References
1. Li, X., Yang, L., Xue, F., and Zhou, H. \Time series
prediction of stock price using deep belief networks
with intrinsic plasticity", In: 29th Chinese Control and
Decision Conference (CCDC), 2 IEEE, pp. 1237{1242
(2017).
2. Kara, Y., Acar Boyacioglu, M., and Baykan, OK.
\Predicting direction of stock price index movement
using arti cial neural networks and support vector
machines: The sample of the Istanbul stock exchange",
Expert Systems with Applications, 38(5), pp. 5311{
5319 (2011).
3. Guresen, E., Kayakutlu, G., and Daim, T.U. \Using
arti cial neural network models in stock market index
prediction", Expert Systems with Applications, 38(8),
pp. 10389{10397 (2011).
4. Zhang, X., Zhang, Y., Wang, S., Yao, Y., Fang,
B., and Yu, P.S. \Improving stock market prediction
via heterogeneous information fusion", arXiv Preprint
arXiv: 1801.00588 (2018).
5. Zhang, J., Cui, S., Xu, Y., Li, Q., and Li, T. \A
novel data-driven stock price trend prediction system",
Expert Systems with Applications, 97(1) pp. 60{69
(2018).
6. Patel, J., Shah, S., Thakkar, P., and Kotecha, K.
\Predicting stock market index using fusion of machine
learning techniques", Expert Systems with Applications,
42(4), pp. 2162{2172 (2015).
7. Yu, L., Dai, W., and Tang, L. \A novel decomposition
ensemble model with extended extreme learning
machine for crude oil price forecasting", Engineering
Applications of Arti cial Intelligence, 47, pp. 110{121
(2016).
8. Niu, M., Hu, Y., Sun, S., and Liu, Y. \A novel hybrid
decomposition-ensemble model based on VMD and
HGWO for container throughput forecasting", Applied
Mathematical Modelling, 57, pp. 163{178 (2018).
9. Ravi, V., Pradeepkumar, D., and Deb, K. \Financial
time series prediction using hybrids of chaos theory,
multi-layer perceptron and multi-objective evolutionary
algorithms", Swarm and Evolutionary Computation,
36, pp. 136{149 (2017).
10. Fama, E.F. and Malkiel, B.G. \Ecient capital markets:
A review of theory and empirical work", The
Journal of Finance, 25(2), pp. 383{417 (1970).
11. Lo, A.W. and MacKinlay, A.C. \Stock market prices
do not follow random walks: Evidence from a simple
speci cation test", The Review of Financial Studies,
1(1), pp. 41{66 (1988).
12. Tsai, C-F., Lin, Y-C., Yen, D.C., and Chen, YM.
\Predicting stock returns by classi er ensembles",
Applied Soft Computing, 11(2), pp. 2452{2459 (2011).
13. Zhong, X. and Enke, D. \Forecasting daily stock
market return using dimensionality reduction", Expert
Systems with Applications, 67, pp. 126{139 (2017).
14. Wang, J-J., Wang, J-Z., Zhang, Z-G., and Guo, S-P.
\Stock index forecasting based on a hybrid model",
Omega, 40(6), pp. 758{766 (2012).
15. Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega,
J.P., and Oliveira, A.L. \Computational intelligence
and nancial markets: A survey and future
directions", Expert Systems with Applications, 55, pp.
194{211 (2016).
16. Lin, L., Wang, F., Xie, X., and Zhong, S. \Random
forests-based extreme learning machine ensemble for
multi-regime time series prediction", Expert Systems
with Applications, 83, pp. 164{176 (2017).
17. Adebiyi, A.A., Adewumi, A.O., and Ayo, C.K. \Comparison
of ARIMA and arti cial neural networks models
for stock price prediction", Journal of Applied
Mathematics, 2014, pp. 1{7 (2014).
18. Tkac, M. and Verner, R. \Arti cial neural networks
in business: Two decades of research", Applied Soft
Computing, 38(1), pp. 788{804 (2016).
19. Arevalo, A., Ni~no, J., Hernandez, G., and Sandoval, J.
\High-frequency trading strategy based on deep neural
networks", In: International Conference on Intelligent
Computing, Springer, pp. 424{436 (2016).
20. Chong, E., Han, C., and Park, F.C. \Deep learning
networks for stock market analysis and prediction:
Methodology, data representations, and case studies",
Expert Systems with Applications, 83, pp. 187{205
(2017).
21. Hassan, M.R., Nath, B., and Kirley, M. \A fusion
model of HMM, ANN and GA for stock market
forecasting", Expert Systems with Applications, 33(1),
pp. 171{180 (2007).
22. Asadi, S., Hadavandi, E., Mehmanpazir, F., and
Nakhostin, M.M. \Hybridization of evolutionary Levenberg
Marquardt neural networks and data preprocessing
for stock market prediction", Knowledge-
Based Systems, 35, pp. 245{258 (2012).
23. Gocken, M., Ozcalc, M., Boru, A., and Dosdogru,
A.T. \Integrating metaheuristics and arti cial neural
networks for improved stock price prediction", Expert
Systems with Applications, 44, pp. 320{331 (2016).
24. Chang, P-C. and Liu, C-H. \A TSK type fuzzy
rule based system for stock price prediction", Expert
Systems with Applications, 34(1), pp. 135{144 (2008).
25. Yu, L., Chen, H., Wang, S., and Lai, K.K. \Evolving
least squares support vector machines for stock market
trend mining", Evolutionary Computation, IEEE
Transactions on 2009, 13(1), pp. 87{102 (2009).
26. Huang, C., Yang, F., and Lee, C. \The strategy of
investment in the stock market using modi ed support
vector regression model", Scientia Iranica, Transaction
E, Industrial Engineering, 25(3), pp. 1629{1640
(2018).
410 M.T. Faghihi Nezhad and B. Minaei Bidgoli]/Scientia Iranica, Transactions E: Industrial Engineering 28 (2021) 395{411
27. Esfahanipour, A. and Aghamiri, W. \Adapted neurofuzzy
inference system on indirect approach TSK fuzzy
rule base for stock market analysis", Expert Systems
with Applications, 37(7), pp. 4742{4748 (2010).
28. Chen, Y-S., Cheng, C-H., Chiu, C-L., and Huang, S-T.
\A study of ANFIS-based multi-factor time series models
for forecasting stock index", Applied Intelligence,
45(2), pp. 277{292 (2016).
29. Shen, W., Guo, X., Wu, C., and Wu, D. \Forecasting
stock indices using radial basis function neural networks
optimized by arti cial sh swarm algorithm",
Knowledge-Based Systems, 24(3), pp. 378{385 (2011).
30. Enke, D. and Mehdiyev, N. \Stock market prediction
using a combination of stepwise regression analysis, differential
evolution-based fuzzy clustering, and a fuzzy
inference neural network", Intelligent Automation &
Soft Computing, 19(4), pp. 636{648 (2013).
31. Khashei, M., Bijari, M., and Ardali, GAR. \Improvement
of auto-regressive integrated moving average
models using fuzzy logic and arti cial neural networks
(ANNs)", Neurocomputing, 72{128(4), pp. 956{967
(2009).
32. Babu, C.N., Reddy, B.E., and Babu, C.N. \A movingaverage
lter based hybrid ARIMA-ANN model for
forecasting time series data", Applied Soft Computing,
23, pp. 27{38 (2014).
33. Tsai, C-F. and Chiou, Y-J. \Earnings management
prediction: A pilot study of combining neural networks
and decision trees", Expert Systems with Applications,
36(3), pp. 7183{7191 (2009).
34. Adhikari, R., and Agrawal, R. \A combination of
arti cial neural network and random walk models for
nancial time series forecasting", Neural Computing
and Applications, 24(6), pp. 1441{1449 (2014).
35. Freitas, P.S. and Rodrigues, A.J. \Model combination
in neural-based forecasting", European Journal of Operational
Research, 173(3), pp. 801{814 (2006).
36. Rather, A.M., Agarwal, A., and Sastry, V. \Recurrent
neural network and a hybrid model for prediction
of stock returns", Expert Systems with Applications,
42(6), pp. 3234{3241 (2015).
37. Andrawis, R.R., Atiya, A.F., and El-Shishiny, H.
\Forecast combinations of computational intelligence
and linear models for the NN5 time series forecasting
competition", International Journal of Forecasting,
27(3), pp. 672{688 (2011).
38. Dietterich, T.G. \Ensemble methods in machine learning",
In: International Workshop on Multiple Classi-
er Systems, Springer, pp. 1{15 (2000).
39. Yu, L., Lai, K.K., and Wang, S. \Multistage RBF
neural network ensemble learning for exchange rates
forecasting", Neurocomputing, 71(16{18), pp. 3295{
3302 (2008).
40. Xiao, Y., Xiao, J., Lu, F., and Wang, S. \Ensemble
ANNs-PSO-GA approach for day-ahead stock Eexchange
prices forecasting", International Journal of
Computational Intelligence Systems, 7(2), pp. 272{290
(2013).
41. Ballings, M., Van den Poel, D., Hespeels, N., and
Gryp, R. \Evaluating multiple classi ers for stock price
direction prediction", Expert Systems with Applications,
42(20), pp. 7046{7056 (2015).
42. Maknickien_e, N. \Prediction capabilities of evolino
RNN ensembles", In: Computational Intelligence,
Edn., Springer, pp. 473{485 (2016).
43. Atsalakis, G.S. and Valavanis, K.P. \Surveying stock
market forecasting techniques-Part II: Soft computing
methods", Expert Systems with Applications, 36(3),
pp. 5932{5941 (2009).
44. Cheng, C., Xu, W., and Wang, J. \A comparison
of ensemble methods in nancial market prediction",
In: Computational Sciences and Optimization (CSO),
2012 Fifth International Joint Conference on, IEEE,
pp. 755{759 (2012).
45. Atsalakis, G.S. and Valavanis, K.P. \Forecasting
stock market short-term trends using a neuro-fuzzy
based methodology", Expert Systems with Applications,
36(7), pp. 10696{10707 (2009).
46. Wang, G., Hao, J., Ma, J., and Jiang, H. \A comparative
assessment of ensemble learning for credit
scoring", Expert Systems with Applications, 38(1), pp.
223{230 (2011).
47. Cubiles-De-La-Vega, M-D., Blanco-Oliver, A., Pino-
Mejas, R., and Lara-Rubio, J. \Improving the management
of micro nance institutions by using credit
scoring models based on statistical learning techniques",
Expert Systems with Applications, 40(17), pp.
6910{6917 (2013).
48. Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde,
E., Havlin, S., Bunde, A., and Stanley, H.E. \Multifractal
detrended
uctuation analysis of nonstationary
time series", Physica A: Statistical Mechanics and its
Applications, 316(1), pp. 87{114 (2002).
49. Lei, L. \Wavelet neural network prediction method
of stock price trend based on rough set attribute
reduction", Applied Soft Computing, 62, pp. 923{932
(2018).
50. Ferreira, T.A., Vasconcelos, G.C., and Adeodato, P.J.
\A new intelligent system methodology for time series
forecasting with arti cial neural networks", Neural
Processing Letters, 28(2), pp. 113{129 (2008).
51. De Oliveira, F.A., Nobre, C.N., and Zarate, L.E.
\Applying arti cial neural networks to prediction of
stock price and improvement of the directional prediction
index-Case study of PETR4, Petrobras, Brazil",
Expert Systems with Applications, 40(18), pp. 7596{
7606 (2013).
52. Yazdani, M., Zandieh, M., Tavakkoli-Moghaddam, R.,
and Jolai, F. \Two meta-heuristic algorithms for the
dual-resource constrained
exible job-shop scheduling
problem", Scientia Iranica, Transactions E, Industrial
Engineering, 22(3), p. 1242 (2015).
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