Page 297 - DLIS402_INFORMATION_ANALYSIS_AND_REPACKAGING

Information Analysis and Repackaging

Notes 14.6 Information and Active Management

Where and how does information arise in active management? Active managers, as opposed to passive
managers, apply information to achieve superior returns relative to a benchmark. Passive managers
simply try to replicate the performance of the benchmark. They have no information. Active managers
use information to predict the future exceptional return on a group of stocks. The emphasis is on
predicting alpha, or residual return: beta-adjusted return relative to a benchmark. We want to know
what stocks will do better than average, and what stocks will do worse, on a riskadjusted basis.
So, when we talk about information in the context of active management, we are really talking
about alpha predictors. For any set of data pertaining to stocks, we can ask: Do these data help
predict alphas? We will even call this data a predictor. In general, any predictor is made up of
signal plus noise. The signal is linked with future stock returns. The noise masks the signal and
makes the task of information analysis both difficult and exciting. Random numbers contain no
signal, only noise. Information analysis is an effort to find the signal-tonoise ratio.
A predictor will cover a number of time periods and a number of stocks in each time period. The
information at the beginning of period t is a data item for each stock. The data item can be as simple
as +1 for all stocks on a recommended buy list and -1 for all stocks on a sell list. On the other hand,
the data can be a precise alpha, 2.15% for one stock,-3.72% for another, and so on.
Other predictors might be scores. Crude scores can be a grouping of stocks into categories, a more
refined version of the buy and sell idea. Other scores might be a ranking of the stocks along some
dimension. Notice it is possible to start with alphas and produce a ranking. It is possible to start
with a ranking and produce other scores such as four for the stocks in the highest quartile, down to
one for the stocks in the lowest quartile.
The predictors can be publicly available information such as consensus earnings forecasts, or they
can be derived data, such as a change in consensus earnings forecasts. Predictors are limited only
by availability and imagination. In examples that we follow throughout the article, we use book-to-
price data in the United Kingdom to generate return predictors according to various standard
schemes. For instance, we can generate a buy list and a sell list by ranking all U.K. stocks according
to book-to-price ratio, and placing the top half on the buy list and the bottom half on the sell list.
The intent of this and other examples is not to suggest novel new strategies, but simply to illustrate
information analysis techniques. Underlying the book-to-price examples is the hypothesis that book-
to-price ratios contain information concerning future stock returns, and, in particular, that high
book-to-price stocks will outperform low book-to-price stocks. Is this hypothesis true? How much
information is contained in book-to-price ratios? We will apply information analysis and find out.
Information analysis is a two-step process:
Step 1: Turn predictions into portfolios, and
Step 2: Evaluate the performance of those portfolios.
Step 1 transforms the information into a concrete object: a portfolio. Step 2 then analyzes the
performance of the portfolio. Information analysis is flexible. There are a great many ways to turn
predictions into portfolios and a great many ways to evaluate performance. We will explore many
of these alternatives below.
Step 1: Information into Portfolios
Let’s start with Step 1: turning predictions into portfolios.
As we have predictions for each time period, we generate portfolios for each time period. 2 now
there are a great many ways to generate portfolios from predictions, and the procedure selected can
depend on the type of prediction. Here are six possibilities. For each case, we provide the general
idea, and then discuss how to apply this to data concerning book-to price ratios in the U.K. Later we
analyze the performance of these portfolios.

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