Date of Degree
PhD (Doctor of Philosophy)
Anand M. Vijh
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
In this dissertation, I study corporate activities, and their predictive abilities of market returns.
The first chapter examines the determinants of industry merger waves. We propose a continuous merger activity variable (MAV) as an alternative to discrete industry merger waves. We find that the ranking of MAV within a quarter is associated with strong patterns in before and after industry returns and operating performance. During 1985-2015, bucket 1 containing industries with lowest MAV rank earns alpha of 0.30% per month higher than bucket 12 containing industries with highest MAV rank.
The second chapter examines the predictive ability of many corporate activities, including mergers and acquisitions, insider trading, share repurchases, etc. Using machine learning approaches, we find that an aggregate index of corporate activities has substantial predictive power of future market returns both in- and out-of-sample, and yields much greater economic gain for a mean-variance investor. We further find that the predictive ability of the corporate index stems from its information content about future cash flows and expected corporate investments and that the corporate index performs particularly well for stocks with greater information asymmetry.
The third chapter examines the relationship between firm valuation and takeover activity, using the European debt crisis as a laboratory. The European debt crisis in mid-2011 caused a wide-spread redemption of money market mutual funds (MMFs) with high exposure to European borrowers. The shock to a group of MMFs induced a reduction in the volume of repurchase agreements (repos) that the funds were engaged in. The resulting decline in the amount of capital available to support equity positions (furnished as collateral by the repo counterparties) potentially lead to downward price pressure on these stocks. I find that the temporary shock in firm valuation triggers more opportunistic takeover bids as suggested by prior studies, but it does not increase the takeover success rate. Overall, my findings present a more complicated picture to the relationship between market valuation and takeover activities than suggested by prior studies.
Corporate Finance, Investments, Machine Learning, Mergers and Acquisitions, Predictability
xii, 168 pages
Includes bibliographical references (pages 160-068).
Copyright © 2018 Bo Meng
Meng, Bo. "Corporate finance and machine learning." PhD (Doctor of Philosophy) thesis, University of Iowa, 2018.