Abstract
Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨ıve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies that profit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.
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References
Mok H M, Causality of interest rate, exchange rate and stock prices at stock market open and close in Hong Kong, Asia Pacific Journal of Management, 1993, 10(2): 123–143.
Xie H B, Fan K K, and Wang S Y, The role of Japanese Candlestick in DVAR model, Journal of Systems Science & Complexity, 2015, 28(5): 1177–1193.
Laboissiere L A, Fernandes R A, Lage G G, Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks, Applied Soft Computing, 2015, 35: 66–74.
Kao L J, Chiu C C, Lu C J, et al., Integration of nonlinear independent component analysis and support vector regression for stock price forecasting, Neurocomputing, 2013, 99(1): 534–542.
Xiong T, Bao Y, and Hu Z, Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting, Knowledge-Based Systems, 2014, 55(55): 87–100.
Kao L J, Chiu C C, Lu C J, et al., A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting, Decision Support Systems, 2013, 54(3): 1228–1244.
Xiao Y, Xiao J, Liu J, et al., A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting, Journal of Systems Science & Complexity, 2014, 27(1): 225–236.
Atsalakis G S and Valavanis K P, Surveying stock market forecasting techniques–Part II: Soft computing methods, Expert Systems with Applications, 2009, 36(3): 5932–5941.
Atsalakis G S and Valavanis K P, Surveying stock market forecasting techniques-part I: Conventional methods, Journal of Computational Optimization in Economics and Finance, 2010, 2(1): 45–92.
Cranger W J and Pesaran M H, Economic and statistical measures of forecast accuracy, Journal of Forecasting, 2000, 19(7): 537–560.
Yang J, Cabrera J, and Wang T, Nonlinearity, data-snooping, and stock index ETF return predictability, European Journal of Operational Research, 2010, 200(2): 498–507.
Eberhart R C, Shi Y, and Kennedy J, Swarm Intelligence, Elsevier, 2001.
Chiong R, Neri F, and McKay R I, Nature that breeds solutions. Ed. by Chiong R. Nature-Inspired Informatics for Intelligent Applications and Knowledge Discovery: Implications in Business, Science and Engineering. Chapter 1. Hershey, PA: Information Science Reference, 2009, 1–24.
Bao Y K, Hu Z Y, and Xiong T, A PSO and pattern search based memetic algorithm for SVMs parameters optimization, Neurocomputing, 2013, 117(14): 98–106.
Bao Y K and Liu Z T, A fast grid search method in support vector regression forecasting time series. Eds. by Corchado E, Yin H, Botti V, et al., Intelligent Data Engineering and Automated Learning — Proceedings of IDEAL 2006, LNCS 4224. Berlin: Springer-Verlag, 2006, 504–511.
Leung M T, Daouk H, and Chen A S, Forecasting stock indices: A comparison of classification and level estimation models, International Journal of Forecasting, 2000, 16(2): 173–190.
Alexander S S, Price movements in speculative markets: Trends or random walks, Industrial Management Review, 1961, 2(2): 7–26.
Inghelbrecht K, Heyman D, Pauwels S, et al., Technical trading rules in emerging stock markets, World Academy of Science, Engineering and Technology, 2012, 59: 2241–2264.
Andrada-Félix J, Fernádez-Rodríguez F, García-Artiles M D, et al., An empirical evaluation of non-linear trading rules, Studies in Nonlinear Dynamics & Econometrics, 2003, 7(3): Article 4.
Xiong T, Bao Y, Hu Z, et al., Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms, Information Sciences, 2015, 305: 77–92.
Chang B M, Tsai H H, and Yen C Y, SVM-PSO based rotation-invariant image texture classification in SVD and DWT domains, Engineering Applications of Artificial Intelligence, 2016, 52: 96–107.
Hsu C W, Chang C C, and Lin C J, A practical guide to support vector classification, Department of Computer Science, National Taiwan University, 2003. Available at: http://www.csie.ntu. edu.tw/˜cjlin/papers/guide/guide.pdf, 2003.
Wang P, Weise T, and Chiong R, Novel evolutionary algorithms for supervised classification problems: An experimental study, Evolutionary Intelligence, 2011, 4(1): 3–16.
Lo S L, Chiong R, and Cornforth D, Using support vector machine ensembles for target audience classification on Twitter, PLoS One, 2015, 10(4): e0122855.
Lo S L, Chiong R, and Cornforth D, Ranking of high-value social audiences on Twitter, Decision Support Systems, 2016, 85: 34–48.
Hu Z, Bao Y, Xiong T, et al., Hybrid filter-wrapper feature selection for short-term load forecasting, Engineering Applications of Artificial Intelligence, 2015, 40: 17–27.
Hu Z, Bao Y, Chiong R, et al., Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection, Energy, 2015, 84: 419–431.
Chang C C and Lin C J, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2001, 2(3): 27.
Tsai C F and Hsiao Y C, Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches, Decision Support Systems, 2010, 50(1): 258–269.
Xie H B, Bian J Z, Wang M X, et al., Is technical analysis informative in UK stock market? Evidence from decomposition-based vector autoregressive (DVAR) model, Journal of Systems Science & Complexity, 2014, 27(1): 144–156.
Sorjamaa A, Hao J, Reyhani N, et al., Methodology for long-term prediction of time series, Neurocomputing, 2007, 70(16–18): 2861–2869.
Trelea I C, The particle swarm optimization algorithm: Convergence analysis and parameter selection, Information Processing Letters, 2003, 85(6): 317–325.
Ben Taieb S, Bontempi G, Atiya A F, et al., A review and comparison of strategies for multistep ahead time series forecasting based on the NN5 forecasting competition, Expert Systems with Applications, 2012, 39(8): 7067–7083.
Marcellino M, Stock J H, Watson MW, A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series, Journal of Econometrics, 2006, 135(1–2): 499–526.
Demsar J, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 2006, 7(1): 1–30.
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This research was supported by the Natural Science Foundation of China under Grant Nos. 71601147, 71571080, and 71501079, Fundamental Research Funds for the Central Universities under Grant No. 104-413000017, and the China Postdoctoral Science Foundation under Grant No. 2015M582280.
This paper was recommended for publication by Editor WANG Shouyang.
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Hu, Z., Bao, Y., Chiong, R. et al. Profit guided or statistical error guided? a study of stock index forecasting using support vector regression. J Syst Sci Complex 30, 1425–1442 (2017). https://doi.org/10.1007/s11424-017-5293-7
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DOI: https://doi.org/10.1007/s11424-017-5293-7