New tools and technologies to streamline IR

PART TWO: The changing role of Consensus

Now there is an adage in the market that when a company beats consensus, their shares advance and when a company misses consensus, their shares go down. Long only and hedge funds try to capture this alpha by shorting shares that miss consensus and buying shares that beat it, the effect usually lasts for two days in the market so a lot of alpha can be generated if the miss/beat strategy works.

So is this adage true? A 2013 article from McKinsey and Company poured some cold water on the theory. Entitled “Avoiding the Consensus-Earnings Trap”, the article stated that the promise of meeting or beating consensus estimates and the perils of missing them are profoundly overstated.

But still consensus seems to be important, so is there something inherent about these consensus numbers that we are missing? Traditional aggregators such as Bloomberg, Thomson Reuters, FactSet etc primarily get their data points from the sell side analysts following the stocks as we have said before.

Newer data aggregators are coming on the scene that “crowdsource” consensus data, taking estimates not only from the sell side but from the buyside and the general public, they also clean up these data sets to provide a consensus that is more accurate and may have more predictive value than that from existing suppliers.

Whisper numbers are very important in the US market as they are a better predictor for share price movements pre and post earnings, and a company called Estimize is producing crowdsourced data that is giving quant funds a good supply of alpha, particularly on the short side of the market. Earnings Whispers state they are more accurate than consensus 71.2% of the time and Estimize, 60% of the time and growing in accuracy over time. The key for these new aggregators is they poll the buyside for their consensus in addition to the sellside, they smooth data by removing out-of-date estimates and they bias towards the most accurate analysts predictions based on historic results.

So we can see how these new consensus plus models can add value by capturing alpha for investment firms, but what can we learn from this for IR departments and how they go about adopting technologies such as crowdsourcing?

As the US tech giants such as Apple, Amazon, Google and Facebook are moving into the life science sector through acquisitions and technology incubators, the life science sector must ensure they are embracing new technologies and tools to stay competitive. Although crowdsourcing is not new, the term was first coined in 2005, its utility to an IR department is obvious in both the consensus arena and other areas such as perception studies. Artificial Intelligence and cloud-based analytics also have their place in investor targeting and surveillance and even the blockchain may have some use in creating more direct, efficient and safer technologies for shareholder communications.

By Optimum

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