The US housing market is the world’s largest and more than two-thirds of US homeowners have a mortgage. The 2008-2009 financial crisis was fueled in large part by a collapse in the value of mortgage-backed securities in the US. This crisis crushed US pension funds, investment banks, hedge funds, asset managers, insurers, and individual investors and homeowners.
How did the survivors learn the lessons of this collapse and re-tool?
One of the largest fund management companies in the US called on Julia to help solve a large complex mortgage optimization problem. This helps the US economy by providing proper valuation of mortgages, needed investment and liquidity for the mortgage industry, investment opportunities and returns for investors, and improved security and stability for the mortgage market, the housing market, and the US and global financial system.
Switching to Julia reduced the amount of time required to complete optimization from 558.095 seconds to 1.833 seconds – a speed increase of 304x.
Switching to Julia also reduced the number of iterations from 3,110 iterations to 50 iterations – a gain of 62x.
Their [analysts / data scientists / quantitative analysts / algorithmic traders] have expanded their use of Julia to model fixed income portfolios, and have seen similar gains in speed and productivity across their business.