<![CDATA[WUJ]]><![CDATA[info@ejournals.eu]]>http://www.ejournals.eu/20171119ejournals.eu.article.901903010610.4467/20838476SI.16.014.619520<![CDATA[Complexity, financial portfolio, overfitting, sample size, variable selection]]>00ED031001Number10102<![CDATA[Schedae Informaticae]]>03<![CDATA[2016]]>01<![CDATA[Volume 25]]>1A01<![CDATA[Sarunas RAUDYS]]>2A01<![CDATA[Aistis RAUDYS]]>3A01<![CDATA[Gene BIZIULEVICIENE]]>4A01<![CDATA[Zidrina PABARSKAITE]]>0101<![CDATA[Portfolio Inputs Selection from Imprecise Training Data]]>01en0017703020PLN03<![CDATA[This paper explores very acute problem of portfolio secondary overfitting.
We examined the financial portfolio inputs random selection optimization
model and derived the equation to calculate the mean Sharpe ratio in dependence
of the number of portfolio inputs, the sample size L used to estimate
Sharpe ratios of each particular subset of inputs and the number of times the
portfolio inputs were generated randomly. It was demonstrated that with the
increase in portfolio complexity, and complexity of optimization procedure we
can observe the over-fitting phenomena. Theoretically based conclusions were
confirmed by experiments with artificial and real world 60,000-dimensional 12
years financial data.]]>012017032415000401http://dx.doi.org/10.4467/20838476SI.16.014.6195