University of Illinois Urbana-Champaign

Essays in financial markets and institutions

Tremacoldi Rossi, Pedro

Content Files
TREMACOLDIROSSI-DISSERTATION-2022.pdf

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https://hdl.handle.net/2142/115893

Description

Title
Essays in financial markets and institutions
Author(s)
Tremacoldi Rossi, Pedro
Issue Date
2022-07-08
Director of Research (if dissertation) or Advisor (if thesis)
Irwin, Scott H.
Doctoral Committee Chair(s)
Irwin, Scott H.
Committee Member(s)
  • Bernhardt, Dan
  • Robe, Michel A.
  • Janzen, Joe
Department of Study
Agr & Consumer Economics
Discipline
Agricultural & Applied Econ
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Financial Markets
  • Market Microstructure
  • Technology Adoption
Abstract
This dissertation consists of three chapters on financial markets and institutions. The first essay studies several dimensions of how trading automation in stock markets impacted trading firms and workers. Electronic trading migration in the early 2000s was the most important technological change in the history of stock markets. However, our understanding of its impacts on industry profits and their distribution across firms, as well as on workers that witnessed a dramatic transformation in the task content of their jobs from physical and intuitive thinking to highly skilled programming and IT knowledge, remains limited by data opacity and lack of appropriate variation. To answer these questions, I combine a novel dataset of the uni- verse of broker-dealer firms and licensed professional traders in the US with variation to exposure to trading automation induced by the SEC’s major redesign of stock trading. I find that areas with greater exposure to trading automation, measured as higher pre-existing IT infrastructure, experience larger increases in total trading profits. In the same high-exposure areas, larger firms increase their share of industry profits, a distributional effect driven by an intensive margin response of surviving firms increasing profit levels. Consistent with higher barriers to entry and setup costs imposed by higher technology requirements, the entry of new broker-dealers decreases more in areas with higher automation, and both the largest and the smallest firms experience higher survival rates. While these larger firms represent multi-billion-dollar investment banks and other brokers that cater to institutional and large investors, very small firms are mainly specialized boutique brokers with large relative IT capital investments and more likely to generate revenue from proprietary trading activities. I also find that stock traders experience large decreases in employment, both when comparing these workers to traders plausibly unaffected by the stock market redesign policy, including commodities traders, and when comparing stock traders in areas with differential degrees of exposure to trading automation. Traders that manage to remain employed in broker-dealers are more likely to switch markets, becoming investment advisors, bond traders, or working in other segments with lower degrees of automation relative to stocks. They are also more likely to switch between firms, usually with a firm size penalty. Consistent with trading automation being a skill-biased technical change, more skilled traders experience wage gains over less skilled individuals. Additionally, the employment of non-traditional professionals in the industry, including computer scientists and software engineers grows, albeit being insufficient to offset losses in trader employment. The second essay (join with Scott H. Irwin) focuses on a crucial aspect of financial markets: liquidity measurement. We investigate how and why tools developed to estimate bid-ask spread with daily data often misbehave in several contexts and provide researchers with simple tests to empirically assess the magnitude of estimation bias from using these proxies. We first show that poorly designed horse-race studies measuring the performance of simple bid-ask spread estimators in modern stock markets lead to inflated performance results and mask the existence of a systematic relationship between higher liquidity and estimation bias. We then derive a framework that explains the determinants of the bias using a popular and recently developed spread estimator, the high-low measure, showing how these common misbehavior factors are equivalent to well-known bias sources in older spread estimators. We suggest that these biases cannot be completely corrected for in any estimator sharing the same properties and implemented with sample quantities instead of population moments, and unfortunately are likely to perform poorly in modern markets, where liquidity cost levels tend to be sufficiently low. Finally, we offer a number of tools to successfully bound the estimation bias in any empirical context. The third essay (joint with Scott H. Irwin and Conner Naughton) studies the prevalence and effects of market manipulation by algorithmic traders. We focus on self-trading, a predatory practice that entices traders to submit orders on a certain side of the market by creating fictitious trades where both counterparties are the same trader. We show how microstructure features of electronic markets enable a much higher level of sophistication in the use of self-trading and study how different quoting strategies impact market quality and order flow.
Graduation Semester
2022-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/115893
Copyright and License Information
Copyright 2022 Pedro Tremacoldi Rossi

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