Updated: Jun 18
In this article we provide insights into which companies have the highest and lowest expenses relative to revenues. This is another way of looking at profitability performance, but with an added focus on efficiency, i.e. how much money do businesses spend to generate sales and profits.
This analysis builds on our earlier article concerning the amount of cash 1,069 companies had on their balance sheet prior to the Covid-19 lockdown. With sustained pressures on revenues and cash flow, businesses with the highest expenses are going to exhaust their cash reserves faster. When their cash reserves are exhausted and revenues are no longer greater than expenses, businesses essentially run out of options to pay their bills and become insolvent.
Prepare for record year of insolvencies
According to official statistics from the Insolvency Service the legal system in England, Scotland and Wales typically deals with between 16,477 and 25,330 corporate insolvencies per year. This year is certainly not a typical year. In the Red Flag Alert produced by Begbies Traynor Group Plc, the UK's leading independent insolvency firm, factors including Brexit and the coronavirus pandemic have pushed more than 509,000 businesses into financial distress.
While the Government has introduced unprecedented support measures to help business through this crisis, including the Coronavirus Business Interruption Loan Scheme (CBILS), many firms have struggled to gain access to the government-backed loans as evidenced by the total sum of cash reserves within the banking sector, which account for 221% the sum of cash across all other sectors combined. See our earlier analysis for more details "Cash is king: which industries have enough cash to survive Covid-19 lockdown?".
Desperate times call for desperate measures
Many of the leading insolvency practitioners believe there is insufficient capacity within the legal system to cope with insolvency rates that are greater than 5 times the annual average. As a consequence, the UK government recently published draft legislation (the Corporate Insolvency and Governance Bill) on 20 May 2020, which aims to provide greater opportunities for businesses to restructure debt and survive the COVID-19 lockdown. The Government intends to ask Parliament to expedite progress of the Bill.
What does the data tell us?
For some of our readers who are not familiar with statistical visualisations, the box-whisker charts below provide an extremely useful way to view the full spectrum of performance across sectors, using a sample of 1,069 companies. Each sector is ranked according to the median of expenses divided by revenues (the dark blue line within the boxes). By looking at expenditures relative to revenues rather than profit margins alone, we can see which sectors are efficient in generating profits and which sectors are inefficient.
The blue dots to the right hand side of the chart represent companies generating significantly large losses. The blue dots to the left of the charts represent companies generating above average profits. As you can see, there is a significant skew towards companies generating large losses, i.e. the larger number of blue dots on the right hand side outnumber the number of blue dots on the left. What this tells us is that even among the best performing sectors there are still companies generating large losses. These companies have a low probability of surviving the Covid-19 lockdown.
Which sectors have the highest expenses relative to revenues?
The illustration below presents the bottom 20 sectors, i.e. sectors with the highest expenses relative to revenues. As you can see, the vast majority of businesses are loss making with the dark pink line (the median of expenses divided by revenues) being greater than 100%. Furthermore, you can see that there are a large number of dots to the right hand side, which represent companies generating significant losses relative to their revenues.
How to extract intelligence from the data?
Below is an extract from our internal analytics platform, which is an interactive analytics tool we use for market intelligence, risk management, and business development purposes. When we hover over each data point (i.e. a dot, median, inter quartile range) we get to see the name of the companies that are outperforming, or under-performing their peer group.
How can this type of analytics be useful to your decision making process?
Through our own experiences we have seen many businesses come to a complete standstill when senior leadership teams have been overwhelmed with information and the volume of important decisions requiring action quickly. Many decision makers suffer from decision fatigue during crisis situations because they become overwhelmed processing large quantities of information, making it difficult to rank and prioritise actions and resulting in "paralysis by analysis".
For example, some of the key questions that have emerged from our stakeholders in the energy sector have been:
Should we stick with an energy supplier when there are clear signs the supplier could become insolvent in the next few months? How will insolvencies impact our energy supply chain? Can we find a new energy supplier on short notice?
Should we reduce credit terms with strategic customers that are experiencing major shortfalls in sales revenues and cash flow? How will a reduction in credit terms impact our own sales revenues with customers and our own cash flow?
Can we afford to reduce investment in talent, research and development? How will that impact the long-term prospects of our business?
All of these questions come with high-impact consequences but if you have the data and analytical evidence to inform decision making you're in a better position to reduce risks from speculative decision making.
Through our engagement with various stakeholders in the investment management and banking sector, there is increasing demand for our services to help them understand which industries stand to benefit from the next economic cycle, so that they can reposition their lending and investment portfolios towards sectors of the economy that will generate the highest returns on invested capital.
By using our market intelligence and data science solutions they are looking to move money away from sectors with weak growth potential and invest in high-performing companies in sectors with strong growth potential.
In our next article we will be presenting data-driven insights concerning cash conversion cycles, the levels of debt within each sector, and how a reliance on debt to financially engineer better returns on equity is negatively impacting businesses ability to adapt to new market environments.
If you find this article interesting and feel that our research and analytics capabilities could help you through this crisis please feel free to get in touch.
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