文章基本信息

标题：Forecasting Daily Returns: A Comparison Of Neural Networks With Parametric Regression Analysis
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作者：Dimitrios Angelidis ; Katerina Lyroudi ; Athanasios Koulakiotis 等
期刊名称：International Business & Economics Research Journal
印刷版ISSN：1535-0754
电子版ISSN：2157-9393
出版年度：2011
卷号：5
期号：1
语种：English
出版社：The Clute Institute for Academic Research
摘要：Since the seminal work of Fama (1965), many researchers have found that the actual distribution of stock returns, for the USA market, is significantly non-normal. Our study is focusing on the examining stock returns predictability for the Hellenic market given some macroeconomic variables. The objective is to use the given information set to reach an optimal way for forecasting. Hence, two basic models for forecasting are examined; a multivariable OLS regression approach and a non-parametric neural network approach and we compare them, based on the minimum forecasted error. Then, the approach that gives the minimum forecasting error is selected. The results indicated that better forecasting approach between the selected two ones is the neural network regression, since it has the smaller mean absolute percent error.