Applications of artificial intelligence in behavioral finance getting benefit from extended data sources

Abstract

Behavioral finance is the core of modern finance, which relies heavily on the value of data information that is highly in line with the characteristics of technological development. In this era of big data, artificial intelligence (AI) has penetrated into every aspect of our lives. It has brought revolutionary changes to various fields such as finance. Furthermore, as the development of machine learning in AI technology and various machine learning technologies have been widely used to perform different the field of behavioral finance. Especially, deep learning has shown excellent performance in the tasks of behavioral finance. Overall, this research aims to address the problems in different application scenarios of behavioral finance with the support of AI technology. The main novelty of this research is to build a proven application framework，different behavioral financial scenarios benefit from AI technology. To examine the feasibility of the proposed frameworks, this study collects human behavior data from social media and financial news, that employ machine learning technology to analyze three application scenarios in behavioral finance. This study successfully extracted human behavior information through natural language processing (NLP), which thereby helps to predict the stock market of the company. Meanwhile, it also successfully explored the impact of user-generated content (UGC) on company performance, which can enrich the feature extraction for company performance evaluation through customer reviews. The experimental results of this study can help to prove that AI technology is useful in application scenarios of behavioral finance and also promote in other similar application scenarios. In summary, This study provides a marketing strategy for the marketer of the company and business decisions for the managers of the company.