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

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  • Sermpinis, Georgios
  • Stasinakis, Charalampos
  • Hassanniakalager, Arman

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 behavior 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 favor 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.

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  • Sermpinis, Georgios & Stasinakis, Charalampos & Hassanniakalager, Arman, 2017. "Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds," European Journal of Operational Research, Elsevier, vol. 263(2), pages 540-558.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:2:p:540-558
    DOI: 10.1016/j.ejor.2017.06.019
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    1. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29, January.
    2. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    3. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    4. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    5. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    6. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    7. Manfred Gilli & Dietmar Maringer & Peter Winker, 2008. "Applications of Heuristics in Finance," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 26, pages 635-653, Springer.
    8. YichaoWu, & Liu, Yufeng, 2007. "Robust Truncated Hinge Loss Support Vector Machines," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 974-983, September.
    9. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    10. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    11. Nicholas G. Hall & Marc E. Posner, 2007. "Performance Prediction and Preselection for Optimization and Heuristic Solution Procedures," Operations Research, INFORMS, vol. 55(4), pages 703-716, August.
    12. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
    13. Wu, Shaomin & Akbarov, Artur, 2011. "Support vector regression for warranty claim forecasting," European Journal of Operational Research, Elsevier, vol. 213(1), pages 196-204, August.
    14. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    15. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    16. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    17. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    18. Shapiro, Arnold F., 2000. "A Hitchhiker's guide to the techniques of adaptive nonlinear models," Insurance: Mathematics and Economics, Elsevier, vol. 26(2-3), pages 119-132, May.
    19. Dana G. Popescu & Pascale Crama, 2016. "Ad Revenue Optimization in Live Broadcasting," Management Science, INFORMS, vol. 62(4), pages 1145-1164, April.
    20. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2013. "ETF arbitrage: Intraday evidence," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3486-3498.
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    Cited by:

    1. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    2. Gary S. Anderson & Alena Audzeyeva, 2019. "A Coherent Framework for Predicting Emerging Market Credit Spreads with Support Vector Regression," Finance and Economics Discussion Series 2019-074, Board of Governors of the Federal Reserve System (U.S.).
    3. Filipa Fernandes & Charalampos Stasinakis & Zivile Zekaite, 2019. "Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery," Annals of Operations Research, Springer, vol. 282(1), pages 87-118, November.
    4. Jinchai Lin & Kaiwei Zhu & Zhen Liu & Jenny Lieu & Xianchun Tan, 2019. "Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation," Energies, MDPI, vol. 12(11), pages 1-28, June.
    5. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.
    6. Kang, Haijun & Zong, Xiangyu & Wang, Jianyong & Chen, Haonan, 2023. "Binary gravity search algorithm and support vector machine for forecasting and trading stock indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 507-526.

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