How is SOLVE Px (Beta) Different: A New AI Modeling Approach From SOLVE

Transcript: How is SOLVE Px (Beta) different? Many traditional algorithmic linear modeling approaches look at historical relationships between bond characteristics and their effect on pricing. And it works fairly well for more liquid securities where there’s more data, there are more trades, there are more quotes, but that approach doesn’t work quite well for bonds with less data points, bonds that have more complex, less common characteristics. The SOLVE AI is significantly different in its ability to price securities for a number of reasons. One is the model is specifically constructed to come up with the next prediction of a trade. It looks at a number of datasets. It looks at trade data. It looks at quotes. It looks at reference data. And it’s trained over time to minimize the prediction error between where the model thinks a trade should happen and where the actual trade happens. It’s phenomenal at understanding complex statistical relationships in data sets. And as a result, it’s able to look at those three categories of data, which we then boil down into hundreds of feature inputs that the AI gets trained on, which ultimately minimizes that prediction error. So, the big difference is the older linear approaches are definitionally backwards looking. They’re very static in their nature. Where the SOLVE AI gets retrained on a daily basis, it understands different market environments, different volatility environments, and it is specifically optimized to predict the next trend.

Disclaimer:  SOLVE offerings are not intended to constitute investment advice, do not seek to value any security, and do not purport to meet the objectives or needs of specific individuals or accounts.