This research aims to analyse how managers react to firm's financial conditions in the Management Discussion and Analysis (MD&A). To do so we appeal to text mining techniques such as natural language processing and sentiment analysis. The main assumption is that the MD&A content may vary depending on financial and economic conditions companies are experiencing. The study is conducted on a sample of US listed financial companies which experienced, between 1998 and 2011, different financial conditions, namely: 1) companies which filed for Chapter 11, thus having a high risk of bankruptcy; 2) companies not filing for Chapter 11, but with a medium risk of bankruptcy according to their economic and financial performance ratios; 3) companies not filing for Chapter 11, with a healthy financial situation. The expected results might let emerge an association between the bankruptcy risk levels and the content of the MD&A. In fact, MD&As issued by companies with higher bankruptcy risk are expected to be more optimistic, proactive and appealing, composed of more verbose and complex periods than the MD&As released by healthy companies. This research provides support for stakeholders, as it suggests to read MD&As with a critical interpretation, considering that a very optimistic and positive tone may actually hide an alarm signal. Taking advantage from the text mining analysis, it makes some elements explicit that would otherwise remain implicit or even hidden behind complex periods and sentences. Future insights provided by this research are related to the possible association between the MD&A and the audit opinion. Recent studies demonstrated that auditing firms often fail in predicting the bankruptcy risk of distressed companies. Perhaps, such an error could be due to the role of MD&A, as auditors may consider its content and its tone as signals of future improvements. Through a matching analysis between MD&A and the related audit opinion, further results may be found out.
|Titolo:||Management Discussion & Analysis in the US financial companies: a data mining analysis|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|