What happens to the speed of trading as technology advances? How do we move from automated button pressing machines to ultra-fast algorithms? What surprising impact does the rain have on the trading windows of financial markets?

Donald MacKenzie is a professor of sociology at the University of Edinburgh and also the author of several books. His most recent works are Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets and An Engine, Not a Camera: How Financial Models Shape Markets.

Greg and Donald discuss the intersection of sociology and finance, exploring how financial models not only describe markets but also actively influence them. Donald explains the concept of performativity, where financial theories shape market behavior, and contrasts qualitative sociological methodologies with quantitative financial studies. Their conversation also touches on the history and impact of technologies and regulatory environments that have transformed financial trading, highlighting contributions from notable academics and instances of feedback loops between theory and practice.

*unSILOed Podcast is produced by University FM.*

Episode Quotes:

Chicago pits vs. algorithms

28:34: For, say, investment management firms that have to buy and sell large portfolios of assets, there’s little doubt that the modern world of automated trading has benefits, but it also has downsides. I mean, the benefit is, quite simply, of course, that automated systems are a lot cheaper than human beings in colored jackets running around in Chicago’s pits or on the floor of the New York Stock Exchange. But, at the same time, of course, if you are trying to sell or buy a very large position, then you do leave electronic traces that trading algorithms can pick up on and make money out of.

Why financial models shapes markets like engine not camera

04:31: An engine does things, it's not a camera—at least in our ordinary thinking about cameras, where you take the photograph and the landscape remains the same. An engine does stuff, it changes its environment.

The power of shared signals in trading success

34:11: The secret of my success is I realized quite early on that there were things—signals, as they would be called in the field—inputs to algorithms that everybody knew about and that everybody knew that everybody knew about. So it wasn't like I had an unsuccessful attempt, way back to research statistical arbitrage and dare nobody would tell you what exactly they were trading off of. But I think they're trading because everybody knows that if you're trading shares, then a move in the relevant index future is a very, very important signal. Everybody knows that, and everybody knows that. Everybody knows that.

Finance beyond numbers, the human side of quantitative work

02:30: Finance as an academic field, and indeed of course finance as a practice, is typically highly quantitative. And to get into the technology, quantitative work can be great, but to really get into it you’ve got to talk to people. Ideally, you want to go see things, so the methodology is more qualitative than quantitative, and it probably would not be the best of ideas.

Show Links:

Recommended Resources:

• William F. Sharpe
• Thomas Morton
• Fischer Black
• Coase Theorem
• Mark Rubinstein
• Eric Budish
• John O'Brien
• Portfolio Insurance
• Milton Friedman
• Commodity Futures Trading Commission
• U.S. Securities and Exchange Commission
• Leo Melamed
• The Library of Mistakes

Guest Profile:

• Faculty Profile at the University of Edinburgh
• Forbes.com Profile
• Wikipedia Profile

His Work:

• Amazon Author Page
• Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets
• An Engine, Not a Camera: How Financial Models Shape Markets
• Do Economists Make Markets? On the Performativity of Economics
• Material Markets: How Economic Agents are Constructed
• Inventing Accuracy: An Historical Sociology of Nuclear Missile Guidance

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