Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data

Huijian Han (), Zhiming Li and Zongwei Li
Additional contact information
Huijian Han: Department of Computer Science, Shandong University of Finance and Economics, Jinan 250014, China
Zhiming Li: Department of Management Science and Engineering, Shandong University of Finance and Economics, Jinan 250014, China
Zongwei Li: Agricultural Bank of China Limited Shandong Branch, Jinan 250001, China

Sustainability, 2023, vol. 15, issue 4, 1-12

Abstract: The consumer confidence index is a leading indicator of regional socioeconomic development. Forecasting research on it helps to grasp the future economic trends and consumption trends of regional development in advance. The data contained on the Internet in the era of big data can truly and timely reflect the current economic trends. This paper constructs a conceptual framework for the relationship between the consumer confidence index and web search keywords. It employed six machine learning and deep learning models: the BP neural network, the convolutional neural network, support vector regression, random forest, the ELMAN neural network, and the extreme learning machine to predict the consumer confidence index. The study shows that the use of machine learning models has a better prediction effect on the consumer confidence index. Compared with other models, the BP neural network and the convolutional neural network have lower error indicators and higher model accuracy, which helps decision-makers forecast the consumer confidence index. Consumers search for various goods and prices, as well as macroeconomics, to understand the economic conditions of the market, which affects the consumer confidence index and consumption decisions. Therefore, web search data can be used to predict consumer confidence. Future research can be extended to other macro indicator-related prediction studies. It is important to promote market consumption and confidence, improve consumption policies, and promote national prosperity.

Keywords: deep learning; CNN; machine learning; consumer confidence index; prediction; web search (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/4/3100/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/4/3100/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:4:p:3100-:d:1062002

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().