":11,"title":["Modeling Traders\u2019 Behavior with Deep Learning and Machine Learning Methods: Evidence from BIST 100 Index"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1177-1711","authenticated-orcid":true,"given":"Afan","family":"Hasan","sequence":"first","affiliation":[{"name":"Sabanci School of Management, Sabanci University, Istanbul, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9553-669X","authenticated-orcid":true,"given":"Oya","family":"Kal\u0131ps\u0131z","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Yildiz Technical 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