Discovering appropriate friends for monetary evaluation is a vexing activity that requires cautious consideration of companies’ underlying economics, accounting decisions, and monetary assertion presentation. However with out comparable monetary assertion data, peer benchmarking might yield much less significant and even deceptive insights that negatively affect earnings forecasts.
In a recent study revealed in The Accounting Evaluation, we developed a technique to determine comparable companies for benchmarking and analyzed its implications for analyst outcomes and valuation with multiples. On this put up, we are going to spotlight the salient particulars, a few of which can shock you.
There are other ways to outline peer companies, equivalent to industry membership, stock index membership, closeness in market capitalization, and similarity in value drivers (e.g., P/E ratio, return-on-invested capital, and development).
As a substitute for conventional classifications, researchers have examined new methods to determine peer companies, equivalent to traders’ co-search, depth of companies’ filings with the SEC’s EDGAR, and inventory data on Yahoo! Finance.
These broadly utilized strategies fail to immediately deal with a vital facet of agency benchmarking: the provision of key monetary assertion data for peer companies. When a number of monetary assertion line objects are lacking for a peer agency, analysts wrestle to derive significant inferences from the comparative to the focal agency’s monetary statements.
Our monetary assertion benchmarking (FSB) measure goals to fill this hole. The information and code are freely out there on our website.
Capturing the Diploma of Overlap Between Monetary Assertion Gadgets
Constructed on the Jaccard similarity coefficient, pairwise FSB captures the diploma of overlap in monetary assertion objects reported by two companies, with scores starting from 0 (no overlap) to 1 (full overlap). The upper the FSB rating, the higher the benchmarking data out there to exterior customers.
As an example, if the focal agency has reported 270 objects, 200 of which overlap with 220 objects reported by the peer agency, the FSB rating is 0.69 (200 / (270 + 220 – 200). To place this into context, the typical rating for analyst-chosen friends in our pattern is 0.68.
Assuming that FSB is a useful metric in capturing the similarity of two companies’ underlying economics and accounting decisions, we count on it to be positively correlated with analysts’ decisions of peer companies.
Our pattern of analyst-chosen friends comes from a Evaluation of Accounting Research article, “Analysts’ choice of peer companies.” By manually screening greater than 2,500 sell-side fairness analysts’ experiences, the authors extracted information on comparable peer companies chosen for the focal agency in every report.
In our examine, for every analyst-chosen peer agency, we chosen an identical agency in the identical {industry} that was not chosen however which had an identical dimension and valuation a number of. The outcomes present that analysts have a tendency to decide on peer companies which are extra akin to a focal agency from a monetary assertion benchmarking perspective.
When FSB is greater by one-standard-deviation, the chance of being chosen as a peer agency by an analyst will increase by 13%.
Increased FSBs Improve Accuracy of Earnings Forecasts
Does selecting friends with greater FSBs have optimistic implications for analyst efficiency? We discover that when the typical FSB of the set of analyst-chosen peer companies is one-standard-deviation greater, the accuracy of analysts’ earnings forecasts will increase by about 23%.
When deciding on peer companies, search for companies which have extra comparable monetary statements to the focal agency, even when meaning looking outdoors the focal agency’s principal {industry}. In actual fact, solely 40% of the analyst-chosen peer companies function in the identical product market because the focal agency.
Which firms do you suppose can be good peer companies to decide on when analyzing Colgate-Palmolive? Morningstar lists Procter & Gamble and Unilever as prime friends for the corporate. Regardless of being listed on a US inventory trade, Unilever has a modest 0.69 FSB rating with Colgate-Palmolive.
That is possible as a result of the corporate makes use of Worldwide Monetary Reporting Requirements to arrange its monetary statements. Utilizing completely different accounting requirements reduces comparability because of variations within the recognition and presentation guidelines. In distinction, P&G and Colgate-Palmolive have a better FSB rating of 0.77, suggesting a higher comparability than Unilever and Colgate-Palmolive.
In distinction to Morningstar’s method, Google Finance creates an inventory of peer companies primarily based on traders’ co-search exercise. Notably, among the many peer companies Google Finance lists for Colgate-Palmolive is Coca-Cola. Though this statement could seem unintuitive at first blush, our methodology means that, from a monetary assertion benchmarking perspective, Coca-Cola can be a wonderful match on this case as a result of its FSB rating with Colgate-Palmolive is effectively above the typical at 0.82. This will clarify why traders extensively co-search the monetary data of the 2 firms.
Validation and Testing
After validating and testing the pairwise FSB metric, we aggregated information throughout all {industry} friends of the focal agency to grasp how simple it’s to benchmark a agency’s monetary statements general. This course of yielded a big panel of firm-level FSB information. Additionally, to counterpoint our methodology, we decomposed FSB on the monetary assertion stage, producing separate FSB scores for the earnings assertion, stability sheet, and assertion of money flows.
Whereas analysts’ consensus earnings and web debt forecasts are extra correct when firm-level FSB is excessive (i.e., it’s simple to benchmark and perceive a agency’s monetary statements), earnings assertion and stability sheet benchmarking play completely different roles in these outcomes.
We discover that the Earnings assertion FSB rating predicts the accuracy of earnings forecasts however not web debt forecasts. In distinction, stability sheet FSB rating predicts the accuracy of web debt forecasts, however not earnings forecasts. In financial phrases, a one-standard-deviation enhance in earnings assertion (stability sheet) FSB is related to a 17.3% (12.1%) extra correct consensus earnings (web debt) forecasts. These findings spotlight that benchmarking advantages depend upon the context of the evaluation.
For the Investor: Trade, Trade-Measurement, or FSB Friends
Past optimistic analyst outcomes, a key query for traders is whether or not selecting peer companies primarily based on FSB improves valuation with comparables. To this finish, we in contrast the predictive skill of the valuation multiples fashioned utilizing FSB-based friends to these of the fashions using conventional strategies for peer agency choice, equivalent to industry- and size-based friends. Particularly, we regressed the long run enterprise value-to-sales ratio (EVS) of the focal agency on the typical EVS calculated for 3 units of friends: (1) {industry} friends, (2) industry-size friends, and (3) FSB friends.
When predicting one-year, two-year, and three-year-ahead EVS, the fashions utilizing the typical valuation a number of of FSB-based friends constantly outperform these using solely industry- and industry-size friends. As an example, the R-squared of the mannequin predicting one-year-ahead EVS will increase from 24.8% to 31.8% when the typical EVS of the 4 highest FSB friends is included within the mannequin.
In conclusion, we word that FSB is an easy, easy measure summarizing the overlap in peer companies’ underlying economics and accounting decisions, that are the important thing elements that form monetary statements. FSB is accessible on the pairwise and agency ranges, permitting exterior customers to search out appropriate peer companies for numerous functions, together with relative efficiency, compensation, and valuation benchmarking.
