We study how investors' use of machine learning methods affects corporate financial reporting decisions. Our model consists of a representative investor who employs a regularized regression (e.g., lasso) to infer value from earnings reports, along with a firm manager who may bias earnings...

We examine the impact of regulatory scrutiny on mutual fund disclosures. Based on a sample of 16,161 comment letter conversations between the Securities and Exchange Commission’s Division of Investment Management and mutual funds from 2004 to 2019, we find that: (i) while comment letters...

We study how the automation of information acquisition activities in the asset management industry impacts the role humans play in such activities. We find that machine-based automation of SEC filing downloads frees up the human workforce and allows humans to research a broader set of portfolio...

Mutual funds’ switches to monthly holding disclosures reduce the efficiency of corporate investments. Consistent with a crowding-out mechanism, the evidence suggests that monthly portfolio disclosures discourage information production activities by other market participants and, consequently,...

Purpose: The purpose of this paper is to study the road segmentation problem of cross-modal remote sensing images. Design/methodology/approach: First, the baseline network based on the U-net is trained under a large-scale dataset of remote sensing imagery. Then, the cross-modal training data...

We examine the extent to which current operating cash flows are incorporated in future earnings targets in executive compensation. Using target and actual compensation earnings per share (EPS) disclosed in proxy statements for large U.S. public companies, we find that revision of the following...