Portfolio Construction Using the Bloomberg
"Portfolio Construction and Revision" is a sub-heading of "Portfolio Management and Wealth Planning" (Part X of CFA Institute Candidate Body of Knowledge, or CBOK). As portfolio management and wealth planning are significant disciplines that CFA® candidates and charterholders find themselves engaged in on a day-to-day basis, it is useful to explore functionality within the terminal setting that can be applied to these tasks.
As it relates to the Portfolio Management portion of the CBOK curriculum, Bloomberg offers a powerful function to assist in the management of both individual and institutional portfolios. The Portfolio Optimization (PORT OP) tool is used to construct, hedge, and re-balance your client portfolios with ease.
Among an extensive array of functionality, the PORT OP offers:
Support of multi-asset class portfolios (equities, fixed income, currencies, commodities, futures)
Support of many asset types that can be included in a portfolio, such as ETFs, mutual funds, individual equities, and individual fixed-income instruments
Support of multiple constraints within a portfolio, including long/short exposure, sector and industry weights, and asset type weights
Customization of goals and constraints, both hard and soft
Seamless integration of Bloomberg data and portfolio analytics
Once an optimization profile is complete, terminal users can then apply the optimizer to an extensive universe of securities to filter down to a manageable list of securities to use in the portfolio.
Users can find optimal weights for each security to meet given risk and expected return objectives and select a small set of securities to reach these goals from a large investment universe.
Portfolio optimization is a process for making intelligent choices among a vast set of possible investment decisions often involving trade-offs between conflicting goals and limited resources. It involves evaluating investment decisions to allocate capital optimally to achieve investors' goals, views, and mandates.
Fundamentally, the original portfolio optimization model seeks to maximize return for a given amount of risk. This is the basis of Modern Portfolio Theory, as given by Markowitz (further reading can be found at https://www.math.ust.hk/~maykwok/courses/ma362/07F/markowitz_JF.pdf).
The basic model is intuitive in that it highlights the basics between the risk and return trade-off, and how that benefits the portfolio construction process. But it is limited by its academic assumptions, and its use is not always practical for the contemporary portfolio manager. Such factors as long/short constraints, maximum number of positions, maximum turnover, excluded industries and securities, etc. can pose difficulty when building and maintaining a client portfolio. This is where the power of the optimizer comes in.
Portfolio Optimizer (PORT OP) Inputs
Once an investor’s investment policy statement has been formalized and return and risk objectives and attendant constraints have been defined, the next step is to create inputs to feed to the system.
Some of the basic possible inputs are:
Portfolio to be optimized, be it an existing portfolio, or in the case of the image below, an all-cash portfolio
Goal(s), such as "minimize active total risk"
Trade universe (bulk list of possibilities)
Security level properties and bounds
The main page of PORT OP looks like:
On this page, a user can provide all basic inputs.
The goal of optimization drives the process. It specifies the maximization or minimization of an element of the desired outcome of the portfolio. Examples include minimizing the portfolio active risk, maximizing expected return, or minimizing tracking error.
Beyond goal setting, a portfolio manager generally will want to place several conditions that must be satisfied to meet the allocation profile. Some general constrains could be limiting technology stock exposure to 10% of the portfolio, fixed-income holdings limited to 30% investment grade, or specific country or region exposure (i.e. emerging markets) to a maximum of 15%.
A key input into the process is directing the optimizer to select securities from a universe of securities. This can be an existing portfolio housed on the Bloomberg terminal, an equity index, such as the S&P 500, or a customized list derived from an equity screen.
Note that a user can specify exclusions, as well. These might be at the sector or individual security level.
Once all goals, constraints, and trade universe are specified, the optimizer will produce a trade list that will satisfy all goals and constraints.
Over time, optimization models can be easily adjusted for changing objectives, investor preferences, or market conditions.
About the Author
Scott Kamenir is Chief Investment Officer and Managing Principal of Waypoint Intelligence, LLC. In this role, he is responsible for developing the firm’s investment models and managing all client accounts. Mr. Kamenir has been actively managing investment portfolios since 1997, when he began his career as an adviser with Morgan Stanley Dean Witter 25 & Co. He has also served as a financial adviser with JP Morgan Chase and an analyst at National City Bank's private client group, and worked on the institutional side of PNC Capital Advisors (formerly Allegiant Asset Management).