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Portfolio Inputs Selection from Imprecise Training Data

Publication date: 24.03.2017

Schedae Informaticae, 2016, Volume 25, pp. 177 - 188

https://doi.org/10.4467/20838476SI.16.014.6195

Authors

,
Sarunas Raudys
Vilnius University, 3 Universiteto St, LT-01513 Vilnius
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,
Aistis Raudys
Vilnius University, 3 Universiteto St, LT-01513 Vilnius
All publications →
,
Gene Biziuleviciene
Vilnius University, 3 Universiteto St, LT-01513 Vilnius
All publications →
Zidrina Pabarskaite
Vilnius University, 3 Universiteto St, LT-01513 Vilnius
All publications →

Abstract

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.

References

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Information

Information: Schedae Informaticae, 2016, pp. 177 - 188

Article type: Original research article

Titles:

English:

Portfolio Inputs Selection from Imprecise Training Data

Authors

Vilnius University, 3 Universiteto St, LT-01513 Vilnius

Vilnius University, 3 Universiteto St, LT-01513 Vilnius

Vilnius University, 3 Universiteto St, LT-01513 Vilnius

Vilnius University, 3 Universiteto St, LT-01513 Vilnius

Published at: 24.03.2017

Article status: Open

Licence: None

Percentage share of authors:

Sarunas Raudys (Author) - 25%
Aistis Raudys (Author) - 25%
Gene Biziuleviciene (Author) - 25%
Zidrina Pabarskaite (Author) - 25%

Article corrections:

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Publication languages:

English

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