Sensitivity analysis

The clearest real-world example of sensitivity analysis that I have ever seen is one by oil and gas company Shell about the impact of changes in the oil price. The price sensitivity at Shell group level is $6 billion of cash flow from operations per annum per $10 per barrel Brent oil price movement. If the oil price goes up by $10 per barrel, they expect $6 billion of incremental cash flow from operations. If the oil price goes down by $10 per barrel, they expect a decrease of $6 billion of cash flow from operations. This sensitivity statement comes with a disclaimer: this price sensitivity is appropriate for smaller price changes, and is best used for full-year numbers. The “format” of this sensitivity analysis is: what is the effect of a change in absolute terms of an input variable (oil price) on the absolute amount of a target variable (cash flow from operations).

⏱️TIMESTAMPS⏱️
0:00 Sensitivity analysis example
1:07 Sensitivity analysis in impairment testing
2:25 Definition of sensitivity analysis
3:16 Set KPIs before doing sensitivity analysis
3:25 Sensitivity of financial statements
6:19 Sensitivity of volume vs price
6:38 One factor vs multiple factor sensitivity analysis
7:52 Linear sensitivity analysis
8:18 Convex and concave sensitivity

Commodity trading and mining company Glencore provides a sensitivity analysis in the part of the annual report that discusses the review of assets for impairment. For each cash generating unit with limited headroom relative to their estimated recoverable value, a sensitivity impact of potential 10% movements in the most sensitive assumptions is provided. For Coal South Africa, a 10% fall in coal price assumptions would lead to a possible impairment of $703 million. For Mopani, a fall of 10% in the copper price assumption would lead to a possible impairment of $181 million, while a 10% reduction in the estimated annual production over the life of the mine could result in an impairment of $116 million. Similar sensitivity analyses are done for cash generating units involved in the extraction and production of nickel, oil, and zinc. The “format” of this #sensitivityanalysis is: what is the effect of a change in percentage terms of an input variable (10%) on the absolute amount of a target variable (millions of $ of impairment charges).

These examples lead us to a definition of sensitivity analysis: the process of estimating how target variables change in relation to changes in input variables. What is the effect of a change in input variable x on target variable f(x)? What is the effect of a change in the oil price on cash flow from operations? What is the effect of a change in revenue on profitability? What is the effect of a change in estimated project benefits on net present value? The key to sensitivity analysis is to identify the most significant assumptions that affect an output: which input variables have the strongest impact on the target variables? Prior to starting a sensitivity analysis, you first need to decide what the key performance indicators (target variables) are!

In a lot of situations, people performing a sensitivity analysis mistakenly assume that input variables and output variables are linked in a linear way: a 20% change in the inputs is expected to have double the effect on the outputs versus a 10% change in the inputs.

The impacts of input variables on target variables could however be exponential, not linear! In the case of a “positive” scenario, this would be called convexity: exponential growth in the target variable. This is something you try to look for, and benefit from! In the case of a “negative” scenario, this would be called concavity: exponential decline in the target variable. This is something you try to avoid, build defenses against, in order not to get hurt, or “blow up”! When doing a sensitivity analysis, try to detect the sensitivity of an outcome to different sized shocks.

What does sensitivity analysis give us? In the words of Nassim Taleb: performing sensitivity analysis on assumptions does not eliminate the risk, but identifies which assumptions are key to conclusions, and thus merit close scrutiny.

Related video: sensitivity analysis in Excel    • Sensitivity analysis in Excel  

Philip de Vroe (The Finance Storyteller) aims to make accounting, finance and investing enjoyable and easier to understand. Learn the business and accounting vocabulary to join the conversation with your CEO at your company. Understand how financial statements work in order to make better investing decisions, and improve your #financialanalysis . Philip delivers #financetraining in various formats: YouTube videos, classroom sessions, webinars, and business simulations. Connect with me through Linked In!

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