Forging the Future of Work
From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
Journal of Financial Economics, July 2024
An AI analyst trained to digest corporate disclosures, industry trends, and macroeconomic indicators surpasses most analysts in stock return predictions. Nevertheless, humans win “Man vs. Machine” when institutional knowledge is crucial, e.g., involving intangible assets and financial distress. AI wins when information is transparent but voluminous. Humans provide significant incremental value in “Man + Machine”, which also substantially reduces extreme errors. Analysts catch up with machines after “alternative data” become available if their employers build AI capabilities. Documented synergies between humans and machines inform how humans can leverage their advantage for better adaptation to the growing AI prowess.
Sean Cao, Robert H. Smith School of Business, University of Maryland
EPA Scrutiny and Voluntary Environmental Disclosures
Review of Accounting Studies
Market participants have called on the SEC to address the lack of disclosures about firms’ environmental impacts, investments, and exposures. However, the frictions that obstruct the flow of environmental information are not well understood. I shed light on these frictions by examining whether scrutiny by the Environmental Protection Agency (EPA) restricts the firm’s voluntary environmental disclosures in earnings conference calls. Consistent with the notion that EPA scrutiny gives rise to disclosure frictions, I find a negative relation between EPA scrutiny and the environmental disclosures of scrutinized firms. This negative relation is concentrated among firms without environmental expert directors, suggesting that environmental governance mitigates the chilling effect of EPA scrutiny. In terms of disclosure quality, I show that environmental disclosures include fewer quantitative details under EPA scrutiny. Collectively, these findings provide insights into the frictions that restrict the flow of environmental information to market participants, an important issue given the SEC’s efforts to improve current disclosure practices.
Mark Zakota, Assistant Professor, Robert H. Smith School of Business, University of Maryland
Identifying Competitors in Geographical Markets Using the CSIS Method
Journal of Marketing Research
Businesses with physical footprints – hotels, retailers, and restaurants – must identify the competitors that matter most. Traditional approaches using brand tier or proximity often fail in dynamic or asymmetric markets. We introduce the Conditional Sure Independence Screening (CSIS) method to marketing to identify true competitors based on their pricing influence on a focal firm’s demand. CSIS is computationally efficient, robust to spatial mis-specifications, and effective for identifying, asymmetric, even non-local, and segment-specific competition. It is also an effective variable selection technique.
In applying CSIS to U.S. hotel data our analysis shows that competition intensity varies not only by location or market segments, but that asymmetry is common – many hotels influence others without being influenced in return. Our methodology enables smarter, data-driven pricing and benchmarking and helps tailor strategy to segment and seasonality. In addition, it is scalable across industries such as retail, services, and hospitality.
Xian Gu, Assistant Professor, Kelley School of Business, Indiana University; P.K. Kannan, Dean's Chair in Marketing Science, Smith School of Business, University of Maryland