Faculty Sponsor(s)
Xiaoyue Luo
Location
Jereld R. Nicholson Library: Grand Avenue
Subject Area
Mathematics
Description
In our modern competitive market, businesses are seeking efficient and innovative platforms to remain profitable and prepared, especially in the uncertain world of the financial stock market. One possible avenue for improving stock market returns that companies can turn to is harnessing a substantial volume of information, known as big data. However, because of the nature of big data, distilling and analyzing the vast amount of information can require complex analytical methods. Using a keyword selection process based on word frequency, we were able to filter out the data amongst the noise and derive a sector-specific keyword list. This list, used in combination with a previously created trading method along with the implementation of a thresholding technique, allowed us to develop a more specific trading strategy focused on different market sectors. Our results show that the use of thresholding techniques in addition to the Google Trends strategy may improve returns in the stock market.
Recommended Citation
Shannon, William; Moranchel, Jennifer; and Luo, Xiaoyue, "Big Data and the Stock Market: Distilling Data to Improve Stock Market Returns" (2018). Linfield University Student Symposium: A Celebration of Scholarship and Creative Achievement. Event. Submission 14.
https://digitalcommons.linfield.edu/symposium/2018/all/14
Big Data and the Stock Market: Distilling Data to Improve Stock Market Returns
Jereld R. Nicholson Library: Grand Avenue
In our modern competitive market, businesses are seeking efficient and innovative platforms to remain profitable and prepared, especially in the uncertain world of the financial stock market. One possible avenue for improving stock market returns that companies can turn to is harnessing a substantial volume of information, known as big data. However, because of the nature of big data, distilling and analyzing the vast amount of information can require complex analytical methods. Using a keyword selection process based on word frequency, we were able to filter out the data amongst the noise and derive a sector-specific keyword list. This list, used in combination with a previously created trading method along with the implementation of a thresholding technique, allowed us to develop a more specific trading strategy focused on different market sectors. Our results show that the use of thresholding techniques in addition to the Google Trends strategy may improve returns in the stock market.
