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Time series processes can be classified to three models, linear models, stochastic
models and chaotic models. Based on these classification the linear models are
forecastable, the stochastic models are unforecastable and the chaotic models are
semi forecastaable. The previouse researches in the modeling and forecasting of the
stock price usually try to prove that, the fluctuations of the share prices in Tehran
Stock Exchange are not random walks in spite of the existance similarity to the
random walks. Indeed the market has a chaotic behavior. This means that, the
Efficient Market Hypothesis (EMH) is failed. Therefore by using a complex and
powerfull models such as artificial neural networks, one can forecast stock prices in
tehran stock merket. This paper proposed another approach to modeling and
forecasting of the share price. This approach is based on the Stochastic Differential
Equations. The modeling is based on the Black- Scholes pricing model. Comparison
the simulation result with the linear ARIMA model, indicates that the proposed
structrure, provides an accurate next step and the long term share prices and daily
returns forecasting.
JEL Classification: C5, C53.