Professors’ picks offers a weekly curated selection of FT articles by and for business school faculty to connect classrooms to current events and to develop students’ critical thinking.
Read all submissions at www.ft.com/bschoolpicks. Save this link in myFT to receive emails alerting you to each new edition. Search the tags for relevant teaching topics. Encourage students to join the debate in the comments section beneath the article.
Financial Risk Management
Vanguard chief warns of ‘financial exploitation’ by prediction markets
The big state gamble on prediction markets
Tags: Prediction markets, Investing vs gambling, Financial regulation, Ethics in finance
Summary: In 1906, the Victorian polymath Francis Galton came across a weight-judging competition at a Plymouth country fair. Eight hundred participants, from expert butchers to indifferent clerks, paid sixpence to guess the weight of a slaughtered ox. Galton expected a demonstration of collective folly, believing the average voter was capable of very little. Instead, the average guess, 1,197 pounds, came within a pound of the true weight. This “wisdom of crowds” episode suggested that under the right conditions, a crowd can be not merely intelligent, but uncannily precise. A century later, platforms like Polymarket and Kalshi attempt to industrialise this logic, treating the future not as a mystery, but as something to be priced, aggregated and settled. The question is whether modern markets still preserve the conditions that make crowds wise.
Classroom application: In line with what I hope to achieve via “Eight Bridges” (a set of eight curated programmes to connect classrooms with industry events for fostering students’ critical thinking and improving their employability), a classroom discussion of these articles forces students to connect financial market design (incentives, information, structure) with real-world regulation and ethics. It sharpens their ability to distinguish between investing, speculation and gambling, and to see how business models and product design can blur those boundaries.
Questions:
- Galton’s fair illustrates four conditions for collective intelligence: diversity, independence, decentralised knowledge and an aggregation mechanism. In today’s prediction platforms, which of these conditions are most compromised? How would you redesign markets to restore them without killing liquidity or engagement?
- John Maynard Keynes described the stock market as a “beauty contest” in which participants pick the faces others will find prettiest, rather than those they themselves like. When prediction-markets prices are tweeted, charted and fed into campaigns, traders start trading on other traders’ beliefs rather than underlying facts. At what point do these Keynesian “beauty contest” dynamics undermine informational efficiency, and what should regulators or platforms do to curb reflexive, screen-watching behaviour — where prices reflecting beliefs about beliefs — without suppressing price discovery?
- Data suggest that a large majority of users on some platforms lose money, while a tiny fraction captures the bulk of profits. Does such extreme pay-off concentration represent a healthy reward for skill and information or evidence of an exploitative structure?
- The CFTC as a regulator resembles a Galton‑era ox‑cart trying to catch a bullet train, juggling derivatives, crypto and prediction markets with limited resources and muddled rules. What two institutional changes would most improve its ability to police prediction markets?
- Insider‑trading concepts were built for identifiable corporate insiders, not pseudonymous event traders. One subsection of CFTC Rule 40.11 — meant to fence off contracts on terrorism, war, assassination and gaming — appears to ban such contracts outright; another outlines a 90-day review to assess whether they are contrary to the public interest. Should regulators aim to: (a) fully ban insider trading on prediction markets, (b) tolerate it with strong enforcement only against the worst abuses, or (c) carve out specific groups/events (eg public officials, war markets) for strict prohibition?
- Vanguard’s boss calls many prediction markets “a form of financial exploitation” and stresses a “really important distinction between investing and gambling”. On what criteria should regulators, platforms and asset managers distinguish between legitimate speculation and exploitative gambling? How might the incentives of different business models (Vanguard, Polymarket/Kalshi, Robinhood) shape how each defines and positions the boundary between investing and gambling?
Krishnan Ranganathan, Guest faculty at Indian business schools
Source: https://www.ft.com/content/4b4b9cde-71e6-4539-980e-bd1f3e96d43c
