Abstract

It is intuitively clear that certain fashions exist in the investment industry: at times we witness technology fashion or emerging markets fashion. In this paper we describe a methodology that allows us to diagnose and measure these style trends or fashions in institutional investment management. Using readily available manager performance databases and a return-based style analysis technique introduced by William F. Sharpe, we were able to identify style cycles for US domestic equity money managers.

Our approach can be used to measure deficiencies and biases in the managers’ performance databases. It can be used by money managers to design their marketing strategies. It can be applied to fixed income and international markets, and used as a basis for arbitrage and style rotation strategies.

Introduction

The goal of this study is to identify certain trends or cycles which exist in the institutional investment industry. If we consider the US equity market, it is reasonable to assume that in some periods growth stocks are fashionable, while during other periods value stocks. Very often this fashion is driven by the past performance, while sometimes it is just marketing hype. Securities fashion drives the demand for new investment products and we witness an increased money flow into the fashionable products — either mutual funds or institutional accounts.

One way to identify this fashion would seem to be measure account cash flows. Unfortunately, these data are not available for most accounts, some accounts represent a mixture of styles, and some money managers tend to misrepresent their style in order to move to a favorable style category. The best way to overcome these problems would be to analyze the actual portfolios historically for all accounts. This would be a large-scale project and, again, the portfolios will not be available for the majority of small accounts.

We will use a different approach, comparing the dynamic of style allocations of managed portfolios to the market. The increased demand for, say, value management would increase the supply of value money managers and/or assets in value portfolios. In this case the portion of value stocks in institutional portfolios will be higher than in the market as a whole. In this case we say that there is a value trend. In the same way we can describe growth and size trends.

We will derive these style allocations by using only portfolio performance data (total returns) and historical assets under management, figures which are available from numerous manager databases’ providers.

One would expect that the fashion trends are driven by style performance — that some of the managers are trend followers that would sell the worst performing assets and buy the best performing, other managers would overreact to the bad or good performance of the style, and so on. In our study we will link this behavior with the investment style performance.

Data Selection

The selection of managers’ historical performance and asset data was crucial for our research.

We examined several manager performance databases that are available on the market: PSN from Effron Enterprises, M-Search from Mobius Group, NELSON from Nelson Publications, PIPER from Rogers, Casey & Associates. All of them, except PIPER, have survivorship bias — they don’t keep managers/products that went out of business in the database. This gap is quite understandable because the vendors provide data to the consultants who perform manager searches and who are concerned with the products that are available at the time of search.

We have selected Nelson Publications’ Manager Performance Database as a comprehensive and apparently reliable source of both asset and return data.

The following graph shows the distribution of assets under management of US equity products at the end of 1993. Note that the X-axis is intentionally made logarithmic because of the log-normal nature of capitalization figures. The line graph represents cumulative assets under management.

Evidently, about 50% of all assets are concentrated in less than 4% of all products, offered by those money managers with the assets greater than $2.5 bill.

In this study we will examine the distribution of style between these large and small products.

Building A Composite Index

Our first step was to test the reliability of performance and asset data. About 80% of all managers, in compliance with AIMR reporting standards, report the performance of all their assets, while the rest of the managers would report, on average, about 60% of their accounts’ performance — apparently the best performing portfolios. One would expect that the composite index of all managers would significantly outperform a broad US market index.

In order to test that hypothesis, we created a composite index for the US domestic equity managers. We started the index at the end of 1983 with the assets given at the year end. We continued this index in 1984 compounding quarterly returns (buy-and-hold). At the end of 1984 we rebalanced to the assets given in the database. Note that the number of managers can increase (it cannot decrease because this database has only survivors). This process is very similar to the creation of any capitalization weighted index. We continued this process up to the June 1994 rebalancing to the provided asset weights at the every year end. We will call the resulting index the institutional index.

In addition to the total composite index we have created in like manner two indices that represent the top 50% and the bottom 50% of the cumulative product capitalization. We constructed these indices to have exactly one-half of the total assets at the end of each year.

We compared performance of the institutional indices thus created to the market benchmark selected for this study, the Russell 3000 Index. The choice of a market benchmark is very important. In their study, Lakonishok, Shleifer, and Vishny [1992] compared equal-weighted and value-weighted composite returns of 769 managed portfolios to the S&P 500 Index. They observed the managers’annual average return for the 1983-1989 period to be 17.7%, compared to 19% for the S&P 500 Index ; based on 1.3% difference, they concluded that active money management subtracted rather than added value. The S&P 500 Index was selected as a benchmark in their study because they assumed that plan sponsors were hiring money managers primarily to beat the S&P500 Index. We think that this approach to the manager selection process is oversimplified. There are plenty of small-cap, value, and growth money managers that are valued for the consistency of their style and their ability to beat style-specific benchmarks, not just the S&P 500 Index. Therefore, a broad market index would be more appropriate for this and similar studies. Interestingly, the average annual return of the Russell 3000 Index for 1983-1989 was 17.7%, which matches the performance of the managers in the Lakonishok study! The only conclusion one can make is that managers in the study had pretty good coverage of the US equity market.

The performance of our reconstituted indices compared to the Russell 3000 is given in Table 2 below. During the whole 10 year period, the tracking numbers look very good.

Table 1 Institutional Indices compared to Russell 3000

January 1984 — June 1994