Brian M. Greenberg

MDT Advisers employs fundamental analysis and uses bottom-up stock selection with a disciplined quantitative process. The process selects stocks based on fundamental variables, controls risk through diversification constraints, and controls turnover by considering the impact of trading costs. The firm provides equity advice, rather than fixed income or money market investment strategies.

MDT utilizes their 'Optimum Q' family of strategies, a disciplined, quantitative approach to investing in U.S. equities. Each strategy employs a variant of the same Optimum Q model first introduced in 1991. There are currently four strategies. The Optimum Q portfolios are traded daily. For Optimum Q - All Cap Core, stocks are selected from a universe of domestic stocks. For the style-specific strategies, stocks are selected from sub-universes that roughly correspond to their benchmark indexes. Diversification constraints are used to control risk and the market impact of trades. Proprietary optimization software determines the best portfolio by maximizing the overall stock selection score subject to the diversification constraints. The model then generates trades that represent the difference between the best portfolio and the current portfolio. Stock selection scores take account of trading costs, so trades are generated only to the extent they are expected to be profitable after trading cost. Each trade generated by the model is examined by the investment team to verify that the model's decision is based on accurate and current information. The team only overrides the model in a few specific situations such as when a review of recent corporate news reveals breaking information that has not yet been incorporated into the database. Optimum Q models are constructed using techniques analyzing at least 15 years of historical data, depending on the strategy. The data is drawn from an in-house database built and maintained by the Optimum Q team. Using proprietary, parallel-processing software and high-speed servers, the team runs hundreds of thousands of historical simulations to find the combination of parameter values that maximize compound annual return subject to constraints on risk as measured by beta and tracking error.

Functions as a College/University

Functions as a College/University