Asset Volatility and Financial Sustainability - Jiahua Zhou (European Scientific Journal, ESJ, 2023)
This study aims to observe companies’ sustainability with fundamental-based volatility measures. We use delisting as a proxy to observe how asset volatility can interact with abnormal earnings fluctuation to impact firms’ sustainability. Abnormal assets and earning volatility are signals of risk. Accounting literature documented evidence that earnings management can hide severe risks with abnormal asset fluctuation. This paper uses a PCA logistic regression model to predict companies’ delisting. We borrow the Six Sigma methodologies to measure the volatility of financial statement items. Then the PCA analysis reduces the data dimensions to twelve factors. The following logistic regression with the panel data provides significant evidence for this prediction. The result shows that assets’ abnormal fluctuation is a risk signal concurring with the earnings management literature. One takeaway for accounting policymaking is that companies must disclose detailed explanations if asset volatility is beyond a red line. As SFAS 151 requires direct disclosure of abnormal excess capacity costs, companies must disclose abnormal asset volatility. The paper contributes to accounting literature from two perspectives. First, this paper captures firms’ sustainability from the accounting perspective with fundamental measures from quarterly financial reports. It provides a comprehensive way to detect inherent risks. Second, the PCA logistic regression model offers a comprehensive analysis to derive useful information from many attributes, and it can avoid multiple col-linearity issues.