In finance and accounting, relative to quantitative methods traditionally used, textual analysis becomes popular recently despite of its substantially less precise manner. In an overview of the literature, we describe various methods used in textual analysis, especially machine learning. By comparing their classification performance, we find that neural network outperforms many other machine learning techniques in classifying news category. Moreover, we highlight that there are many challenges left for future development of textual analysis, such as identifying multiple objects within one single document.
Machine learning, Textual analysis, Finance, Accounting, Media news, Sentiment, Information
Finance | Finance and Financial Management
The Journal of Finance and Data Science
GUO, Li; SHI, Feng; and TU, Jun.
Textual analysis and machine leaning: Crack unstructured data in finance and accounting. (2016). The Journal of Finance and Data Science. 2, (3), 153-170. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/5407
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.