What is Computational Finance?

Often referred to as financial engineering, computational finance is a process that relies on the application of several factors in order to arrive at conclusions regarding such matters as investments in stocks and bonds, futures trading, and hedging on stock market activity. Generally speaking, the wide umbrella of computational finance will employ the disciplines of mathematical science, number theories, and the use of computer simulations to explore the potential risks as well as the probably outcomes of any such transaction. Here are a few examples of how computational finance is used each day in a number of different scenarios.

One of the most common applications of computational finance is within the arena of investment banking. Because of the sheer amount of funds involved in this type of situation, computational finance comes to the fore as one of the tools used to evaluate every potential investment, whether it be something as simple as a new start-up company or a well established fund. Computational finance can help prevent the investment of large amounts of funding in something that simply does not appear to have much of a future.

Another area where computational finance comes into play is the world of financial risk management. Stockbrokers, stockholders, and anyone who chooses to invest in any type of investment can benefit from using the basic principles of computational finance as a way of managing an individual portfolio. Running the numbers for individual investors, just alike for larger concerns, can often make it clear what risks are associated with any given investment opportunity. The result can often be an individual who is able to sidestep a bad opportunity, and live to invest another day in something that will be worthwhile in the long run.

In the business world, the use of computational finance can often come into play when the time to engage in some form of corporate strategic planning arrives. For instance, reorganizing the operating structure of a company in order to maximize profits may look very good at first glance, but running the data through a process of computational finance may in fact uncover some drawbacks to the current plan that were not readily visible before.

Being aware of the complete and true expenses associated with the restructure may prove to be more costly than anticipated, and in the long run not as productive as was originally hoped. Computational finance can help get past the hype and provide some realistic views of what could happen, before any corporate strategy is implemented.