On the Use of Technical Analysis Indicators for Stock Market Price Movement Direction Prediction
| Autor: | Ishak Aykurt, Mehmet S. Aktas, Ramazan Faruk Oguz, Yasin Uygun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Decision support system
Computer science Data stream mining Movement (music) 0211 other engineering and technologies Stock market price 02 engineering and technology Stock price Technical analysis Technical indicator 0202 electrical engineering electronic engineering information engineering Econometrics 020201 artificial intelligence & image processing Time series 021101 geological & geomatics engineering |
| Zdroj: | SIU |
| Popis: | Technical indicators are algorithms that take financial time series data as input and predict price movement directions based on mathematical calculations. Technical analysts can predict the stock price trends by interpreting the results of different technical indicators. In this study, we investigate the prediction of price movement directions based on the use of technical indicators in learning algorithms. We explore the technical indicators that provide the most successful prediction when they are used together in learning algorithms. In this paper, we investigate the technical indicators that lead to the most successful price movement direction prediction. To do this, we explore all possible combinations of indicators with various machine learning algorithms. Here, a decision support system is proposed to predict price movement direction on financial time series data by using technical indicators in machine learning algorithms. |
| Databáze: | OpenAIRE |
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