Essays on (Frictions in) Corporate Finance
A new thesis reveals how cognitive, market, and regulatory frictions shape corporate finance decisions, challenging the belief that managers always track financial markets. Using a machine learning-based Index of Attention to Financial Markets (IAFM) from 98,000 earnings calls, the study finds managers who focus on markets are more sensitive to market conditions. It also shows that small increases in stock transaction costs weaken environmentally-minded investors’ influence, and clear regulations boost digital debt crowdfunding. The research mainly uses U.S. data.
What it examines
This thesis studies how different types of frictions—managerial attention, market structure, and regulation—affect corporate finance decisions. Using new data and empirical methods, it measures how managers pay attention to financial markets, how stock liquidity impacts environmental policies, and how regulatory clarity shapes digital financing.
What it concludes
The research shows that managerial attention, stock liquidity, and clear regulations have real effects on corporate decisions and sustainability. These findings can help improve financial market policies, guide sustainable investing, and design better regulations. Future research could further explore these frictions in other markets and settings.
Evidence objects
A groundbreaking machine learning-based Index of Attention to Financial Markets (IAFM) analyzes 98,000 earnings call transcripts, revealing that managers market focus drives investment and financing decisions, supporting price feedback and market timing theories.
key_findings bullet 1 · key_findings · validation V0
A natural experiment shows that even minor increases in stock transaction costs weaken the influence of environmentally-minded 'value' investors on corporate environmental policies, highlighting how market frictions can undermine sustainable finance efforts.
key_findings bullet 2 · key_findings · validation V0
Clear, detailed regulations significantly boost digital financingespecially debt crowdfundingby reducing uncertainty, while the thesiss innovative use of text analysis and natural experiments is notable; however, findings mainly reflect U.S. data, limiting global relevance.
key_findings bullet 3 · key_findings · validation V0
This dissertation uniquely applies advanced NLP (word embeddings, word2vec) to measure managerial attention, leverages a natural experiment to separate value from values in sustainable investing, and analyzes regulatory claritys impact on digital finance. Its originality, novel methodologies, and large-scale data make it compelling and significant for quantitative finance research.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
- … I develop a machine learning framework that measures firm-level managerial attention using earnings call transcripts. This produces an Index of Attention to Financial …
Source row: 730 · abstract type: snippet