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

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. © 2019 Elsevier B.V.

Vukovic D. 1, 2 , Vyklyuk Y.3, 4 , Matsiuk N.5 , Maiti M.6
  • 1 Finance and credit department, Faculty of Economics, People's Friendship University of Russia (RUDN University), 117198, Miklukho-Maklaya str.6, Moscow, Russian Federation
  • 2 Geographical Institute “Jovan Cvijic” SASA, Djure Jaksicca 9, Belgrade, 11000, Serbia
  • 3 Institute of Laser and Optoelectronic Intelligent Manufacturing, College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China
  • 4 PHEI “Bukovinian university”, Vice-rector on science and international relations, Darwina str. 2A, Chernivtsi, 58000, Ukraine
  • 5 Yuriy Fedkovych Chernivtsi National University, Department of Business, Trade and Exchange Activities, 2 Kotsjubynskyi Str., Chernivtsi, 58012, Ukraine
  • 6 Department for Finance, St. Petersburg School of Economics and Management, National Research University Higher School of Economics, Kantemirovskaya ulitsa 3A, Office 331, Sankt Petersburg, 194100, Russian Federation
Ключевые слова
Neural networks; Returns; Risk; Sharpe ratio
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