Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds

Sermpinis, G. , Stasinakis, C. and Hassanniakalager, A. (2017) Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds. European Journal of Operational Research, 263(2), pp. 540-558. (doi: 10.1016/j.ejor.2017.06.019)

Abstract

This study introduces a Reverse Adaptive Krill Herd - Locally Weighted Support Vector Regression (RKH-LSVR) model. The Reverse Adaptive Krill Herd (RKH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. In RKH-LSVR, the RKH optimizes the locally weighted Support Vector Regression (LSVR) parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading six ETF stocks on a daily basis over the period 2010-2015. The RKH-LSVR's efficiency is benchmarked against a set of traditional SVR structures and simple linear and non-linear models. The trading application is designed in order to validate the robustness of the algorithm under study and to provide empirical evidence in favour of or against the Adaptive Market Hypothesis (AMH). In terms of the results, the RKH-LSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the time varying trading performance of the models under study validates the AMH theory.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Stasinakis, Professor Charalampos and Hassanniakalager, Arman and Sermpinis, Professor Georgios
• Authors: Sermpinis, G., Stasinakis, C., and Hassanniakalager, A.
• College/School: College of Social Sciences > Adam Smith Business School; College of Social Sciences > Adam Smith Business School > Accounting and Finance; College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: European Journal of Operational Research
• Publisher: Elsevier
• ISSN: 0377-2217
• ISSN (Online): 1872-6860
• Published Online: 10 June 2017
• Copyright Holders: Copyright © 2017 Elsevier
• First Published: First published in European Journal of Operational Research 263(2): 540-558
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher