Abstract
Although the vast majority of fundamental analysts believe that technical analysts’ estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. 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.
Highlights
Investors are thought to be one of the most important drivers of volatility in stock prices as a result of repetitive patterned trading behavior. is leads to the idea that stock prices are following the trends that form the basis of technical analysis [2]
We investigate the benefits of Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classifiers in making decisions on market direction
In order to compare the performance of classification techniques according to the prepared dataset, four different machine learning methods were used in three different time periods and bidirectional operations were tested on six different threshold values, resulting in a total of 144 aspects
Summary
Investors are thought to be one of the most important drivers of volatility in stock prices as a result of repetitive patterned trading behavior. is leads to the idea that stock prices are following the trends that form the basis of technical analysis [2]. Patterned trading behavior does not seem logical to some, it is known that investors are using it to predict market trends and predict future price movements effectively. Technical and quantitative analysis uses mathematical and statistical tools to determine the most appropriate time for investors to initiate and close their orders, which means instructions for buying or selling on a trading venue. While these traditional approaches serve to some extent their purpose, new techniques emerging in Complexity computational intelligence such as machine learning and data mining have been used to analyze financial information. While the vast majority of previous financial engineering research focuses on complex computational models such as Neural Networks [17,18,19,20] and Support Vector Machines [21, 22], there is research based on new deep learning models that yield better results in nonfinancial applications [23, 24]
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