Author's School

Olin Business School

Language

English (en)

Date of Award

4-26-2024

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Xiumin Martin

Committee Members

na

Abstract

Financial institutions, e.g., mutual funds and banks, are important entities in the capital markets. They make decisions based on accounting information. Further, their activities influence information production on the firm side, because interests are aligned between institutional investors and firms either through contracting or through market price pressure. Within this context, my dissertation focuses on the interactions between financial institutions and firms. In Chapter 1, I study how institutional investors’ myopia affects firms’ voluntary disclosures. This research question is motivated by the concurrent debate in accounting theory about whether myopia leads to more or less voluntary disclosures. I explore the SEC regulation that required mutual funds to mandatorily report holdings more frequently as a natural experiment that increases institutional investors’ myopia, and study its impact on portfolio firms’ issuance of earnings guidance. I find firms that experience larger increase in investors’ myopia are more likely to issue earnings guidance. In particular, they issue earnings guidance that are below the analysts’ consensus, which helps them to better meet-or-beat the quarter end earnings target. In Chapter 2 with Ran Duchin, Roni Michaely and Xiumin Martin, we study how personal connections matter in loan granting decisions. Previous theoretical and empirical studies find borrowers connected to lenders are more likely to receive loans, either due to lower information asymmetry, or favoritism, and are unlikely to distinguish between the two mechanisms. We explore the Paycheck Protection Program (PPP) as part of the Coronavirus Aid, Relief, and Economic Security Act (CARES Act). Banks carry minimum screening responsibilities in granting the PPP loans, since the funding is supported by the government and borrowers do not need to repay the debt as long as certain criteria are met. The unique feature of the PPP mutes the lower information channel in loan decisions and we are able to provide a cleaner estimate of how favoritism matters. In Chapter 3 with Xiumin Martin, Xiaoxiao Tang and Yifang Xie, we employ an unsupervised machine learning algorithm to identify the decentralized network among lenders in the syndicate market. We study how the network reduces coordination costs and promotes collective actions among lenders when a new syndicate is formed. We find with more participant lenders coming from the same clique as the lead lender does, the syndicate is more likely to go through renegotiations before maturity. Since renegotiations required voting from both lead lenders and participant lenders and are considered as a Pareto improvement to both the borrowing firms and the lending institutions, our findings suggest that the decentralized networks can serve to improve coordination and contracting efficiency.

Comments

Accounting

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