A monetary policy prediction model based on deep learning

Applying neural network and error t-value test, this study trains and analyzes 28 interest rate changes of China’s macro-monetary policy and the mutual influences between reserve adjustments and financial markets for 51 times from 2000 to 2018 according to the data correlation between financial mark...

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Veröffentlicht in:	Neural computing & applications Jg. 32; H. 10; S. 5649 - 5668
1. Verfasser:	Lu, Minrong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.05.2020 Springer Nature B.V
Schlagworte:	Advances in Parallel and Distributed Computing for Neural Computing Artificial Intelligence Back propagation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data correlation Data Mining and Knowledge Discovery Data processing Datasets Deep learning Error analysis Feature extraction Image Processing and Computer Vision Interest rates Model accuracy Monetary policy Mutation Neural networks Prediction models Principal components analysis Probability and Statistics in Computer Science Securities markets Training Monetary policy Neural network Financial risks Time series
ISSN:	0941-0643, 1433-3058
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Abstract	Applying neural network and error t-value test, this study trains and analyzes 28 interest rate changes of China’s macro-monetary policy and the mutual influences between reserve adjustments and financial markets for 51 times from 2000 to 2018 according to the data correlation between financial market and monetary policy. Through the principal component analysis, the bilateral financial risk system and data set are established, and the data set pre-process and dimensionality reduction are carried out to extract the most informative features. Six training cases are designed with processed features, and then the cases are input to each neural network model for combined prediction. Firstly, based on backpropagation neural network (BP), the forecasting model of monetary policy is established. Then, considering the importance characteristics of financial index data, expert weights based on BP, are introduced to propose weights backpropagation (WBP) model. On the basis of the timing characteristics of financial market, the WBP model is improved and the timing weights backpropagation (TWBP) model is proposed. Experiments show that different training cases bring out various effects. The accuracy rate of interest rate and reserve change value is lower than the original value after training. The mutation after data processing affects the learning of neural network. At the same time, the WBP and TWBP models improve according to the importance and timing characteristics of financial indicators have less errors in results, and the TWBP model has higher accuracy. When the number of hidden layers is 3, good results can be obtained, but in manifold training of the timing cycle, the efficiency of that is not as good as the WBP model.
AbstractList	Applying neural network and error t-value test, this study trains and analyzes 28 interest rate changes of China’s macro-monetary policy and the mutual influences between reserve adjustments and financial markets for 51 times from 2000 to 2018 according to the data correlation between financial market and monetary policy. Through the principal component analysis, the bilateral financial risk system and data set are established, and the data set pre-process and dimensionality reduction are carried out to extract the most informative features. Six training cases are designed with processed features, and then the cases are input to each neural network model for combined prediction. Firstly, based on backpropagation neural network (BP), the forecasting model of monetary policy is established. Then, considering the importance characteristics of financial index data, expert weights based on BP, are introduced to propose weights backpropagation (WBP) model. On the basis of the timing characteristics of financial market, the WBP model is improved and the timing weights backpropagation (TWBP) model is proposed. Experiments show that different training cases bring out various effects. The accuracy rate of interest rate and reserve change value is lower than the original value after training. The mutation after data processing affects the learning of neural network. At the same time, the WBP and TWBP models improve according to the importance and timing characteristics of financial indicators have less errors in results, and the TWBP model has higher accuracy. When the number of hidden layers is 3, good results can be obtained, but in manifold training of the timing cycle, the efficiency of that is not as good as the WBP model.
Author	Lu, Minrong
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Cites_doi	10.1109/TPDS.2018.2877359 10.1016/j.ins.2014.02.122 10.1109/TPDS.2014.2308221 10.1109/TPDS.2018.2871189 10.1109/TPDS.2019.2907537 10.1007/s10479-016-2113-8 10.1016/j.ijforecast.2010.02.015 10.1016/j.knosys.2012.05.003 10.1016/j.jfs.2013.01.004 10.1016/j.eneco.2016.11.016 10.1016/j.ins.2018.06.045 10.1162/089976699300016340 10.1109/TPDS.2013.270 10.3938/jkps.71.444 10.1109/TPDS.2016.2603511 10.1109/MSP.2017.2663138 10.1109/TEVC.2008.928176 10.1162/neco.2008.11-07-647 10.1016/j.camwa.2010.08.054 10.1111/j.1540-5915.1998.tb01582.x 10.1007/s10489-018-1308-x 10.1111/jofi.12521 10.1016/j.chaos.2018.01.008 10.1111/risa.12801 10.1016/j.neunet.2011.12.004 10.1109/TPDS.2014.2385698 10.1080/07350015.1990.10509779 10.1007/s11071-014-1455-5 10.1126/science.1248506 10.1038/nature14539 10.2307/3215342 10.1109/TFUZZ.2012.2226890 10.1109/INMIC.2005.334420 10.3390/su10103765 10.1016/j.knosys.2005.11.015 10.1016/j.jedc.2016.01.003 10.1016/j.ecolecon.2018.05.011 10.1109/TII.2019.2909473 10.1109/ICASSP.2013.6639346 10.1007/s00521-015-1848-5 10.1109/TCBB.2018.2868088 10.1057/palgrave.jors.2601415 10.1126/science.1243089 10.1007/s10489-006-0001-7 10.1016/j.ins.2014.09.038 10.1016/j.ins.2017.04.028 10.1016/j.neunet.2017.03.004
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References	Chen, Li, Tang, Yu, Li (CR22) 2017; 28 Lazer, Kennedy, King (CR23) 2014; 343 Yu-Hui, Wei, Shou-Ning (CR42) 2010; 3 Kayalar, Küçüközmen, Selcuk-Kestel (CR10) 2017; 61 Zhang, Wang (CR18) 2017; 34 Yuming, Li, He, Zhang, Li (CR14) 2015; 26 Fratianni, Marchionne (CR35) 2013; 9 Li, Chiang (CR9) 2013; 21 CR33 CR30 Chiu, Pun, Wong (CR24) 2017; 37 Ebrahimpour, Nikoo, Masoudnia (CR27) 2011; 27 Jie, Wang (CR47) 2017; 90 Xiao, Li, Li (CR48) 2017; 405 Antipov, Meade (CR8) 2002; 53 Asadi, Hadavandi, Mehmanpazir (CR41) 2012; 35 Ganda (CR12) 2019; 5 Lecun, Bengio, Hinton (CR38) 2015; 521 Mandic, Chambers (CR56) 1999; 11 Chen, Li, Bilal, Zhou, Li, Yu (CR29) 2018; 30 CR43 Desai, Bharati (CR49) 2010; 29 Yu, Chen, Wang (CR26) 2009; 13 Li, Tang, Li (CR7) 2014; 25 Li, Yang, Li (CR21) 2015; 26 Qian, Rasheed (CR34) 2007; 26 Yuming, Li, Jingtong, Li (CR11) 2014; 270 CR15 CR13 Lin, Shang, Zhou (CR51) 2014; 78 CR54 CR53 CR52 Araújo (CR19) 2012; 28 CR50 Lin, Sun, Yu (CR3) 2018; 262 Song, Lee, Lee (CR39) 2018; 49 Wu, Lu (CR37) 2010; 60 Chung, Shin (CR20) 2010; 10 Lengnick, Wohltmann (CR4) 2016; 64 Chen, Chen (CR28) 2015; 294 Dafermos, Nikolaidi, Galanis (CR5) 2018; 152 Einav, Levin (CR17) 2014; 346 CR25 Patwary, Lee, Nobi (CR36) 2017; 71 Pang, Zhou, Pan (CR40) 2018; 1 Cao, Long, Chao (CR32) 2015; 26 Maknickiené, Maknickas (CR46) 2013; 14 O’Connor, Madden (CR44) 2006; 19 Platen, Rebolledo (CR6) 1996; 33 Bengio, Delalleau (CR31) 2009; 21 Black, Devereux, Lundborg (CR1) 2017; 72 Chen, Li, Yang, Xiao, Xie, Li (CR45) 2019; 30 Riccioni, Cerqueti (CR2) 2018; 107 Leshno, Lin, Pinkus (CR55) 1991; 6 Chen, Tiao (CR16) 1990; 8 EMH Lin (4319_CR3) 2018; 262 R Ebrahimpour (4319_CR27) 2011; 27 Kenli Li (4319_CR7) 2014; 25 L Yu (4319_CR26) 2009; 13 L Einav (4319_CR17) 2014; 346 Xu Yuming (4319_CR14) 2015; 26 4319_CR33 XPS Zhang (4319_CR18) 2017; 34 4319_CR30 Kenli Li (4319_CR21) 2015; 26 Yuedan Chen (4319_CR45) 2019; 30 VS Desai (4319_CR49) 2010; 29 M Lengnick (4319_CR4) 2016; 64 C Li (4319_CR9) 2013; 21 Y Dafermos (4319_CR5) 2018; 152 N Maknickiené (4319_CR46) 2013; 14 M Leshno (4319_CR55) 1991; 6 Jianguo Chen (4319_CR29) 2018; 30 4319_CR25 A Antipov (4319_CR8) 2002; 53 Y Bengio (4319_CR31) 2009; 21 MY Chen (4319_CR28) 2015; 294 RDA Araújo (4319_CR19) 2012; 28 Jianguo Chen (4319_CR22) 2017; 28 T Yu-Hui (4319_CR42) 2010; 3 N O’Connor (4319_CR44) 2006; 19 4319_CR50 SE Black (4319_CR1) 2017; 72 Y Lecun (4319_CR38) 2015; 521 4319_CR54 S Asadi (4319_CR41) 2012; 35 D Lazer (4319_CR23) 2014; 343 MC Chiu (4319_CR24) 2017; 37 4319_CR52 4319_CR53 DP Mandic (4319_CR56) 1999; 11 4319_CR15 Xu Yuming (4319_CR11) 2014; 270 4319_CR13 EU Patwary (4319_CR36) 2017; 71 Z Cao (4319_CR32) 2015; 26 E Platen (4319_CR6) 1996; 33 X Pang (4319_CR40) 2018; 1 DE Kayalar (4319_CR10) 2017; 61 Y Song (4319_CR39) 2018; 49 H Chung (4319_CR20) 2010; 10 Guoqing Xiao (4319_CR48) 2017; 405 X Wu (4319_CR37) 2010; 60 J Riccioni (4319_CR2) 2018; 107 4319_CR43 C Chen (4319_CR16) 1990; 8 B Qian (4319_CR34) 2007; 26 F Ganda (4319_CR12) 2019; 5 M Fratianni (4319_CR35) 2013; 9 W Jie (4319_CR47) 2017; 90 A Lin (4319_CR51) 2014; 78
References_xml	– volume: 72 start-page: 100 issue: 5 year: 2017 end-page: 105 ident: CR1 article-title: On the origins of risk-taking in financial markets publication-title: J Financ – volume: 343 start-page: 1203 issue: 6176 year: 2014 ident: CR23 article-title: Big data. the parable of google flu: traps in big data analysis publication-title: Science – volume: 30 start-page: 965 issue: 5 year: 2018 end-page: 976 ident: CR29 article-title: A bi-layered parallel training architecture for large-scale convolutional neural networks publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2877359 – volume: 107 start-page: 186 year: 2018 end-page: 194 ident: CR2 article-title: Regular paths in financial markets: investigating the benford’s law publication-title: Chaos Solitons Fractals – volume: 11 start-page: 1069 issue: 5 year: 1999 end-page: 1077 ident: CR56 article-title: Relating the slope of the activation function and the learning rate within a recurrent neural network publication-title: Neural Comput – volume: 34 start-page: 14 issue: 3 year: 2017 end-page: 35 ident: CR18 article-title: Signal processing for finance, economics, and marketing: concepts, framework, and big data applications publication-title: IEEE Signal Process Mag – volume: 53 start-page: 953 issue: 9 year: 2002 end-page: 960 ident: CR8 article-title: Forecasting call frequency at a financial services call centre publication-title: J Oper Res Soc – volume: 270 start-page: 255 year: 2014 end-page: 287 ident: CR11 article-title: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues publication-title: Inf Sci – volume: 21 start-page: 1601 issue: 6 year: 2009 ident: CR31 article-title: Justifying and generalizing contrastive divergence publication-title: Neural Comput – ident: CR54 – volume: 33 start-page: 601 issue: 3 year: 1996 end-page: 613 ident: CR6 article-title: Principles for modelling financial markets publication-title: J Appl Probab – volume: 3 start-page: 442 year: 2010 end-page: 445 ident: CR42 article-title: Research on stock quotation based on improved rough lattice model publication-title: IEEE – volume: 61 start-page: 162 year: 2017 end-page: 173 ident: CR10 article-title: The impact of crude oil prices on financial market indicators: copula approach publication-title: Energy Econ – ident: CR25 – volume: 6 start-page: 861 issue: 6 year: 1991 end-page: 867 ident: CR55 article-title: Original contribution: multilayer feedforward networks with a nonpolynomial activation function can approximate any function publication-title: Neural Comput – volume: 35 start-page: 245 issue: 15 year: 2012 end-page: 258 ident: CR41 article-title: Hybridization of evolutionary levenberg-marquardt neural networks and data pre-processing for stock market prediction publication-title: Knowledge-Based Syst – volume: 27 start-page: 804 issue: 3 year: 2011 end-page: 816 ident: CR27 article-title: Mixture of mlp-experts for trend forecasting of time series: a case study of the tehran stock exchange publication-title: Int J Forecast – volume: 26 start-page: 25 issue: 1 year: 2007 end-page: 33 ident: CR34 article-title: Stock market prediction with multiple classifiers publication-title: Appl Intell – ident: CR15 – volume: 78 start-page: 485 issue: 1 year: 2014 end-page: 494 ident: CR51 article-title: Cross-correlations and structures of stock markets based on multiscale MF-DXA and PCA publication-title: Nonlinear Dyn – ident: CR50 – volume: 26 start-page: 196 issue: 1 year: 2015 end-page: 205 ident: CR21 article-title: Performance analysis and optimization for spmv on gpu using probabilistic modeling publication-title: IEEE Trans Parallel Distrib Syst – volume: 26 start-page: 1839 issue: 8 year: 2015 end-page: 1847 ident: CR32 article-title: Spiking neural network-based target tracking control for autonomous mobile robots publication-title: Neural Comput Appl – volume: 37 start-page: 1532 issue: 8 year: 2017 ident: CR24 article-title: Big data challenges of high-dimensional continuous-time mean-variance portfolio selection and a remedy publication-title: Risk Analysis – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: CR38 article-title: Deep learning publication-title: Nature – volume: 1 start-page: 1 year: 2018 end-page: 21 ident: CR40 article-title: An innovative neural network approach for stock market prediction publication-title: J Supercomput – volume: 29 start-page: 405 issue: 2 year: 2010 end-page: 423 ident: CR49 article-title: The efficacy of neural networks in predicting returns on stock and bond indices publication-title: Decis Sci – volume: 10 start-page: 3765 year: 2010 ident: CR20 article-title: Genetic algorithm-optimized long short-term memory network for stock market prediction publication-title: Sustainability – volume: 28 start-page: 919 issue: 4 year: 2017 end-page: 933 ident: CR22 article-title: A parallel random forest algorithm for big data in spark cloud computing environment publication-title: IEEE Trans Parallel Distrib Syst – ident: CR43 – volume: 9 start-page: 69 issue: 1 year: 2013 end-page: 89 ident: CR35 article-title: The fading stock market response to announcements of bank bailouts publication-title: J Financ Stabil – volume: 21 start-page: 567 issue: 3 year: 2013 end-page: 584 ident: CR9 article-title: Complex neurofuzzy arima forecasting–a new approach using complex fuzzy sets publication-title: IEEE Trans Fuzzy Syst – ident: CR53 – ident: CR30 – volume: 64 start-page: 148 year: 2016 end-page: 165 ident: CR4 article-title: Optimal monetary policy in a new Keynesian model with animal spirits and financial markets publication-title: J Econ Dyn Control – ident: CR33 – volume: 13 start-page: 87 issue: 1 year: 2009 end-page: 102 ident: CR26 article-title: Evolving least squares support vector machines for stock market trend mining publication-title: IEEE Trans Evol Comput – volume: 8 start-page: 83 issue: 1 year: 1990 end-page: 97 ident: CR16 article-title: Random level-shift time series models, arima approximations, and level-shift detection publication-title: J Bus Econ Stat – volume: 152 start-page: 219 year: 2018 end-page: 234 ident: CR5 article-title: Climate change, financial stability and monetary policy publication-title: Ecol Econ – volume: 71 start-page: 444 issue: 7 year: 2017 end-page: 451 ident: CR36 article-title: Changes of hierarchical network in local and world stock market publication-title: J Kor Phys Soc – volume: 405 start-page: 207 year: 2017 end-page: 226 ident: CR48 article-title: Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries publication-title: Inf Sci – volume: 26 start-page: 3208 issue: 12 year: 2015 end-page: 3222 ident: CR14 article-title: A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems publication-title: IEEE Trans Parallel Distrib Syst – volume: 14 start-page: 403 issue: 2 year: 2013 end-page: 413 ident: CR46 article-title: Financial market prediction system with evolino neural network and delphi method publication-title: Physiologia Plantarum – volume: 60 start-page: 2476 issue: 8 year: 2010 end-page: 2487 ident: CR37 article-title: Exponential synchronization of weighted general delay coupled and non-delay coupled dynamical networks publication-title: Comput Math Appl – volume: 28 start-page: 61 year: 2012 end-page: 81 ident: CR19 article-title: A morphological perceptron with gradient-based learning for Brazilian stock market forecasting publication-title: Neural Netw – volume: 5 start-page: 11 year: 2019 end-page: 15 ident: CR12 article-title: The environmental impacts of financial development in oecd countries: a panel gmm approach publication-title: Environ Sci Pollut Res – ident: CR52 – volume: 90 start-page: 8 year: 2017 end-page: 20 ident: CR47 article-title: Forecasting stochastic neural network based on financial empirical mode decomposition publication-title: Neural Netw – ident: CR13 – volume: 262 start-page: 579 issue: 2 year: 2018 end-page: 603 ident: CR3 article-title: Systemic risk, financial markets, and performance of financial institutions publication-title: Ann Oper Res – volume: 294 start-page: 227 issue: 2 year: 2015 end-page: 241 ident: CR28 article-title: A hybrid fuzzy time series model based on granular computing for stock price forecasting publication-title: Inf Sci – volume: 30 start-page: 923 issue: 4 year: 2019 end-page: 938 ident: CR45 article-title: Performance-aware model for sparse matrix-matrix multiplication on the sunway taihulight supercomputer publication-title: IEEE Trans Parallel Distrib Syst – volume: 49 start-page: 897 year: 2018 end-page: 911 ident: CR39 article-title: A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction publication-title: Appl Intell – volume: 19 start-page: 371 issue: 5 year: 2006 end-page: 378 ident: CR44 article-title: A neural network approach to predicting stock exchange movements using external factors publication-title: Knowledge-Based Syst – volume: 346 start-page: 124 issue: 6210 year: 2014 end-page: 130 ident: CR17 article-title: Economics in the age of big data publication-title: Science – volume: 25 start-page: 2867 issue: 11 year: 2014 end-page: 2876 ident: CR7 article-title: Energy-efficient stochastic task scheduling on heterogeneous computing systems publication-title: IEEE Trans Parallel Distrib Syst – volume: 270 start-page: 255 year: 2014 ident: 4319_CR11 publication-title: Inf Sci doi: 10.1016/j.ins.2014.02.122 – volume: 26 start-page: 196 issue: 1 year: 2015 ident: 4319_CR21 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2014.2308221 – volume: 1 start-page: 1 year: 2018 ident: 4319_CR40 publication-title: J Supercomput – volume: 30 start-page: 923 issue: 4 year: 2019 ident: 4319_CR45 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2871189 – ident: 4319_CR53 – ident: 4319_CR50 doi: 10.1109/TPDS.2019.2907537 – volume: 262 start-page: 579 issue: 2 year: 2018 ident: 4319_CR3 publication-title: Ann Oper Res doi: 10.1007/s10479-016-2113-8 – volume: 27 start-page: 804 issue: 3 year: 2011 ident: 4319_CR27 publication-title: Int J Forecast doi: 10.1016/j.ijforecast.2010.02.015 – volume: 35 start-page: 245 issue: 15 year: 2012 ident: 4319_CR41 publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2012.05.003 – volume: 30 start-page: 965 issue: 5 year: 2018 ident: 4319_CR29 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2877359 – volume: 9 start-page: 69 issue: 1 year: 2013 ident: 4319_CR35 publication-title: J Financ Stabil doi: 10.1016/j.jfs.2013.01.004 – volume: 61 start-page: 162 year: 2017 ident: 4319_CR10 publication-title: Energy Econ doi: 10.1016/j.eneco.2016.11.016 – ident: 4319_CR30 doi: 10.1016/j.ins.2018.06.045 – volume: 11 start-page: 1069 issue: 5 year: 1999 ident: 4319_CR56 publication-title: Neural Comput doi: 10.1162/089976699300016340 – volume: 25 start-page: 2867 issue: 11 year: 2014 ident: 4319_CR7 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2013.270 – volume: 71 start-page: 444 issue: 7 year: 2017 ident: 4319_CR36 publication-title: J Kor Phys Soc doi: 10.3938/jkps.71.444 – volume: 28 start-page: 919 issue: 4 year: 2017 ident: 4319_CR22 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2016.2603511 – volume: 34 start-page: 14 issue: 3 year: 2017 ident: 4319_CR18 publication-title: IEEE Signal Process Mag doi: 10.1109/MSP.2017.2663138 – volume: 13 start-page: 87 issue: 1 year: 2009 ident: 4319_CR26 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2008.928176 – volume: 21 start-page: 1601 issue: 6 year: 2009 ident: 4319_CR31 publication-title: Neural Comput doi: 10.1162/neco.2008.11-07-647 – volume: 60 start-page: 2476 issue: 8 year: 2010 ident: 4319_CR37 publication-title: Comput Math Appl doi: 10.1016/j.camwa.2010.08.054 – ident: 4319_CR54 – volume: 6 start-page: 861 issue: 6 year: 1991 ident: 4319_CR55 publication-title: Neural Comput – volume: 29 start-page: 405 issue: 2 year: 2010 ident: 4319_CR49 publication-title: Decis Sci doi: 10.1111/j.1540-5915.1998.tb01582.x – volume: 5 start-page: 11 year: 2019 ident: 4319_CR12 publication-title: Environ Sci Pollut Res – volume: 49 start-page: 897 year: 2018 ident: 4319_CR39 publication-title: Appl Intell doi: 10.1007/s10489-018-1308-x – volume: 72 start-page: 100 issue: 5 year: 2017 ident: 4319_CR1 publication-title: J Financ doi: 10.1111/jofi.12521 – volume: 107 start-page: 186 year: 2018 ident: 4319_CR2 publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2018.01.008 – volume: 37 start-page: 1532 issue: 8 year: 2017 ident: 4319_CR24 publication-title: Risk Analysis doi: 10.1111/risa.12801 – volume: 28 start-page: 61 year: 2012 ident: 4319_CR19 publication-title: Neural Netw doi: 10.1016/j.neunet.2011.12.004 – volume: 26 start-page: 3208 issue: 12 year: 2015 ident: 4319_CR14 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2014.2385698 – volume: 8 start-page: 83 issue: 1 year: 1990 ident: 4319_CR16 publication-title: J Bus Econ Stat doi: 10.1080/07350015.1990.10509779 – volume: 78 start-page: 485 issue: 1 year: 2014 ident: 4319_CR51 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1455-5 – volume: 343 start-page: 1203 issue: 6176 year: 2014 ident: 4319_CR23 publication-title: Science doi: 10.1126/science.1248506 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 4319_CR38 publication-title: Nature doi: 10.1038/nature14539 – volume: 14 start-page: 403 issue: 2 year: 2013 ident: 4319_CR46 publication-title: Physiologia Plantarum – ident: 4319_CR13 – volume: 33 start-page: 601 issue: 3 year: 1996 ident: 4319_CR6 publication-title: J Appl Probab doi: 10.2307/3215342 – volume: 21 start-page: 567 issue: 3 year: 2013 ident: 4319_CR9 publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2012.2226890 – ident: 4319_CR43 doi: 10.1109/INMIC.2005.334420 – volume: 10 start-page: 3765 year: 2010 ident: 4319_CR20 publication-title: Sustainability doi: 10.3390/su10103765 – volume: 19 start-page: 371 issue: 5 year: 2006 ident: 4319_CR44 publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2005.11.015 – volume: 64 start-page: 148 year: 2016 ident: 4319_CR4 publication-title: J Econ Dyn Control doi: 10.1016/j.jedc.2016.01.003 – volume: 152 start-page: 219 year: 2018 ident: 4319_CR5 publication-title: Ecol Econ doi: 10.1016/j.ecolecon.2018.05.011 – ident: 4319_CR25 doi: 10.1109/TII.2019.2909473 – ident: 4319_CR33 doi: 10.1109/ICASSP.2013.6639346 – ident: 4319_CR52 – volume: 26 start-page: 1839 issue: 8 year: 2015 ident: 4319_CR32 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1848-5 – ident: 4319_CR15 doi: 10.1109/TCBB.2018.2868088 – volume: 53 start-page: 953 issue: 9 year: 2002 ident: 4319_CR8 publication-title: J Oper Res Soc doi: 10.1057/palgrave.jors.2601415 – volume: 3 start-page: 442 year: 2010 ident: 4319_CR42 publication-title: IEEE – volume: 346 start-page: 124 issue: 6210 year: 2014 ident: 4319_CR17 publication-title: Science doi: 10.1126/science.1243089 – volume: 26 start-page: 25 issue: 1 year: 2007 ident: 4319_CR34 publication-title: Appl Intell doi: 10.1007/s10489-006-0001-7 – volume: 294 start-page: 227 issue: 2 year: 2015 ident: 4319_CR28 publication-title: Inf Sci doi: 10.1016/j.ins.2014.09.038 – volume: 405 start-page: 207 year: 2017 ident: 4319_CR48 publication-title: Inf Sci doi: 10.1016/j.ins.2017.04.028 – volume: 90 start-page: 8 year: 2017 ident: 4319_CR47 publication-title: Neural Netw doi: 10.1016/j.neunet.2017.03.004
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Snippet	Applying neural network and error t-value test, this study trains and analyzes 28 interest rate changes of China’s macro-monetary policy and the mutual...
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SubjectTerms	Advances in Parallel and Distributed Computing for Neural Computing Artificial Intelligence Back propagation Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data correlation Data Mining and Knowledge Discovery Data processing Datasets Deep learning Error analysis Feature extraction Image Processing and Computer Vision Interest rates Model accuracy Monetary policy Mutation Neural networks Prediction models Principal components analysis Probability and Statistics in Computer Science Securities markets Training
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Title	A monetary policy prediction model based on deep learning
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