Financial engineering

Financial engineering

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Financial engineering is a multidisciplinary field involving financial theory, the methods of engineering, the tools of mathematics and the practice of programming.[1] It has also been defined as the application of technical methods, especially from mathematical finance and computational finance, in the practice of finance.[2]

Financial engineering draws on tools from applied mathematics, computer science, statistics and economic theory.[3]. In broadest definition, anyone who uses technical tools in finance could be called a financial engineer, for example any computer programmer in a bank or any statistician in a government economic bureau. However, most practitioners restrict the term to someone educated in the full range of tools of modern finance and whose work is informed by financial theory.[4] It is sometimes restricted even further, to cover only those originating new financial products and strategies.[5]


Related terms

Computational finance and mathematical finance are both subfields of financial engineering. Computational finance is a field in computer science and deals with the data and algorithms that arise in financial modeling. Mathematical finance is the application of theoretical mathematics to finance.[5]

Quant is a broad term that covers any person who uses math for practical purposes, including financial engineers. Quant is often taken to mean “financial quant,” in which case it is similar to financial engineer.[6] The difference is that is it possible to be a theoretical quant, or a quant in only one specialized niche in finance, while “financial engineer” usually implies a practitioner with broad expertise.[7]

“Rocket scientist” is an older term reserved for the first generation of financial quants who arrived on Wall Street in the late 1970s and early 1980s.[8] While basically synonymous with financial engineer, it implies adventurousness and fondness for disruptive innovation.[9] Rocket scientists were usually trained in applied mathematics, statistics or finance; and spent their entire careers in risk-taking.[10] They were not hired for their mathematical talents, they either worked for themselves or applied mathematical techniques to traditional financial jobs.[4][9] The later generation of financial engineers were more likely to have PhDs in mathematics or physics and often started their careers in academics or non-financial fields.[11]

The first degree programs in financial engineering were set up in the early 1990s. The number and size of programs has grown rapidly, so now some people use the term “financial engineer” to mean someone who has a degree in the field.[3]

An older use of the term “financial engineering” that is less common today is aggressive restructuring of corporate balance sheets. It is generally (but not always) a disparaging term, implying that someone is profiting from paper games at the expense of employees and investors.[12]


The main applications of financial engineering[13][14] are to:


The best known critic of financial engineering is Nassim Taleb [15] who argues that it replaces common sense and leads to disaster. Many other authors have identified specific problems in financial engineering that caused catastrophes: Aaron Brown[16] named confusion between quants and regulators over the meaning of “capital”, Felix Salmon[17] fingered the Gaussian copula, Ian Stewart[18] criticized the Black-Scholes formula, Pablo Triana[19] dislikes Value-at-Risk and Scott Patterson [20][21] accused quantitative traders and later high-frequency traders.

A gentler criticism came from Emanuel Derman[22] who heads a financial engineering degree program at Columbia University. He blames over-reliance on models for financial problems.


  1. ^ “Overview”. Columbia University Department of Industrial Engineering and Operations Reseach. Retrieved 2012-07-22.
  2. ^ Tanya S. Beder and Cara M. Marshall, Financial Engineering: The Evolution of a Profession, Wiley (June 7, 2011) 978-0470455814
  3. ^ a b “What is Financial Engineering?”. International Association of Financial Engineers. Retrieved 2012-07-22.
  4. ^ a b Salih N. Neftci, Principles of Financial Engineering, Academic Press (December 15, 2008) 978-0123735744
  5. ^ a b Robert Dubil, Financial Engineering and Arbitrage in the Financial Markets, Wiley (October 11, 2011) 978-0470746011
  6. ^ Espen Gaarder Haug, Derivatives Models on Models, Wiley (July 24, 2007) 978-0470013229
  7. ^ Richard R. Lindsey and Barry Schachter (editors), How I Became a Quant: Insights from 25 of Wall Street’s Elite, Wiley (August 3, 2009) 978-0470452578
  8. ^ Emanuel Derman, My Life as a Quant: Reflections on Physics and Finance, Wiley (September 16, 2004) 978-0471394204
  9. ^ a b Aaron Brown, Red-Blooded Risk: The Secret History of Wall Street, Wiley (October 11, 2011) 978-1118043868
  10. ^ Aaron Brown, The Poker Face of Wall Street, Wiley (March 31, 2006) 978-0470127315
  11. ^ Dan Stefanica, A Primer for the Mathematics of Financial Engineering, FE Press (April 4, 2008) 978-0979757600
  12. ^ Michael Pomerleano and William Shaw (editors), Corporate Restructuring: Lessons from Experience, World Bank Publications (April 2005) 978-0821359280
  13. ^ Marek Capiski and Tomasz Zastawniak, Mathematics for Finance: An Introduction to Financial Engineering, Springer (November 25, 2010) 978-0857290816
  14. ^ David Ruppert, Statistics and Data Analysis for Financial Engineering, Springer (November 17, 2010) 978-1441977861
  15. ^ < Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Random House (April 17, 2007) 978-1400063512
  16. ^ Whodunit? Rocket Scientists on Wall Street. Minyanville. Retrieved 2012-07-22.
  17. ^ Recipe for Disaster: The Formula that Killed Wall Street. Wired. Retrieved 2012-07-22.
  18. ^ The Mathematical Equation that Caused the Banks to Crash. Wired. Retrieved 2012-07-22.
  19. ^ < Pablo Triana, The Number That Killed Us: A Story of Modern Banking, Flawed Mathematics, and a Big Financial Crisis , Wiley (December 6, 2011) 978-0470529737
  20. ^ < Scott Patterson, The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, Crown Business (February 2, 2010) 978-0307453372
  21. ^ < Scott Patterson, Dark Pools: High-Speed Traders, A.I. Bandits, and the Threat to the Global Financial System, Crown Business (June 12, 2012) 978-0307887177
  22. ^ Emanuel Derman, Models.Behaving.Badly.: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life, Free Press (July 24, 2012) 978-1439164990

See also

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