In 2009, after completing my studies in Finance and Banking at the University of Piraeus, I joined the Master’s in Financial Engineering program at the University of Michigan, Ann Arbor on a Fulbright Scholarship. Financial Engineering is a multidisciplinary field that involves topics from Finance, Economics, Engineering, Mathematics, and Statistics. In other words, it is a specialization that uses tools from Mathematics, Statistics, and Engineering to answer questions in Finance and Economics. Such questions usually range from the creation of financial products – to cover the current and future investment and risk needs of investors – to the pricing and risk assessment of existing financial products and investment schemes. Financial engineers are therefore, expected to have deep optimization and mathematical modelling skills, and programming expertise. Financial engineers however, are not financial economics. That is, they are not concerned with questions pertaining to aggregate wealth improvement, optimal (corporate) decision making, or the stability of the financial system. The graduates of financial engineering programs typically become (financial) traders, although, due to their diverse skillset, they are highly sought as analysts, consultants, and researchers in wealth and risk management companies, investment banks, hedge funds, and federal agencies.
More often than not, a financial engineering program is considered equivalent to a financial mathematics or computational finance program. Although that is in general true, there are important differences among these programs. Financial engineering programs put considerable emphasis on their multidisciplinary character and do not concentrate in one specific discipline, i.e., they usually devote the same amount of time to finance, mathematics/statistics, and engineering. Financial mathematics and computational finance, in contrast, tend to concentrate more on mathematics and programming, respectively. As a result, these three programs tend to attract students from different backgrounds that are also absorbed, on average, by different positions within similar industries. Financial mathematics tend to be preferred for more research oriented positions and computational finance for more programming oriented positions. A typical financial engineering cohort is comprised of engineers, statisticians, mathematicians, and economists.