ReferencePoint Methodology Overview
GSTCAP develops algorithms to help investors profit from cyclicality in capital markets by tilting the odds in your favor. Our ReferencePoint Algorithms are designed to generate excess returns versus underlying indices or exchange traded funds (ETFs) that target specific investment exposures. ReferencePoint algorithms produce exposure coefficients for the major sources of systematic portfolio risk investors face each day. This includes sectors, industries, market capitalization ranges and factors like momentum.
The ReferencePoint methodology is based on the observation that systematic investment exposures are sources of risk premia that have performance cycles characterized by periods of substantial drawdown and capital appreciation. This behavior is driven by shifting investor expectations and preferences for asset risk characteristics over a market cycle. These shifting preferences provide large return opportunities for investors.
GSTCAP exploits this behavior for our clients with algorithms that change investment exposure in response to output from data driven behavioral models. These models signal changes in investor preferences for investment characteristics that lead to directional changes in asset prices. The coefficients change infrequently because investors preference change infrequently.
The ReferencePoint methodology is coded to be forward looking. While the data we apply is widely available in the market, our algorithms use it in a more insightful and systematic manner than other market participants. Algorithms enable GSTCAP to prioritize output from multiple models and apply procedures in a continuous manner to adapt to different market and economic environments. In addition, the use of algorithms and data science enable us to test data for information content useful in models, simulate how models work with one another, ignore market noise and control unintended return consequences. Algorithms also enable us to integrate this intelligence in a single number, the ReferencePoint coefficient, managers can use to make investment decisions.
Output from our algorithms is designed to help active managers and serve product issuers. They can be viewed as a third-party analysts working to improve your performance. Investors that need to make market positioning or industry allocation decisions, for example, can use exposure coefficients that put probabilities on their side. Product issuers can use our model portfolios to create value added products from indices and existing ETFs that can be delivered to the market in various structures.