No 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 BioPharma, Banking, Information Technology, Oil & 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 (2010-15) 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.

We textually analyze 10-K texts from EDGAR during 1995-2009 to score firms' investment opportunity sets on multiple dimensions. We identify 646 unique key words that predict future investments and group them into 62 factors. Industry-specific factors include Bio-Pharmaceutical, Banking, Information Technology, Oil & Gas and Semiconductor , while more general factors include Impairment, Debt Intensity, Executive Changes, Preferred Stock Buyback and Capital Seeking. Our multi-dimensional measures of firms' investment opportunities outperform Tobin's Q and/or industry-fixed effects, in predicting out-of-sample future (2010-15) investments and related corporate policies, and even inform incrementally over lagged dependent variables. Our IOS factors outperform Tobin's Q more in subsamples with less efficient market prices, i.e., when Tobin's Q is a noisier signal of investment opportunities.

No abstract

No abstract