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Stock market timing is a very difficult task because of the complexity of the market. Since there are various factors affecting the market and therefore it is not a simple task to predict future stock price and its trend. This paper aims to apply advanced tools and algorithms such as the artificial neural networks (ANN) to model nonlinear processes and predict future stock price and its trend. More specifically, this study explores the abilities of the ANN to enhance the effectiveness of the technical analysis indicators to predict stock trend signals. Using a sample of 50 companies in the Tehran Stock Exchange (TSE), the results indicate that the ANN is capable to predict the direction of the short term movement in the future stock price. After considering the transaction costs, the results confirm that there is not significant difference among the returns gained from the ANN method, buy and hold strategy, and the most profitable technical indicators in the market when the trend is increasing. While, the ANN model yields higher returns compared to buy and hold strategy in the market when the trend is decreasing. Nevertheless, in the case of decreasing trend, the finding confirms the trend indicators (moving averages) achieve the highest returns.

Keywords: Stock Market Timing, Artificial Neural Networks, technical analysis, Technical Indicators, buy and hold strategy

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