Portfolio Analysis

Overview

The Portfolio Analysis module provides users the ability to build optimized portfolios of multiple managers targeting our predictive manager skill metric while controlling for a wide variety of portfolio risks and exposures.

The Portfolio Analysis module provides an efficient way to maximize our forward looking measure of manager skill within your customized risk constraints. It replaces raw alpha estimates based on historical performance with the alpha estimates generated by our Skill Analysis model which our research has found to be more accurate and persistent. This Module’s optimization framework also provides users with the flexibility to target various objectives and constraints. Users have flexibility to constrain the portfolio along all commonly used attributes as well as input custom attributes of their own.

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Purpose: Aapryl’s Portfolio Analysis allows users to choose the proportions of various assets that will build the optimal portfolio based on various criteria.  Aapryl’s Optimizer module goes beyond the industry standard Mean-Variance Optimization programs that use risk and return to create an efficient frontier of portfolios.   Aapryl allows users to optimize on various other metrics including Aapryl’s proprietary portfolio skill measure.  This allows users to create portfolios that maximize the level of manager skill within given constraint parameters.

Glossary of Terms:

• Projected Manager Skill:  Aapryl’s  proprietary measure of forward looking manager skill that can be used as a target to maximize in the module.
• Total Return:   Forward looking total return of a portfolio that can be used as target to maximize in the module
• Standard Deviation:A commonly used risk measure that is a measure of the variance of data points relative to the mean.
• Tracking Error:  Sometimes called active risk; it is the standard deviation difference between a portfolio’s returns and its benchmark’s returns.  It can be used as a target to minimize in the optimizer module.
• Standard Downside Deviation: A metric similar to standard deviation but it looks only at negative variances from the mean.  It can be used as a target to minimize in the optimizer module.
• Downside Tracking Error: Similar to tracking error, but focuses on portfolio returns below the benchmark. It can be used as a target to minimize in the optimizer module.
• Min Weight & Max Weight: Portfolio constraints that can be used to set ceilings and floors to the potential allocation weights of a given asset in the optimization.

Description of Methodology:   Aapryl uses industry standard methodology to calculate optimized portfolios.  Aapryl allows users to select the group of assets to be optimized, set constraints on their weights and choose the criteria that will be both maximized and minimized through the optimization.  A unique feature of Aapryl’s optimizer module is that it allows users to maximize on Aapryl’s proprietary projected manager skill measure as well as total return.  Aapryl allows users to minimize on Standard Deviation, Tracking Error, Standard Downside Deviation, and Downside Tracking Error.

Information Provided:  Aapryl is able to use the information generated in the optimization to provide users with charts and graphs that contain an abundance of useful information which includes the following:

• Efficient Frontier Charts: A set of optimal portfolios that have the highest return (or projected manager skill) at given levels or risk (or other downside measure).
• Cyclical Manager Positioning:  Demonstrates the market cycle positioning analysis for a manager product in a prioritized manner by showing dominant and recent market cycles.

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