The article below was published on Inside Market Data about Lucena’s decision support platform QuantDesk™.
Lucena Preps Support for Performance, Proprietary Data by Vicki Chan
Startup analytics vendor Lucena Research is making the results of historical back-testing analysis of its predictions available via its QuantDesk decision support platform, along with the ability to upload proprietary data sources, to help users evaluate investment decisions.
The vendor last week officially launched its QuantDesk decision support platform, following last month’s release of a scaled-down version of QuantDesk for the Bloomberg App Portal that includes a basic version of the Price Forecaster and Portfolio Optimizer modules, though not its Hedge Finder module (IMD, Nov. 16), and is now seeking to educate potential users of the platform’s value as it begins showcasing its platform to potential clients beyond its existing early adopter hedge funds and registered investment advisors, says Lucena chief executive Erez Katz.
Part of that go-to-market strategy includes adding historical analysis of QuantDesk’s recommendations to the platform, to show—for each prediction generated by the platform—how the predictions would have performed over the past two years, with brief explanations of the results to help users understand what is considered a good result.
The vendor is also adding the ability for firms to upload proprietary data sources to the Price Forecaster, to enable clients to customize the results they can derive from the platform, Katz says. In particular, users want to be able to upload their own proprietary data files for running price correlations—for example, location-based cellular data to predict retail sales figures, or municipal bond data to correlate with commodity price movements—Katz adds.
The platform’s statistical machine-learning engine can process the data to determine how predictive it is and incorporate the predictive elements in its forecasts of stock prices, says Lucena chief technology officer Tucker Balch, adding that these forecasts can then also be fed into the Portfolio Optimizer, as the estimates of stock price movement will affect fund allocations.
In addition, QuantDesk can help users evaluate how predictive their proprietary data actually is of price movements, Katz says. “We’re not providing trading algorithms, but we’re providing the tools for users to make algos better,” he adds.
