Author(s)

Yifeng Ye ¹, Rui Ma ¹

Affiliation(s)

¹ School of Computing, Zhuhai College of Science and Technology, Zhuhai, Guangdong, 519040, China

Corresponding Author

Rui Ma

ABSTRACT

With the vigorous development of the Internet and the rapid popularization of artificial intelligence technology, financial market forecasting and analysis become more convenient and accurate. In this context, this article aims to explore the relationship between financial news and stock price trends, and conduct in-depth analysis using artificial intelligence related algorithms. By utilizing natural language processing techniques, we can quantify and analyse a large amount of financial news, thereby predicting the rise and fall trends of stock indices. We adopted artificial intelligence related technologies, combined with financial news text data, and used a series of algorithm models for analysis. By annotating the rise and fall of stock indices and corresponding news text data, and conducting multiple independent experiments, we divided the dataset into training and testing sets to verify the accuracy and reliability of our model. The research results show that financial news has a certain degree of accuracy in predicting the rise and fall of stock indices, and we observe that there is a certain lag in the response of stock indices to financial news. This means that the stock market's response to news is not immediate, but rather has a certain degree of delay. In addition, we also found some correlation differences between different markets. The research results of this article not only provide new ideas and methods for predicting and analysing financial markets, but also provide a new perspective for people to better understand the relationship between financial news and stock price trends.

KEYWORDS

Stock Indices, Financial News, Artificial Intelligence, Model Fundamentals

CITE THIS PAPER

Yifeng Ye, Rui Ma, Research on the Correlation between Financial News Text Analysis and Stock Market Fluctuations Using Artificial Intelligence Algorithm Models. Financial Engineering and Risk Management (2024) Vol. 7: 124-130. DOI: http://dx.doi.org/10.23977/ferm.2024.070217.

REFERENCES

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