Implement Multi-Alpha Investing Thesis

Active Allocator can be a great way to build active investing portfolios and programs. We have built cutting edge models to support fund manager selection, portfolio allocation, and market exposure hedging.

We follow a simple three-step process.

  • First, we measure alpha ( in other words, skills) and beta ( in other words, market exposure, when a rising market tide lifts all ships) by removing biases in fund manager returns.
  • Second, we integrate multiple information sources in creating a forecast alpha.
  • Third we account for unique forms of risk by penalizing managers for downside risk and integrating forecast risk into the portfolio construction process.

In short Active Allocator provides you with a mechanism to implement an active investment management strategy. We have tested for around 30 classes of Alpha already. We help create core portfolios that combine various alpha sources across a broad range of asset classes and strategies including: Equities, fixed income, foreign exchange, real estate securities, commodities, and special opportunities.

Our approach is closely tied to active investment management, which separates passive risk (beta) from active risk (alpha). The central principles of active investment management that we use are:

  •  Risk and return can be separated into passive and active components.
  • Passive return, or beta, comes from systematic, undiversifiable risk. Examples of these are returns from indexes such as the S&P 500. An investor can gain beta exposure very cheaply and therefore should not pay active management fees for these returns.
  • Active returns, or alphas, are generated through information asymmetry, structural inefficiency, security selection, and market timing. These returns are uncorrelated with beta.
  • In constructing portfolios, an investor should obtain beta exposure in the cheapest manner possible, and then add alpha exposure in proportion to one’s risk tolerance.

At Active Allocator, we unearth and calculate alphas properly. Then we rank them. We figure out correlations, or lack of correlation, across alphas.

We may, for example, compute correlations on a pairwise basis. In order to reduce the estimation error in these correlations, we group fund managers by strategy. Our portfolio construction system then determines the allocations by running an optimization to identify the portfolio with the highest information ratio – in other words the highest alpha per unit of risk; Not on a historical basis, but on a projected basis. In addition, we also account for the downside risk and forecast risk of each strategy when determining allocations to multiple alpha sources.

Our approach is finding the most efficient sources of excess returns and transferring them onto a benchmark. The benchmark itself can be cheaply accessed by investing in say tracker ETFs for example. If properly implemented, this strategy can substantially improve returns over a benchmark.

The questions we ask are:

  • How does an alpha strategy create value?
  • How should alpha be measured?
  • What types of instruments are best suited to an alpha strategy?
  • How can one implement an alpha strategy in practice?

By identifying and measuring changing market exposures — or dynamic Betas — of fund managers on a real-time basis, we are able to break down a manager’s returns and risks over the long run into three components:

(i) Beta: the portion created by the passive average long-run market exposure.

(ii)Timing Alpha: the portion created by proactive variations in market exposure around the passive average exposure over time. If on average the manager increases his exposure to markets when they go up, and decreases exposure to these markets when they go down, the manager will generate positive returns through market timing. Comparing the returns from a manager’s average market exposures to the returns from the manager’s time varying market exposures can therefore help determine value added from market timing.

(iii) Security Selection Alpha: the portion remaining, or residual, which is due to security selection and stock picking skills.

Author: Sameer_Jain

Partner. Sameer Jain is founder of FinTech, the world’s first portal that seamlessly integrates traditional, illiquid and alternative investments within portfolios. Prior to this he was Chief Economist & Managing Director at AR Capital. Before that he headed Investment Content & Strategy at UBS Alternative Investments. At UBS, he served as a non-voting member of the Wealth Management Research investment committee, and as a capital allocator was responsible for all illiquid investing including fund manager selection and due diligence across the platform. Prior to UBS he headed product development & investment research at Citigroup Alternative Investments that managed over $75 billion of alternative investments across hedge funds, managed futures, private equity, credit structures, infrastructure and real estate. Here he led a team that developed proprietary models for portfolio strategy and asset allocation with alternative investments, provided investment support and research to pension plans, sovereign wealth funds, endowments as well as internal clients including Citi Private Bank. Before this he was with Cambridge Alternative Investments and SunGard (System Access) where he travelled to over 80 countries for work across Europe, Asia, Middle-East and Africa. He has written over 30 academic and practitioner articles on alternative investments with thousands of downloads at SSRN, presented at over a hundred industry conferences and has coauthored a book, Active Equity Management. Mr. Jain has multiple degrees in engineering, management, public administration and policy and is a graduate of Massachusetts Institute of Technology and Harvard University. He is a recipient of the Alfred Sloan Fellowship and subsequently was a Fellow of Public Policy and Management at the Harvard Kennedy School of Government for a year. He holds Series 7 and 66 securities licenses.

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