Driving our investment process is Hillsdale’s proprietary research engine. This engine provides us with a key information edge with over 140 years of capital market history and 40 years of fundamental data collected in five frequencies. An open source platform allows us to quickly integrate and evaluate data from any potential source. Once selected for further study, potential alpha sources are purified and transformed by cleaning, scrubbing, validating and researching. This new information is then available for testing using a variety of tools and techniques.
The information in our proprietary research engine is at the heart of all our testing in:
- Capital Markets and Conditionality
- Proprietary Factors
- Investment Strategy and Design
Our investment process uses a proprietary, multi-factor, multi-frequency ranking approach to stock selection implemented through a rigorous risk management framework. Inputs are carefully extracted from many different sources and are distilled or transformed into proprietary factors and forecasts. Model construction is derived from our fundamental, expectational and technical research reflecting the diversity of agents and investment styles prevalent in the actual marketplace. Such multi-dimensionality leads to a core investment style with an objective of adding value through varying market conditions.
Within the active portfolio, securities are reviewed regularly for their adherence to specific decision rules and for their contribution to increasing return and/or reducing risk.
The investment process is fully integrated across capital markets and factor research, return forecasting, portfolio construction, risk and factor monitoring, performance measurement and client reporting. The search for new variables and factors that either predict or control equity returns is ongoing. All new data items, data sources or algorithms resulting in either increased return or reduced risk are immediately fed through to all applicable portfolios. Once established, all portfolio risk and return characteristics are monitored daily against pre-established tolerances. This way portfolio managers can evaluate if disparities in actual performance are “random” or in need of corrective action.