Due to rapid changes in innovations and inventions in the world, a country can claim it has a safe and healthy economy which has a sufficient capital market and financial sector. In many countries, stock exchange market is the central core of capital market; one of the important subjects which is in the center of attention for investors in this market is how they can choose stocks and find appropriate time for selling and purchasing. In this regard there are several methods. One of the techniques of choosing stock is technical analysis. Technical Analysis is based on the amount of supply and demand. Technicians forecast the future trend of stock prices by using different graphs, historical prices and transaction volume of stocks. Forecasting in technical analysis does not mean obtaining an exact graph of future; it means finding the appropriate time for entering the market or departure, so that the return of selling and purchasing stocks in stock exchange market by using forecasting signals of technical analysis will significantly be better than return of selling and purchasing in this market without using forecasting signals of technical analysis. One of the most important instruments of technical analysis is moving average. In this paper, we investigate the efficiency of using technical analysis (moving average) in Tehran Stock Exchange Market, in three levels of total index, different industries and firm's index. Hypothesis: In this survey, we use two line techniques or simultaneous use of 10 days and 20 days Moving Average, as a pattern in technical analysis, for finding and testing the selling and buying signals. The hypotheses of the survey are: 1. When we use the forecasting signals of technical analysis on the broad indices level, the rate of return on trading in Tehran Stock Exchange is significantly higher than when we do not use these techniques. 2. When we use the forecasting signals of technical analysis on industries level, the rate of return on trading in Tehran Stock Exchange is significantly higher than when we do not use these techniques. 3. When we use the forecasting signals of technical analysis on industries level, the rate of return on trading in Tehran Stock Exchange is significantly higher than when we do not use these techniques. 4. When we use the forecasting signals of technical analysis on the broad indices (T) level, the rate of return on trading in Tehran Stock Exchange is significantly higher than the rate of return when we do not use these techniques on the industry (i). 5. When we use the forecasting signals of technical analysis on the industry (i), the rate of return on trading in Tehran Stock Exchange is significantly higher than the rate of return when we do not use these techniques on the industry (j). In this research, the beneficial use of 10 days and 20 days moving average predictor signals, the index of‍ technical analysis and type of delay time index have been studied. Moving average rule, divided the total amount of the samples into two groups (related period‍ to buying and selling symptoms) divided‍, depending on what the moving average‍ yields. If the short term moving average is higher than long term moving average‍, this is a buy signal and if the short term moving average ‍is ‍lower than long term moving average‍, this is a sales signal. Investors buy‍ when the short term moving average moves ‍from below into the long term moving average, and stop acting in market ‍until the short term moving average‍ moves from up into long term moving average. Return or profit for each buy or maintenance strategy signal and any sale or not holding strategy signal, has been calculated. Then returns from the use of technical analysis and return from not using this method are compared. Therefore, the principal aim of this study is comparing returns ‍from shares ownership and lack of shares ownership when 10 day and 20 days moving average predictor signals have buy and sales suggestions with similar value obtained using buy, maintenance, or not to use of 10-days and 20 days moving average predictor signals. In this study, it was observed that the use of 10 days and 20 days moving average predictor signals in Tehran Stock Exchange has acceptable performance in Tehran Stock Exchange broad indices level, industries and firms, so using technical analysis in Tehran Stock Exchange Market has acceptable significance in all levels. The results also identified that use of 10 days and 20 days moving average predictor signals has higher efficiency than not using this method, and efficiency of using 10 days and 20 days moving average predictor signals in Tehran Stock Exchange board indices level has higher efficiency and more significance than using technical analysis in groups (various industries) level. Also efficiency of using 10 days and 20 days moving average predictor signals in groups level is higher than efficiency of using technical analysis in the firms level and has more significant. Discussion and We conclude that by using forecasting signals of technical analysis, we can have an appropriate return from an insufficient capital market. In this study, for first time ‍Stock price data of Iran exchange were used to investigate the efficiency of using Technical analysis (10 days and 20 days moving average) in Tehran Stock Exchange, in three levels of total index, groups ‍and firms‍; It was observed that using Technical analysis‍ in Tehran Stock Exchange, at all levels, has higher efficiency than not using this method‍, and efficiency of using Technical analysis in Tehran Stock Exchange in general index level is more appropriate and has ‍more significance than using ‍technical analysis in groups level. The efficiency of using Technical analysis in industries level is ‍higher and more significant ‍than using technical analysis in firms level.

This article investigates the usefulness of applying the technical analysis to the world gold market by using daily world gold price data per ounce in the US Dollar during last 37 years and the Relative Strength Index which is a directional indicator of the technical analysis. The results of this study indicate that the use of buy and sell signals derived from this method using the Relative Strength Index Junction 50 on the world gold market over the last 37 years has been significantly beneficial.
The average yield of gold ownership in the gold market, when the relative strength index technical analyses of predictive signals, signs are sold, the value is negative. The average return on equity (or maintenance) of gold in the gold market, when relative strength index technical analysis of the predictive signals, signs are purchasing, the value is positive. This article is also a useful model to provide technical analysis of gold prices .

In this paper, we use GMDH neural network based on Genetic Algorithm to model and forecast the price of gasoline using two approaches; Deductive Method and Technical Analysis. The results of deductive method indicate that the accuracy of prediction could reach up to 96% and in technical analysis could reach up to 99%. Furthermore the comparison reveals that the GMDH neural networks model consistently outperforms the regression model used in this study.

Investment in stock exchange is one of investments to increase investors wealth. There are three various methods for investment of stock exchange: fundamental analysis, technical analysis and Buy & Hold strategy. In this research, effectiveness of using technical analysis in Tehran Stock Exchange researched by focus on several important and applied indicators. Seven valid indicators in two type of technical analysis; trend and oscilators. This research is period of 4 years from beginning of 1380 to ending of 1383 s.br. including of both Bull period (beginning of 1380 to mid of 1382) and Bear period (mid of 1382 to end of 1383) and 62 of best companies aspect of the number of trading days and with considering of trading costs. The results demonstrate that each of technical analysis indicators individually and combination of trend technical analysis indicators compared with Buy & Hold strategy cannot create high and abnormal return for their investors and chartists. But, combination of oscilator technical analysis indicators, specially relative strength index and stochastics, can create high return relative to Buy & Hold strategy significantly.

The study aims at determining whether the tools and techniques of technical analysis are applicable to the Tehran Stock Exchange. The technical analysis goes against the fulfillment of the weak form of the Efficient Market Hypothesis. In this research we have assessed one simple form of technical analysis named by Dual Simple Moving Average rule. To that end, we have used daily data for prices of the 35 firms of the Tehran Stock Exchange, covering the one-year period from 21 March 2004 to 20 March 2005. In the present investigation, the continuation of the past prices is confirmed and so the weak form of the Efficient Market Hypothesis is refused. Also our findings indicate that in the one year periods, returns mean obtained from studying DSMA both with and without considering transactions costs is equal to that achieved from the buy and hold strategy.

Generally speaking Technical Analysis Rules are a set of methods which concentrate on prices trends in order to predict the prices trend in the future. It must be mentioned that Technical Analysis Rules are many and varied and in order to make effective outcome, one must select the most useful methods which are depends on the market situation. In this paper, the profitability of 46 popular technical trading rules such as “moving average”, “trading range breakout”, “Bollinger bands”, “stochastic”, “RSI” and “MACD” in 22 company in Tehran Stock Exchange (TSE) from 1995-2005 have been evaluated. The small nature, short-selling constraints, lack of analyst coverage, and loose insider trading regulation suggest that the TSE equity market may be has less efficient. This cause that technical analysis has more potential to be profitable. To overcome the shortfalls of the t-test methodology within our test sample, the significance of the technical trading rules have been analyzed by using the bootstrap methodology with Random Walk null models. The numerical results indicate that technical trading rules have predictive ability in the mentioned companies. This result rejects the null hypothesis that the returns to be earned from applying trading rules values are equal to those achieved from a naive buy and hold strategy. This research also illustrates that among all 46 Technical Analysis Rules, “moving average” and “stochastic” are the most effective trading rules.