A HYBRID INTELLIGENT MODEL USING TECHNICAL AND FUNDAMENTAL ANALYSIS TO FORECASTING STOCK PRICE INDEX

作者：Shavandi Hassan^*; Alizadeh Parisa

来源：Economic Computation and Economic Cybernetics Studies and Research, 2010, 44(2): 95-112.

• Forecasting
• Stock price index
• Technical analysis
• Fundamental analysis
• Neural networks
• Fuzzy inference system

In this paper we develop a hybrid forecasting model which combines artificial intelligence and technical analysis to predict short-term stock price index. The results show that using technical indices as neural network's inputs yields good performance in forecasting short-term prices, but this model cannot predict long-term prices well. To overcome this shortcoming we have exploited a fuzzy inference system based on analyzing the historical effects of macro economic variables on the stock markets' indices. Our forecasting models differ from the other ones in two main aspects: the first one is analyzing previous macroeconomics trends in order to build a Mamdani FIS and the second one is providing two different techniques for short-term and long-term predictions.
These models allow decision makers to forecast prices by using minimum data and calculations. The good performance of the proposed model is confirmed by real stock market data.

• 出版日期2010