Measuring Multidimensional Investment Opportunity Sets with 10-K Text
通过分析公司10-K文本，识别出445个预测未来投资的关键词并归纳为43个因子，构建多维投资机会集指标，比托宾Q和行业固定效应更准确预测企业投资及相关政策。
ABSTRACT We show that firms' investment opportunity sets (IOS) are multidimensional. Analyzing Form 10-K texts, we identify 445 unique keywords that predict firms' future investments during 1995–2009 and combine them into 43 underlying factors. Industry-specific factors include Bio-Pharma, Banking, Information Technology, Oil and Gas, and Retail Stores, while more general factors include Equity Intensity, Debt Intensity, Lease, Going Concern, and Acquisition. These factors form our multidimensional measures of IOS. They outperform Tobin's Q and/or industry fixed effects, in predicting future out-of-sample (2010–2015) investments and related corporate policies, and even inform incrementally over lagged dependent variables. We trace the factors' improved predictive power to their multidimensional nature, which captures IOS-related variation within and between industries, and stability in IOS that allows 10-K texts to be more informative. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G35; M41; M21.