Our Research
How we use consensus-driven quantitative models
to identify promising European stocks.
At the European Investor, our mission is to help you make better investment decisions by continuously identifying Europe-listed stocks exhibiting the most potential. Now, how do we accomplish that?
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What is so different in our approach?
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How do we identify promising companies?
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How is risk management integrated in our models?
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 stocks is to compute aggregate consensus indicators. This 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:
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First, 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.
Those differ from expressed previsions in the sense that we are no longer 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 gathered. 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
(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
Although compiled consensus on individual stocks are the main building block of our methodology, another metric plays a critical role in consensus computation: the general sentiment regarding a complete sector (or industry).
Economic sectors are used to categorise companies based on the type of business they operate. Think about Industrials, telecom, technology, Healthcare, raw materials, etc.
Why is this information critical when computing consensus? Because the economic sector of a company indicates the general context in which it operates. And statistics show that this context has a notable influence on the future performance of the stocks composing this sector.
Note that here is a major distinction to be made here, between defensive and cyclical sectors:
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Defensives activities (like consummer staples, utilities, health care etc...) tend to offer predictable performance in all economic conditions, mainly because they offer products and services that are necessities.
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Cyclicals industries, on the other hand, include activities that tend to be dependant on economic growth and the general state of the economy. Those includes electronics, raw materials, Luxury, financials or real estate.
Markets tend to cycle between those two opposites poles following the fluctutation of the underlying economy, a movement called sectorial rotation. Those cycles clearly show in sectorial sentiment indexes, which track opinions and demand focused on markets segments. In other words, not only do we compute consensus indicators on individual stocks, but we also compute it for complete industries.
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 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, as buying decisions from institutional investors mechanically influence related stocks trends.
In short, this momentum angle offers us a new and complementary dataset, allowing us to refine our analysis.
Now, will consensus-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.