Job Market Paper
Presentation: FMA Annual Meeting 2021, Georgia State University 2021, Global Finance Conference 2021, Dayton Summer Finance Workshop 2021, AFA Poster Session 2021, MFA 2022, Atlanta Rising Scholar Symposium in Finance 2022
I use EDGAR web traffic to study the effects of investors’ information learning activities in mergers and acquisitions. On one hand, investors improve their understanding of the merger by searching for peer firms. Specifically, high abnormal downloads in peer firms enhance the positive link between market reactions and post-merger performance. On the other hand, investors update their expectations on both peer firms and future takeover activities. At the firm level, high abnormal downloads reduce peer firms’ information asymmetry and increase their probability of being the next target(acquirer). At the deal level, high abnormal downloads reduce external monitoring costs and increase value creation in subsequent mergers of peer firms. Overall, information learning improves takeover market efficiencies.
I study the effects of the connections between CEO candidates and board on CEO appointment decisions and post-succession firm efficiencies. I find that candidates who have prior connections to the hiring firms’ board members are more likely to be hired as CEOs. The positive effects of connections on candidates’ succession probability increase with CEO labor market frictions but vary with candidates’ influence and monitoring strength for internally hiring firms. I also find that, following successions, firms that hire externally with more connections perform better and experience more active reconstructing activities, while firms that hire internally with more connections perform worse and exhibit weak monitoring. Moreover, connections with available candidates affect a firm’s decision to hire internally or externally.
Can Technology Help Overcome Contractual Incompleteness? Evidence from Blockchain Laws
(with Mark Chen, Sophia Hu, and Qinxi Wu)
Presentation: FMA Annual Meeting 2021, Georgia State University 2021, Lone Star Conference 2021, Shanghai-Edinburgh-London Fintech Conference 2021, Southwestern University of Finance and Economics 2021
To what extent can blockchain technology help alleviate contracting problems in supplier-customer relationships? We examine this issue by exploiting a quasi-natural experiment based on the staggered adoption of state laws that increased firms’ ability to develop, adopt, and use blockchains in business and commerce. We find that, after being subjected to a legislative shock, firms with more relationship-specific outputs exhibit significantly more positive changes in Tobin’s Q, R&D, and blockchain-related innovation. Also, such firms appear to reduce their reliance on vertical integration strategies in favor of less integrative alliances and joint ventures. For supplier firms with high relationship specificity, a pro-blockchain state law also leads to increased acquisition of new, in-state customers as well as greater retention of existing in-state customers. Overall, our results suggest that blockchain technology can help remedy constraints and inefficiencies arising from contractual incompleteness in supply-chain relationships.
FinTech companies and FinTech mergers are increasingly prevalent over the past decades. We explore the motives and consequences of FinTech mergers. FinTech firms have a greater likelihood of becoming targets in mergers and acquisitions (M&As). FinTech mergers increase the valuation of combined firms by 2.15%, and such value creation is significantly greater than non-FinTech mergers. Acquirers adopt cutting-edge technologies through M&As and subsequently become FinTech firms. Moreover, FinTech mergers generate spillover effects to peer firms in the merging industries. We document positive market reactions to the industry peers in the short term and FinTech merger waves in the long term.
Labor-Displacing Innovation, Firm Value, and Productivity
(with Mark Chen)
Presentation: Georgia State University 2022
We provide evidence on the incidence of labor-displacing technological innovations and how they affect innovating firms’ value and productivity. Using recent advances in machine learning and natural language processing, we empirically identify labor-displacing innovations from patent texts and firm-level employment data. We show that labor-displacing patents are, on average, less valuable to their innovators than non-displacing patents that are otherwise similar along various quality dimensions. We also document that receiving a labor-displacing patent has a negative, causal effect on a firm’s workforce productivity and efficiency. These adverse effects are more pronounced for firms that employ a higher share of skilled workers. Overall, our findings suggest that labor-displacement effects can substantially lower the private returns to corporate innovation.
Board Interlocks and Innovation Spillover
(with Mark Chen, Sophia Hu, and Qinxi Wu)
Presentation: FMA Annual Meeting 2022 , The 8th Annual International Corporate Governance Society (ICGS) Conference 2022
This paper examines whether board interlocks—direct links created when an individual serves concurrently on two or more corporate boards—foster spillovers of knowledge and innovation between firms. We exploit firm-specific tax price of innovation that combines local changes in R&D tax incentives and the locations of inventors to capture exogenous variation in corporate innovation. Using a variety of patent-based measures, we show that innovation at a “source” firm has a greater positive effect on the quantity, quality, and similarity of innovation at “downstream” firms when interlocks are present. We also document that knowledge spillovers are larger when an interlocking director is younger, non-busy, or appointed after the incumbent downstream CEO. Spillovers are reduced, however, when the downstream board is more independent, has fewer directors with advanced education background, or has less collective experience in high-tech industries. Overall, our findings suggest that board interlocks provide an important channel by which scientific knowledge and innovation can flow between firms.
The Real Effects of Common Analyst Coverage on Mergers and Acquisitions
(with Omesh Kini)
We examine the influence of common analysts on the likelihood and subsequent outcomes of mergers and acquisitions (M&As). Consistent with the idea that analysts’ coverage decisions reflect unique aspects of relatedness between portfolio firms and that analysts produce coverage-specific information, we find that shared analysts between a pair of firms increase the likelihood of a deal announcement between them. This effect is stronger when there is greater uncertainty about potential deal synergies and firm valuation, and also gets enhanced after the passage of Regulation FD. Conditional on deal success, shared analysts are associated with higher combined wealth effects, higher acquirer wealth effects, and lower target wealth effects as measured by announcement-period abnormal returns. In addition, in multiple acquirer successful contests, acquirers with common analysts are more likely to complete (withdraw from) potentially value creating (destroying) deals. These results suggest that there is better matching between acquirers and target firms with shared analysts, but these analysts tend to benefit bidders. As further evidence that is consistent with better matching between merger-pairs, we find greater post-merger improvement in combined firm operating performance, which is accompanied by both higher employment growth and growth in the number of establishments, for deals with shared analysts. Using a sample of deals that exogenously failed as benchmarks, we confirm that the effect of shared analysts on the improvement in operating performance is potentially causal. Overall, these results suggest that common analysts, by covering firm pairs, play a unique information dissemination and monitoring role in M&A deals.
Local Labor Discrepancies and Corporate Geographic Diversification
(With Lixin Huang and Omesh Kini)