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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 39700 (01)
- University: Sharif University of Technology
- Department: Industrial Engineering
- Advisor(s): Shavandi, Hassan
- Abstract:
- This research proposes a novel hybrid method for stock price index forecasting relying on technical analysis, the fundamental analysis of capital market, neural networks technology, and fuzzy logic. The new method shows a good performance comparing complicated and time-consuming forecasting methods. Several factors influence stock price; for example, an important one is the previous trend of stock price. Fortunately, by developing technical analysis and introducing the various indices of this method the maximum use of historical data is made in order to forecast future prices. Another kind of factors is macro-economic variables that their influence on the long-term trends of the stock markets has been completely confirmed. In the proposed model for forecasting stock index price four technical indices (RSI, MCDA, MA5, and ROC) and two fundamental variables (monthly crude oil price and monthly unemployment rate) has been used to forecaste NYSE Composite index. First, technical indices are used as neural network’s inputs to forecast short-term prices. At the next step, in order to increase long-term forecasting precision two fundamental variables will be analyzed by using a mamdani fuzzy system and long-term price will be forecasted. It wil be shown that in long-term forecasting fuzzy inference system has better performance that neural network.
- Keywords:
- Forecasting ; Stock Price ; Neural Network ; Fuzzy Logic ; Technical Analysis ; Fundamental Analysis
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