I am a Ph.D. candidate in finance from the University of Mannheim. My research focuses on empirical asset pricing and market microstructure.Â
I am on the 2025-2026 finance job market.
I am a Ph.D. candidate in finance from the University of Mannheim. My research focuses on empirical asset pricing and market microstructure.Â
I am on the 2025-2026 finance job market.
Empirical Asset Pricing,
Market Microstructure
Presented at: 3rd Bonn-Cologne-Frankfurt-Mannheim PhD Conference, DGF conference 2025
Abstract: Could efforts to restrain algorithmic trading result in increased information rents for corporate insiders? I test this hypothesis by exploiting the U.S. SEC Tick Size Pilot Program, in which temporarily coarser tick sizes reduced algorithmic trading effectiveness. I find that insider purchases increased by approximately 30\% during the program while remaining at least as profitable as before. These effects coincided with informed outsider trading intensity moving in the opposite direction, suggesting that algorithmic trading primarily serves as a competitive advantage for outside investors in order execution. The increased insider purchases are more pronounced in smaller firms and firms with lower institutional ownership, cautioning against restrictions on algorithmic trading in financial markets with insufficient insider trading oversight.
This table presents results indicating that algorithmic trading shifts informed trading activities from corporate insiders to outsider investors.
The key independent varaible is the predicted changes in algorithmic trading proxies using the quote treatment in the U.S. tick size pilot program as an instrument. The dependent variables are insider purchase intensity (purchaseInShrout), insider purchase propensity (isPurchase), and outsider informed trading intensity (ITI).
These results support the notion that outsider investors could leverage on developments in trade execution technology to offset the informational advantages of insiders.