| Title |
Predicting stock market movements using network science: an information theoretic approach
|
|---|---|
| Published in |
Applied Network Science, October 2017
|
| DOI | 10.1007/s41109-017-0055-y |
| Pubmed ID | |
| Authors |
Minjun Kim, Hiroki Sayama |
X Demographics
The data shown below were collected from the profiles of 72 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 20 | 28% |
| United Kingdom | 4 | 6% |
| Spain | 2 | 3% |
| Australia | 2 | 3% |
| Mexico | 2 | 3% |
| Bosnia and Herzegovina | 1 | 1% |
| Italy | 1 | 1% |
| South Africa | 1 | 1% |
| Paraguay | 1 | 1% |
| Other | 8 | 11% |
| Unknown | 30 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 38 | 53% |
| Scientists | 31 | 43% |
| Science communicators (journalists, bloggers, editors) | 2 | 3% |
| Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Taiwan | 1 | 1% |
| Unknown | 92 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 19% |
| Student > Master | 11 | 12% |
| Student > Bachelor | 10 | 11% |
| Researcher | 10 | 11% |
| Student > Doctoral Student | 4 | 4% |
| Other | 12 | 13% |
| Unknown | 28 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 24% |
| Economics, Econometrics and Finance | 10 | 11% |
| Business, Management and Accounting | 6 | 6% |
| Physics and Astronomy | 5 | 5% |
| Mathematics | 4 | 4% |
| Other | 14 | 15% |
| Unknown | 32 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 54. 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 21 July 2019.
All research outputs
#798,482
of 25,850,376 outputs
Outputs from Applied Network Science
#14
of 590 outputs
Outputs of similar age
#16,452
of 334,976 outputs
Outputs of similar age from Applied Network Science
#2
of 10 outputs
Altmetric has tracked 25,850,376 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 590 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. This one has done particularly well, scoring higher than 97% of its peers.
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 334,976 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.