Faced with MiFID II research challenges, such as research unbundling, buy-side firms are using StarMine to ‘analyze the analysts’ and decide where to allocate spending on sell-side reports.
- How is the asset management industry reacting to research unbundling?
- How can analytics help you be more discerning about what research you read?
- What content sets can help you enhance your investment process?
The long arm of MiFID II has certainly impacted the buy-side community this year.
In Asia, we’ve had discussions from Korea to Melbourne and Mumbai to Tokyo as customers seek to understand how other firms are adapting, and what best practice looks like.
One area where we are seeing huge demand is analyst analytics.
Many customers may be aware of the StarMine brand. This was an acquisition in 2008 which remains the global benchmark for analyst performance measurement.
Many sell-side directors of research rely on StarMine as a rigorous, impartial, fair measure of performance which forms an important component in analyst compensation.
MiFID II research benefits
StarMine measures sell-side analysts on two distinct skills — their estimates accuracy and their stock-sorting skill (do their buys outperform their holds, do their holds outperform their sells?).
The latter skill is also measured in an industry context and forms the basis of the annual StarMine Analyst Awards that run in many markets globally.
From a buy-side perspective, this analytics data performs several roles.
It allows the buy-side to understand whose research to start with, who to call with a question, and whose meetings or roadshows to take.
In today’s budget-conscious environment and with MiFID II impacting the industry, these analytics provide invaluable context that can help firms understand where to allocate their limited sell-side research spend.
Measuring sell-side performance
On the basis of feedback from our buy-side customers, we have now launched a standalone Analyst Report that firms can use to gain a broad market understanding of sell-side performance — whether globally, in individual markets or by industries and sectors.
StarMine then goes further — using this data to create a Smart Estimate — a better consensus number that dynamically removes stale estimates, and with the subset of remaining estimates places more weight on the most recent estimates and more accurate numbers.
The difference between the I/B/E/S consensus and the Smart Estimate is known as a Predicted Surprise. In fact, we have recently released a research note on how this Smart Estimate can be used in a risk management or idea generation context, assisting institutional investors with forecast earnings misses.
Another unique analytic is our Bold Estimate, where we highlight the top 10 percent of analysts who have issued a non-consensus estimate. So not only does this analyst have a great track record, they’ve put their neck out with a non-consensus estimate.
We provide these analytics in our quantitative database, QA Direct (and imminent cloud solution), and via a data feed for integration into your proprietary system. The analytics are also available in our back-testing solution, QA Point and our Eikon financial desktop.
Additional layer of insight
Stepping back, many commonly used content sets in the investment process are approaching commodification.
The above are clear examples showing where we provide an additional layer of insight, taking well understood content and creating value.
It’s also about us helping the asset management community in an environment where research costs are rising and margins remain pressured.
The rise of inexpensive beta solutions also mean that tools to assist in providing alpha, and mitigating portfolio volatility remain in great demand.
In an environment where challenges and disruption abound, these are timely reminders of the unique insight we deliver and our ongoing commitment to the institutional asset management community.
If you’d like to know more please get in touch, or come along to the Buyside Summit at the Conrad Hotel, Hong Kong on 17 April.