Portfolio selection is an important problem in area of finance. Researchers have always tried to work with a variety of methods and strategies to achieve this important issue.In this research has been trying to present a new approach for portfolio selection with use of lower tail dependence and Extreme value theory.We show theoretically that lower tail dependence (χ), a measure of the probability that a portfolio will suffer large losses given that the market does, contains important information for risk-averse investors. We then estimate χ for a sample of stocks and show that it differs systematically from other risk measures including variance, semi-variance, skewness, kurtosis, beta, and coskewness. In out-of-sample tests, portfolios constructed to have low values of χ outperform the market index, and portfolios with high values of χ. Our results indicate that χ is conceptually important for risk-averse investors, differs substantially from other risk measures, and provides useful information for portfolio selection.
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