The chaos engulfing the world’s financial markets is remarkable in its severity and complexity. The latest stage has seen the collapse of Lehman Brothers, the sale of Merrill Lynch, and the Fed stepping in to rescue insurance giant AIG with an $85 billion loan. Today, the world’s central banks pumped in $180 billion in cash in an effort to resuscitate the global money markets.
As financial turmoil accelerates, it is worth rereading The Blow-Up (requires free registration), an article that we ran in November 2007 examining the way that quantitative analysis contributed to the credit crisis that has gradually deepened ever since.
Much of what’s happening currently connects back to this: the application of incredibly complex mathematical and statistical techniques to financial markets. An article in yesterday’s Financial Times highlights how the failure of mathematical modeling to accurately foresee market behavior is now exposing even seemingly safe institutions such as AIG to the wider credit mess:
On a wider level, AIG failed to see how the fate of supersenior [pools of debt previously considered safe] could be linked to behaviour in other parts of the financial world. For what has made the price falls so vicious this year is that all the institutions that had previously piled this “boring” supersenior on their books have needed to sell at once. Hence the development of a vicious, downward spiral.
These institutions can hardly be blamed. This morning I spoke with Jiang Wang, a professor at the Laboratory for Financial Engineering at MIT’s Sloan School of Management. He says that the models used by big financial institutions simply aren’t engineered to cope with the kind of severe conditions we are now seeing:
“Quantitative models/tools have served finance well at the micro level, such as valuation techniques, trading strategies, and specific risk analysis and product design. However, they are not at the level of capturing system wide risks and dynamics, and not intended to be. Much more work and data are needed here.”
Unfortunately, as the situation worsens, it becomes even harder to predict what will happen next.
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
Data analytics reveal real business value
Sophisticated analytics tools mine insights from data, optimizing operational processes across the enterprise.
Driving companywide efficiencies with AI
Advanced AI and ML capabilities revolutionize how administrative and operations tasks are done.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.