Macroeconomic indicators alone can predict the monthly closing price of major U.S. indices: Insights from artificial intelligence, time-series analysis and hybrid models
[Display omitted] •A 2-stage method is proposed to predict the 1-month ahead price for 13 U.S. indices.•Ensembles of macroeconomic factors alone are more predictive than time-series models.•Errors in time-series models are explained by the ensembles of macroeconomic factors.•A decision support syste...
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| Veröffentlicht in: | Applied soft computing Jg. 71; S. 685 - 697 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.10.2018
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| ISSN: | 1568-4946, 1872-9681 |
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| Abstract | [Display omitted]
•A 2-stage method is proposed to predict the 1-month ahead price for 13 U.S. indices.•Ensembles of macroeconomic factors alone are more predictive than time-series models.•Errors in time-series models are explained by the ensembles of macroeconomic factors.•A decision support system for predicting the monthly stock price is presented.•The code is freely available for investors and researchers.
This paper proposes a two-stage approach that can be used to investigate whether the information hidden in macroeconomic variables (alone) can be used to accurately predict the one-month ahead price for major U.S stock and sector indices. Stage 1 is constructed to evaluate the hypothesis that the price for different indices is driven by different economic indicators. It consists of three phases. In phase I, the data is automatically acquired using freely available APIs (application programming interfaces) and prepared for analysis. Phase II reduces the set of potential predictors without the loss of information through several variable selection methods. The third phase employs four ensemble models and three time-series models for prediction. The prediction performance of the seven models are compared using the Mean Absolute Percent Error (and two additional metrics). If the hypothesis were to be true, one expects that the performance of the ensemble models to outperform the time-series models since the information in the economy is more important than the information in previous prices. In Stage 2, a hybrid approach of the recurring neural network used for time-series prediction (i.e., the LSTM) and the ensemble models is constructed to examine the secondary hypothesis that the residuals from the time-series models are not random and can be explained by the macroeconomic indicators. To test the two hypotheses, the monthly closing prices for 13 U.S. stock and sector indices and the corresponding values for 23 macroeconomic indicators were collected from 01/1992–10/2016. Based on the case study, the four ensembles prediction performance were superior to that of the three time-series models. The MAPE of the best model for a given index was < 1.87%. The Stage 2 results also show that the three evaluation metrics (RMSE, MAPE and MAE) can be typically improved by 25–50% by incorporating the information hidden in the macroeconomic indicators (through the ensemble approach). Thus, this paper shows that, for the analysis period and the indices studied, the macro-economic indicators are leading predictors of the price of 13 U.S. sector indices. |
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| AbstractList | [Display omitted]
•A 2-stage method is proposed to predict the 1-month ahead price for 13 U.S. indices.•Ensembles of macroeconomic factors alone are more predictive than time-series models.•Errors in time-series models are explained by the ensembles of macroeconomic factors.•A decision support system for predicting the monthly stock price is presented.•The code is freely available for investors and researchers.
This paper proposes a two-stage approach that can be used to investigate whether the information hidden in macroeconomic variables (alone) can be used to accurately predict the one-month ahead price for major U.S stock and sector indices. Stage 1 is constructed to evaluate the hypothesis that the price for different indices is driven by different economic indicators. It consists of three phases. In phase I, the data is automatically acquired using freely available APIs (application programming interfaces) and prepared for analysis. Phase II reduces the set of potential predictors without the loss of information through several variable selection methods. The third phase employs four ensemble models and three time-series models for prediction. The prediction performance of the seven models are compared using the Mean Absolute Percent Error (and two additional metrics). If the hypothesis were to be true, one expects that the performance of the ensemble models to outperform the time-series models since the information in the economy is more important than the information in previous prices. In Stage 2, a hybrid approach of the recurring neural network used for time-series prediction (i.e., the LSTM) and the ensemble models is constructed to examine the secondary hypothesis that the residuals from the time-series models are not random and can be explained by the macroeconomic indicators. To test the two hypotheses, the monthly closing prices for 13 U.S. stock and sector indices and the corresponding values for 23 macroeconomic indicators were collected from 01/1992–10/2016. Based on the case study, the four ensembles prediction performance were superior to that of the three time-series models. The MAPE of the best model for a given index was < 1.87%. The Stage 2 results also show that the three evaluation metrics (RMSE, MAPE and MAE) can be typically improved by 25–50% by incorporating the information hidden in the macroeconomic indicators (through the ensemble approach). Thus, this paper shows that, for the analysis period and the indices studied, the macro-economic indicators are leading predictors of the price of 13 U.S. sector indices. |
| Author | Li, Chen Tsai, Yao-Te Martinez, Waldyn Barth, James R. Megahed, Fadel M. Weng, Bin Lu, Lin |
| Author_xml | – sequence: 1 givenname: Bin surname: Weng fullname: Weng, Bin email: bzw0018@auburn.edu organization: Department of Industrial & Systems Engineering, Auburn University, AL 36849, USA – sequence: 2 givenname: Waldyn surname: Martinez fullname: Martinez, Waldyn email: martinwg@miamioh.edu organization: Farmer School of Business, Miami University, Oxford, OH 45056, USA – sequence: 3 givenname: Yao-Te surname: Tsai fullname: Tsai, Yao-Te email: yaottsai@fcu.edu.tw organization: Department of International Business, Feng Chia University, Taiwan 40724, ROC – sequence: 4 givenname: Chen surname: Li fullname: Li, Chen email: czl0053@auburn.edu organization: Department of Agricultural Economics, Auburn University, AL 36849, USA – sequence: 5 givenname: Lin surname: Lu fullname: Lu, Lin email: lzl0032@auburn.edu organization: Department of Industrial & Systems Engineering, Auburn University, AL 36849, USA – sequence: 6 givenname: James R. surname: Barth fullname: Barth, James R. email: barthjr@auburn.edu organization: Raymond J. Harbert College of Business, Auburn University, AL 36849, USA – sequence: 7 givenname: Fadel M. orcidid: 0000-0003-2194-5110 surname: Megahed fullname: Megahed, Fadel M. email: fmegahed@miamioh.edu organization: Farmer School of Business, Miami University, OH 45056, USA |
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| References | de Oliveira, Nobre, Zarate (bib0330) 2013; 40 Hochreiter, Schmidhuber (bib0160) 1997; 9 Bollen, Mao, Zeng (bib0035) 2011; 2 Ahangar, Yahyazadehfar, Pournaghshband (bib0155) 2010; 7 Ou, Wang (bib0100) 2009; 3 Pyo, Lee, Cha, Jang (bib0120) 2017; 12 Ican, Çelik (bib0085) 2017; 9 Sadaei, Enayatifar, Lee, Mahmud (bib0135) 2016; 40 Flannery, Protopapadakis (bib0215) 2002; 15 Tsai, Lin, Yen, Chen (bib0110) 2011; 11 Mahajan, Dey, Haque (bib0220) 2008 Opitz, Maclin (bib0290) 1999 Fama (bib0005) 1965; 38 Hajizadeh, Ardakani, Shahrabi (bib0075) 2010; 2 D.P. Kingma, J. Ba, Adam: A method for stochastic optimization Szegö (bib0395) 2002; 26 Hyndman, Khandakar (bib0350) 2007 Huang, Nakamori, Wang (bib0095) 2005; 32 Lai, Fan, Huang, Chang (bib0315) 2009; 36 Rahman, Sidek, Tafri (bib0200) 2009; 3 Hyndman, O’Hara-Wild, Bergmeir, Razbash, Wang (bib0345) 2017 Kazem, Sharifi, Hussain, Saberi, Hussain (bib0385) 2013; 13 Sadorsky (bib0180) 1999; 21 Case, Quigley, Shiller (bib0195) 2005; 5 Quinlan (bib0280) 1996; vol. 1 Gottschlich, Hinz (bib0390) 2014; 59 Chen, Roll, Ross (bib0210) 1986 Ryan, Ulrich (bib0230) 2017 Grudnitski, Osburn (bib0140) 1993; 13 . McTaggart, Daroczi, Leung (bib0235) 2016 Box, Jenkins, Reinsel, Ljung (bib0355) 2015 Kiersz (bib0040) 2015 Patel, Shah, Thakkar, Kotecha (bib0130) 2015; 42 Park, Ratti (bib0185) 2008; 30 Malkiel (bib0015) 2003; 17 Prechter, Parker (bib0030) 2007; 8 Akita, Yoshihara, Matsubara, Uehara (bib0165) 2016 Kilian, Park (bib0190) 2009; 50 Hamao, Masulis, Ng (bib0205) 1990; 3 L. Breiman, Bias, variance, and arcing classifiers, Technical Report 460. Dag, Topuz, Oztekin, Bulur, Megahed (bib0245) 2016; 86 Ghalanos (bib0360) 2018 Drucker, Cortes, Jackel, LeCun, Vapnik (bib0265) 1994; 6 Nofsinger (bib0025) 2005; 6 Vaisla, Bhatt (bib0080) 2010; 2 Zhang (bib0370) 2003; 50 Atsalakis, Valavanis (bib0070) 2009; 36 Tsai, Hsiao (bib0105) 2010; 50 Da Silva, Hruschka, Hruschka (bib0115) 2014; 66 Arlot, Celisse (bib0250) 2010; 4 Guresen, Kayakutlu, Daim (bib0325) 2011; 38 Pai, Lin (bib0375) 2005; 33 Smith (bib0020) 2003; 93 Kim (bib0090) 2003; 55 Kuhn (bib0260) 2008; 28 Schapire, Freund, Bartlett, Lee (bib0285) 1998; 26 Loomis (bib0045) 2012 Breiman (bib0300) 2001; 45 Johnson, Watson (bib0225) 2011; 20 H. Jia, Investigation into the effectiveness of long short term memory networks for stock price prediction Wiesmeier, Barthold, Blank, Kögel-Knabner (bib0320) 2011; 340 Enke, Thawornwong (bib0065) 2005; 29 Schumaker (bib0335) 2013; 54 Wang, Wang, Zhang, Guo (bib0055) 2012; 40 Cootner (bib0010) 1964 Breiman (bib0270) 1996; 24 Maclin, Opitz (bib0305) 2011; 11 Poon, Granger (bib0060) 2003; 41 Chen, Zhou, Dai (bib0175) 2015 Liu, Tian, Li (bib0380) 2012; 98 Taylor (bib0340) 2000; 19 Meinshausen (bib0310) 2006; 7 Kryzanowski, Galler, Wright (bib0145) 1993; 49 Maman, Yazdi, Cavuoto, Megahed (bib0400) 2017; 65 Lewis (bib0050) 2015 Kao, Chiu, Lu, Chang (bib0125) 2013; 54 Quinlan (bib0240) 2014 Hamid, Iqbal (bib0150) 2004; 57 Dag, Oztekin, Yucel, Bulur, Megahed (bib0255) 2017; 94 Dietterich (bib0295) 2000 Szegö (10.1016/j.asoc.2018.07.024_bib0395) 2002; 26 Ican (10.1016/j.asoc.2018.07.024_bib0085) 2017; 9 10.1016/j.asoc.2018.07.024_bib0170 Vaisla (10.1016/j.asoc.2018.07.024_bib0080) 2010; 2 Case (10.1016/j.asoc.2018.07.024_bib0195) 2005; 5 Flannery (10.1016/j.asoc.2018.07.024_bib0215) 2002; 15 Kilian (10.1016/j.asoc.2018.07.024_bib0190) 2009; 50 Breiman (10.1016/j.asoc.2018.07.024_bib0300) 2001; 45 Zhang (10.1016/j.asoc.2018.07.024_bib0370) 2003; 50 Loomis (10.1016/j.asoc.2018.07.024_bib0045) 2012 Huang (10.1016/j.asoc.2018.07.024_bib0095) 2005; 32 Chen (10.1016/j.asoc.2018.07.024_bib0175) 2015 Rahman (10.1016/j.asoc.2018.07.024_bib0200) 2009; 3 Arlot (10.1016/j.asoc.2018.07.024_bib0250) 2010; 4 Ou (10.1016/j.asoc.2018.07.024_bib0100) 2009; 3 Pai (10.1016/j.asoc.2018.07.024_bib0375) 2005; 33 McTaggart (10.1016/j.asoc.2018.07.024_bib0235) 2016 Kazem (10.1016/j.asoc.2018.07.024_bib0385) 2013; 13 Gottschlich (10.1016/j.asoc.2018.07.024_bib0390) 2014; 59 Mahajan (10.1016/j.asoc.2018.07.024_bib0220) 2008 Meinshausen (10.1016/j.asoc.2018.07.024_bib0310) 2006; 7 Schumaker (10.1016/j.asoc.2018.07.024_bib0335) 2013; 54 Smith (10.1016/j.asoc.2018.07.024_bib0020) 2003; 93 Ryan (10.1016/j.asoc.2018.07.024_bib0230) 2017 Maclin (10.1016/j.asoc.2018.07.024_bib0305) 2011; 11 Prechter (10.1016/j.asoc.2018.07.024_bib0030) 2007; 8 Patel (10.1016/j.asoc.2018.07.024_bib0130) 2015; 42 Liu (10.1016/j.asoc.2018.07.024_bib0380) 2012; 98 Atsalakis (10.1016/j.asoc.2018.07.024_bib0070) 2009; 36 Hyndman (10.1016/j.asoc.2018.07.024_bib0345) 2017 Wang (10.1016/j.asoc.2018.07.024_bib0055) 2012; 40 Poon (10.1016/j.asoc.2018.07.024_bib0060) 2003; 41 Dag (10.1016/j.asoc.2018.07.024_bib0255) 2017; 94 Opitz (10.1016/j.asoc.2018.07.024_bib0290) 1999 Tsai (10.1016/j.asoc.2018.07.024_bib0105) 2010; 50 Drucker (10.1016/j.asoc.2018.07.024_bib0265) 1994; 6 Malkiel (10.1016/j.asoc.2018.07.024_bib0015) 2003; 17 Hamid (10.1016/j.asoc.2018.07.024_bib0150) 2004; 57 Breiman (10.1016/j.asoc.2018.07.024_bib0270) 1996; 24 Guresen (10.1016/j.asoc.2018.07.024_bib0325) 2011; 38 Hochreiter (10.1016/j.asoc.2018.07.024_bib0160) 1997; 9 Bollen (10.1016/j.asoc.2018.07.024_bib0035) 2011; 2 Johnson (10.1016/j.asoc.2018.07.024_bib0225) 2011; 20 Grudnitski (10.1016/j.asoc.2018.07.024_bib0140) 1993; 13 Sadaei (10.1016/j.asoc.2018.07.024_bib0135) 2016; 40 Akita (10.1016/j.asoc.2018.07.024_bib0165) 2016 Kiersz (10.1016/j.asoc.2018.07.024_bib0040) 2015 Kuhn (10.1016/j.asoc.2018.07.024_bib0260) 2008; 28 Taylor (10.1016/j.asoc.2018.07.024_bib0340) 2000; 19 Kim (10.1016/j.asoc.2018.07.024_bib0090) 2003; 55 Chen (10.1016/j.asoc.2018.07.024_bib0210) 1986 10.1016/j.asoc.2018.07.024_bib0275 Da Silva (10.1016/j.asoc.2018.07.024_bib0115) 2014; 66 Quinlan (10.1016/j.asoc.2018.07.024_bib0280) 1996; vol. 1 Lewis (10.1016/j.asoc.2018.07.024_bib0050) 2015 Ahangar (10.1016/j.asoc.2018.07.024_bib0155) 2010; 7 Dietterich (10.1016/j.asoc.2018.07.024_bib0295) 2000 Box (10.1016/j.asoc.2018.07.024_bib0355) 2015 Ghalanos (10.1016/j.asoc.2018.07.024_bib0360) 2018 Lai (10.1016/j.asoc.2018.07.024_bib0315) 2009; 36 Tsai (10.1016/j.asoc.2018.07.024_bib0110) 2011; 11 Pyo (10.1016/j.asoc.2018.07.024_bib0120) 2017; 12 Nofsinger (10.1016/j.asoc.2018.07.024_bib0025) 2005; 6 Maman (10.1016/j.asoc.2018.07.024_bib0400) 2017; 65 Wiesmeier (10.1016/j.asoc.2018.07.024_bib0320) 2011; 340 Cootner (10.1016/j.asoc.2018.07.024_bib0010) 1964 Fama (10.1016/j.asoc.2018.07.024_bib0005) 1965; 38 Sadorsky (10.1016/j.asoc.2018.07.024_bib0180) 1999; 21 Hyndman (10.1016/j.asoc.2018.07.024_bib0350) 2007 10.1016/j.asoc.2018.07.024_bib0365 Kao (10.1016/j.asoc.2018.07.024_bib0125) 2013; 54 Kryzanowski (10.1016/j.asoc.2018.07.024_bib0145) 1993; 49 Park (10.1016/j.asoc.2018.07.024_bib0185) 2008; 30 Quinlan (10.1016/j.asoc.2018.07.024_bib0240) 2014 Enke (10.1016/j.asoc.2018.07.024_bib0065) 2005; 29 Dag (10.1016/j.asoc.2018.07.024_bib0245) 2016; 86 Hajizadeh (10.1016/j.asoc.2018.07.024_bib0075) 2010; 2 Schapire (10.1016/j.asoc.2018.07.024_bib0285) 1998; 26 de Oliveira (10.1016/j.asoc.2018.07.024_bib0330) 2013; 40 Hamao (10.1016/j.asoc.2018.07.024_bib0205) 1990; 3 |
| References_xml | – volume: 17 start-page: 59 year: 2003 end-page: 82 ident: bib0015 article-title: The efficient market hypothesis and its critics publication-title: J. Econ. Perspect. – volume: 57 start-page: 1116 year: 2004 end-page: 1125 ident: bib0150 article-title: Using neural networks for forecasting volatility of s&p 500 index futures prices publication-title: J. Bus. Res. – year: 2007 ident: bib0350 article-title: Automatic Time Series for Forecasting: the Forecast Package for R, No. 6/07 – volume: 21 start-page: 449 year: 1999 end-page: 469 ident: bib0180 article-title: Oil price shocks and stock market activity publication-title: Energy Econ. – volume: 28 start-page: 1 year: 2008 end-page: 26 ident: bib0260 article-title: Caret package publication-title: J. Stat. Softw. – start-page: 423 year: 2008 end-page: 426 ident: bib0220 article-title: Mining financial news for major events and their impacts on the market publication-title: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1 – year: 2015 ident: bib0040 article-title: Here's How Badly Warren Buffett Beat the Market – volume: 98 start-page: 415 year: 2012 end-page: 424 ident: bib0380 article-title: Comparison of two new arima-ann and arima-kalman hybrid methods for wind speed prediction publication-title: Appl. Energy – volume: 54 start-page: 1228 year: 2013 end-page: 1244 ident: bib0125 article-title: A hybrid approach by integrating wavelet-based feature extraction with mars and SVR for stock index forecasting publication-title: Decis. Support Syst. – volume: 54 start-page: 1370 year: 2013 end-page: 1379 ident: bib0335 article-title: Machine learning the harness track: crowdsourcing and varying race history publication-title: Decis. Support Syst. – volume: 7 start-page: 983 year: 2006 end-page: 999 ident: bib0310 article-title: Quantile regression forests publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 109 year: 2010 ident: bib0075 article-title: Application of data mining techniques in stock markets: a survey publication-title: J. Econ. Int. Finance – reference: D.P. Kingma, J. Ba, Adam: A method for stochastic optimization, – volume: 42 start-page: 2162 year: 2015 end-page: 2172 ident: bib0130 article-title: Predicting stock market index using fusion of machine learning techniques publication-title: Expert Syst. Appl. – volume: 93 start-page: 465 year: 2003 end-page: 508 ident: bib0020 article-title: Constructivist and ecological rationality in economics publication-title: Am. Econ. Rev. – volume: 38 start-page: 10389 year: 2011 end-page: 10397 ident: bib0325 article-title: Using artificial neural network models in stock market index prediction publication-title: Expert Syst. Appl. – volume: 30 start-page: 2587 year: 2008 end-page: 2608 ident: bib0185 article-title: Oil price shocks and stock markets in the us and 13 European countries publication-title: Energy Econ. – start-page: 1 year: 2000 end-page: 15 ident: bib0295 article-title: Ensemble methods in machine learning publication-title: International Workshop on Multiple Classifier Systems – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib0270 article-title: Bagging predictors publication-title: Mach. Learn. – volume: 7 start-page: 38 year: 2010 end-page: 46 ident: bib0155 article-title: The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in tehran stock exchange publication-title: Int. J. Comput. Sci. Inf. Secur. – volume: 38 start-page: 34 year: 1965 end-page: 105 ident: bib0005 article-title: The behavior of stock-market prices publication-title: J. Bus. – volume: 50 start-page: 258 year: 2010 end-page: 269 ident: bib0105 article-title: Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches publication-title: Decis. Support Syst. – reference: L. Breiman, Bias, variance, and arcing classifiers, Technical Report 460. – volume: 8 start-page: 84 year: 2007 end-page: 108 ident: bib0030 article-title: The financial/economic dichotomy in social behavioral dynamics: the socionomic perspective publication-title: J. Behav. Finance – start-page: 383 year: 1986 end-page: 403 ident: bib0210 article-title: Economic forces and the stock market publication-title: J. Bus. – volume: 36 start-page: 5932 year: 2009 end-page: 5941 ident: bib0070 article-title: Surveying stock market forecasting techniques – Part II: Soft computing methods publication-title: Expert Syst. Appl. – volume: 6 start-page: 144 year: 2005 end-page: 160 ident: bib0025 article-title: Social mood and financial economics publication-title: J. Behav. Finance – year: 2015 ident: bib0355 article-title: Time Series Analysis: Forecasting and Control – volume: 33 start-page: 497 year: 2005 end-page: 505 ident: bib0375 article-title: A hybrid arima and support vector machines model in stock price forecasting publication-title: Omega – year: 2017 ident: bib0230 article-title: Quantmod: Quantitative Financial Modelling Framework. R Package Version 0.4-12 – year: 2016 ident: bib0235 article-title: Quandl: Api Wrapper for quandl.com. R Package Version 2.8.0 – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0300 article-title: Random forests publication-title: Mach. Learn. – year: 2018 ident: bib0360 article-title: Introduction to the Rugarch Package.(Version 1.3-1), Tech. Rep., Technical Report v – volume: 3 start-page: 28 year: 2009 ident: bib0100 article-title: Prediction of stock market index movement by ten data mining techniques publication-title: Mod. Appl. Sci. – volume: 26 start-page: 1651 year: 1998 end-page: 1686 ident: bib0285 article-title: Boosting the margin: a new explanation for the effectiveness of voting methods publication-title: Ann. Stat. – year: 2012 ident: bib0045 article-title: Buffett Beats the SP for the 39th Year – volume: 40 start-page: 132 year: 2016 end-page: 149 ident: bib0135 article-title: A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting publication-title: Appl. Soft Comput. – volume: 13 start-page: 947 year: 2013 end-page: 958 ident: bib0385 article-title: Support vector regression with chaos-based firefly algorithm for stock market price forecasting publication-title: Appl. Soft Comput. – volume: 29 start-page: 927 year: 2005 end-page: 940 ident: bib0065 article-title: The use of data mining and neural networks for forecasting stock market returns publication-title: Expert Syst. Appl. – volume: 32 start-page: 2513 year: 2005 end-page: 2522 ident: bib0095 article-title: Forecasting stock market movement direction with support vector machine publication-title: Comput. Oper. Res. – volume: 26 start-page: 1253 year: 2002 end-page: 1272 ident: bib0395 article-title: Measures of risk publication-title: J. Bank. Finance – volume: 11 start-page: 2452 year: 2011 end-page: 2459 ident: bib0110 article-title: Predicting stock returns by classifier ensembles publication-title: Appl. Soft Comput. – year: 2015 ident: bib0050 article-title: The Big Short: Inside the Doomsday Machine (movie tie-in) – volume: 94 start-page: 42 year: 2017 end-page: 52 ident: bib0255 article-title: Predicting heart transplantation outcomes through data analytics publication-title: Decis. Support Syst. – volume: 12 start-page: e0188107 year: 2017 ident: bib0120 article-title: Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets publication-title: PLoS ONE – volume: 40 start-page: 758 year: 2012 end-page: 766 ident: bib0055 article-title: Stock index forecasting based on a hybrid model publication-title: Omega – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib0160 article-title: Long short-term memory publication-title: Neural Comput. – volume: 55 start-page: 307 year: 2003 end-page: 319 ident: bib0090 article-title: Financial time series forecasting using support vector machines publication-title: Neurocomputing – volume: 2 start-page: 2104 year: 2010 end-page: 2109 ident: bib0080 article-title: An analysis of the performance of artificial neural network technique for stock market forecasting publication-title: Int. J. Comput. Sci. Eng. – start-page: 2823 year: 2015 end-page: 2824 ident: bib0175 article-title: A LSTM-based method for stock returns prediction: a case study of china stock market publication-title: 2015 IEEE International Conference on Big Data (Big Data), IEEE – volume: 49 start-page: 21 year: 1993 end-page: 27 ident: bib0145 article-title: Using artificial neural networks to pick stocks publication-title: Financ. Anal. J. – volume: 2 start-page: 1 year: 2011 end-page: 8 ident: bib0035 article-title: Twitter mood predicts the stock market publication-title: J. Comput. Sci. – volume: 9 start-page: 100 year: 2017 ident: bib0085 article-title: Stock market prediction performance of neural networks: a literature review publication-title: Int. J. Econ. Finance – volume: 13 start-page: 631 year: 1993 end-page: 643 ident: bib0140 article-title: Forecasting s&p and gold futures prices: an application of neural networks publication-title: J. Futures Mark. – volume: 15 start-page: 751 year: 2002 end-page: 782 ident: bib0215 article-title: Macroeconomic factors do influence aggregate stock returns publication-title: Rev. Financ. Stud. – volume: 86 start-page: 1 year: 2016 end-page: 12 ident: bib0245 article-title: A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival publication-title: Decis. Support Syst. – volume: 65 start-page: 515 year: 2017 end-page: 529 ident: bib0400 article-title: A data-driven approach to modeling physical fatigue in the workplace using wearable sensors publication-title: Appl. Ergonom. – volume: 4 start-page: 40 year: 2010 end-page: 79 ident: bib0250 article-title: A survey of cross-validation procedures for model selection publication-title: Stat. Surv. – volume: 20 start-page: 89 year: 2011 end-page: 98 ident: bib0225 article-title: Can changes in the purchasing managers’ index foretell stock returns? An additional forward-looking sentiment indicator publication-title: J. Invest. – year: 2014 ident: bib0240 article-title: C4.5: Programs for Machine Learning, Morgan Kaufmann Series in Machine Learning – volume: 11 start-page: 169 year: 2011 end-page: 198 ident: bib0305 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. – volume: 5 start-page: 1 year: 2005 end-page: 23 ident: bib0195 article-title: Comparing wealth effects: the stock market versus the housing market publication-title: Adv. Macroecon. – volume: 40 start-page: 7596 year: 2013 end-page: 7606 ident: bib0330 article-title: Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of petr4, petrobras, Brazil publication-title: Expert Syst. Appl. – start-page: 169 year: 1999 end-page: 198 ident: bib0290 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. – volume: 66 start-page: 170 year: 2014 end-page: 179 ident: bib0115 article-title: Tweet sentiment analysis with classifier ensembles publication-title: Decis. Support Syst. – volume: 3 start-page: 281 year: 1990 end-page: 307 ident: bib0205 article-title: Correlations in price changes and volatility across international stock markets publication-title: Rev. Financ. Stud. – volume: vol. 1 start-page: 725 year: 1996 end-page: 730 ident: bib0280 publication-title: Bagging, Boosting, and c4. 5, AAAI/IAAI – volume: 340 start-page: 7 year: 2011 end-page: 24 ident: bib0320 article-title: Digital mapping of soil organic matter stocks using random forest modeling in a semi-arid steppe ecosystem publication-title: Plant Soil – year: 2017 ident: bib0345 article-title: Forecast: Forecasting Functions for Time Series and Linear Models. R Package Version 8.2 – year: 1964 ident: bib0010 article-title: The Random Character of Stock Market Prices – reference: . – volume: 59 start-page: 52 year: 2014 end-page: 62 ident: bib0390 article-title: A decision support system for stock investment recommendations using collective wisdom publication-title: Decis. Support Syst. – volume: 3 start-page: 95 year: 2009 ident: bib0200 article-title: Macroeconomic determinants of malaysian stock market publication-title: Afr. J. Bus. Manag. – volume: 36 start-page: 3761 year: 2009 end-page: 3773 ident: bib0315 article-title: Evolving and clustering fuzzy decision tree for financial time series data forecasting publication-title: Expert Syst. Appl. – volume: 19 start-page: 299 year: 2000 end-page: 311 ident: bib0340 article-title: A quantile regression neural network approach to estimating the conditional density of multiperiod returns publication-title: J. Forecast. – volume: 50 start-page: 1267 year: 2009 end-page: 1287 ident: bib0190 article-title: The impact of oil price shocks on the us stock market publication-title: Int. Econ. Rev. – volume: 6 start-page: 1289 year: 1994 end-page: 1301 ident: bib0265 article-title: Boosting and other ensemble methods publication-title: Neural Comput. – reference: H. Jia, Investigation into the effectiveness of long short term memory networks for stock price prediction, – start-page: 1 year: 2016 end-page: 6 ident: bib0165 article-title: Deep learning for stock prediction using numerical and textual information publication-title: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE – volume: 41 start-page: 478 year: 2003 end-page: 539 ident: bib0060 article-title: Forecasting volatility in financial markets: a review publication-title: J. Econ. Lit. – volume: 50 start-page: 159 year: 2003 end-page: 175 ident: bib0370 article-title: Time series forecasting using a hybrid arima and neural network model publication-title: Neurocomputing – volume: 11 start-page: 2452 issue: 2 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0110 article-title: Predicting stock returns by classifier ensembles publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.10.001 – volume: 29 start-page: 927 issue: 4 year: 2005 ident: 10.1016/j.asoc.2018.07.024_bib0065 article-title: The use of data mining and neural networks for forecasting stock market returns publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2005.06.024 – year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0345 – volume: 4 start-page: 40 year: 2010 ident: 10.1016/j.asoc.2018.07.024_bib0250 article-title: A survey of cross-validation procedures for model selection publication-title: Stat. Surv. doi: 10.1214/09-SS054 – volume: 38 start-page: 10389 issue: 8 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0325 article-title: Using artificial neural network models in stock market index prediction publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2011.02.068 – volume: 50 start-page: 258 issue: 1 year: 2010 ident: 10.1016/j.asoc.2018.07.024_bib0105 article-title: Combining multiple feature selection methods for stock prediction: union, intersection, and multi-intersection approaches publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2010.08.028 – year: 2018 ident: 10.1016/j.asoc.2018.07.024_bib0360 – volume: 93 start-page: 465 issue: 3 year: 2003 ident: 10.1016/j.asoc.2018.07.024_bib0020 article-title: Constructivist and ecological rationality in economics publication-title: Am. Econ. Rev. doi: 10.1257/000282803322156954 – start-page: 1 year: 2000 ident: 10.1016/j.asoc.2018.07.024_bib0295 article-title: Ensemble methods in machine learning – volume: 19 start-page: 299 issue: 4 year: 2000 ident: 10.1016/j.asoc.2018.07.024_bib0340 article-title: A quantile regression neural network approach to estimating the conditional density of multiperiod returns publication-title: J. Forecast. doi: 10.1002/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V – start-page: 2823 year: 2015 ident: 10.1016/j.asoc.2018.07.024_bib0175 article-title: A LSTM-based method for stock returns prediction: a case study of china stock market publication-title: 2015 IEEE International Conference on Big Data (Big Data), IEEE doi: 10.1109/BigData.2015.7364089 – volume: 2 start-page: 2104 issue: 6 year: 2010 ident: 10.1016/j.asoc.2018.07.024_bib0080 article-title: An analysis of the performance of artificial neural network technique for stock market forecasting publication-title: Int. J. Comput. Sci. Eng. – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.asoc.2018.07.024_bib0300 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 54 start-page: 1228 issue: 3 year: 2013 ident: 10.1016/j.asoc.2018.07.024_bib0125 article-title: A hybrid approach by integrating wavelet-based feature extraction with mars and SVR for stock index forecasting publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2012.11.012 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.asoc.2018.07.024_bib0160 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 20 start-page: 89 issue: 4 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0225 article-title: Can changes in the purchasing managers’ index foretell stock returns? An additional forward-looking sentiment indicator publication-title: J. Invest. doi: 10.3905/joi.2011.20.4.089 – volume: 49 start-page: 21 issue: 4 year: 1993 ident: 10.1016/j.asoc.2018.07.024_bib0145 article-title: Using artificial neural networks to pick stocks publication-title: Financ. Anal. J. doi: 10.2469/faj.v49.n4.21 – volume: 98 start-page: 415 year: 2012 ident: 10.1016/j.asoc.2018.07.024_bib0380 article-title: Comparison of two new arima-ann and arima-kalman hybrid methods for wind speed prediction publication-title: Appl. Energy doi: 10.1016/j.apenergy.2012.04.001 – volume: 42 start-page: 2162 issue: 4 year: 2015 ident: 10.1016/j.asoc.2018.07.024_bib0130 article-title: Predicting stock market index using fusion of machine learning techniques publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.10.031 – volume: 5 start-page: 1 issue: 1 year: 2005 ident: 10.1016/j.asoc.2018.07.024_bib0195 article-title: Comparing wealth effects: the stock market versus the housing market publication-title: Adv. Macroecon. – year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0230 – ident: 10.1016/j.asoc.2018.07.024_bib0275 – volume: 11 start-page: 169 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0305 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. – ident: 10.1016/j.asoc.2018.07.024_bib0365 – volume: 2 start-page: 1 issue: 1 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0035 article-title: Twitter mood predicts the stock market publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2010.12.007 – volume: 36 start-page: 5932 issue: 3 year: 2009 ident: 10.1016/j.asoc.2018.07.024_bib0070 article-title: Surveying stock market forecasting techniques – Part II: Soft computing methods publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.07.006 – volume: 13 start-page: 631 issue: 6 year: 1993 ident: 10.1016/j.asoc.2018.07.024_bib0140 article-title: Forecasting s&p and gold futures prices: an application of neural networks publication-title: J. Futures Mark. doi: 10.1002/fut.3990130605 – volume: 86 start-page: 1 year: 2016 ident: 10.1016/j.asoc.2018.07.024_bib0245 article-title: A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2016.02.007 – volume: 28 start-page: 1 issue: 5 year: 2008 ident: 10.1016/j.asoc.2018.07.024_bib0260 article-title: Caret package publication-title: J. Stat. Softw. – volume: 40 start-page: 7596 issue: 18 year: 2013 ident: 10.1016/j.asoc.2018.07.024_bib0330 article-title: Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index-case study of petr4, petrobras, Brazil publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.06.071 – year: 2015 ident: 10.1016/j.asoc.2018.07.024_bib0040 – volume: 15 start-page: 751 issue: 3 year: 2002 ident: 10.1016/j.asoc.2018.07.024_bib0215 article-title: Macroeconomic factors do influence aggregate stock returns publication-title: Rev. Financ. Stud. doi: 10.1093/rfs/15.3.751 – volume: 33 start-page: 497 issue: 6 year: 2005 ident: 10.1016/j.asoc.2018.07.024_bib0375 article-title: A hybrid arima and support vector machines model in stock price forecasting publication-title: Omega doi: 10.1016/j.omega.2004.07.024 – volume: 9 start-page: 100 issue: 11 year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0085 article-title: Stock market prediction performance of neural networks: a literature review publication-title: Int. J. Econ. Finance doi: 10.5539/ijef.v9n11p100 – volume: 3 start-page: 95 issue: 3 year: 2009 ident: 10.1016/j.asoc.2018.07.024_bib0200 article-title: Macroeconomic determinants of malaysian stock market publication-title: Afr. J. Bus. Manag. – volume: 36 start-page: 3761 issue: 2 year: 2009 ident: 10.1016/j.asoc.2018.07.024_bib0315 article-title: Evolving and clustering fuzzy decision tree for financial time series data forecasting publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.02.025 – volume: 40 start-page: 758 issue: 6 year: 2012 ident: 10.1016/j.asoc.2018.07.024_bib0055 article-title: Stock index forecasting based on a hybrid model publication-title: Omega doi: 10.1016/j.omega.2011.07.008 – volume: 8 start-page: 84 issue: 2 year: 2007 ident: 10.1016/j.asoc.2018.07.024_bib0030 article-title: The financial/economic dichotomy in social behavioral dynamics: the socionomic perspective publication-title: J. Behav. Finance doi: 10.1080/15427560701381028 – ident: 10.1016/j.asoc.2018.07.024_bib0170 – start-page: 423 year: 2008 ident: 10.1016/j.asoc.2018.07.024_bib0220 article-title: Mining financial news for major events and their impacts on the market publication-title: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1 doi: 10.1109/WIIAT.2008.309 – volume: 17 start-page: 59 issue: 1 year: 2003 ident: 10.1016/j.asoc.2018.07.024_bib0015 article-title: The efficient market hypothesis and its critics publication-title: J. Econ. Perspect. doi: 10.1257/089533003321164958 – volume: 3 start-page: 28 issue: 12 year: 2009 ident: 10.1016/j.asoc.2018.07.024_bib0100 article-title: Prediction of stock market index movement by ten data mining techniques publication-title: Mod. Appl. Sci. doi: 10.5539/mas.v3n12p28 – year: 2015 ident: 10.1016/j.asoc.2018.07.024_bib0050 – start-page: 1 year: 2016 ident: 10.1016/j.asoc.2018.07.024_bib0165 article-title: Deep learning for stock prediction using numerical and textual information publication-title: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE – volume: 2 start-page: 109 issue: 7 year: 2010 ident: 10.1016/j.asoc.2018.07.024_bib0075 article-title: Application of data mining techniques in stock markets: a survey publication-title: J. Econ. Int. Finance – volume: 50 start-page: 159 year: 2003 ident: 10.1016/j.asoc.2018.07.024_bib0370 article-title: Time series forecasting using a hybrid arima and neural network model publication-title: Neurocomputing doi: 10.1016/S0925-2312(01)00702-0 – volume: 59 start-page: 52 year: 2014 ident: 10.1016/j.asoc.2018.07.024_bib0390 article-title: A decision support system for stock investment recommendations using collective wisdom publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2013.10.005 – volume: 55 start-page: 307 issue: 1 year: 2003 ident: 10.1016/j.asoc.2018.07.024_bib0090 article-title: Financial time series forecasting using support vector machines publication-title: Neurocomputing doi: 10.1016/S0925-2312(03)00372-2 – volume: 40 start-page: 132 year: 2016 ident: 10.1016/j.asoc.2018.07.024_bib0135 article-title: A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.11.026 – volume: vol. 1 start-page: 725 year: 1996 ident: 10.1016/j.asoc.2018.07.024_bib0280 – volume: 65 start-page: 515 year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0400 article-title: A data-driven approach to modeling physical fatigue in the workplace using wearable sensors publication-title: Appl. Ergonom. doi: 10.1016/j.apergo.2017.02.001 – volume: 340 start-page: 7 issue: 1–2 year: 2011 ident: 10.1016/j.asoc.2018.07.024_bib0320 article-title: Digital mapping of soil organic matter stocks using random forest modeling in a semi-arid steppe ecosystem publication-title: Plant Soil doi: 10.1007/s11104-010-0425-z – volume: 38 start-page: 34 issue: 1 year: 1965 ident: 10.1016/j.asoc.2018.07.024_bib0005 article-title: The behavior of stock-market prices publication-title: J. Bus. doi: 10.1086/294743 – volume: 12 start-page: e0188107 issue: 11 year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0120 article-title: Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the Korean stock markets publication-title: PLoS ONE doi: 10.1371/journal.pone.0188107 – volume: 94 start-page: 42 year: 2017 ident: 10.1016/j.asoc.2018.07.024_bib0255 article-title: Predicting heart transplantation outcomes through data analytics publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2016.10.005 – volume: 26 start-page: 1253 issue: 7 year: 2002 ident: 10.1016/j.asoc.2018.07.024_bib0395 article-title: Measures of risk publication-title: J. Bank. Finance doi: 10.1016/S0378-4266(02)00262-5 – year: 2014 ident: 10.1016/j.asoc.2018.07.024_bib0240 – start-page: 383 year: 1986 ident: 10.1016/j.asoc.2018.07.024_bib0210 article-title: Economic forces and the stock market publication-title: J. Bus. doi: 10.1086/296344 – volume: 57 start-page: 1116 issue: 10 year: 2004 ident: 10.1016/j.asoc.2018.07.024_bib0150 article-title: Using neural networks for forecasting volatility of s&p 500 index futures prices publication-title: J. Bus. Res. doi: 10.1016/S0148-2963(03)00043-2 – year: 2016 ident: 10.1016/j.asoc.2018.07.024_bib0235 – volume: 6 start-page: 1289 issue: 6 year: 1994 ident: 10.1016/j.asoc.2018.07.024_bib0265 article-title: Boosting and other ensemble methods publication-title: Neural Comput. doi: 10.1162/neco.1994.6.6.1289 – year: 2007 ident: 10.1016/j.asoc.2018.07.024_bib0350 – volume: 3 start-page: 281 issue: 2 year: 1990 ident: 10.1016/j.asoc.2018.07.024_bib0205 article-title: Correlations in price changes and volatility across international stock markets publication-title: Rev. Financ. Stud. doi: 10.1093/rfs/3.2.281 – year: 2012 ident: 10.1016/j.asoc.2018.07.024_bib0045 – volume: 7 start-page: 38 issue: 2 year: 2010 ident: 10.1016/j.asoc.2018.07.024_bib0155 article-title: The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in tehran stock exchange publication-title: Int. J. Comput. Sci. Inf. Secur. – volume: 30 start-page: 2587 issue: 5 year: 2008 ident: 10.1016/j.asoc.2018.07.024_bib0185 article-title: Oil price shocks and stock markets in the us and 13 European countries publication-title: Energy Econ. doi: 10.1016/j.eneco.2008.04.003 – volume: 7 start-page: 983 issue: June year: 2006 ident: 10.1016/j.asoc.2018.07.024_bib0310 article-title: Quantile regression forests publication-title: J. Mach. Learn. Res. – year: 1964 ident: 10.1016/j.asoc.2018.07.024_bib0010 – volume: 32 start-page: 2513 issue: 10 year: 2005 ident: 10.1016/j.asoc.2018.07.024_bib0095 article-title: Forecasting stock market movement direction with support vector machine publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2004.03.016 – start-page: 169 year: 1999 ident: 10.1016/j.asoc.2018.07.024_bib0290 article-title: Popular ensemble methods: an empirical study publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.614 – volume: 66 start-page: 170 year: 2014 ident: 10.1016/j.asoc.2018.07.024_bib0115 article-title: Tweet sentiment analysis with classifier ensembles publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2014.07.003 – year: 2015 ident: 10.1016/j.asoc.2018.07.024_bib0355 – volume: 13 start-page: 947 issue: 2 year: 2013 ident: 10.1016/j.asoc.2018.07.024_bib0385 article-title: Support vector regression with chaos-based firefly algorithm for stock market price forecasting publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.09.024 – volume: 21 start-page: 449 issue: 5 year: 1999 ident: 10.1016/j.asoc.2018.07.024_bib0180 article-title: Oil price shocks and stock market activity publication-title: Energy Econ. doi: 10.1016/S0140-9883(99)00020-1 – volume: 50 start-page: 1267 issue: 4 year: 2009 ident: 10.1016/j.asoc.2018.07.024_bib0190 article-title: The impact of oil price shocks on the us stock market publication-title: Int. Econ. Rev. doi: 10.1111/j.1468-2354.2009.00568.x – volume: 41 start-page: 478 issue: 2 year: 2003 ident: 10.1016/j.asoc.2018.07.024_bib0060 article-title: Forecasting volatility in financial markets: a review publication-title: J. Econ. Lit. doi: 10.1257/.41.2.478 – volume: 6 start-page: 144 issue: 3 year: 2005 ident: 10.1016/j.asoc.2018.07.024_bib0025 article-title: Social mood and financial economics publication-title: J. Behav. Finance doi: 10.1207/s15427579jpfm0603_4 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.asoc.2018.07.024_bib0270 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1023/A:1018054314350 – volume: 26 start-page: 1651 year: 1998 ident: 10.1016/j.asoc.2018.07.024_bib0285 article-title: Boosting the margin: a new explanation for the effectiveness of voting methods publication-title: Ann. Stat. – volume: 54 start-page: 1370 issue: 3 year: 2013 ident: 10.1016/j.asoc.2018.07.024_bib0335 article-title: Machine learning the harness track: crowdsourcing and varying race history publication-title: Decis. Support Syst. doi: 10.1016/j.dss.2012.12.013 |
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•A 2-stage method is proposed to predict the 1-month ahead price for 13 U.S. indices.•Ensembles of macroeconomic factors alone are more... |
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| Title | Macroeconomic indicators alone can predict the monthly closing price of major U.S. indices: Insights from artificial intelligence, time-series analysis and hybrid models |
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