TY - JOUR
TI - Portfolio Inputs Selection from Imprecise Training Data
AU - Raudys, Sarunas
AU - Raudys, Aistis
AU - Biziuleviciene, Gene
AU - Pabarskaite, Zidrina
TI - Portfolio Inputs Selection from Imprecise Training Data
AB - 
	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.
VL - 2016
IS - Volume 25
PY - 2017
SN - 1732-3916
C1 - 2083-8476
SP - 177
EP - 188
DO - 10.4467/20838476SI.16.014.6195
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/portfolio-inputs-selection-from-imprecise-training-data
KW - sample size
KW - variable selection
KW - Complexity
KW - financial portfolio
KW - overfitting