The strategy of investment in the stock market using modified support vector regression model

Document Type : Article


1 Da-Yeh University

2 Department of International Business Management, Da-Yeh University

3 Department of Maritime Information and Technology, National Kaohsiung Marine University


Stock indices forecasting has become a popular research issue in recent years. Although many statistical time series models have been applied in stock indices forecasting, they are limited to certain assumptions. Accordingly, the traditional statistical time series models might not be suitable for forecasting real-life stock indices data. Hence, this paper proposes a novel forecasting model to assist investors in determining a strategy for investments in the stock market. The proposed model is called the modified support vector regression model, which is composed of the correlation coefficient method, the sliding window algorithm and the support vector regression model. The results show that the forecasting accuracy of the proposed model is more stable than the existing models in terms of average and standard deviation of the root mean square error (RMSE) and the mean absolute percentage error (MAPE). Accordingly, the proposed model would be used to assist investors in determining a strategy for investing in stocks.


Main Subjects

1. Donate, J.P., Sanchez, G.G., and De Miguel, A.S.
\Time series forecasting. a comparative study between
an evolving arti cial neural networks system and statistical
methods", Int. J. Artif. Intell. Tools, 21(01),
1250010 (2012).
2. Tseng, C.-H., Cheng, S.-T., Wang, Y.-H., and Peng,
J.-T. \Arti cial neural network model of the hybrid
EGARCH volatility of the Taiwan stock index option
prices", Physica A, 387(13), pp. 3192-3200 (2008).
3. Yu, H.-K. \Weighted fuzzy time series models for
TAIEX forecasting", Physica A, 349(3-4), pp. 609-624
4. Cheng, C.-H., Chen, T.-L., Teoh, H.J., and Chiang, C.-
H. \Fuzzy time-series based on adaptive expectation
model for TAIEX forecasting", Expert Syst. Appl.,
34(2), pp. 1126-1132 (2008).
5. Leu, Y., Lee, C.-P., and Jou, Y.-Z. \A distance-based
fuzzy time series model for exchange rates forecasting",
Expert Syst. Appl., 36(4), pp. 8107-8114 (2009).
6. Lee, C.-P., Lin, W.-C., and Yang, C.-C. \A strategy
for forecasting option price using fuzzy time series and
least square support vector regression with a bootstrap
model", Sci. Iran., 21(3), pp. 815-825 (2014).
7. Leu, Y., Lee, C.-P., and Hung, C.-C. \A fuzzy time
series-based neural network approach to option price
forecasting", In Intelligent Information and Database
Systems, N. Nguyen, M. Le and J. Swiatek, pp. 360-
369, Springer Berlin Heidelberg (2010).
8. Yang, C.-C., Leu, Y., and Lee, C.-P. \A dynamic
weighted distanced-based fuzzy time series neural network
with bootstrap model for option price forecasting",
Rom. J. Econ. Forecast, 2014(2), pp. 115-129
9. Lee, L.-W., Wang, L.-H., Chen, S.-M., and Leu, Y.-H.
\Handling forecasting problems based on two-factors
high-order fuzzy time series", IEEE Trans. Fuzzy Syst.,
14(3), pp. 468-477 (2006).
10. Chen, S.-M. \Forecasting enrollments based on highorder
fuzzy time series", Cybern. Syst., 33(1), pp. 1-16
11. Liang, X., Zhang, H., and Li, X. \A simple method of
forecasting option prices based on neural networks", In
Next-Generation Applied Intelligence, B.-C. Chien, T.-
P. Hong, S.-M. Chen, and M. Ali, pp. 586-593, Springer
Berlin Heidelberg (2009).
12. Huang, C.-F. \A hybrid stock selection model using
genetic algorithms and support vector regression",
Appl. Soft. Comput., 12(2), pp. 807-818 (2012).
13. Wang, Y.-H. \Nonlinear neural network forecasting
model for stock index option price: hybrid GJRGARCH
approach", Expert Syst. Appl., 36(1), pp. 564-
570 (2009).
1640 C.-H. Huang et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 1629{1640
14. Panda, C. and Narasimhan, V. \Forecasting exchange
rate better with arti cial neural network", J. Policy
Model., 29(2), pp. 227-236 (2007).
15. Wei, L.-Y., Cheng, C.-H., and Wu, H.-H. \A hybrid
ANFIS based on n-period moving average model to
forecast TAIEX stock", Appl. Soft. Comput., 19, pp.
86-92 (2014).
16. Wang, Y., Wang, B., and Zhang, X. \A new application
of the support vector regression on the construction
of nancial conditions index to CPI prediction",
Procedia Comput. Sci., 9, pp. 1263-1272 (2012).
17. Chen, S.-M. and Kao, P.-Y. \TAIEX forecasting based
on fuzzy time series, particle swarm optimization
techniques and support vector machines", Inf. Sci.,
247, pp. 62-71 (2013).
18. Chen, S.-M. \Forecasting enrollments based on fuzzy
time series", Fuzzy Sets Syst., 81(3), pp. 311-319
19. Song, Q. and Chissom, B.S. \Forecasting enrollments
with fuzzy time series - Part I", Fuzzy Sets Syst., 54(1),
pp. 1-9 (1993).
20. Song, Q. and Chissom, B.S. \Forecasting enrollments
with fuzzy time series-part II", Fuzzy Sets Syst., 62(1),
pp. 1-8 (1994).
21. McCulloch, W.S. and Pitts, W. \A logical calculus of
the ideas immanent in nervous activity", Bull. Math.
Biol., 5(4), pp. 115-133 (1943).
22. Lee, C.-P., Shieh, G.-J., Yiu, T.-J., and Kuo, B.-J.
\The strategy to simulate the cross-pollination rate for
the co-existence of genetically modi ed (GM) and non-
GM crops by using FPSOSVR", Chemometrics Intell.
Lab. Syst., 122, pp. 50-57 (2013).
23. Drucker, H., Burges, C.J.C., Kaufman, L., Smola,
A.J., and Vapnik, V. \Support vector regression machines",
NIPS'1996, pp. 155-161 (1996).
24. Basak, D., Pal, S., and Patranabis, D.C. \Support
vector regression", Neural Inf. Process Lett. Rev., 11,
pp. 203-224 (2007).
25. Kapoor, P. and Bedi, S.S. \Weather forecasting using
sliding window algorithm", ISRN Signal Processing,
2013, p. 5 (2013).
26. Smola, A.J. and Scholkopf, B. \A tutorial on support
vector regression", Stat. Comput., 14(3), pp. 199-222
27. Kleinpeter, M.A. \Multivariable analysis: A practical
guide for clinicians", 2nd Edn., J. Natl. Med. Assoc.,
99(6), pp. 684-684 (2007).
28. Ahlburg, D.A. \How accurate are the U.S. bureau
of the census projections of total live births?", J.
Forecast., 1(4), pp. 365-374 (1982).
29. Ou, S.-L. \Forecasting agricultural output with an
improved grey forecasting model based on the genetic
algorithm", Comput. Electron. Agric., 85, pp. 33-39
30. Coshall, J.T. \Combining volatility and smoothing
forecasts of UK demand for international tourism",
Tourism Manage., 30(4), pp. 495-511 (2009).
31. Gani, A., Mohammadi, K., Shamshirband, S., Altameem,
T.A., Petkovic, D., and Ch, S. \A combined
method to estimate wind speed distribution based on
integrating the support vector machine with re
algorithm", Environ. Prog. Sustain. Energy, 35(3), pp.
867-875 (2016).
32. Hadavandi, E., Shahrabi, J., and Shamshirband, S. \A
novel Boosted-neural network ensemble for modeling
multi-target regression problems", Eng. Appl. Artif.
Intell., 45, pp. 204-219 (2015).