The Application of Technical Analysis in Stock Price Forecasting: Non-linear Probability Models and Artificial Neural Networks

Stock price forecasting is one of the main challenges in stock market which investors and analysts are faced with. To forecast the future prices and future trend, different tools have been used among which we can refer to technical and fundamental analysis. It is noticed that technical analysis has good performance in short-time forecasting. Hence, in this paper, technical analysis has been used to estimate the probability function of stock prices. To forecast the direction of stock price movement in the following day, artificial neural networks (ANN), Logit, Probit, and extreme value models are utilized. To evaluate the performance of proposed models, daily values of Iran Khodro company stock are considered as a real case study. The nonparametric test of equality of ratios shows that the difference between the forecasting results of different models is not statistically significant. However, according to forecasting error criterion, the Probit model is more efficient than other mentioned models

Application of Value at Risk in Risk Management of Oil Revenue in Iran

Crude oil price risk is crucial for oil exporting countries. Consequently, developing a risk hedging mechanism has great importance for these countries. Given that Value at Risk (VaR) is one of the most powerful tools for evaluating price risk, this paper has tried to design a mechanism for risk management of Iranian oil revenues using the VaR measure. In this regard, Autoregressive Conditional Heteroskedasticity models including GARCH, CGARCH and EGARCH with different destiny distribution functions are utilized for calculating VaR of OPEC crude oil price in the period of 6 October 2005 to 29 August 2015. The results show that CGARCH model with t-student distribution outperforms the other methods in terms of forecast error measures. The implementation of CGARCH model with using the data of Iranian oil production in 2014 reveals that the proposed model can lead to a significant surplus income