Investors in E&Ps have suffered over the last few years as poor exploration success has exacerbated reducing returns (caused by increased capex and higher costs of capital). Sentiment in the sector has fallen, but is this measurable?
We think not, but there are some indicators that may be weak proxies.
Percentage discount of oil shares to estimated value (consensus analyst value)
We start by making the bold assumption that an approximation to the value of the company is the average target price as published by stock analysts.
Taking an index of London-based E&P companies over time and comparing the analyst valuations with actual share prices shows that the market-applied discount to E&Ps has changed little since January 2012. The apparent discount that the market applies is around 40%.
Recent movements (such as Dec 2014-Feb 2014) are more likely the lag of analysts updating models than an actual move in sentiment (shares prices more quickly reflected the oil price falls than analysts).
When examining individual companies, it is important to note the size effect. As companies become larger, more investors and analysts cover the company and we believe this is why we see the correlation between market cap and discount to consensus target price, as below. This view of an efficient market hypothesis would also explain why the range of uncertainty decreases with size as companies grow in market cap.
It is also true that the smaller E&Ps are those with the greatest uncertainty in outcomes – this is perhaps the result of having fewer exploration targets, which does not mesh particularly well with the application of probabilistic CoS in general use.
Given this, one would assume it fair that this uncertainty would give rise to a greater discount to target prices, which tend to use consistent principles across the space.
Objections to this approach
Readers can very easily raise objections to this approach. Consensus target prices will be affected by the analyst’s over-riding macro assumptions (assume $80/bbl or $100/bbl for long-term oil prices) and risk application (how is the level of exploration value to apply calculated). For example, in the boom years of mid 2000’s, analysts and investors were more likely to include more exploration value, whereas it is our impression that inclusion of longer-dated exploration is less likely now.
Without a full database of analyst models/notes, it is not possible to separate out these effects.
If this is true, it is up to the analysts to adapt their approaches to take this into account. We will expand on this theme in later blog posts.