Research in the Application of Big Data in the Field of Stock Investment
- DOI
- 10.2991/978-94-6463-064-0_99How to use a DOI?
- Keywords
- Big Data; investment; finance; Artificial Intelligence
- Abstract
In some fields like stock investment, every decision matters. To decrease the risk that may be caused by monitoring large quantities of information artificially and increase the security, investors usually use Big Data to process information. In this paper, we are going to discuss how Big Data works in stock investment, the model and theory that is used in the application of Big Data investment, how the prospect of Big Data Investment is, what the advantages and the disadvantages of using Big Data in stock investment are, and how Big Data makes the stock investment more efficient. The data and information indicate that the diversity, accuracy and high-speed of Big Data makes stock investment more efficient. In investment, Big Data works in 3 dimensions which are momentum, value and profitability, by using different models like Behavioral Event Analysis, Funnel Analysis and Retention Analysis Model. Although it is acknowledged that Big Data is quite useful and beneficial in stock investment, it can not be denied that the use of Big Data still needs some improvement.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wanying Qi AU - Michelle Sun PY - 2022 DA - 2022/12/27 TI - Research in the Application of Big Data in the Field of Stock Investment BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 965 EP - 973 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_99 DO - 10.2991/978-94-6463-064-0_99 ID - Qi2022 ER -