On January 25 and 27 in Chicago and New York, respectively, Wolfram, in conjunction with NVIDIA, hosted a seminar themed “Optimizing Financial Modeling” to showcase how Mathematica and CUDA can be applied within the financial industry. Full presentations and a white paper on CUDA programming with Mathematica are available for download on the seminar page.

Dr. Phillip Zecher, Chief Risk Officer of EQA Partners, detailed how Mathematica is used in every facet of his firm’s operation, and NVIDIA’s Senior CUDA Consultant John Ashley explained how CUDA programming is changing financial computation.

My talk concerned Mathematica 8′s broad functionality for finance. Each capability is deserving of a full seminar unto itself, so because of the sheer number of topics and functions, I was only able to briefly touch on a few examples from each category. A full list of financial tools in Mathematica is available in the online documentation. The following TabView presents an overview of the new financial functions:

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When the founders of the global hedge fund EQA Partners, LP, wanted to build a comprehensive system for handling all functions of their company, they chose Mathematica. In this video, EQA’s Chief Risk Officer Philip Zecher describes how using Mathematica improves their ability to quickly analyze market conditions and respond to investors’ data requests, and reduces data errors.

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Finance professionals have been using Mathematica for years to optimize portfolios, develop and refine analytic risk models, rapidly prototype products and trading strategies, deploy analysis tools over the web, and much more.

Not surprisingly, we’ve seen increased interest in Mathematica‘s financial applications stemming from the current economic struggles. Accurate models and analyses are in demand to determine the best way to get the world’s economy back on track and prevent future crises.

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Fannie Mae financial economist Bernard Gress attributes part of the current economic crisis to reliance on outdated statistical models. He’s taking a fresh approach to the problem—using Mathematica to create a genetic algorithm system for predicting economic data related to housing prices. In this video, he describes the advantages of developing new mortgage forecasting models with Mathematica.

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Yesterday it became known that a 31-year-old trader at Société Générale had been performing fraudulent transactions in the futures markets that ended up costing the French bank more than 7 billion USD. Some news sources have even speculated that the large market losses on Monday were caused by SocGen unwinding the bad trades. These market losses, on their turn, were the main cause behind the US Federal Reserve’s decision to cut interest rates by 75 basis points on Tuesday.

I was chatting about the topic with a coworker today when he mentioned—as people often do when these things happen—that this value was larger than the economy of many countries. Sure, but how many? As we do all the time, we fired up Mathematica to put things into perspective. And indeed, it’s trivial to get Mathematica to answer that question: you just use the built-in function CountryData to generate a list of all countries in the world, and select those whose gross domestic product (at market rates) is smaller than 7 billion.

The SocGen rogue trader managed to annihilate an amount of money that surpasses the yearly output of the economy of 112 countries, among them Madagascar, Mozambique and war-torn Afghanistan, all of which have population sizes larger than 15 million. A more visual way to present this fact is to use the "FullPolygon" property in CountryData to generate countries’ shapes, and then color those countries with a GDP below 7 billion USD:

It took me less than a minute to visualize my office mate’s comparison, and indeed this is a type of visualization that a lot of Mathematica users can just pull out of their hats—thanks to the effort that was put into designing Mathematica to make sure that all of its functionality fits together.

For more examples of CountryData, visit The Wolfram Demonstrations Project.

The Federal Reserve cut the federal funds rate this week for the first time in four years.

And it happens that I am working on a new economics data function for Mathematica—so I wanted to see what typically results after such a reduction in the federal funds rate.

The Fed makes much of its data available on the web through the FRED II database. So, all I had to do was point Mathematica’s powerful Import function to the site, and I instantly had the data in Mathematica for analysis. It took one line of code.

A couple of short Mathematica evaluations later, and I had a list of all the previous occasions when this rate fell 0.5% or more. I immediately noticed that these large drops sometimes come in “runs,” and decided to focus on the large cuts in each such sequence. I found 15 of these which go back to 1954.

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