Firm-Specific Industries, Volatility, and Return: A Text-Based Network Industrial Classification Approach
研究发现基于文本网络行业分类（TNIC）的企业特定行业是波动性和异常收益的关键驱动因素，利用TNIC波动性构建的多空策略能获得传统行业分类无法解释的正向非因子收益。
This study finds that the firm-specific industry of the text-based network industrial classification (TNIC) is a key driver of return volatility and abnormal return. Rationally, firm-specific industries provide a unique set of competitors that share more fundamentals than those from fixed industrial classifications. The TNIC return volatility is positively associated with higher firm volatility. It also explains the abnormal returns not captured by the six-factor asset-pricing model of Fama and French. Finally, this study explores asset-pricing implications by examining a long position in stocks with high TNIC volatility and a short position in stocks with low TNIC volatility. This long–short investment strategy delivered significant and positive non-factor-related returns that are higher than the same investment strategy applied to fixed industrial classifications such as Standard Industrial Classification and Fama and French classification. <b>TOPICS:</b>Security analysis and valuation, factor-based models, quantitative methods, statistical methods, performance measurement <b>Key Findings</b> ▪ Industry return volatility based on the TNIC is an underlying factor in explaining a firm’s volatility and return. ▪ Sorting firms into investment portfolios based on TNIC industry volatility provides significant abnormal return not explained by the traditional industrial classifications. ▪ Industry grouping methods (i.e., TNIC) may enhance the performance of asset-pricing models.