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Intraday trend prediction of stock indices with machine learning approaches

Overview of attention for article published in The Engineering Economist, May 2023
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Mentioned by

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2 X users

Citations

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2 Dimensions

Readers on

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9 Mendeley
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Title
Intraday trend prediction of stock indices with machine learning approaches
Published in
The Engineering Economist, May 2023
DOI 10.1080/0013791x.2023.2205841
Authors

Pan Tang, Xin Tang, Wentao Yu

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 11%
Student > Bachelor 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 5 56%
Readers by discipline Count As %
Economics, Econometrics and Finance 2 22%
Computer Science 1 11%
Unknown 6 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 July 2023.
All research outputs
#19,187,140
of 23,778,637 outputs
Outputs from The Engineering Economist
#68
of 99 outputs
Outputs of similar age
#179,507
of 263,602 outputs
Outputs of similar age from The Engineering Economist
#1
of 2 outputs
Altmetric has tracked 23,778,637 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 99 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,602 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them