Neural network forecasting in prediction Sharpe ratio: Evidence from EU debt market

Darko Vuković (), Yaroslav Vyklyuk, Natalia Matsiuk and Moinak Maiti

Physica A: Statistical Mechanics and its Applications, 2020, vol. 542, issue C

Abstract: This study analyzes a neural networks model that forecast Sharpe ratio. The developed neural networks model is successful to predict the position of the investor who will be rewarded with extra risk premium on debt securities for the same level of portfolio risk or a greater risk premium than proportionate growth risk. The main purpose of the study is to predict highest Sharpe ratio in the future. Study grouped the data on yields of debt instruments in periods before, during and after world crisis. Results shows that neural networks is successful in forecasting nonlinear time lag series with accuracy of 82% on test cases for the prediction of Sharpe-ratio dynamics in future and investor‘s portfolio position.

Keywords: Risk; Returns; Neural networks; Sharpe ratio (search for similar items in EconPapers)
JEL-codes: C88 G20 G23 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:542:y:2020:i:c:s0378437119318655

DOI: 10.1016/j.physa.2019.123331

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