The financial world is made up of several very technical skills, including computational finance. While most of the finance sector is concerned with actually making transactions that range from stock trades to corporate acquisitions, the computational side of the profession is more concerned with algorithms, modeling, and the computer software that powers most of the modern financial world. This means that professionals who work in this part of the financial industry often come from varied academic backgrounds, including computer science, information systems, business administration, and finance.
Origins in the 1950s: Problem-Solving Through Algorithms
In the early 1950s, one financial professional decided to develop a new method of portfolio selection that relied more on standard deviation and variance than it did on intuition and recommendation. This practice of creating data models associated with various investment tools and financial decisions became known as computational finance, and it advanced rapidly alongside the technology revolution that ensued throughout the remaining 50 years of the 20th century. As the field developed, it came to rely on massive algorithms and “big data,” which analyzed a full history of similar financial transactions, past financial performance, and future expected growth.
In classes on computer programming and the computational side of finance, professors often note that this is the “practical” or “applied” side of the profession, since it relies on data and analysis first and foremost. The algorithms that have their roots in 1950s efficiency are currently used to make small trades on the stock market, generate investment recommendations to individual investors, and manage investment-related retirement accounts with minimal human intervention as their value is maximized. The field focuses on numerical methods in investment, rather than the mathematical proofs that non-computational methods used in the past, and still use today in a few cases. Though not foolproof, algorithmic financial computation has been approved for widespread use in large-scale investment trading, algorithmic stock trading, quantitative investing, and algorithmic investment management at some firms.
Getting into the Field: What Does it Take?
Finance itself is one of the most highly competitive job sectors in the United States. There is no exception for the more computational side of the industry: Applicants in this industry often compete with several hundred people for each opening, and they’re judged as much on their experience as they are on the pedigree of their academic qualifications. Because this particular field is so involved with both finances and advanced computing, most applicants have typically studied either computer science or finance at the undergraduate level. The most qualified applicants, and the ones most likely to get the job, usually have a dual Master of Science degrees in finance and computer science. Some have a Master of Business Administration in place of the Finance degree, though they’ve usually pursued a concentration in finance.
Pedigree counts, too. Many of the largest finance firms look for computer and computational professionals from Ivy League schools or those that rank in the top 20 finance programs on a nationwide or worldwide basis. For smaller firms, however, pedigree counts a bit less and experience counts a bit more. The high stakes of this particular job explain why experience and academic prestige are valued so highly: One wrong algorithmic programming choice could lead to financial disaster for individuals, retirement firms, stocks, corporations, and many other stakeholders. It’s important that those professionals who are hired can do the job well and do it right.
A Growing Area of Hiring for Financial Professionals
Finance is an increasingly technical profession that depends on the advances made in computing over the past half-century. When it comes to making predictable, valuable trades and investment choices, that means that most firms are hiring a large number of computational finance professionals who show the right computer science expertise, financial knowledge, and algorithmic comprehension to get the job done in line with corporate expectations.
