Managing portfolio risk in the “Golden Age of fraud”
How new technology and a sceptical mindset can provide protection
A decade long, central bank driven, bull market (pre-Covid) along with high levels of retail participation, Silicon Valley’s fake-it-till-you-make-it approach, Trumpian post-truth #fakenews and lax regulation led famed investor Jim Chanos to recently opine during an FT interview that the current point in time is:
“a golden age for fraud [and] a really fertile field for people to play fast and loose with the truth, and for corporate wrongdoers to get away with it for a long time”.
Jim Chanos, July 2020
This should perhaps be no surprise, indeed the Kindleberger-Minsky model of bubbles and crashes highlights how frauds tend to increase in good times as everyone tries to get in on the act of making an easy buck. The Insull Empire in the 1930s, Enron and Worldcom in the early 2000s, Madoff in 2008 and Wirecard in 2020 — this is a small list of some of the world’s largest financial frauds and it is therefore no coincidence that these have all been unearthed in the aftermath of bull market euphoria.
Covid’s downdraft and subsequent recovery, bifurcating markets in the process, only seems to have exacerbated investor risks. Currently cheap stocks are as cheap as they have been and expensive ones more expensive, as Chanos adds in his interview there is “a heady witch’s brew for trouble”.
Equally Jeremy Grantham, in his most recent GMO quarterly update, puts it another way “we are in the top 10% of historical price earnings ratio for the S&P on prior earnings and simultaneously are in the worst 10% of economic situations, arguably even the worst 1%!”
Compounding these structural concerns is the creeping usage of EBITDA, presumably pandering to the private equity playbook and $2trln dry powder. The coining of EBITDAC, earnings before interest, tax, depreciation, amortisation and Covid, is the tip of a growing iceberg as company management seem determined to steer the market away from focusing on actual cashflow or reported profit, even though EBITDA as a metric has been widely castigated since 2000 at least. Indeed analysing consensus on Bloomberg demonstrates this with the number of data points for consensus EBITDA consistently more than equivalent EBIT data points for the average stock in Europe.
The fragility of this EBITDA iceberg is that most of the time the focus is on “adjusted” EBITDA. The chart on the right shows the mentions of “adjusted” in its various guises across European companies’ earnings calls. FT Alphaville did a good post on the staggering level of adjustments some companies employ at the back at the end of last year highlighting a chart from US firm Zion Research which showed the average word count for the definition of EBITDA, at 90 US tech firms that file credit agreement documentation, was 700 words!
So as we sit on the cliff edge of fraud and financial manipulation how can investors try and prepare for a period of higher systemic risks in equity investing? While we have already written about the value of avoiding homophily and echo chambers in specific stocks this post is more about using machine intelligence and new technology to help investors get ahead of the risk curve.
Given investing is about buying and holding stocks, most of the current tools available are designed with the intention of helping find the right buying points for stocks based on momentum, underlying returns or technical factors. As Bethany McClean, author of The Smartest Guys in the Room and one of the journalists who helped uncover Enron’s fraud, says in a brilliant podcast “scepticism doesn’t travel in business”. A contrasting, and indeed more sceptical, approach would be to systematically try and find those companies with the most negative red flags and avoid them (see excerpt below from Dragonfly). While that might seem like a pessimistic way of investing it is somewhat reinforced by the fact the most successful investors have made the majority of their excess returns in bear markets.
Returning to the earlier theme of outright frauds, as the most extreme version of stocks with red flags, there are lessons to be learned from the best. Financial Shenanigans (on its Fifth edition) by Howard Schilit remains one of the foremost texts and screening for some of the more mainstream measures of accounting risk that Schilit highlights, like high receivables or high inventory, is relatively easy using Bloomberg tools.
Screening the myriad information hidden within the multiple footnotes and disclosures is where the real juice lies. Proprietary development of our own natural language tools allows us to be increasingly accurate with picking data out of tables or text and, for our Dragonfly technology, these [developments] have enabled us to start dissecting receivable ageing, provision releases and other elements of receivable movement like accruals and growth in “soft assets”. Unlike high level financial statements these details tend to be more heterogeneous as the two examples below attest — on the left the Dutch Akzo Nobel and on the right Germany’s Duerr.
This is just the start however as annual reports, most running to 150+ pages, contain a wealth of information waiting to be decoded: management remuneration targets and structure, customer concentration, accounting policies, risk factor scoring, subsidiary information etc etc…
Beyond annual reports the world becomes your oyster but some areas which might prove useful to screen are things like Glassdoor review scores and keywords, website traffic and quality, Linkedin departures, director histories, customer reviews…the list goes on.
There is also value in silence. As anyone who uses Tripadvisor as a tool to research a hotel or restaurant while on holiday will attest, one negative review among 100s of positives can actually be a distraction and lead you to make a poor decision. In many ways the same can be said about looking for red flags in stocks. Not only would you rather not be bombarded with information that isn't relevant — the restaurant only served fish from a fish hater — but also you probably don’t mind about the odd one or two — I was sat next to the loos which was unpleasant from the person drawing the table plan short straw.
Equally the same is true for stock screening. What is perhaps more important is when red flags compound one another, especially if interlinked, this can be incredibly revealing. For example imagine a stock has the following red flags:
- small number of audit meetings
- Low number of independent directors on audit committee
- recently changed auditor
- large number of accounting red flags incl DSO
- change in revenue recognition policy
- annual bonus linked to revenue and adjusted EBITDA
- EBITDA to operating cash conversion very low
Together these start to form a rather more worrying signal than each in isolation. Using machine intelligence we can make this signal register much more loudly for the potential investor (or avoider).
A structural increase in risk from a decade long bull market and loose capital allocation has been exacerbated by the way Covid has distorted markets and made management even more focused on adjustments. More than ever being alive to those risks and systematically looking for ways to protect investors ahead of time is critically important. We believe we are creating a unique tool that can provide that first level safety blanket. If you would like to understand more or arrange a 24hr trial of Dragonfly please get in touch.
