Public and Private Information: Firm Disclosure, SEC Letters, and the JOBS Act

Abstract

This paper examines the impact of the recently passed Jumpstart Our Business Startups (JOBS) Act on the behavior of market participants. Using the JOBS Act — which relaxed mandatory information disclosure requirements — as a natural experiment on firms’ choices of the mix of hard, accounting information and textual disclosures, we find that relative to a peer group of firms, initial public offering (IPO) firms reduce accounting disclosures and change textual disclosures. Because it allows a partial revelation of IPO quality, only textual disclosures affect underpricing. We also find that the Securities and Exchange Commission (SEC) changes its behavior post-JOBS Act in responding to draft registration statements. Specifically, the SEC’s comment letters to firms are more negative in tone, and more forceful in their recommendations, focusing on quantitative information. Finally, under the JOBS Act, investors place more emphasis on the information produced by the SEC when pricing the stock. Returns following public release of the letters vary by about 4% based on letter tone.

The SEC Filing Review Process: A Survey and Future Research Opportunities*

“…Studies examining the probability of receiving specific types of comment letters expand their models based on their particular research setting, such as auditor turnover disclosures in 8‐K filings (Ettredge et al 2011), compensation disclosures in proxy statements (Robinson et al 2011), IPO or merger registration statements (Agarwal et al 2017; Johnson et al 2020; Liu et al 2020; Schuldt and Vega 2018; Wang 2018), non‐GAAP reporting (Donelson et al 2020; Jo and Yang 2020), filings from foreign companies (Boone et al 2021; Gietzmann and Isidro 2013; Linthicum et al 2017; Naughton et al 2018), and other specific disclosures or accounting applications 16 . Prior literature finds evidence of spillover in types of comments across auditors (Baugh and Schmardebeck 2020; Bills et al 2020) and industry peers (Brown et al 2018).…”

“…Quantitatively, prior literature has examined cross‐sectional variation in comment letters based on observable features such as the number of unique topics discussed (Heese et al 2017), the number of filings (Bozanic et al 2019), and the length of the conversation in days or rounds (Cassell et al 2013; Gunny and Hermis 2020). 23 Qualitatively, previous research using textual analysis of comment letters has focused on the length in the number of characters or words (Johnson et al 2020; Lowry et al 2020; Shen and Tan 2020), readability (Ballestero and Schmidt 2019; Cassell et al 2019), negative tone (Agarwal et al 2017; Chantziaras et al 2021; Ege et al 2020), linguistic strength (Liu and Moffitt 2016), parsing of staff information (Ege et al 2020; Kubic 2021), or identification of specific topics (Dechow et al 2016). More sophisticated incorporation of textual analysis includes latent Dirichlet allocation to classify comment letters textually as important or unimportant based on their ability to predict future restatements and write‐downs (Ryans 2021) and Kullback‐Leibler divergence to map textual components of comment letters to changes in future disclosures (Lowry et al 2020).…”