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Artificial Intelligence Applied to Stock Market Trading: A Review

Ferreira, Fernando G. D. C., Gandomi, Amir H., Cardoso, Rodrigo T. N.·2021·IEEE Access
DOI 10.1109/access.2021.3058133
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Research works printed but not linked: 95

  1. 10.1023/A:1008768404046 · DOI
  2. 10.1016/j.eswa.2006.08.001 · DOI
  3. 10.1007/s00521-011-0721-4 · DOI
  4. 10.1016/j.eswa.2011.04.210 · DOI
  5. 10.1145/1389095.1389387 · DOI
  6. 10.1016/j.dss.2013.02.006 · DOI
  7. 10.1016/j.physa.2009.03.028 · DOI
  8. 10.1016/j.cor.2004.03.016 · DOI · Paper page
  9. 10.1016/j.eswa.2007.11.062 · DOI
  10. 10.1016/j.eswa.2007.05.035 · DOI · Paper page
  11. 10.1109/CIFER.1995.495230 · DOI
  12. 10.1016/j.dss.2013.09.013 · DOI
  13. 10.1016/j.knosys.2010.05.004 · DOI
  14. 10.1023/B:ANOR.0000039513.99038.c6 · DOI
  15. 10.1016/j.eswa.2005.06.024 · DOI
  16. 10.1016/S0377-2217(02)00881-0 · DOI
  17. 10.1016/j.cor.2005.06.017 · DOI
  18. 10.1016/j.swevo.2018.01.009 · DOI
  19. 10.1016/j.ejor.2017.11.054 · DOI
  20. mendonça, 2020, Multi-attribute decision making applied to financial portfolio optimization problem, Expert Syst Appl, 158, 10.1016/j.eswa.2020.113527 · DOI
  21. lo, 2007, Efficient markets hypothesis, The New Palgrave Dictionary of Economics
  22. 10.1016/j.knosys.2018.10.034 · DOI
  23. 10.3390/e22080840 · DOI
  24. murphy, 1999, Technical Analysis of the Financial Markets A Comprehensive Guide to Trading Methods and Applications
  25. 10.1109/CEC.2006.1688603 · DOI
  26. 10.1109/BigDataService.2019.00035 · DOI
  27. 10.1109/HICSS.2004.1265201 · DOI
  28. 10.2307/2975974 · DOI
  29. 10.1016/j.ejor.2013.08.035 · DOI
  30. 10.1016/S0377-2217(98)00252-5 · DOI
  31. 10.1016/j.eswa.2017.05.018 · DOI
  32. 10.1007/978-981-10-8201-6_5 · DOI
  33. 10.1016/j.knosys.2012.05.003 · DOI
  34. 10.1016/j.csda.2004.02.004 · DOI
  35. 10.1109/ICOMET.2018.8346382 · DOI
  36. 10.1142/S0129065797000409 · DOI
  37. 10.2200/S00416ED1V01Y201204HLT016 · DOI
  38. 10.1016/j.cor.2005.01.027 · DOI
  39. 10.3390/app9173509 · DOI
  40. 10.1016/S0169-2070(98)00003-X · DOI
  41. 10.1016/j.knosys.2014.04.022 · DOI
  42. 10.1016/S0165-0114(98)00399-6 · DOI
  43. 10.1016/j.eswa.2010.10.027 · DOI
  44. kansal, 2019, Optimized feature extraction based artificial 1301 intelligence technique for empirical analysis of stock market data, Int J Innov Technol Exploring Eng, 8, 10.35940/ijitee.J8837.0881019 · DOI
  45. 10.1016/j.eswa.2005.09.020 · DOI
  46. 10.1186/s40854-019-0140-6 · DOI
  47. 10.1109/MC.2010.2 · DOI
  48. 10.1109/CIFER.1996.501826 · DOI
  49. 10.1016/j.knosys.2018.01.016 · DOI
  50. 10.1016/j.knosys.2019.03.029 · DOI
  51. 10.1016/j.eswa.2019.05.018 · DOI
  52. 10.5267/j.msl.2015.7.003 · DOI
  53. 10.1145/1462198.1462204 · DOI
  54. 10.1007/s11771-016-3061-9 · DOI
  55. smailovi?, 2013, Predictive sentiment analysis of Tweets A stock market application, 7947, 77
  56. 10.1016/0305-0548(95)00030-5 · DOI
  57. 10.1016/j.eswa.2015.07.052 · DOI
  58. pagolu, 2017, Sentiment analysis of Twitter data for predicting stock market movements, Proc Int Conf Signal Process Commun Power Embedded Syst, 1345
  59. 10.1016/j.dss.2016.02.013 · DOI
  60. 10.1016/j.eswa.2014.07.040 · DOI · Paper page
  61. 10.1016/j.epsr.2010.05.006 · DOI
  62. 10.1109/TEVC.2012.2196800 · DOI
  63. 10.1016/S0305-0548(03)00063-7 · DOI
  64. 10.1109/JSYST.2018.2794462 · DOI
  65. 10.1016/S0378-4266(02)00271-6 · DOI
  66. 10.1080/00036846.2018.1564115 · DOI
  67. 10.21314/JOR.2000.038 · DOI · Paper page
  68. 10.1016/j.eswa.2013.08.028 · DOI
  69. 10.1109/MCI.2018.2866727 · DOI
  70. 10.1186/s40854-019-0138-0 · DOI
  71. 10.1109/IJCNN.1999.830772 · DOI
  72. 10.1007/s12559-018-9609-2 · DOI
  73. 10.1016/j.ejor.2012.04.023 · DOI
  74. zadrozny, 2019, A comparison of neural network methods for accurate sentiment analysis of stock market tweets, Proc ECML PKDD Workshops MIDAS PAP, 11054, 51
  75. tsai, 2017, The information content of implied volatility skew: Evidence on Taiwan stock index options, Data Sci Pattern Recognit, 1, 48
  76. 10.1016/j.eswa.2011.02.075 · DOI
  77. 10.1002/spe.2915 · DOI
  78. 10.1016/j.eswa.2016.02.006 · DOI
  79. chang, 2018, How A I Traders Will Dominate Hedge Fund Industry
  80. 10.1016/S0305-0548(99)00074-X · DOI
  81. 10.1016/j.eswa.2009.02.062 · DOI
  82. 10.1007/s10878-018-0364-9 · DOI
  83. 10.1016/j.physa.2015.02.060 · DOI
  84. 10.1016/j.eswa.2017.04.030 · DOI · Paper page
  85. 10.1109/CEC.2005.1554896 · DOI
  86. 10.1016/S0377-2217(02)00784-1 · DOI
  87. takahashi, 2007, Analysis of the Relation Between Stock Price Returns and Headline News Using Text Categorization, 4693, 1339
  88. 10.1016/j.eswa.2018.08.003 · DOI
  89. takahashi, 2006, Analysis of Stock Price Return Using Textual Data and Numerical Data Through Text Mining, 4252, 310
  90. 10.1016/S0377-2217(02)00774-9 · DOI
  91. 10.1109/ICSMC.1998.725072 · DOI
  92. 10.36713/epra4361 · DOI
  93. 10.1016/j.neucom.2011.12.013 · DOI
  94. 10.1016/S0957-4174(01)00047-1 · DOI
  95. 10.1016/S0957-4174(02)00079-9 · DOI

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Metadata

Authors
Ferreira, F. G. D. C., Gandomi, A. H., & Cardoso, R. T. N.
Affiliations
  • Department of Mathematical and Computational Modeling, Centro Federal de Educação Tecnológica de Minas Gerais, Belo Horizonte, Brazil
  • Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021
Volume
9
Pages
30898-30917
ISSN
2169-3536
DOI
10.1109/access.2021.3058133
Cited by
187
References
2
Licence
creativecommons.org/licenses/by/4.0/legalcode (vor)
Funders
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (001 (Proc. 88881.361790/2019-01))

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