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首页> 外文期刊>Trends in Applied Sciences Research >Stock Price Prediction of the Most Profitable Stock Exchange in the Asia During the Global Financial Crisis: A Comparative Study of Tehran Stock Exchange
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Stock Price Prediction of the Most Profitable Stock Exchange in the Asia During the Global Financial Crisis: A Comparative Study of Tehran Stock Exchange

机译：全球金融危机期间亚洲最赚钱的证券交易所的股价预测：德黑兰证券交易所的比较研究

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摘要

The purpose of this study is to provide a useful model for predicting the stock prices of companies listed in the Tehran Stock Exchange (TSE) during the Global Financial Crisis (GFC). Linear and exponential regression method and Artificial Neural Networks (ANNs) were used for this purpose. Then a comparison was done between the methods to determine the most effective of them for predicting the stock prices in the TSE. In this study, the stock prices were modelled by using the variables of the growth rate of industrial products, companies' net assets, inflation, oil prices, earning per share and price ratio of dividends. The study uses a body of data from 250 companies in the years, 2008-2011. The results showed that the correlation coefficients for the linear and exponential regressions are equal to 30.5 and 35.1%, respectively and for ANNs, they are 86.041%. This shows the tangible superiority of ANNs for predicting stock prices compared to classical methods. This means that investors must use scientific methods to forecast stock prices instead of using traditional methods, especially during the GFC.
机译：这项研究的目的是提供一个有用的模型，以预测在全球金融危机（GFC）期间在德黑兰证券交易所（TSE）上市的公司的股价。为此，使用了线性和指数回归方法以及人工神经网络（ANN）。然后，对这两种方法进行了比较，以确定最有效的方法来预测TSE中的股票价格。在这项研究中，使用工业产品增长率，公司净资产，通货膨胀，石油价格，每股收益和股息价格比率的变量对股票价格进行建模。该研究使用了2008-2011年间250家公司的大量数据。结果表明，线性回归和指数回归的相关系数分别等于30.5％和35.1％，而人工神经网络的相关系数则为86.041％。这表明与传统方法相比，人工神经网络在预测股票价格方面具有明显优势。这意味着投资者必须使用科学方法来预测股票价格，而不是使用传统方法，尤其是在全球金融危机期间。

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