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Fina Technologies Aiming to Build Smarter Computer Models for Wall Street

Xconomy Boston — 

Wall Street is no stranger to computer models, which have been used for more than a decade by fund managers and traders to beat the market. But many of the models developed by the so-called “quants” have proved insufficiently prescient—they didn’t do a very good job, for example, of predicting the subprime mortgage crisis. Using computer modeling software initially developed to help pharmaceutical firms find biological targets for new drugs, Cambridge, MA-based Fina Technologies is hoping to recharge the troubled financial sector.

Fina has licensed its so-called “reverse engineering/forward simulation” (REFS) technology from Cambridge’s Gene Network Sciences, which has been developing and applying REFS for drug companies for several years. Under the REFS approach, software analyzes massive amounts of historical data to divine causal links, then makes forward projections based on the connections it’s discovered.

Fina boosted its prospects last month by closing a $4.5 million first-round financing from a pool of investors led by Reed Elsevier Ventures, the venture arm of the media and publishing conglomerate Reed Elsevier. Josh Holden, the CEO of Fina (and an early angel investor in Gene Network Sciences), talked to me recently about how the startup plans to use the cash to fund efforts to promote the REFS technology in the financial world.

Investment computer models—which are basically algorithms based on investment hypotheses—have had varied success. Holden, an MIT-trained engineer with more than a decade of experience in the investment world at Deutsche Bank and other financial institutions, says that a chronic problem is trying to jam too many variables or parameters into these algorithms. Fina aims to overcome this over-modeling problem with the REFS platform, which has shown that it can handle the massive amounts of data about biochemical signaling pathways in the human body needed to make predictions about how various drug candidates will affect the system.

“What we’re trying to do, at the most fundamental [level], is to automate the scientific method,” Holden says, “which is basically to propose a hypothesis, test whether that hypothesis is true in the presence of experimental data, [and] compare it to other hypotheses out there all with an eye toward controlling for over-fitting and complexity.”

The firm’s technology integrates input from multiple models before making predictions about the market, Holden explains, rather than using one algorithm overloaded with parameters. The upshot is that traders at hedge funds could predict changes in the market 5 minutes to 30 minutes before they happen, then buy or sell ahead of time to capitalize on the upward or downward shift in price, he says.

The startup has built sample models with the platform that make Holden confident that the technology makes the correct predictions about 55 to 60 percent of the time (which may not sound much better than flipping a coin, but can translate into big wins over time). The firm’s confidence in its technology is apparent in its business model: it makes money by taking a cut of the upside that the platform provides to funds, Holden says.

The company’s patent license from Gene Network Sciences covers uses of the technology in e-commerce, government, and other non-life sciences sectors, but Fina determined that the financial industry was the best place to start outside of life sciences because of demand for better models among investment funds and other trading operations, Holden says.

Still, Fina has some large competitors in the business applying quantitative modeling and other technologies to investment strategy. Some of the larger and more well-established players in this field include New York-based investment firm DE Shaw Group, which boasts that it has spent more than twenty years and hundreds of millions of dollars developing quantitative investment models, and Renaissance Technologies, a hedge fund that uses computer-based models to predict market changes and to automate trades.

Holden is well aware of his competition, and he remains confident that Fina’s platform has capabilities that other technologies lack. He should know; he built financial models on Wall Street for a long time. After graduating from MIT in 1993 with a master’s degree in electrical engineering, Holden wound up working on the foreign exchange desk for Goldman Sachs in London. He later moved on to Deutsche and most recently worked for Countrywide.

In 2000, Holden met Gene Network Sciences founder and CEO Colin Hill and became what he characterizes as a small investor in the firm. Holden says he was impressed with the startup and Hill’s quantitative approach to drug discovery. He appreciated the firm’s idea that large computer models of cells could identify biological targets for drugs in silico, helping pharma outfits save time and money on physical experiments.

Gene Network Sciences is a major shareholder in Fina, and Hill is deeply involved in Fina as the spinout firm’s chairman. In fact, Fina’s technical staff and main operations are at Gene Network Sciences’s offices in Cambridge. Holden has led the startup from his primary office in Santa Monica, CA, and his home in Utah, while flying to the East Coast frequently to work with his staff in Cambridge.

Holden says that more spinouts based on Gene Network Sciences’s REFS technology could be in the offing. So stay tuned.

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  • Will this help wall street be able to do things faster than they are now? I mean, they are pretty good at what they do but I guess upgrading their systems will only help.