Financial Crisis and Big Data
Posted on March 3rd, 2021 by Emilio Miles
In chapter 3 of Weapons of Math Destruction, Cathy O’Neil talks about her journey in the finance and big data industries. She talks about working for a Hedge Fund, called Shaw, which used mathematical models for their own benefit and to the detriment of others. This was one of the reasons she quickly started to become disillusioned with WMDs. During the 2008 stock market collapse, Shaw found a way to do well despite other businesses failing. This was because they employed a strategy similar to one in baseball betting where instead of betting on the winner of the game, you could bet on whether a batter would bunt at least one, but no more than twice the whole game, for example. They saw that there were other micro-opportunities from which to make money during the crisis.
Mortgages were being made more easily accessible to everyone with little tricks to convince people to buy and inevitably default on their loans. The same things were happening with credit cards. Once disaster finally hit the mainstreams, that’s when people started to see what the algorithms were doing. Millions of Americans were losing their homes and jobs. Credit card defaults were at an all time high. The suffering that was being experienced by the populace was massive.
This was when Ms. O’Neil decided to quit her job at Shaw. She was disappointed in the role that mathematics had played in the disaster. WMDs had caused this. People had been using formulas to impress rather than clarify. She decided to quit in order to fight against and fix the financial WMDs. So she started working at a company, called RiskMetrics Group, as an expert that analyzed risk. When she saw that the refusal to acknowledge risk ran deep in finance, it was time to move again and started working for Big Data instead.
At this job, she analyzed traffic in websites and used consumer information to decide what kind of ads to present them. She saw many similarities between finance and Big Data. The biggest difference was that instead of moving markets, she now predicted people’s clicks. Big Data scouted talent from the same prestigious universities and payed attention to the same factors, such as SAT scores and college admissions. In both cultures, wealth was not a means to get by but was rather tied to personal worth. Once again, disillusioned with what she had learned, she quit her job in order to investigate these issues.
I think the biggest takeaway from this chapter is the importance of management of mathematical models in business. Misuse of these models can cause disasters such as the stock market crash in 2008. Millions of people lost their jobs and were thrown deep into debt. It shows the harm that mathematics can do when placed into the wrong hands.