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A Hybrid Stock Trading Strategy and Stock Portfolio Creation on the Stock Exchange Using a Combination of New Data Mining Techniques and Technical Analysis

Kamroo, Saeed | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 51940 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): khedmati, Majed
  7. Abstract:
  8. By expanding the use of IT and public access to financial markets, the number of players in this area has increased and the nonlinearity of the market has become more complex. Hence, investors need specific strategies that can make profitable investment by determining the time of purchase and sale of stocks. The purpose of this research is to provide a stock trading framework for strategic portfolio management. This framework uses daily values of 18 indicators of technical analysis as features and daily trading signals as data labels for training various machine learning models, such as support vector regression, k nearest neighbors, decision tree, artificial neural network and random forest. Finally, the trained model, using technical indicators of each day, determines one of the categories of buy, hold, sell or not buying for a stock. Customized strategies have been used to build trading signals. Various models are made using different signals, applying or not applying reduce dimensions of data and various techniques of machine learning. To compare these models, we use the stock information of the five Nasdaq companies. Then they were used to build 60 stock portfolios. The results showed that 53 portfolios have gained more profit than stock portfolios with buying and holding stocks strategy, and 20 stock portfolios have more than doubled profit of the old portfolio. In addition, it has been shown that the two factors studied, the machine learning technique and the signals used in study are effective in the portfolio's profitability
  9. Keywords:
  10. Classification ; Machine Learning ; Stock Trading Strategy ; Stock Portfolio ; Technical Analysis

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