Measuring Multidimensional Investment Opportunity Sets with 10-K Text

Abstract

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.