Math and the Financial Crisis

In 2008, the stock market faced one of its biggest down turns in history, and its cause is pinpointed to one very specific part of the market.

In Cathy O’Neil’s novel “Weapons of Math Destruction,” she goes into detail about her time spent as a quantitative analyst at D.E. Shaw during the financial crisis. Joining the firm prior to the collapse, she became very familiar with the finance sector’s heavy utilization of mathematical models for all facets of the industry. From assessing risk to making trading decisions, mathematical models were leveraged to give “smart money” the ability to outperform “dumb money.” It was at this point, she became disillusioned with the absolute power mathematical models had over the entire sector.

Working as a quantitative analyst, your role is to build mathematical models that accurately predict pattern based phenomena in equity, commodity, and derivative pricing. Understanding, and subsequently exploiting, these moments of arbitrage is an extremely lucrative position that can indefinitely provide virtually risk-less returns on investment. Working with huge swaths of money to exploit these minute differences in prices causes some cognitive dissonance between the exploiter and the exploitee. O’Neil was under the assumption that these models were not really hurting anyone, but were just making the market more efficient by closing down discrepancies.

Unlike her position, dealing with commodities and derivative pricing, there were many other places that mathematical models were running the show. Primarily, they were used to assess risk in most financial vehicles, specifically mortgages. Eventually, this is what led to the 2008 financial crisis, and is the reason it is dubbed the subprime mortgage crisis. Lenders were issuing mortgages like crazy, because the money was cheap to lend and offered long term, sustainable returns. Additionally, these mortgages were often packaged up into a bundle of mortgages and sold to investment firms looking to collect the returns and assume the risk. These bundles of mortgages were sent off to credit rating agencies to determine the risk level, and this is where WMDs came into play. Credit rating agencies had been using mathematical models to determine the risk of underlying securities for years, but eventually, there was reason to start fibbing about the quality of these mortgages. Investment firms, like D.E. Shaw, Lehman Brothers, and the like, were no longer interested in buying junk mortgages with high risks of default because they were not acceptable hedges of risk. So the credit agencies now had pressure from investment firms to start tweaking their models so that they could sell higher rated mortgage securities as investment vehicles. Packaged in these “AAA” (the highest possible rating) mortgage backed securities were janitors and maids with mortgages for $750,000, a dollar amount well beyond their means. Eventually, the market shifted, interest rates rose, and people were no longer able to afford their mortgages, so these high quality securities were now full of defaulting borrowers. Foreclosures commenced, and investment firms were now holding property instead of safe investment securities, and portfolios were full of risky assets that yielded little to no return.

Come 2008, and worsening in 2009, the financial crisis was in full effect and market makers like D.E. Shaw et. al. were in a bad spot. Mathematical models, and specifically WMDs, had reared their ugly heads and showed the world how dangerous biased models can be. Although the original intention of these models were sound, reduce the bias in rating mortgages and objectively assess the risk of default, but they were tweaked and tuned to give the results the developers wanted, not the ones they needed to hear. In turn, the subprime mortgage crisis was an important, though harrowing, lesson for the finance sector. Mathematical models are powerful tools, and used properly, can give valuable insight traditional investors are unable to see; however, used improperly, and especially at scale, they can become dangerous weapons that can harm not only your firm, or your sector, but bring the world’s most powerful economy to its knees.