About

I am a doctoral candidate in Finance at Columbia Business School. I will be available for interviews at the 2020 ASSA meeting in San Diego. 

  • Curriculum Vitae: CV

  • Email: dmei19[at]gsb.columbia.edu

  • Research Fields: Empirical Corporate Finance, Innovation, Merger and Acquisition 

Reference

Wei Jiang (Chair)

Arthur F. Burns Professor of Free and Competitive Enterprise

Columbia Business School

Finance & Economics Division

(212) 854 9002
wj2006@columbia.edu

Kairong Xiao

Assistant Professor

Columbia Business School
kairong.xiao@gsb.columbia.edu

Daniel Wolfenzon

Stefan H. Robock Professor of Finance and Economics
Columbia Business School

Finance & Economics Division
(212) 851 1803
dw2382@gsb.columbia.edu

Olivier M. Darmouni

Assistant Professor

Columbia Business School
omd2109@columbia.edu

 

Research

Technology Development and Corporate Mergers (Job Market Paper)

Presented at USC Marshall Ph.D. Conference in Finance; Empirics and Methods in Economics Conference; Meeting of Minds@HKU Forum

Abstract: I examine the motives as well as consequences of M&As between companies with varying degrees of technological overlap. High-overlap merging deals, with more collaboration between the inventors from the merging companies, produce more patents and go deeper in the existing fields. In contrast, low-overlap deals, with a higher percentage of new inventors, experience larger technology shifts and develop patents in unexplored areas with higher commercial value. Importantly, M&A completion facilitates technology transformation to a greater degree than the two companies, especially pairs with low overlap, could have accomplished on their own. Overall, the direction of innovation is an important motive for technology-driven acquisitions.

 

Published Papers

- Activist Arbitrage in M&A Acquirers, Finance Research Letters, 2019, with Wei Jiang and Tao Li.

- Influencing Control: Jawboning in Risk Arbitrage, Journal of Finance, 2018, with Wei Jiang and Tao Li.​

 

- Appraisal: Shareholder Remedy or Litigation Arbitrage?, Journal of Law and Economics, 2016, with Wei Jiang, Tao Li, and Randall S. Thomas.

Work in Progress

- Technology, Information, and Firm Boundary, with Miao Liu. 

How does information shape the firm boundary? We tackle this question by examining firms’ strategy when entering a new technology field. We examine whether increased corporate patent disclosures, as induced by the American Inventor’s Protection Act, facilitate firms to enter new technology fields. We first find that increased public information on technology facilitates more technology acquisition for non-innovative firms, defined as firms that do not report R&D in their financial statements. Second, we document that, for a given technology field, the impact of AIPA is more prominent for firms whose technological distance is larger from the field. Third, we show that the impact of AIPA is larger for technology fields that advance more rapidly.​

- Innovation, Management, and Compensation

 

I match the information on inventors with the database of detailed executive compensation from 2006 through 2018. It allows me to obtain a unique sample of executives who potentially assume two different roles, as both manager and inventor, at the same time. In theory, their compensation packages should trade off risk sharing, which is classic in the principal-agent problem, versus failure tolerance, which is established in the innovation incentives literature. I can utilize the within-firm variation by comparing innovative and non-innovative executives’ compensation packages. In addition, I can also utilize the time-series variation by comparing the same executive’s compensation packages during different time periods with varying intensities of inventing activities.

- Debt Collection Technology, with Guangyu Cao and Daheng Yang.

 

We obtain a unique dataset from one of the largest debt collection companies in China. It contains all debtors' hard information that is observable to the loan officers and the debt collectors. In addition, we have the recordings and transcripts of all the phone calls between collectors and debtors. Based on the data on debt collection and repayment, we examine the optimal approaches to re-collect debts for different groups of populations. Utilizing the fact that debtors are randomly assigned to collectors, we conduct preliminary analysis and show that these calls are more effective when the debtors are college students and elder people. The transcripts and recordings of these calls enable us to examine the underlying mechanism in more detail.

- Signaling Values and Cross-Border Mergers, with Rebecca DeSimone.

We observe a large increase in cross-border M&As by multinational firms from emerging markets. Signaling cultural fit and legitimacy can be important as these deals face higher regulatory scrutiny. In this context, we are examining the impact of value-signaling between firms in cross-border M&As. We first construct a firm-level corporate social responsibility (CSR) profile based on a new dataset of more than 250 million media-reported events. We then ask if a change in the professed CSR of the acquirer towards the target predicts the launch of M&A. If so, is that M&A more likely to complete? What about the profitability and productivity of the combined firm? As the media database contains rich details of both statements and actions of the firms, we can utilize these events to determine the mechanism of the effect – what explains the credibility of successful acquirer signals?

Data

Matching USPTO Patent Assignees to Compustat Public Firms and SDC Private Firms

This data project is a systematic effort to match assignee names on USPTO patent records, sometimes abbreviated or misspelled, to all public and private firms that have been involved in alliances and M&A in Compustat and SDC Platinum databases. The current coverage runs from 1976 to 2017 for all public firms in Compustat and from 1985 to 2017 for all private firms in SDC Platinum.

The algorithm leverages the Bing web search engine and significantly improves upon fuzzy name matching, a common practice in the literature. This document presents a step-by-step guide to the searching and matching algorithm. All codes are publicly available on the GitHub page: 

https://github.com/danielm-github/patentsmatch_bingsearchapproach

This data is used in my job market paper "Technology Development and Corporate Mergers" and other projects including "Technology, Information, and Firm Boundary" (with Miao Liu). It will be made publicly available after the paper is published. I acknowledge the research grant from the Finance Division and the Deming Center at Columbia Business School.

 
 

Teaching

  • MBA. Advanced Corporate Finance, instructed by Neng Wang, Spring 2018

  • Executive Education. The Debevoise Business Education Program, Fall 2016

  • Ph.D. Financial Econometrics II Panel Data, instructed by Wei Jiang, Spring 2015

 

© 2019 by Danqing Mei.