Every day in the financial news, you will see a reference to price targets for individual stocks disseminated by sell-side analysts from firms like Goldman Sachs and Morgan Stanley. Such price targets represent these analysts’ estimate of the potential upside price for a particular company.
Sell-side analysts are a large and well-embedded part of the broad market ecosystem. These analysts provide research coverage on companies to generate trading commissions from investors. Sell-side analysts typically have a very narrow industry focus. As a result, these analysts are known to produce in-depth company research that reflects detailed company and industry information from various sources, including relationships with management teams, channel checks, and industry sources. The efforts of sell-side analysts culminate in the price target that these analysts assign to each company that they cover. These price targets are widely published and represent the sell-side analyst’s overall outlook for a company based on the totality of the analyst’s research of a company/industry. To be sure, it is widely acknowledged that sell-side research has shortcomings given the influence that conflicts of interests and business relationships can have on a sell-side analyst’s outlooks. However, due to these analysts’ presumed expertise, it is still common for the sell-side research community’s price targets to carry a degree of weight and influence in investors’ minds.
While conventional wisdom would suggest that these sell-side price targets are predictive of future stock returns given analyst expertise or the in-depth research they conduct, as a behavioral manager, we recognize that this assumption should be objectively tested. Conventional wisdom and rules of thumb often cause investors to fall victim to behavioral biases that can lead to sub-optimal investments. To demonstrate the effectiveness of price targets to generate outperformance, we have summarized the implied return test results from the consensus sell-side analyst price target for a company in the table and chart below. This is calculated by taking the consensus sell-side price target and dividing it by the current price. The table on the left displays the average twelve-month returns by decile over the test period. The first decile represents companies that should perform the best based on the sell-side price target relative to current prices. Conversely, the tenth decile represents the returns of companies with the lowest price target relative to current prices and are thus the companies that sell-side analysts are the least enthusiastic about. It is important to note that these deciles are sector neutral, meaning that each decile is comprised of the same sector weights as the overall universe. The chart on the right breaks out how the factor performed throughout the test period, with the data specifically representing the rolling twelve-month return spread of the top three (best) deciles of the factor versus the factor average. The results reflect a trailing decade test period run with a quarterly frequency and with the Russell 3000 used as the universe.
The test results above directly oppose the notion that a company with aggressive sell-side analyst price targets indicates future outperformance. These price targets are worse than a random factor with no predictive power in stock selection, given that the companies with the most elevated analyst price target predictions relative to the current price notably underperform the average company in the universe. As a result, this data exhibits that an investor would be better off either focusing on the companies that sell-side analysts do not like or shorting the ones they do. Furthermore, the time-series data shows that these poor results for analyst price targets are not concentrated in a specific period. Companies with the highest price target implied return have only outperformed the average decile in an abysmal 31% of periods.
The consistent poor returns generated by companies with the most ambitious sell-side analyst price targets begs the question of why investors seek to be influenced by these price targets in the first place, especially when considering that the issues above with sell-side independence are no secret. We believe that this phenomenon of sell-side price targets carrying undue influence is primarily the result of investors falling victim to the authority bias or the tendency to attribute greater accuracy and knowledge to persons of perceived authority than they may possess. In particular, investors can come to either explicitly or implicitly view sell-side analysts as authoritative given the amount of information and alleged expertise that such analysts have due to their very specialized industry focus. However, as is evident in the test results, simply having more information, crunching more numbers, or conducting “deeper” research into a company does not necessarily translate to generating accurate predictions. Instead, though these analysts are heralded as experts, sell-side outlooks routinely reflect a wide range of behavioral biases that the analysts themselves fall victim to when analyzing a company. As a result, their predictions can be widely inaccurate. While seeking vast arrays of company information or conducting in-depth fundamental research is certainly not an inherently negative endeavor, simply accumulating more information or analysis will not offset the detrimental effects of behavioral biases and inefficient decision making.
While there are pitfalls to using sell-side analyst price target data in its most basic form to select stocks, an understanding of investor behavior can provide insight into how to transform these price targets into data that effectively identifies companies that will outperform. A more effective utilization of this data is the price target diffusion factor. Price target diffusion measures the degree to which analysts raise or lower their price targets and is calculated as the number of recent upward price target revisions minus the number of recent downward price target revisions divided by the total number of price targets. The behavioral impetus for focusing on the directional change in the data rather than the simple implied return from the price target is that, as was discussed, the absolute price target level generally reflects the adverse effects of each sell-side analyst’s own behavioral biases and heuristics. Conversely, incremental upward and downward directional revisions to price targets more accurately measure whether a company’s operating position has improved or worsened based on the latest incremental data rather than a specific analyst’s own behavioral biases.
The test results of the price target diffusion factor are below. The first decile of the data reflects companies with the highest number of upward price target revisions relative to the total number of price targets. In contrast, the tenth decile reflects companies with the highest number of negative price target revisions relative to the total number of price targets. Similar to the previous data, the results are sector neutralized and reflect a trailing decade test period run with a quarterly frequency and with the Russell 3000 used as the universe.
As shown above, price diffusion, the directional measure of the revisions in analysts' price targets, possesses an ability to identify companies that will outperform with a strong consistency throughout time. Thus, there is a stark contrast in the efficacy of price target diffusion and the implied return from sell-side price targets despite these two factors being derived from the same underlying data set. These results show that while the desire to rely on absolute sell-side price targets is detrimental to stock selection, this price target data can be transformed by using behavioral principles into an effective tool for identifying stocks that will outperform.
We believe that these results exhibit the multiple aspects of benefits that can stem from utilizing a focus and understanding of investor behavior when making investment decisions. First, leveraging a behavioral awareness can bring the obvious "defensive" benefit of resisting the natural cognitive tendency to fall victim to a problematic market indicator such as the headline price targets from sell-side analysts. However, this research also illustrates the often lesser-known "offensive" benefit of focusing on investor behavior, given that such a focus can result in avoiding one's own behavioral mistakes and utilizing data in a differentiated way that adds meaningful value to stock selection.
Any forecasts, figures, opinions, or investment techniques and strategies explained are Hillcrest Asset Management, LLC's as of the date of publication. They are considered to be accurate at the time of writing, but no warranty of accuracy is given, and no liability in respect to error or omission is accepted. They are subject to change without reference or notification. The views contained herein are not to be taken as advice or a recommendation to buy or sell any investment. The material should not be relied upon as containing sufficient information to support any investment decision.
Data is provided by various sources and prepared by Hillcrest Asset Management, LLC and has not been verified or audited by an independent accountant. Test results are not indicative of future results and should not be relied upon. While the information provided above is not based on the performance of any individual security or group of securities, the methodology used to provide the information can be obtained by contacting Hillcrest Asset Management, LLC.
The views and opinions expressed herein are the author's views and opinions and do not necessarily reflect those of Nasdaq, Inc.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.