In bank runs and market crashes, it matters how ideas ‘catch’

The Twitter-fuelled run on Silicon Valley Bank and its fallout in markets has sparked renewed interest in how financial contagion spreads through webs of connections, causing fortunes to sometimes turn on a dime.

“There were a couple of Tweets and then this thing went down much faster than has happened in history,” Citigroup chief executive Jane Fraser said in a Bloomberg interview in March. “It’s a complete game-changer from what we’ve seen before.”

Contagion through new social media channels and old-fashioned word of mouth may also explain why the first casualty of a California bank run was a respectably capitalised firm nearly 6,000 miles away, albeit a troubled one in other respects.

The power of viral narratives cascading through networks of contacts, as witnessed in recent months, raises new questions, too. How far do outcomes depend on the composition and topology of the network — that’s to say, the balance of different actors in the system and who influences whom?

It’s a question academics have been thinking about since the late 1990s when research pointed to how releasing information to certain combinations of informed and impressionable actors can produce chaotic results.

“If the world were made up of Warren Buffetts, bubbles and crashes wouldn’t happen,” says Tim Hellmann, an associate professor in economics at the UK’s University of Southampton. A certain balance of Buffetts and other types of investors, though, might create the bumpier conditions investors would recognise.

Hellman is investigating how the make-up of markets — putting all other factors aside — might play a role in booms and busts. By studying different networks of rational agents in a computer model, he and his colleagues have been able to show that how the agents are linked can explain a lot. Network effects on their own are capable of creating bubbles and cycles, for example.

Through analysis of every possible configuration of linkages it emerges that different set-ups lead to different market patterns — some healthy and calm, others more feverish. The effects come down to the network centrality of certain agents, something that can be quantified with metrics like those employed at Google to rate web pages based on their number of links.

Hellmann’s model mimics a financial market, but the logic could apply to other pieces of the ecosystem such as banking deposits.

One type of network in particular leads to markets where prices bubble and crash, Hellmann says. These are networks in which a few “closed” groups of highly influential agents — the Warren Buffetts or Peter Thiels of the world — mix with a broader mass of more impressionable actors. Closed means the influencers are stubborn in their views, largely ignoring what others think. For market participants, it’s a familiar description.

What does the power of network effects mean in practice? For one, it’s an idea that could help shape policy. Hellmann is talking to the European Investment Bank, the Bank of England and the OECD about collaborating on a project to look at the spread of green investing, for example. If the popularity of green investing hinges on the views of a few key influencers, policy-makers might consider measures to nudge such influencers in a preferred direction.

Similar thinking could also be coming to bank regulation. The Financial Stability Board is examining the role social media played in the bank runs that brought down Credit Suisse, SVB and others.

In the meantime, a growing understanding of networks suggests regulators and risk managers should be tracking how ideas spread in networks and on social media, as well as what those ideas are and who is pushing them.