Analyst Impact and Performance


New York – We have talked at length about the impact that analysts can have on stock prices and what particular remedies to prescribe for this problem with respect to performance measurement of analyst’s recommendations. Recently, we have seen an extreme example of the potential for an analyst to affect stock prices. Performance measurement which denies, or at least diminishes the analyst’s ability to affect prices is a key part of a performance evaluation system. Still the old adage continues to ring true “good research may not be consistent with good performance”.

The most recent example of analyst impact is the case Citigroup analyst Bhatia, who called for E*trade to experience a run on its banking operations and potentially declare bankruptcy. Other analysts covering the stock were either in the hold category (66%) or the buy category (33%) and there was apparently no immanent crisis facing E*Trade. In general, analyst impact is a function of the volatility (uncertainty) in the market and the level of surprise that the recommendation provides.

From a performance standpoint, Mr. Bhatia was the only analyst that got the call right. From an investor perspective, Mr. Bhatia destroyed shareholder wealth. E*Trade’s shares fell by 59% on Monday, even though the recommendation was unfounded. But the topic of this blog is neither the integrity nor the competence of Mr. Bhatia. Rather we are interested in this case as it relates to performance measurement.

If the call were logged prior to the price move, the analyst would have looked psychic in his ability to time his recommendations. Further, we would have logged a 59% improvement in his overall performance. This assumes that the overall performance is measured by the synthetic returns that would have been achieved if an investor had invested in and shorted or sold all the analyst’s recommendations over some period of time.

One model of coping with analyst impact in performance analysis is to lag the recommendation. Some rules lag the recommendation to the close of business, unless it is after 3PM, after which it is awarded the following day’s closing price. Others use the following day’s closing price regardless. For Mr. Bhatia, a one day lag in the selection of the price used to open the short position would have resulted in a 41% loss on that recommendation.

Of course, the loss is more consistent with the timing that even a savvy retail account could have reasonably acted upon the recommendation. Indeed, one could argue that it is the retail account that the performance measurement systems were designed to protect in the first place.

In the final analysis, the object of performance measurement is to find good research. However, in the case of the Citi analyst, performance measurement and good research diverged radically. And while it is clearly not feasible for a performance measurement system to be concerned with the distinction of being “right for the wrong reason, or wrong for the right reason” this is the Achilles’ heel of performance measurement as it relates to analyst impact.


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