Understanding the MD&A Section in Financial Reports through Sentence Classification

• Main Authors: Sung-Ying Fang, 方松營
• Other Authors: Hsin-Min Lu
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/tbttky

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 107 === Textual analysis has been an emerging area in accounting and finance research. With the growing realization that economical and statistical models may not adequately explain the market with conventional quantitative measures alone, there has been extensive empirical literature attempting to incorporate verbal, non-quantitative measures. However, in fields such as natural language processing, there have been extensive progress in machine learning methods, such as new methods of language representations and the application of transfer learning, which has not been commonly used in academic papers featuring textual analysis within financial context. This paper wishes to contribute to the field by preparing a training dataset suitable for a wider array of research questions, and apply a contemporary machine learning method using bidirectional encoding representations from transformers, or BERT. Our sentence classifier aims to correctly classify sentences with respect to their tone, their accounting category or topic based on the context of the sentence, and whether or not the sentence is a forward-looking statement. By applying our sentence classifier to out-of-sample annual filings, we evaluate our dataset and classification method by revisiting a subset of research questions concluded from our literature review. Our dataset and preliminary descriptive analysis align with the results of many empirical models from other studies, most notably with the analysis made by Li (2010). In conclusion, the tone of MD&A sections is mean-reverting, may proxy for economic determinants, and can be useful in inspecting the macro-environment of an industry.