Three essays in corporate finance
There are two foci in my research efforts to produce this dissertation. First, I explore and create novel datasets and methods that can expand our existing arsenal of empirical tools.1 Following that, I deploy these tools to analyze three aspects of information science in social networks and earnings-related voluntary disclosures: Social network connectedness, natural language, and management credibility.
This dissertation has three essays on corporate finance. The first essay is motivated by the friendly board framework of Adams and Ferreira (2007). In this study, we measure the value of board advisory activities using Centrality Slice (CS) - the ratio of the network connectedness of executive directors to non-executive directors. We find that this measure positively relates to firm value, performance-turnover sensitivity, management forecast accuracy, and market reaction to forecast surprises. The results from our instrumented regression suggest that CS is an optimal selection outcome that varies across firms. As such, firms will likely enjoy better advisory benefits if their policies can support high CS in an optimal manner.
The second essay is co-authored with Roger K. Loh. In this study, we add two novel approaches to a large literature on analysts’ conflicts of interests. Using analysts’ tones during peer conference calls, and returns co-movement between their brokerages and hosts to proxy for the level of information advantage, we find that analysts from high returns co-moving brokerages exhibit language patterns that neither signal competition nor collusion. Our results show that the market values tones, with increasing reactions to the level of returns co-movement, consistent with the notion of pricing for competence. We also find that the market is not naïve as it discounts sentiment tones from brokerages sanctioned during the Global Analyst Research Settlements.
The third essay is co-authored with Chiraphol N. Chiyachantana. Using a proprietary set of institutional trading data, we investigate how sophisticated investors utilize the information contained in management earnings forecasts characteristics to formulate their trading strategy. We find that these investors’ responses to a firm’s forecasts are not only increasing in the magnitude of earnings surprise, but also magnified by the firm’s prior forecast accuracy. We reveal transient institutions as the principal traders on these forecast characteristics and show that trading strategies using both forecast surprise and prior forecast accuracy are not only profitable to implement, but also outperform those that rely solely on forecast surprise.