Portfolio Inputs Selection from Imprecise Training Data

• Autorzy: Raudys S. , Raudys A. , Biziuleviciene G. , Pabarskaite Z.

Identyfikatory

DOI

10.4467/20838476SI.16.014.6195

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

complexity; financial portfolio; overfitting; sample size; variable selection

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2016
• Tom: Vol. 25
• Strony: 177--188
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Raudys S.

• Vilnius University

autor

Raudys A.

• Vilnius University

autor

Biziuleviciene G.

• Vilnius University

autor

Pabarskaite Z.

• Vilnius University

Bibliografia

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• [3] Raudys S., Portfolio of automated trading systems: Complexity and learning set size issues. IEEE transactions on neural networks and learning systems, 2013, 24 (3), pp. 448–459.
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• [7] John G.H., Kohavi R., Pfleger K. et. al, Irrelevant features and the subset selection problem. The journal of finance, 1994, pp. 121–129.
• [8] Raudys A., Pabarškaité ˇZ., Discrete portfolio optimisation for large scale systematic trading applications. In: Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on, IEEE, 2012, pp. 1566–1570.
• [9] Bailey D.H., Borwein J.M., de Prado M.L., Zhu Q.J., Pseudomathematics and financial charlatanism: The effects of backtest over fitting on out-of-sample performance. Notices of the AMS, 2014, 61 (5), pp. 458–471.
• [10] Bradley P.S., Fayyad U.M., Mangasarian O.L., Mathematical programming for data mining: Formulations and challenges. INFORMS Journal on Computing, 1999, 11 (3), pp. 217–238.
• [11] Jackowski K., Wozniak M., Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications, 2009, 12 (4), pp. 415–425.
• [12] Tetko I.V., Livingstone D.J., Luik A.I., Neural network studies. 1. Comparison of overfitting and overtraining. Journal of Chemical Information and Computer Sciences, 1995, 35 (5), pp. 826–833.
• [13] Raudys S., Experts’ boasting in trainable fusion rules. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (9), pp. 1178–1182.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).