WHEN MACHINES LEARN TECHNICAL ANALYSIS: AN APPLICATION ON TECHNICAL ANALYSIS WITH MACHINE LEARNING IN BORSA ISTANBUL

There are two approaches to analyzing the value of a stock in financial markets: fundamental analysis and technical analysis. While fundamental analysis focuses on finding the intrinsic value of a stock based on a company's financial condition and current market conditions, technical analysis focuses on identifying trading signals in patterns by examining historical price behavior and statistics. Although technical analysis, which is based on the assumption that past price movements can be an indicator for future price movements, has a predefined set of rules, the interpretation of the results is closely related to the experience of the analyst. Therefore, the interpretive part of technical analysis has a subjective dimension. This subjective dimension and predefined set of rules indicate that machine learning methods with experience-based learning logic can be an important tool in identifying trading signals or predicting price movements. The aim of this study is to investigate the potential use of machine learning algorithms that use technical analysis indicators of stocks traded in Borsa Istanbul as input to predict trading signals and price movements. In the study, technical analysis indicators are analyzed with models based on machine learning methods and the results are compared. The findings show that the addition of machine learning methods to technical analysis strategies increases the predictive power of trading signals and price movements.