Our Selection Model
Learn how we use consensus-driven quantitative research
to identify the most promising European stocks.
During our conversations with clients and partners, questions about our methodology frequently come up:
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How do we identify outperforming companies?
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How do we integrate risk mangement in our approach?
The answer to these questions lies in the statistical selection model that we designed and use on a daily basis. Let us tell you what it's all about, starting with a visual representation:
Step 1:
Aggregating Consensus Data
Our first step in evaluating and comparing stocks is to compute aggregate consensus indicators. This first step consists in compiling opinions and previsions data recently published on all large European listed companies in order to create an objective composite consensus metric.
Concretely, we collect two different type of consensus data that will be combined:
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First, we source all the expressed previsions published by financial analysts regarding a listed stock. This is the simple part, as most research can be obtained through financial data providers.
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Then, we shift our focus to implicit opinions. This means we uncover stocks that institutional investors are actively buying. It differs from expressed previsions in the sense that we are no longer merely tracking published opinions, but the actual buying (and selling) decisions from large investors (like banks, hedge funds and money managers). Implicit opinions are much harder to collect, but offer better forecasting power.
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Last but not least, we assign a "weight" to all the opinions (expressed or implicit) we obtained. This means that we are actively giving more relative importance (weight) to some data over the others. Why? Because not all opinions are created equal, and we want our final results to be influenced by the most reliable experts first.
At this stage, we already have:
(i.) collected previsions published by analysts,
(ii.) integrated buying decisions from large investors and
(iii.) assigned consistent weights to all these data.
Now, we can finally compute our consensus metrics, in the form of 12-month price forecasts range. Those price forecasts form the bedrock of our decision making and our editorial policy.
Step 2:
Integrating Sectorial Sentiment
As we've just seen, compiled consensus on individual stocks are the main building block of our methodology. However, another metric plays a critical role in consensus computation: the general sentiment regarding a complete sector or industry.
As you know, sectors are used to categorise companies based on the type of business they operate. Think about Industry, Telecom, Technology, Healthcare, raw material, etc...
Why is this useful when computing consensus metrics? Because it indicates the context this company operates in, which will have a notable influence on its future performance.
There are is a major distinction to be made here, between defensive and cyclical sectors:
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Defensives activities (like consummer staples, utilities and health care) tend to offer predictable performance in all economic conditions, mainly because they offer products and services that are necessary.
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Cyclicals industries, on the other hand, include activities that tend to be dependant on the general economic context and growth (electronics, materials, Luxury or real estate)
Markets tend to cycle between those two poles following the fluctutation of the underlying economy, a movement called sectorial rotation.
Our research methodology natively integrates sectorial rotation and industry preferences. Concretely, this means that stocks belonging to a sought after sector will be assigned a higher rating than companies associated with (temporarily) less attractive industries.
Step 3:
Incorporating of Momentum Metrics
Momentum is the measure of the the speed of price changes in a stock. It is a metric mostly used to characterize the trend of a company in terms of direction (bullish, bearish, neutral) and quality.
Continuously computing those trend indicators on hundreds of European stocks allow our models:
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to rate companies on variables like in terms of quality, duration and probability of continuation.
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To compare stock (and sectors) and rank them to identify those with the best probability of trend continuation within the next 12 months.
Also, momentum is mechanically correlated with consensus (see step 1), as buying decisions from institutional investors should result in bullish trend.
In short, this momentum angle offers us a new and complementary dataset, allowing us to refine our analysis.
Step 4:
Setting Targets using Historical Volatility
Once one or several promising companies have been identified, time has come to make another important decision: where to set the intermediate price targets? Embedded in all our Best-Ranked selections, price targets are here to encourage our readers to exit winning positions in a progressive and optimized manner.
Defining those targets is a balancing act, as it must be high enough to justify the risk taken the investor (profit maximisation), but realistic enough to maximize the probability it will be reached within 12 months (risk reduction).
Here again, relying on quantitative indicators helps: we define those partial exit levels by analysing the historical volatility of the studied stock or sectors. In other words, we analyse the recent past behaviour of the stock to set exit targets that combine a high probabllity of being reached while offering sufficient profit.
Now, will consesus-based decisions markedly improve the performance and risk management of your portfolio?
Yes, without a doubt. But don't take our words for it, form your own opinion by checking the complete track record of our top-ranked selections.