2022
DOI: 10.1016/j.eswa.2022.116659
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Machine learning techniques and data for stock market forecasting: A literature review

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Cited by 253 publications
(101 citation statements)
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References 188 publications
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“…In their reference paper, de Oliveira, Nobre, and Zarate (2013) also quite clearly summarize the importance of directional predictions and emphasize that what really matters for decision making is predicting directions of movements. However, RMSE is one of the most popular metric in evaluation stock market prediction models; accuracy is also widely preferred likewise RMSE and most of the studies in the recent literature are based on classi cation-based prediction tasks (Kumbure et al, 2022). Moreover, for the stock market direction prediction or classi cation task, accuracy is the only metric to be employed.…”
Section: Methodsmentioning
confidence: 99%
“…In their reference paper, de Oliveira, Nobre, and Zarate (2013) also quite clearly summarize the importance of directional predictions and emphasize that what really matters for decision making is predicting directions of movements. However, RMSE is one of the most popular metric in evaluation stock market prediction models; accuracy is also widely preferred likewise RMSE and most of the studies in the recent literature are based on classi cation-based prediction tasks (Kumbure et al, 2022). Moreover, for the stock market direction prediction or classi cation task, accuracy is the only metric to be employed.…”
Section: Methodsmentioning
confidence: 99%
“…ML is considered the working horse in the new era of the so-called big data. Different machine learning techniques have been applied successfully in diverse fields, such as, from wireless communications ( Tan et al, 2014 ), computer vision ( Khan et al, 2021 , Altantawy et al, 2020 ), finance ( Kumbure et al, 2022 ), entertainment ( Porcino et al, 2022 ), control system ( Hedrea and Petriu, 2021 ) and computational biology to biomedical and medical applications ( Chiang et al, 2014 , Albu et al, 2019 , Upadhyay and Nagpal, 2020 ).…”
Section: Related Workmentioning
confidence: 99%
“…However, the behavior of a stock market is affected by many factors, such as financial and economic policy, business development, and investor psychology [1]. Due to complex internal factors and the changing economic environment, forecasting the stock market is a challenge for researchers of financial data mining [2]. Traditional stock market forecasting methods include securities investment analysis, nonlinear dynamic methods, time series mining, and statistical modeling [3].…”
Section: Introductionmentioning
confidence: 99%
“…Comparison results between EMTL-LS-SVR(L + R) and other algorithms on average rank deviations.Remark: In Table4, set d to represent the absolute value of the difference between average rank deviation and the CD value. * denotes d is between [0, 1], ** denotes d is between[1,2], *** denotes d is between[2,3], **** denotes d is between[3,4].…”
mentioning
confidence: 99%