A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, a...

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Vydané v:	Multimedia systems Ročník 29; číslo 3; s. 1751 - 1770
Hlavní autori:	Wu, Jimmy Ming-Tai, Li, Zhongcui, Herencsar, Norbert, Vo, Bay, Lin, Jerry Chun-Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2023 Springer Nature B.V
Predmet:	Algorithms Arrays Artificial neural networks Business cycles Computer Communication Networks Computer Graphics Computer Science Corporate profits Cryptology Data Storage Representation Deep learning Discriminant analysis Expected values Financial instruments Financial management Genetic algorithms Investments Investors Multimedia Information Systems Neural networks Operating Systems Real estate Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Securities markets Special Issue Paper Stock exchanges Stock prices Time series Trends Long–short-term memory neural network Leading indicators Stock price prediction Convolution neural network
ISSN:	0942-4962, 1432-1882
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Abstract	In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.
AbstractList	In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly.
Author	Vo, Bay Wu, Jimmy Ming-Tai Herencsar, Norbert Lin, Jerry Chun-Wei Li, Zhongcui
Author_xml	– sequence: 1 givenname: Jimmy Ming-Tai surname: Wu fullname: Wu, Jimmy Ming-Tai organization: Shandong University of Science and Technology – sequence: 2 givenname: Zhongcui surname: Li fullname: Li, Zhongcui organization: Shandong University of Science and Technology – sequence: 3 givenname: Norbert surname: Herencsar fullname: Herencsar, Norbert organization: Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology – sequence: 4 givenname: Bay surname: Vo fullname: Vo, Bay organization: Faculty of Information Technology, Ho Chi Minh City University of Technology – sequence: 5 givenname: Jerry Chun-Wei orcidid: 0000-0001-8768-9709 surname: Lin fullname: Lin, Jerry Chun-Wei email: jerrylin@ieee.org organization: Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences
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Cites_doi	10.1016/j.knosys.2018.10.034 10.1016/j.knosys.2020.106218 10.1007/s12652-020-01826-1 10.1016/j.eswa.2013.06.071 10.1016/j.ejor.2017.11.054 10.1109/CEC.2013.6557680 10.1007/s10115-019-01353-2 10.1162/neco.1997.9.8.1735 10.1007/978-3-319-73600-6_15 10.1016/j.dss.2013.02.006 10.1016/j.asoc.2018.05.018 10.1002/spe.2915 10.1016/S1352-2310(97)00447-0 10.1016/j.knosys.2017.09.023 10.1109/NOMS.2018.8406199 10.1109/FG.2017.82 10.1007/11760191_68 10.1007/978-981-10-6463-0_31 10.1016/j.knosys.2020.106548 10.1016/S0957-4174(00)00027-0 10.3390/pr7110845 10.18653/v1/N16-1112 10.1016/j.eswa.2016.09.027 10.1016/j.ins.2020.07.001 10.1109/5.726791 10.1109/ICASSP.2013.6638947 10.5120/8282-1278 10.1007/978-981-15-3308-2_64 10.1109/IJCNN.2017.7966019 10.1109/BigData.2015.7364089 10.1007/s11227-017-2228-y 10.1007/978-3-319-93803-5_58 10.1016/j.knosys.2017.03.018 10.1016/j.eswa.2019.03.029 10.1016/j.patrec.2017.09.013 10.1007/s00521-020-05377-6 10.1371/journal.pone.0180944 10.1016/0261-5606(92)90048-3 10.1016/j.physa.2018.11.061 10.1109/IJCNN.1992.287183 10.1080/00036849200000020 10.3390/app9214514 10.1162/neco.1989.1.2.270
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References	BabaNKozakiMAn intelligent forecasting system of stock price using neural networksInt. Jt. Conf. Neural Netw.1992137137710.1109/IJCNN.1992.287183 HoseinzadeEHaratizadehSCnnpred: Cnn-based stock market prediction using a diverse set of variablesExpert Syst. Appl.201912927328510.1016/j.eswa.2019.03.029 GardnerMWDorlingSArtificial neural networks (the multilayer perceptron)a review of applications in the atmospheric sciencesAtmos. Environ.19983214–152627263610.1016/S1352-2310(97)00447-0 ChenCHLuCYLinCBAn intelligence approach for group stock portfolio optimization with a trading mechanismKnowl. Inf. Syst.202062128731610.1007/s10115-019-01353-2 Cui, Y., Wang, S., Li, J.: Lstm neural reordering feature for statistical machine translation. arXiv preprint. arXiv:151200177 (2015) Di PersioLHoncharOArtificial neural networks architectures for stock price prediction: comparisons and applicationsInt. J. Circ. Syst. Signal Process.2016102016403413 TsaiHHWuMEWuWHThe information content of implied volatility skew: evidence on Taiwan stock index optionsData Sci. Pattern Recogn.2017114853 Han, S., Kang, J., Mao, H., Hu, Y., Li, X., Li, Y., Xie, D., Luo, H., Yao, S., Wang, Y., et al.: Ese: Efficient speech recognition engine with sparse lstm on fpga. In: ACM/SIGDA international symposium on field-programmable gate arrays, pp. 75–84 (2017) Nelson, D.M., Pereira, A.C., de Oliveira, R.A.: Stock market’s price movement prediction with lstm neural networks. In: The international joint conference on neural networks, pp. 1419–1426 (2017) Wu, J.M.T., Tsai, M.H., Xiao, S.H., Liaw, Y.P.: A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction. J. Ambient Intell. Humaniz. Comput. pp. 1–17 (2020). https://doi.org/10.1007/s12652-020-01826-1 HochreiterSSchmidhuberJLong short-term memoryNeural Comput.1997981735178010.1162/neco.1997.9.8.1735 ZhaoZZhangXZhouHLiCGongMWangYHetnerec: Heterogeneous network embedding based recommendationKnowl. Based Syst.202020410621810.1016/j.knosys.2020.106218 Siripurapu, A.: Convolutional networks for stock trading. Stanford Univ Dep Comput Sci, pp. 1–6 (2014) Rani, S., Sikka, G.: Recent techniques of clustering of time series data: a survey. Int. J. Comput. Appl. 52(15), 1–9 (2012) BaoWYueJRaoYA deep learning framework for financial time series using stacked autoencoders and long-short term memoryPloS One2017127e018094410.1371/journal.pone.0180944 Wu, J.M.T., Li, Z., Lin, J.C.W., Pirouz, M.: A new convolution neural network model for stock price prediction. In: International conference on genetic and evolutionary computing, pp. 581–585 (2019) PanJSHuPChuSCNovel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind powerProcesses201971184510.3390/pr7110845 Wu, J.M.T., Wu, M.E., Hung, P.J., Hassan, M.M., Fortino, G.: Convert index trading to option strategies via lstm architecture. Neural Comput. Appl. pp. 1–18 (2020) FischerTKraussCDeep learning with long short-term memory networks for financial market predictionsEur. J. Oper. Res.20182702654669380710310.1016/j.ejor.2017.11.0541403.91387 LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc. IEEE199886112278232410.1109/5.726791 Khaidem, L., Saha, S., Dey, S.R.: Predicting the direction of stock market prices using random forest. arXiv preprint. arXiv:160500003 (2016) HuPPanJSChuSCChaiQWLiuTLiZCNew hybrid algorithms for prediction of daily load of power networkAppl. Sci.2019921451410.3390/app9214514 Yong, B.X., Rahim, M.R.A., Abdullah, A.S.: A stock market trading system using deep neural network. In: Asian simulation conference, Springer, pp. 356–364 (2017) ChenCHYuCHA series-based group stock portfolio optimization approach using the grouping genetic algorithm with symbolic aggregate approximationsKnowl. Based Syst.201712514616310.1016/j.knosys.2017.03.018 Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-fourth international joint conference on artificial intelligence (2015) TaylorMPAllenHThe use of technical analysis in the foreign exchange marketJ. Int. Money Finance199211330431410.1016/0261-5606(92)90048-3 Chen, K., Zhou, Y., Dai, F.: A lstm-based method for stock returns prediction: A case study of china stock market. In: IEEE International conference on big data, pp. 2823–2824 (2015) GhoshABoseSMajiGDebnathNSenSStock price prediction using lstm on indian share marketInt. Conf. Comput. Appl. Ind. Eng.201963101110 Wu, J.M.T., Li, Z., Srivastava, G., Tasi, M.H., Lin, J.C.W.: A graph-based convolutional neural network stock price prediction with leading indicators. Pract. Exp. Softw. (2020). https://doi.org/10.1002/spe.2915 Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International conference on acoustics, speech and signal processing, pp. 6645–6649 (2013) ZhuCYinJLiQA stock decision support system based on dbnsJ. Comput. Inf. Syst.2014102883893 Krollner, B., Vanstone, B.J., Finnie, G.R.: Financial time series forecasting with machine learning techniques: a survey. In: ESANN, pp. 25–30 (2010) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint. arXiv:14126980 (2014) Zhang, X., Chen, Y., Yang, J.Y.: Stock index forecasting using pso based selective neural network ensemble. In: IC-AI, pp. 260–264 (2007) PangXZhouYWangPLinWChangVAn innovative neural network approach for stock market predictionJ. Supercomput.20207632098211810.1007/s11227-017-2228-y Lin, J.C.W., Shao, Y., Djenouri, Y., Yun, U.: Asrnn: A recurrent neural network with an attention model for sequence labeling. Knowl. Based Syst. Vol. 212, p. 106548 (2020) KinghornPZhangLShaoLA hierarchical and regional deep learning architecture for image description generationPattern Recogn. Lett.2019119778510.1016/j.patrec.2017.09.013 Garcia Garcia, A., Orts Escolano, S., Oprea, S., Villena Martinez, V., Garcia Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint. arXiv:170406857 (2017) LongWLuZCuiLDeep learning-based feature engineering for stock price movement predictionKnowl. Based Syst.201916416317310.1016/j.knosys.2018.10.034 GunduzHYaslanYCataltepeZIntraday prediction of borsa istanbul using convolutional neural networks and feature correlationsKnowl. Based Syst.201713713814810.1016/j.knosys.2017.09.023 WilliamsRJZipserDA learning algorithm for continually running fully recurrent neural networksNeural Comput.19891227028010.1162/neco.1989.1.2.270 Gui, D., Zhong, Sh., Ming, Z.: Implicit affective video tagging using pupillary response. In: International conference on multimedia modeling, pp. 165–176 (2018) CaiXHuSLinXFeature extraction using restricted boltzmann machine for stock price predictionIEEE Int. Conf. Comput. Sci. Autom. Eng.201238083 CaoJLiZLiJFinancial time series forecasting model based on ceemdan and lstmPhys. A Stat. Mech. Appl.201951912713910.1016/j.physa.2018.11.061 de OliveiraFANobreCNZarateLEApplying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of petr4, petrobras, brazilExpert Syst. Appl.201340187596760610.1016/j.eswa.2013.06.071 Zhang, X., Tan, Y.: Deep stock ranker: a lstm neural network model for stock selection. In: International conference on data mining and big data, pp. 614–623 (2018) KjKimHanIGenetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price indexExpert Syst. Appl.200019212513210.1016/S0957-4174(00)00027-0 Kuo, S.Y., Kuo, C., Chou, Y.H.: Dynamic stock trading system based on quantum-inspired tabu search algorithm. In: IEEE congress on evolutionary computation, pp. 1029–1036 (2013) ZhaoZZhouHLiCTangJZengQDeepemlan: deep embedding learning for attributed networksInf. Sci.2021543382397413090510.1016/j.ins.2020.07.001 ZhongXEnkeDForecasting daily stock market return using dimensionality reductionExpert Syst. Appl.20176712613910.1016/j.eswa.2016.09.027 Chen, Q.a., Li, C.D.: Comparison of forecasting performance of ar, star and ann models on the chinese stock market index. In: International symposium on neural networks, pp. 464–470 (2006) Azzouni, A., Pujolle, G.: A long short-term memory recurrent neural network framework for network traffic matrix prediction. arXiv preprint. arXiv:170505690 (2017) Jiang, H., Learned-Miller, E.: Face detection with the faster r-cnn. In: IEEE international conference on automatic face and gesture recognition, pp. 650–657 (2017) Chen, C.H., Hsieh, C.Y.: Actionable stock portfolio mining by using genetic algorithms. J. Inf. Sci. Eng. 32(6), 1657–1678 (2016) Bahmani-OskooeeMSohrabianAStock prices and the effective exchange rate of the dollarAppl. Econ.199224445946410.1080/00036849200000020 HagenauMLiebmannMNeumannDAutomated news reading: stock price prediction based on financial news using context-capturing featuresDecis. Support Syst.201355368569710.1016/j.dss.2013.02.006 758_CR22 758_CR23 JS Pan (758_CR10) 2019; 7 758_CR20 E Hoseinzade (758_CR33) 2019; 129 X Zhong (758_CR2) 2017; 67 Z Zhao (758_CR15) 2020; 204 P Hu (758_CR9) 2019; 9 758_CR27 W Long (758_CR7) 2019; 164 MP Taylor (758_CR45) 1992; 11 Kim Kj (758_CR5) 2000; 19 W Bao (758_CR31) 2017; 12 758_CR11 758_CR55 758_CR56 758_CR14 758_CR51 Y LeCun (758_CR24) 1998; 86 758_CR53 758_CR6 758_CR8 758_CR16 X Pang (758_CR43) 2020; 76 H Gunduz (758_CR3) 2017; 137 Z Zhao (758_CR21) 2021; 543 758_CR50 758_CR44 758_CR46 758_CR47 758_CR40 758_CR42 HH Tsai (758_CR39) 2017; 1 N Baba (758_CR18) 1992; 1 FA de Oliveira (758_CR19) 2013; 40 MW Gardner (758_CR17) 1998; 32 A Ghosh (758_CR36) 2019; 63 758_CR48 758_CR49 758_CR1 X Cai (758_CR29) 2012; 3 M Hagenau (758_CR4) 2013; 55 758_CR34 758_CR35 CH Chen (758_CR13) 2017; 125 J Cao (758_CR54) 2019; 519 T Fischer (758_CR28) 2018; 270 758_CR37 758_CR38 L Di Persio (758_CR32) 2016; 10 CH Chen (758_CR12) 2020; 62 RJ Williams (758_CR25) 1989; 1 M Bahmani-Oskooee (758_CR41) 1992; 24 S Hochreiter (758_CR26) 1997; 9 C Zhu (758_CR30) 2014; 10 P Kinghorn (758_CR52) 2019; 119
References_xml	– reference: Lin, J.C.W., Shao, Y., Djenouri, Y., Yun, U.: Asrnn: A recurrent neural network with an attention model for sequence labeling. Knowl. Based Syst. Vol. 212, p. 106548 (2020) – reference: HagenauMLiebmannMNeumannDAutomated news reading: stock price prediction based on financial news using context-capturing featuresDecis. Support Syst.201355368569710.1016/j.dss.2013.02.006 – reference: Wu, J.M.T., Li, Z., Srivastava, G., Tasi, M.H., Lin, J.C.W.: A graph-based convolutional neural network stock price prediction with leading indicators. Pract. Exp. Softw. (2020). https://doi.org/10.1002/spe.2915 – reference: de OliveiraFANobreCNZarateLEApplying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of petr4, petrobras, brazilExpert Syst. Appl.201340187596760610.1016/j.eswa.2013.06.071 – reference: Zhang, X., Tan, Y.: Deep stock ranker: a lstm neural network model for stock selection. In: International conference on data mining and big data, pp. 614–623 (2018) – reference: PanJSHuPChuSCNovel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind powerProcesses201971184510.3390/pr7110845 – reference: ZhaoZZhangXZhouHLiCGongMWangYHetnerec: Heterogeneous network embedding based recommendationKnowl. Based Syst.202020410621810.1016/j.knosys.2020.106218 – reference: HoseinzadeEHaratizadehSCnnpred: Cnn-based stock market prediction using a diverse set of variablesExpert Syst. Appl.201912927328510.1016/j.eswa.2019.03.029 – reference: Krollner, B., Vanstone, B.J., Finnie, G.R.: Financial time series forecasting with machine learning techniques: a survey. In: ESANN, pp. 25–30 (2010) – reference: Wu, J.M.T., Tsai, M.H., Xiao, S.H., Liaw, Y.P.: A deep neural network electrocardiogram analysis framework for left ventricular hypertrophy prediction. J. Ambient Intell. Humaniz. Comput. pp. 1–17 (2020). https://doi.org/10.1007/s12652-020-01826-1 – reference: Jiang, H., Learned-Miller, E.: Face detection with the faster r-cnn. In: IEEE international conference on automatic face and gesture recognition, pp. 650–657 (2017) – reference: Chen, Q.a., Li, C.D.: Comparison of forecasting performance of ar, star and ann models on the chinese stock market index. In: International symposium on neural networks, pp. 464–470 (2006) – reference: BabaNKozakiMAn intelligent forecasting system of stock price using neural networksInt. Jt. Conf. Neural Netw.1992137137710.1109/IJCNN.1992.287183 – reference: KinghornPZhangLShaoLA hierarchical and regional deep learning architecture for image description generationPattern Recogn. Lett.2019119778510.1016/j.patrec.2017.09.013 – reference: ZhongXEnkeDForecasting daily stock market return using dimensionality reductionExpert Syst. Appl.20176712613910.1016/j.eswa.2016.09.027 – reference: Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-fourth international joint conference on artificial intelligence (2015) – reference: Rani, S., Sikka, G.: Recent techniques of clustering of time series data: a survey. Int. J. Comput. Appl. 52(15), 1–9 (2012) – reference: Garcia Garcia, A., Orts Escolano, S., Oprea, S., Villena Martinez, V., Garcia Rodriguez, J.: A review on deep learning techniques applied to semantic segmentation. arXiv preprint. arXiv:170406857 (2017) – reference: LeCunYBottouLBengioYHaffnerPGradient-based learning applied to document recognitionProc. IEEE199886112278232410.1109/5.726791 – reference: GardnerMWDorlingSArtificial neural networks (the multilayer perceptron)a review of applications in the atmospheric sciencesAtmos. Environ.19983214–152627263610.1016/S1352-2310(97)00447-0 – reference: Gui, D., Zhong, Sh., Ming, Z.: Implicit affective video tagging using pupillary response. In: International conference on multimedia modeling, pp. 165–176 (2018) – reference: Siripurapu, A.: Convolutional networks for stock trading. Stanford Univ Dep Comput Sci, pp. 1–6 (2014) – reference: ZhuCYinJLiQA stock decision support system based on dbnsJ. Comput. Inf. Syst.2014102883893 – reference: CaoJLiZLiJFinancial time series forecasting model based on ceemdan and lstmPhys. A Stat. Mech. Appl.201951912713910.1016/j.physa.2018.11.061 – reference: Khaidem, L., Saha, S., Dey, S.R.: Predicting the direction of stock market prices using random forest. arXiv preprint. arXiv:160500003 (2016) – reference: Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International conference on acoustics, speech and signal processing, pp. 6645–6649 (2013) – reference: LongWLuZCuiLDeep learning-based feature engineering for stock price movement predictionKnowl. Based Syst.201916416317310.1016/j.knosys.2018.10.034 – reference: Yong, B.X., Rahim, M.R.A., Abdullah, A.S.: A stock market trading system using deep neural network. In: Asian simulation conference, Springer, pp. 356–364 (2017) – reference: Wu, J.M.T., Wu, M.E., Hung, P.J., Hassan, M.M., Fortino, G.: Convert index trading to option strategies via lstm architecture. Neural Comput. Appl. pp. 1–18 (2020) – reference: ChenCHYuCHA series-based group stock portfolio optimization approach using the grouping genetic algorithm with symbolic aggregate approximationsKnowl. Based Syst.201712514616310.1016/j.knosys.2017.03.018 – reference: Di PersioLHoncharOArtificial neural networks architectures for stock price prediction: comparisons and applicationsInt. J. Circ. Syst. Signal Process.2016102016403413 – reference: Azzouni, A., Pujolle, G.: A long short-term memory recurrent neural network framework for network traffic matrix prediction. arXiv preprint. arXiv:170505690 (2017) – reference: TsaiHHWuMEWuWHThe information content of implied volatility skew: evidence on Taiwan stock index optionsData Sci. Pattern Recogn.2017114853 – reference: FischerTKraussCDeep learning with long short-term memory networks for financial market predictionsEur. J. Oper. Res.20182702654669380710310.1016/j.ejor.2017.11.0541403.91387 – reference: Chen, C.H., Hsieh, C.Y.: Actionable stock portfolio mining by using genetic algorithms. J. Inf. Sci. Eng. 32(6), 1657–1678 (2016) – reference: ZhaoZZhouHLiCTangJZengQDeepemlan: deep embedding learning for attributed networksInf. Sci.2021543382397413090510.1016/j.ins.2020.07.001 – reference: GunduzHYaslanYCataltepeZIntraday prediction of borsa istanbul using convolutional neural networks and feature correlationsKnowl. Based Syst.201713713814810.1016/j.knosys.2017.09.023 – reference: TaylorMPAllenHThe use of technical analysis in the foreign exchange marketJ. Int. Money Finance199211330431410.1016/0261-5606(92)90048-3 – reference: Bahmani-OskooeeMSohrabianAStock prices and the effective exchange rate of the dollarAppl. Econ.199224445946410.1080/00036849200000020 – reference: HuPPanJSChuSCChaiQWLiuTLiZCNew hybrid algorithms for prediction of daily load of power networkAppl. Sci.2019921451410.3390/app9214514 – reference: Chen, K., Zhou, Y., Dai, F.: A lstm-based method for stock returns prediction: A case study of china stock market. In: IEEE International conference on big data, pp. 2823–2824 (2015) – reference: Kuo, S.Y., Kuo, C., Chou, Y.H.: Dynamic stock trading system based on quantum-inspired tabu search algorithm. In: IEEE congress on evolutionary computation, pp. 1029–1036 (2013) – reference: Han, S., Kang, J., Mao, H., Hu, Y., Li, X., Li, Y., Xie, D., Luo, H., Yao, S., Wang, Y., et al.: Ese: Efficient speech recognition engine with sparse lstm on fpga. In: ACM/SIGDA international symposium on field-programmable gate arrays, pp. 75–84 (2017) – reference: Nelson, D.M., Pereira, A.C., de Oliveira, R.A.: Stock market’s price movement prediction with lstm neural networks. In: The international joint conference on neural networks, pp. 1419–1426 (2017) – reference: HochreiterSSchmidhuberJLong short-term memoryNeural Comput.1997981735178010.1162/neco.1997.9.8.1735 – reference: Zhang, X., Chen, Y., Yang, J.Y.: Stock index forecasting using pso based selective neural network ensemble. In: IC-AI, pp. 260–264 (2007) – reference: ChenCHLuCYLinCBAn intelligence approach for group stock portfolio optimization with a trading mechanismKnowl. Inf. Syst.202062128731610.1007/s10115-019-01353-2 – reference: Wu, J.M.T., Li, Z., Lin, J.C.W., Pirouz, M.: A new convolution neural network model for stock price prediction. In: International conference on genetic and evolutionary computing, pp. 581–585 (2019) – reference: KjKimHanIGenetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price indexExpert Syst. Appl.200019212513210.1016/S0957-4174(00)00027-0 – reference: BaoWYueJRaoYA deep learning framework for financial time series using stacked autoencoders and long-short term memoryPloS One2017127e018094410.1371/journal.pone.0180944 – reference: WilliamsRJZipserDA learning algorithm for continually running fully recurrent neural networksNeural Comput.19891227028010.1162/neco.1989.1.2.270 – reference: Cui, Y., Wang, S., Li, J.: Lstm neural reordering feature for statistical machine translation. arXiv preprint. arXiv:151200177 (2015) – reference: GhoshABoseSMajiGDebnathNSenSStock price prediction using lstm on indian share marketInt. Conf. Comput. Appl. Ind. Eng.201963101110 – reference: CaiXHuSLinXFeature extraction using restricted boltzmann machine for stock price predictionIEEE Int. Conf. Comput. Sci. Autom. Eng.201238083 – reference: PangXZhouYWangPLinWChangVAn innovative neural network approach for stock market predictionJ. Supercomput.20207632098211810.1007/s11227-017-2228-y – reference: Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint. arXiv:14126980 (2014) – ident: 758_CR55 – volume: 164 start-page: 163 year: 2019 ident: 758_CR7 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2018.10.034 – ident: 758_CR51 – volume: 204 start-page: 106218 year: 2020 ident: 758_CR15 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2020.106218 – ident: 758_CR23 doi: 10.1007/s12652-020-01826-1 – volume: 40 start-page: 7596 issue: 18 year: 2013 ident: 758_CR19 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.06.071 – volume: 270 start-page: 654 issue: 2 year: 2018 ident: 758_CR28 publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2017.11.054 – ident: 758_CR8 – volume: 3 start-page: 80 year: 2012 ident: 758_CR29 publication-title: IEEE Int. Conf. Comput. Sci. Autom. Eng. – ident: 758_CR6 doi: 10.1109/CEC.2013.6557680 – volume: 62 start-page: 287 issue: 1 year: 2020 ident: 758_CR12 publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-019-01353-2 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 758_CR26 publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – ident: 758_CR53 doi: 10.1007/978-3-319-73600-6_15 – volume: 55 start-page: 685 issue: 3 year: 2013 ident: 758_CR4 publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2013.02.006 – ident: 758_CR47 doi: 10.1016/j.asoc.2018.05.018 – ident: 758_CR56 doi: 10.1002/spe.2915 – volume: 32 start-page: 2627 issue: 14–15 year: 1998 ident: 758_CR17 publication-title: Atmos. Environ. doi: 10.1016/S1352-2310(97)00447-0 – volume: 137 start-page: 138 year: 2017 ident: 758_CR3 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2017.09.023 – ident: 758_CR40 – ident: 758_CR38 doi: 10.1109/NOMS.2018.8406199 – ident: 758_CR46 doi: 10.1109/FG.2017.82 – ident: 758_CR16 doi: 10.1007/11760191_68 – ident: 758_CR22 doi: 10.1007/978-981-10-6463-0_31 – ident: 758_CR34 – ident: 758_CR49 doi: 10.1016/j.knosys.2020.106548 – volume: 19 start-page: 125 issue: 2 year: 2000 ident: 758_CR5 publication-title: Expert Syst. Appl. doi: 10.1016/S0957-4174(00)00027-0 – volume: 7 start-page: 845 issue: 11 year: 2019 ident: 758_CR10 publication-title: Processes doi: 10.3390/pr7110845 – ident: 758_CR50 doi: 10.18653/v1/N16-1112 – volume: 67 start-page: 126 year: 2017 ident: 758_CR2 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.09.027 – volume: 543 start-page: 382 year: 2021 ident: 758_CR21 publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.07.001 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 758_CR24 publication-title: Proc. IEEE doi: 10.1109/5.726791 – ident: 758_CR14 doi: 10.1109/ICASSP.2013.6638947 – volume: 1 start-page: 48 issue: 1 year: 2017 ident: 758_CR39 publication-title: Data Sci. Pattern Recogn. – ident: 758_CR1 doi: 10.5120/8282-1278 – ident: 758_CR20 – ident: 758_CR35 doi: 10.1007/978-981-15-3308-2_64 – volume: 63 start-page: 101 year: 2019 ident: 758_CR36 publication-title: Int. Conf. Comput. Appl. Ind. Eng. – ident: 758_CR44 doi: 10.1109/IJCNN.2017.7966019 – volume: 10 start-page: 403 issue: 2016 year: 2016 ident: 758_CR32 publication-title: Int. J. Circ. Syst. Signal Process. – ident: 758_CR27 doi: 10.1109/BigData.2015.7364089 – volume: 76 start-page: 2098 issue: 3 year: 2020 ident: 758_CR43 publication-title: J. Supercomput. doi: 10.1007/s11227-017-2228-y – ident: 758_CR37 doi: 10.1007/978-3-319-93803-5_58 – volume: 125 start-page: 146 year: 2017 ident: 758_CR13 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2017.03.018 – volume: 10 start-page: 883 issue: 2 year: 2014 ident: 758_CR30 publication-title: J. Comput. Inf. Syst. – volume: 129 start-page: 273 year: 2019 ident: 758_CR33 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.03.029 – volume: 119 start-page: 77 year: 2019 ident: 758_CR52 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2017.09.013 – ident: 758_CR48 doi: 10.1007/s00521-020-05377-6 – volume: 12 start-page: e0180944 issue: 7 year: 2017 ident: 758_CR31 publication-title: PloS One doi: 10.1371/journal.pone.0180944 – volume: 11 start-page: 304 issue: 3 year: 1992 ident: 758_CR45 publication-title: J. Int. Money Finance doi: 10.1016/0261-5606(92)90048-3 – volume: 519 start-page: 127 year: 2019 ident: 758_CR54 publication-title: Phys. A Stat. Mech. Appl. doi: 10.1016/j.physa.2018.11.061 – volume: 1 start-page: 371 year: 1992 ident: 758_CR18 publication-title: Int. Jt. Conf. Neural Netw. doi: 10.1109/IJCNN.1992.287183 – volume: 24 start-page: 459 issue: 4 year: 1992 ident: 758_CR41 publication-title: Appl. Econ. doi: 10.1080/00036849200000020 – ident: 758_CR42 – ident: 758_CR11 – volume: 9 start-page: 4514 issue: 21 year: 2019 ident: 758_CR9 publication-title: Appl. Sci. doi: 10.3390/app9214514 – volume: 1 start-page: 270 issue: 2 year: 1989 ident: 758_CR25 publication-title: Neural Comput. doi: 10.1162/neco.1989.1.2.270
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SubjectTerms	Algorithms Arrays Artificial neural networks Business cycles Computer Communication Networks Computer Graphics Computer Science Corporate profits Cryptology Data Storage Representation Deep learning Discriminant analysis Expected values Financial instruments Financial management Genetic algorithms Investments Investors Multimedia Information Systems Neural networks Operating Systems Real estate Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Securities markets Special Issue Paper Stock exchanges Stock prices Time series Trends
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Title	A graph-based CNN-LSTM stock price prediction algorithm with leading indicators
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