  Prediction markets have demonstrated their value for aggregating collective
expertise. Combinatorial prediction markets allow forecasts not only on base
events, but also on conditional and/or Boolean combinations of events. We
describe a trade-based combinatorial prediction market asset management system,
called Dynamic Asset Cluster (DAC), that improves both time and space
efficiency over the method of, which maintains parallel junction trees for
assets and probabilities. The basic data structure is the asset block, which
compactly represents a set of trades made by a user. A user's asset model
consists of a set of asset blocks representing the user's entire trade history.
A junction tree is created dynamically from the asset blocks to compute a
user's minimum and expected assets.
