Journal article Open Access

# Stock Price Analysis under Extreme Value Theory

Louangrath, P.I.

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<foaf:name>Louangrath, P.I.</foaf:name>
<foaf:givenName>P.I.</foaf:givenName>
<foaf:familyName>Louangrath</foaf:familyName>
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<foaf:name>Bangkok University - International College</foaf:name>
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<dct:title>Stock Price Analysis under Extreme Value Theory</dct:title>
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<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2015</dct:issued>
<dcat:keyword>Extreme Value Theory, risk management, stock price analysis</dcat:keyword>
<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2015-12-31</dct:issued>
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<dct:description>&lt;p&gt;The objective of this paper is to provide a practical tool for stock price evaluation and forecasting under Extreme Value Theory (EVT). We reviewed three existing models: Mordern Portfolio Theory, Black-Scholes, and Jarrow-Rudd models. It was found that these models may not be effective tools where option contract is not part of the investment regime. The data used in this research consist of the daily close price from a period of 30 days from 100 companies in the SET100 index. From the sample distribution F(X), extreme values were separated into a group G(X). A tail index &amp;nbsp;was calculated from G(X)) and the distribution for each security was identified. Using EVT, the threshold value was estimated and used as a tool for risk assessment for each stock. It was found that Thailand&amp;rsquo;s SET100 consists of two groups of stocks according to price distribution. The majority of the stocks are Weibull distributed and the remaining stocks are Fr&amp;eacute;chet distributed. Using Fisher-Tippett-Gnedenko&amp;rsquo;s Generalized Extreme Value calculation for the indication of price volatility, the Weibull group shows the mean value of &amp;nbsp;H(E, mu, sigma), and the Fr&amp;eacute;chet group shows H(E, mu, sigma) = 0.05. The findings may be used as a tool for risk assessment in stock investment. This finding rejects the general assertion that most financial data are fat-tailed distribution. The finding of this paper implies that investors face two categories of stocks: low and high price volatility. The idea of sector diversity becomes secondary. Empirical evidence shows that stocks from different sectors may have the same distribution and stocks of the same sector may have different distributions. Therefore, price volatility index is a better indicator for risk management.&lt;/p&gt;</dct:description>
<dct:description xml:lang="">JEL CODE: C10, C13, C14, C46, E27, G11, G17</dct:description>
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<rdfs:label>Open Access</rdfs:label>
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