The Adaptive Alpha method is based on the thesis that complex networks of risk ebb and flow dynamically across the global financial markets driven by a relentless and idiosyncratic demand for liquidity. Because of these phenomena, liquidity is the best proxy to understand risk.

Traditionally, risk is modeled on volatility, correlations and historical loss events using normal distribution based methods. This approach, however, precludes contemplation of any risk event that has not happened in the past and assumes statistical relationships are empirical constants. Moreover, traditional risk measures were not designed to accommodate speculative trading and derivative investment vehicles, which dominate today’s global financial markets.

We approach financial risk as a chain of liquidity interdependencies. Loss events potentially trigger forced liquidations, which, in turn, potentially impact market prices adversely. To understand these liquidity interdependencies, we apply complex network theory and business process analysis to uncover emerging patterns and temporal correlations in real-time. This is a continuous, analytic process designed to identify unprecedented risk determinants and unexpected changes in liquidity supply and demand. Predictive analytic models, created dynamically by the process, assess current risk conditions and determine the optimal profitability path.