Trying to Gain an Edge: Blackrock Goes a Bit Too Far
Yesterday New York Attorney General Eric T. Schneiderman reached an agreement with Blackrock, the world’s largest money manager, in which Blackrock will cease conducting a survey of Wall Street analysts. Blackrock was using the results in their quantitative models to help select stocks for their investment portfolios. The Attorney General objected to Blackrock’s use of its influence as a gigantic money manager to influence analysts in furnishing them with the data. As improper conduct goes, Blackrock’s survey hardly deserves condemnation. In fact, Blackrock simply agreed to cease the practice and reimburse the Attorney General $400,000 for the cost of the investigation.
|Product Opportunities (1999)|
I am writing about this settlement because it illustrates the unrelenting pressure facing money managers to find some kind of informational advantage as they buy and sell securities. As Chief Investment Officer for the State of North Carolina I helped to build a series of portfolios managed by Barclay’s Global Investors (BGI), which was acquired by Blackrock in 2012. BGI had a huge team of mathematicians, economists, and engineers building complex investment models. I spent many days in BGI’s conference rooms trying to determine if their models were effective. I was always impressed by the fact that BGI acknowledged that their models deteriorated over time, and that they constantly searched for new factors and algorithms to upgrade their models. The settlement with Mr. Schneiderman represents an R&D project that went too far.
In order to understand why Blackrock undertook the survey you need to know a bit about quarterly corporate earnings and their effect on stock prices. When public companies release earnings money managers and their analysts pay attention, frankly too much attention. The casual observer probably expects that positive or rising earnings reports should result in higher stock prices, and negative or falling earnings should result in lower stock prices. That’s not how it works. There are plenty of instances when a company generates a huge increase in profits and its stock plummets. Conversely, there are many times when a company issues dismal financial results and the stock rises. What’s going on?
While the fundamental prospects of a company drive stock prices over the long term, expectations propel stock prices in the short run. In simple terms, if a company reports earnings of 24¢ per share and the expectation was 21¢, the company’s stock will tend to jump. The stock’s price change may be quite extreme because a lot of money manager and traders are playing the same expectations game.
Where does the expectation come from? Typically, money managers spend a great deal of time trying to gauge the consensus earnings expectation for a stock. For many money managers, the earnings estimates produced by analysts at brokerage firms serve as a proxy for the consensus. In the earliest part of my career, collecting this data was an arduous task. A money manager had to do his own survey. With the advent of the computer, companies, such as Zack’s Investment Research, Institutional Brokers' Estimate System (I/B/E/S), and First Call Corp. started to systematically collect Wall Street earnings estimates and sell the data to money managers. When a company’s quarterly results beat or trailed the consensus expectation, it was known as “earnings surprise”. For a period of time, money managers could generate investment performance by speculating on whether a company’s actual earnings would exceed or trail the consensus expectation.
However after a while earnings surprise didn’t drive short-term stock prices as reliably. Companies began managing quarterly earnings to the consensus expectation. In addition, analysts and money managers started gaming the earnings collection services. The earnings expectation was no longer based on the published consensus estimate, but rather the “whisper number,” which was the Wall Street analysts’ unpublished but more informed earnings estimate.
All sorts of quantitative managers parsed the analysts’ earnings estimates to see if they could find an edge. Were certain analysts’ estimates better than others? Was the rate of increase in earnings estimates (momentum) more effective than the estimates themselves? Were earnings estimates in certain sectors or for certain companies more effective predictors of stock performance? Typically, money managers found a temporary edge in slicing and dicing the estimates, only to see it evaporate as other managers attempted to exploit the same algorithm or factor.
Let’s return to Blackrock. Blackrock’s survey was an attempt to systematically capture the whisper number by asking analysts questions that might elicit the analysts’ real expectations for a company. While the surveys were a clever technique to extract useful information, they created a conflict of interest. Blackrock pays massive commissions to Wall Street brokers. In addition, they rate analysts, as do all money managers, which figures in the analysts’ bonuses. I’m sure that Blackrock and its competitors will continue to look for a way to better anticipate quarterly earnings reports.