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About Peer Analysis

 
 
 
 

Peer groups are necessary in stock analysis when one wants to carry out a like-for-like comparison to be used in stock picking, due diligence or portfolio management. Peers share a given degree of similarity in relation to a list of pre-defined attributes which should be tailored to the needs of each user. From an investor point of view, one must go beyond the conventional method of simply identifying peers as rivals or competitors. Both the company financials and the market features of the stock should be considered.

Algorithms to identify peers must perform three main steps: a) select the set of features to compare; b) define similarity ranges for each feature; and c) weight the various similarity yardsticks. In doing this, relationships between companies can be quantified and ranked.

Peer grouping definitions have become increasingly intricate due to the ever growing complexity of listed companies; which range from local and single product firms to global multi-sector conglomerates.

 
 
 
 

Defining Peer Groups

 
 
 
 

It is no longer correct to equate competitors to peers, even in those cases where the activity of a company is overwhelming concentrated in a few areas of business. This is so, both because the future outlook of a company depends on its diversification into new businesses (no matter how little they currently contribute to total revenue) and because companies often engage in simultaneous competition and cooperation with their rivals.

An example of an industry where companies work concurrently in competition and cooperation is the airline industry (composed primarily of single-product companies). Airlines may fight fiercely for the same dollar of passengers travelling a given route (e.g. between London and New York) while at the same time establish route-sharing agreements for other destinations (such as the Far East or Africa).

We can see the necessity for diversification in companies such as Google and Yahoo which obtain the bulk of their revenue by competing for the same online advertising dollar. But because the future fortunes of these companies may depend on other sources of revenue, both companies have been on a spending spree in terms of new acquisitions. For instance, about 99% of Google’s $US 11.8bn revenue for the first 9 months of 2007 came from advertising, but the company expects to raise some revenue from new services such as Google Checkout, a recently introduced merchant account product, and from the hosting of enterprise applications, a service provided by one of its acquisitions, Postini Inc. During the same period Yahoo obtained only about 88% of its $US 5.1bn revenue from advertisers. The remaining revenue is accounted for by other Yahoo services including Internet broadband services, sports, music, games, personals, premium mail offerings and services for small businesses.

The same two companies can be used to illustrate the problems of arbitrary industry classifications. Such problems include companies not changing classification when their business changes and the potential for changes in accordance with the market fads (e.g. technology or energy). For instance, despite Google and Yahoo being rivals in online search advertising, until recently Reuters classified Yahoo as being in the Cyclical Consumer Goods & Services economic sector and in the Advertising/Marketing industry while Google was classified in the Technology sector and in the IT Services & Consulting industry. Yahoo has since been reclassified to match Google but common sense would classify them as Advertising/Marketing companies, given that they both derive the majority of their revenues from advertising and other advertising businesses (e.g. broadcasting) are grouped in the Cyclical Consumer Goods & Services sector.

When classifying companies, data vendors may also consider factors beyond competition, such as the technology used, business models and public perceptions (e.g. herd behavior). Investors may wish to heavily weight the market characteristics of a company’s stock and underweight revenue stream factors, whether defined in terms of source or size. For instance, they may be interested in a list of companies with similar indicators such as stock beta, valuation or index membership.

To provide investors with such flexibility in defining peer groups, SADIF Analytics developed the PeerFinder™ algorithm which takes into account 16 different attributes which are grouped into four main categories: market league, business similarity in terms of income streams, technology and non-financial assets and financial structure. Users can weight each category to suit their needs, but for the purposes of StockMarks™ we keep closer to the “competitors” tradition and give a large weighting to market league and income stream factors.

 
 
 
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