The Impact of Market Sentiment and Financial Reports on Stock Prices: A Machine Learning Approach

DOI

10.2991/978-94-6239-801-6_146How to use a DOI?

Keywords

Stock Price Prediction; Machine Learning; Market Sentiment; Financial Indicators; XGBoost

Abstract

Machine learning will be employed in this paper to investigate the joint effects of market sentiment and corporate financial indicators on short-term stock prices. Natural language processing is used to collect and process sentiment data from financial news and social media, and at the same time, financial indicators are obtained from corporate reports. Logistic Regression, Random Forest, XGBoost, SVM and LSTM are all contained in the same experiment. Combine the financial and sentiment features to construct a prediction model, then evaluate the model. All the models will have the same data processing and evaluation methods in the experiment. According to the above analysis, in addition to financial indicators, sentiment information can also help predict. Random Forest and XGBoost are the best models among those that have been evaluated. Therefore, the data of sentiment can provide us with more immediate information on the market’s anticipation of changes. Combining the sentiment and financial information of a company can help predict changes in stock prices and offer some support for quantitative investment.

Copyright

© 2026 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.

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TY  - CONF
AU  - Fenghui Zhao
PY  - 2026
DA  - 2026/10/23
TI  - The Impact of Market Sentiment and Financial Reports on Stock Prices: A Machine Learning Approach
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 1403
EP  - 1408
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_146
DO  - 10.2991/978-94-6239-801-6_146
ID  - Zhao2026
ER  -