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Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction

  • Hybrid Artificial Intelligence and Machine Learning Technologies
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Abstract

Recently, artificial intelligence technologies have received considerable attention because of their practical applications in various fields. The key factor in this prosperity is deep learning which is inspired by the information processing in biological brains. In this study, we apply one of the representative deep learning techniques multi-channel convolutional neural networks (CNNs) to predict the fluctuation of the stock index. Furthermore, we optimize the network topology of CNN to improve the model performance. CNN has many hyper-parameters that need to be adjusted for constructing an optimal model that can learn the data patterns efficiently. In particular, we focus on the optimization of feature extraction part of CNN, because this is the most important part of the computational procedure of CNN. This study proposes a method to systematically optimize the parameters for the CNN model by using genetic algorithm (GA). To verify the effectiveness of our model, we compare the prediction result with standard artificial neural networks (ANNs) and CNN models. The experimental results show that the GA-CNN outperforms the comparative models and demonstrate the effectiveness of the hybrid approach of GA and CNN.

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Abbreviations

\( C_{t} \) :

Closing price at time t

\( L_{t} \) :

Low price at time t

\( H_{t} \) :

High price at time t

\( {\text{LL}}_{t} \) :

The lowest low in the last t days

\( {\text{HH}}_{t} \) :

Highest high in the last t days

\( {\text{Up}}_{t} \) :

Upward price change at time t

\( {\text{Dw}}_{t} \) :

Downward price change at time t

\( {\text{EMA}}_{t} \) :

Exponential moving average for t days

M :

Typical price which is calculated using \( (H_{t} + L_{t} + C_{t} )/3 \)

m :

Simple moving average which is calculated using \( \left( {\sum\nolimits_{i = 1}^{n} {M_{t - i + 1} } } \right)/n \)

d :

Mean absolute deviation which is calculated using \( \left( {\sum\nolimits_{i = 1}^{n} {\left| {M_{t - i + 1} - m_{t} } \right|} } \right)/n \))

AI:

Artificial intelligence

CNN:

Convolutional neural network

RNN:

Recurrent neural network

ANN:

Artificial neural network

ILSVRC:

ImageNet large-scale visual recognition challenge

GA:

Genetic algorithm

SVM:

Support vector machine

CBR:

Case-based reasoning

EMH:

Efficient market hypothesis

ES:

Exponential smoothing

ARIMA:

Autoregressive integrated moving average

ARCH:

Autoregressive conditional heteroscedasticity

GARCH:

Generalized autoregressive conditional heteroscedasticity

TOPIX:

Tokyo stock exchange price indexes

PNN:

Probabilistic neural network

TDNN:

Time-delay neural network

LDA:

Linear discriminant analysis

QDA:

Quadratic discriminant analysis

EBNN:

Elman back-propagation neural network

PCA:

Principal component analysis

KOSPI:

Korea composite stock price index 200

MV:

Majority vote

WMV:

Weighted majority vote

BC:

Borda count

WBC:

Weighted Borda count

BKS:

Behavior-knowledge space

IBB:

NASDAQ biotechnology index

RBM:

Restricted Boltzmann machine

NMSE:

Normalized mean squared error

RMSE:

Root-mean-squared error

MAE:

Mean absolute error

MI:

Mutual information

LSTM:

Long short-term memory

MNIST:

Mixed national institute of standards and technology

k-NN:

k-nearest neighbor

DTW:

Dynamic time warping

CIFAR10:

Canadian institute for advanced research

PSO:

Particle swarm optimization

SVR:

Support vector regression

RSI:

Relative strength index

MACD:

Moving average convergence divergence

CCI:

Commodity channel index

ReLU:

Rectified linear unit

Adam:

Adaptive moment estimation

References

  1. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge

    MATH  Google Scholar 

  2. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th conference on neural information processing systems (NIPS), pp 1097–1105

  3. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1746–1751

  4. Zheng Y, Liu Q, Chen E, Ge Y, Zhao JL (2016) Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front Comput Sci 10(1):96–112. https://doi.org/10.1007/11704-015-4478-2

    Article  Google Scholar 

  5. Abd-Elazim SM, Ali ES (2016) Load frequency controller design via BAT algorithm for nonlinear interconnected power system. Int J Electr Power Energy Syst 77:166–177. https://doi.org/10.1016/j.ijepes.2015.11.029

    Article  Google Scholar 

  6. Abd-Elazim SM, Ali ES (2016) Imperialist competitive algorithm for optimal STATCOM design in a multimachine power system. Int J Electr Power Energy Syst 76:136–146. https://doi.org/10.1016/j.ijepes.2015.09.004

    Article  Google Scholar 

  7. Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616. https://doi.org/10.1007/s00521-016-2668-y

    Article  Google Scholar 

  8. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  9. Amin AE (2013) A novel classification model for cotton yarn quality based on trained neural network using genetic algorithm. Knowl Based Syst 39:124–132. https://doi.org/10.1016/j.knosys.2012.10.008

    Article  Google Scholar 

  10. Donate JP, Li X, Sánchez GG, de Miguel AS (2013) Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput Appl 22(1):11–20. https://doi.org/10.1007/s00521-011-0741-0

    Article  Google Scholar 

  11. Azadeh A, Mianaei HS, Asadzadeh SM, Saberi M, Sheikhalishahi M (2015) A flexible ANN-GA-multivariate algorithm for assessment and optimization of machinery productivity in complex production units. J Manuf Syst 35:46–75. https://doi.org/10.1016/j.jmsy.2014.11.007

    Article  Google Scholar 

  12. Braun MA, Seijo S, Echanobe J, Shukla PK, del Campo I, Garcia-Sedano J, Schmeck H (2016) A neuro-genetic approach for modeling and optimizing a complex cogeneration process. Appl Soft Comput 48:347–358. https://doi.org/10.1016/j.asoc.2016.07.026

    Article  Google Scholar 

  13. Armaghani DJ, Hasanipanah M, Mahdiyar A, Majid MZA, Amnieh HB, Tahir MM (2018) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 29(9):619–629. https://doi.org/10.1007/s00521-016-2598-8

    Article  Google Scholar 

  14. Zhao M, Ren J, Ji L, Fu C, Li J, Zhou M (2012) Parameter selection of support vector machines and genetic algorithm based on change area search. Neural Comput Appl 21(1):1–8. https://doi.org/10.1007/s00521-011-0603-9

    Article  Google Scholar 

  15. Ahmad I, Hussain M, Alghamdi A, Alelaiwi A (2014) Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components. Neural Comput Appl 24(7–8):1671–1682. https://doi.org/10.1007/s00521-013-1370-6

    Article  Google Scholar 

  16. Raman MG, Somu N, Kirthivasan K, Liscano R, Sriram VS (2017) An efficient intrusion detection system based on hypergraph-Genetic algorithm for parameter optimization and feature selection in support vector machine. Knowl Based Syst 134:1–12. https://doi.org/10.1016/j.knosys.2017.07.005

    Article  Google Scholar 

  17. Tao Z, Huiling L, Wenwen W, Xia Y (2019) GA-SVM based feature selection and parameter optimization in hospitalization expense modeling. Appl Soft Comput 75:323–332. https://doi.org/10.1016/j.asoc.2018.11.001

    Article  Google Scholar 

  18. Shin KS, Han I (1999) Case-based reasoning supported by genetic algorithms for corporate bond rating. Expert Syst Appl 16(2):85–95. https://doi.org/10.1016/S0957-4174(98)00063-3

    Article  Google Scholar 

  19. Ahn H, Kim KJ (2009) Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach. Appl Soft Comput 9(2):599–607. https://doi.org/10.1016/j.asoc.2008.08.002

    Article  Google Scholar 

  20. Abu-Mostafa YS, Atiya AF (1996) Introduction to financial forecasting. Appl Intell 6(3):205–213. https://doi.org/10.1007/BF00126626

    Article  Google Scholar 

  21. Fama EF (1970) Efficient capital markets: a review of theory and empirical work. J Finance 25(2):383–417. https://doi.org/10.2307/2325486

    Article  Google Scholar 

  22. Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32(10):2513–2522. https://doi.org/10.1016/j.cor.2004.03.016

    Article  MATH  Google Scholar 

  23. De Faria EL, Albuquerque MP, Gonzalez JL, Cavalcante JTP, Albuquerque MP (2009) Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Syst Appl 36(10):12506–12509. https://doi.org/10.1016/j.eswa.2009.04.032

    Article  Google Scholar 

  24. Babu CN, Reddy BE (2015) Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model. Appl Comput Inform 11(2):130–143. https://doi.org/10.1016/j.aci.2014.09.002

    Article  Google Scholar 

  25. Cavalcante RC, Brasileiro RC, Souza VL, Nobrega JP, Oliveira AL (2016) Computational intelligence and financial markets: a survey and future directions. Expert Syst Appl 55:194–211. https://doi.org/10.1016/j.eswa.2016.02.006

    Article  Google Scholar 

  26. Kim KJ, Han I (2000) Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Syst Appl 19(2):125–132. https://doi.org/10.1016/s0957-4174(00)00027-0

    Article  MathSciNet  Google Scholar 

  27. Armano G, Marchesi M, Murru A (2005) A hybrid genetic-neural architecture for stock indexes forecasting. Inf Sci 170(1):3–33. https://doi.org/10.1016/j.ins.2003.03.023

    Article  MathSciNet  Google Scholar 

  28. Kara Y, Boyacioglu MA, Baykan ÖK (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Syst Appl 38(5):5311–5319. https://doi.org/10.1016/j.eswa.2010.10.027

    Article  Google Scholar 

  29. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187–205. https://doi.org/10.1016/j.eswa.2017.04.030

    Article  Google Scholar 

  30. Box GE, Jenkins GM (1976) Time series analysis: forecasting and control, revised edn. Holden-Day, Oakland

    MATH  Google Scholar 

  31. Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007. https://doi.org/10.2307/1912773

    Article  MathSciNet  MATH  Google Scholar 

  32. Schwaiger WS (1995) A note on GARCH predictable variances and stock market efficiency. J Bank Finance 19(5):949–953. https://doi.org/10.1016/0378-4266(94)00081-d

    Article  Google Scholar 

  33. Wang JJ, Wang JZ, Zhang ZG, Guo SP (2012) Stock index forecasting based on a hybrid model. Omega 40(6):758–766. https://doi.org/10.1016/j.omega.2011.07.008

    Article  Google Scholar 

  34. Wei LY, Chen TL, Ho TH (2011) A hybrid model based on adaptive-network-based fuzzy inference system to forecast Taiwan stock market. Expert Syst Appl 38(11):13625–13631. https://doi.org/10.1016/j.eswa.2011.04.127

    Article  Google Scholar 

  35. Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques–part II: soft computing methods. Expert Syst Appl 36(3):5932–5941. https://doi.org/10.1016/j.eswa.2008.07.006

    Article  Google Scholar 

  36. Fernandez-Rodrıguez F, Gonzalez-Martel C, Sosvilla-Rivero S (2000) On the profitability of technical trading rules based on artificial neural networks: evidence from the Madrid stock market. Econ Lett 69(1):89–94. https://doi.org/10.1016/s0165-1765(00)00270-6

    Article  MATH  Google Scholar 

  37. Tay FE, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317. https://doi.org/10.1016/s0305-0483(01)00026-3

    Article  Google Scholar 

  38. Lee MC (2009) Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Syst Appl 36(8):10896–10904. https://doi.org/10.1016/j.eswa.2009.02.038

    Article  Google Scholar 

  39. Adebiyi AA, Adewumi AO, Ayo CK (2014) Comparison of ARIMA and artificial neural networks models for stock price prediction. J Appl Math 2014:1–7. https://doi.org/10.1155/2014/614342

    Article  MathSciNet  Google Scholar 

  40. Saad EW, Prokhorov DV, Wunsch DC (1998) Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans Neural Netw 9(6):1456–1470. https://doi.org/10.1109/72.728395

    Article  Google Scholar 

  41. Yu H, Chen R, Zhang G (2014) A SVM stock selection model within PCA. Procedia Comput Sci 31:406–412. https://doi.org/10.1016/j.procs.2014.05.284

    Article  Google Scholar 

  42. Kim KJ, Lee WB (2004) Stock market prediction using artificial neural networks with optimal feature transformation. Neural Comput Appl 13(3):255–260. https://doi.org/10.1007/s00521-004-0428-x

    Article  Google Scholar 

  43. Lu CJ (2013) Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting. Neural Comput Appl 23(7–8):2417–2427. https://doi.org/10.1007/s00521-012-1198-5

    Article  Google Scholar 

  44. Kim MJ, Min SH, Han I (2006) An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Syst Appl 31(2):241–247. https://doi.org/10.1016/j.eswa.2005.09.020

    Article  Google Scholar 

  45. Heaton JB, Polson NG, Witte JH (2017) Deep learning for finance: deep portfolios. Appl Stoch Models Bus Ind 33(1):19–21. https://doi.org/10.1002/asmb.2230

    Article  MathSciNet  MATH  Google Scholar 

  46. Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669. https://doi.org/10.1016/j.ejor.2017.11.054

    Article  MathSciNet  MATH  Google Scholar 

  47. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  48. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  49. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  50. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Proceedings of the European conference on computer vision. Springer, pp 818–833

  51. He F, Zhou J, Feng Z, Liu G, Yang Y (2019) A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Appl Energy 237:103–116. https://doi.org/10.1016/j.apenergy.2019.01.055

    Article  Google Scholar 

  52. Bengio Y (2000) Gradient-based optimization of hyperparameters. Neural Comput 12(8):1889–1900. https://doi.org/10.1162/089976600300015187

    Article  MathSciNet  Google Scholar 

  53. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(1):281–305

    MathSciNet  MATH  Google Scholar 

  54. Jaddi NS, Abdullah S, Hamdan AR (2016) A solution representation of genetic algorithm for neural network weights and structure. Inf Process Lett 116(1):22–25. https://doi.org/10.1016/j.ipl.2015.08.001

    Article  Google Scholar 

  55. Tian D, Deng J, Vinod G, Santhosh TV, Tawfik H (2018) A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants. Neurocomputing 322:102–119. https://doi.org/10.1016/j.neucom.2018.09.014

    Article  Google Scholar 

  56. Ciancio C, Ambrogio G, Gagliardi F, Musmanno R (2015) Heuristic techniques to optimize neural network architecture in manufacturing applications. Neural Comput Appl 27(7):2001–2015. https://doi.org/10.1007/s00521-015-1994-9

    Article  Google Scholar 

  57. LeCun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge

    Google Scholar 

  58. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. https://doi.org/10.1016/j.neucom.2016.12.038

    Article  Google Scholar 

  59. Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  60. Kim HJ, Shin KS (2007) A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets. Appl Soft Comput 7(2):569–576. https://doi.org/10.1016/j.asoc.2006.03.004

    Article  Google Scholar 

  61. Pal SK, Wang PP (1996) Genetic algorithms for pattern recognition. CRC Press, Boca Raton

    MATH  Google Scholar 

  62. Boureau YL, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 111–118

  63. Ali ES, Elazim SA (2018) Mine blast algorithm for environmental economic load dispatch with valve loading effect. Neural Comput Appl 30(1):261–270. https://doi.org/10.1007/s00521-016-2650-8

    Article  Google Scholar 

  64. Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Resour Plan Manag 120(4):423–443. https://doi.org/10.1061/(ASCE)0733-9496(1994)120:4(423)

    Article  Google Scholar 

  65. Vanstone B, Finnie G (2009) An empirical methodology for developing stock market trading systems using artificial neural networks. Expert Syst Appl 36(3):6668–6680. https://doi.org/10.1016/j.eswa.2008.08.019

    Article  Google Scholar 

  66. Kingma DP, Ba JL (2014) Adam: a method for stochastic optimization. In: Proceedings of the 3rd international conference on learning representation (ICLR)

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  1. School of Business, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, 03760, Korea

    Hyejung Chung & Kyung-shik Shin

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Chung, H., Shin, Ks. Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Comput & Applic 32, 7897–7914 (2020). https://doi.org/10.1007/s00521-019-04236-3

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