With the new Financial Services Authority (FSA) directive known as the Retail Distribution Review
(RDR) set to come on-line December 31st, 2012, many are wondering what the impact will be on the landscape for providing financial advice in the United Kingdom. This piece seeks to ask questions and provide answers regarding the practical, real-world implications of the RDR.

Since unveiling the Black-Litterman Global Portfolio Optimization model in AllocationADVISOR in 2004, many have wondered how the Black-Litterman (BL) model could accommodate alternative asset classes. This paper seeks to give practitioners some ideas on how alternatives might be addressed in a BL framework.

Recent market events like the dot-com bust the credit crunch have increased the awareness of “tail events”, i.e. rare, but extreme and traumatic market environments. Skewness and kurtosis are the traditional ways one would measure the impact of extreme market conditions upon a distribution of returns. Although well-established in statistical theory, skewness and kurtosis are often ignored or misunderstood in performance analysis. This paper seeks to give the reader useful definitions and a working knowledge of skewness and kurtosis.

With the release of StyleADVISOR 8.1, Zephyr Associates has expanded the toolset used to understand the Omega ratio. Omega captures all four moments of a distribution: return, standard deviation, skewness, and kurtosis. In addition, Omega can be used to help understand any series of return data, whereas some of the traditional return/risk statistics used in finance are only useful if the returns happen to fall into a somewhat normal distribution pattern. Therefore, Omega is a useful metric when looking at absolute-return strategies or strategies that are not pegged to a market benchmark. When Omega is coupled with a cumulative distribution of returns chart, the two can offer insights into the tail-risk of an investment. Finally, Omega is quite valuable when analyzing a total portfolio’s suitability for meeting a target return.

In July of 2006, Zephyr Associates unveiled a new risk metric called the “pain index”. The pain index is meant to quantify the capital preservation characteristics of a fund, manager, index, or any other data series. In short, the pain index measures the 1) depth, 2) duration, and 3) frequency of losses experienced by a manager. Once established, the pain index can be utilized in a return vs. risk trade-off metric. The “pain ratio” compares the returns versus the loss characteristics using a well-known format.

The α* statistic was introduced by Richard C. Marston in 2004 as a measure of risk-adjusted excess return. The idea of risk-adjusted excess return is based on the premise that comparing the return of a manager to that of a benchmark is inherently unfair. That is because the risk levels of the manager and the benchmark are almost always different. Therefore, comparing the returns of two portfolios with different risk levels amounts to an apples-to-oranges comparison.  The α* statistic normalizes the risks of the manager and benchmark, creating an apples-to-apples comparison.

In Spring of 2010, Zephyr Associates put forth the idea of using William Sharpe’s returns-based style analysis (RBSA) methodology as a valuable tool for analyzing target date funds.  While spelling out the case for using RBSA from an academic, theoretical perspective, Zephyr clients have since requested guidance in using the idea proposed in a real-world setting to compare the performances of two families of target date funds.   This follow-up paper illustrates how the analyst might use the RBSA ideas to these ends.

This paper proposes that the standard set of analytical metrics used in the finance industry did not adequately expose just how preposterous the return series claimed by Bernie Madoff actually was.  However, using Zephyr StyleADVISOR’s newer metrics and graphs focusing on tail risk and capital preservation, the intelligent and diligent analyst would have noticed some disturbing results.

This guide will walk you through the steps and rationale for using Zephyr’s AllocationADVISOR to optimize a collection of active managers. The optimization is a variation of Markowitz’s classic concept of Mean-Variance Optimization and the efficient frontier.

In 1996, Lars Kestner introduced the K-Ratio as a complement to the Sharpe Ratio. With Version 8.1, Zephyr Associates makes the K-Ratio available to StyleADVISOR users. This article explains the use, the meaning, and the exact mathematical definition of the ratio.

This guide will walk you through the steps and rationale for using Zephyr’s AllocationADVISOR’s “Confidence Bands” function.  In short, a Confidence Bands analysis runs a full Monte Carlo analysis for every single portfolio on the efficient frontier and tells the analyst which portfolios have the highest probability of meeting an investor’s goals.

Over the past several years the popularity of target date funds for use in retirement plans has exploded.  Their attractiveness is obvious:  faced with an overwhelming number of investment options in a self-directed 401(k) plan, an unsophisticated investor can instead direct all of his or her contributions to a single, well-diversified mutual fund.  While target date, lifestyle, or glide path funds have made investing easy for the plan participant, the irony is their introduction has made life harder, in some ways, for the fiduciaries whose job it is to analyze such investments.  This study seeks to shed some light on the situation and propose thoughtful, meaningful ways of analyzing such balanced funds.  We will argue that Bill Sharpe’s returns-based style analysis methodology is ideally suited for analyzing target date funds.

The Omega measure was introduced in 2002 by C. Keating and B. Sedgewick ([1]) as a universal performance measure. This article explains the use, the meaning, and the exact mathematical definition of the Omega.

An Introduction for the Practitioner. This article explains the benefits of using the Black-Litterman model (BLM) in conjunction with the time-honored mean variance optimization (MVO). BLM provides a front end to the MVO method that addresses the two main issues practitioners have with it.

This paper introduces a quantitative measure of style drift – the Style Drift Score. The
Style Drift Score measures the variability of a portfolio’s effective asset mix as determined by
returns-based style analysis around the portfolio’s average effective asset mix. The Style Drift
Score frees one from having to examine countless rolling window asset allocation graphs and
rolling window style maps by quantifying the style drift of a portfolio in a single statistic. It is
ideal for screening thousands of portfolios, comparing the style consistency of portfolios, and
monitoring the drift in a portfolio’s style.

One of the most frequently asked questions concerning the mathematics of StyleADVISOR is how exactly we calculate the style attribution coefficients which are displayed in the style map view, the asset allocation view, and the style table. The short answer to this question is very easy: We perform the returns-based style analysis that was invented by Stanford professor and Nobel laureate William F. Sharpe. In this article, I will explain the mathematics of Sharpe's algorithm. As it turns out, a fairly complete and mathematically rigorous description of the algorithm can be given without using a lot of mathematical formalism.

Style analysis, often referred to as returns-based style analysis (hereafter called RBSA), was developed and first introduced by William Sharpe in his landmark article, “Determining a Fund’s Effective Asset Mix.”¹ In 1992, RBSA was made commercially available with the release of StyleADVISOR, a Windows-based software program designed to implement Sharpe’s style analysis. For much of its early history, RBSA was used by a small number of pension plan sponsors and institutional money managers. Today, thousands of investment professionals use RBSA through numerous software programs.

We recently completed an analysis of a large defined contribution program. The plan sponsor wanted to use StyleADVISOR to make changes in the structure. The results may be helpful to you whether you are a plan sponsor, consultant or money manager.

One of the most frequently asked questions concerning the mathematics of StyleADVISOR is how exactly we calculate the style attribution coefficients which are displayed in the style map view, the asset allocation view, and the style table. The short answer to this question is very easy: We perform the returns-based style analysis that was invented by Stanford professor and Nobel laureate William F. Sharpe. In this article, I will explain the mathematics of Sharpe's algorithm. As it turns out, a fairly complete and mathematically rigorous description of the algorithm can be given without using a lot of mathematical formalism.

The goal of this project is to illustrate a technique that can be used by StyleADVISOR/AllocationADVISOR users for going through a large database of managers to create superior performing portfolios. This process starts by putting all managers onto a level playing field and finishes by allocating among the finalists to create the “most efficient” portfolios from those determined to be “most skillful.” By using the power of the computer the user is able to quickly search through a database of thousands of managers and discover many that would otherwise have been overlooked using more traditional due diligence research techniques.

As many people know, StyleADVISOR is not only a style analysis program; it is also a performance analysis program. This includes the ability for the user to easily create their own custom “peer groups” or “universes” of any size for performance comparisons. This document covers this process step-by-step and also some of the most commonly asked questions about this topic. After reading this, you will be on your way to becoming the “Master of the Universes.”

StyleADVISOR is used at a number of universities (Stanford, Wharton, London Business School, Northwestern, etc.). This nineteen page report is a tutorial on style analysis written by Professor Ravi Jagannathan from the Kellogg Graduate School of Business, Northwestern University. This is an excellent primer on style analysis.

Originally presented at our 2001 User's Conference this article looks at how daily data can show shifts in mutual fund holdings over a short time period. On July 13, 2001 the Wall Street Journal reported that a handful of mutual funds had recently increased their exposure to energy stocks. For each fund they gave the percentage of energy exposure on a specific date. We thought that this would be a good test for daily style analysis.

Also presented at our 2001 User's Conference this article looks at how daily data can be used for style and sector monitoring of any manager where daily returns are available.

Also presented at our 2001 User's Conference this article looks at the differences in detecting style shifts using daily versus monthly data using a few different Fidelity funds and the Prudential sector indexes as indices.

Because of our superior methodology, investment professionals are using Zephyr Universes for peer group analysis. This newsletter discusses the Zephyr Universes in detail, including our new domestic fixed income universes, our methodology for the domestic equity universes, how to create your own universes and much more. Also, a further exploration into Monte Carlo simulation, Dr. Becker's Math Corner, information about online training and much more.

You create a PowerPoint presentation easily and directly from StyleADVISOR. This capability is one of many features we are adding to StyleADVISOR to help you work more efficiently. Check out just how easy it can be.

Included in this newsletter are the enhancements made to the latest version of StyleADVISOR including an explanation of the all new custom axis graph and we take a look at the new macro recording tool for changing parameters such as symbols throughout a workbook. Also included is an analysis of detecting a manager's sector changes using daily data and the all new Zephyr WebANALYTICS.

This newsletter features an analysis of using daily data in StyleADVISOR, specifically analyzing style drift and sector analysis.