Category Archives: review

The Best Tools for Investors to Detect Potential Accounting Fraud

A lonely accountant uses advanced internet based analytical models to detect fraud at a publicly-traded firm.

What are the best tools investors can use to detect potential accounting fraud at the firms in which they might invest?

That's not an easy question to answer. That's because accounting fraud is almost invariably an inside job, where most investors are on the outside looking in. Most investors simply don't have the access to information about the transactions that constitute the bulk of accounting fraud where it exists.

But for publicly-traded firms, investors can access their financial statements. If the scope and scale of potential accounting fraud at a firm is big enough, the signs of it can show up in them. Accounting professionals have developed tools to help them identify known signs of fraud within financial statements.

That brings us back to the beginning. What are the best tools investors can use to find the signs of potential accounting fraud in financial statements?

A 2021 paper by Messod Beneish and Patrick Vorst, who evaluated seven fraud prediction models. They sought to identify the tools that could successfully identify firms where real accounting fraud may be occurring, without triggering too many costly false positives in the process. Here's the list of tools they evaluated, which are identified by their creator(s) and the year the tool was introduced:

  1. Beneish (1999) M-Score
  2. Cecchini et al. (2020)
  3. Dechow et al. (2011) F-Score
  4. Amiram et al. (2015) FSD Score
  5. Alawadhi et al. (2020)
  6. Bao et al. (2020)
  7. Chakrabarty et al. (2020) ABF Score

The M-Score was developed by Messod Beneish, so as the analysis goes, we should recognize that he has skin in the game for the evaluation.

Let's cut to the chase and go straight to the conclusion to find out which tools the authors evaluated came out on top in their analysis and why they did:

We compare seven fraud prediction models that have been proposed in prior research. We find that the higher true positive rates in recent models come at the cost of higher false positive rates and that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even when we consider extreme subsamples where a priori firms’ management has higher incentives and/or ability to misreport. We believe this could explain audit practitioners’ apparent reluctance to use these models, despite the fact that models have nearly doubled their success at identifying fraud when compared to the initial models in Beneish (1997, 1999).

For investors, M-Score and the F-Score when used at higher cut-offs are the only models providing a net benefit when applied to the sample as a whole. We conjecture this occurs because the M-Score and the F-Score exploit fundamental signals that have been shown to predict future earnings and returns, and the main component of investors’ false positive costs is the profit foregone (or the loss avoided) by not investing in a falsely flagged firm. In addition, we find that most models are economically viable if applied to top or bottom quintiles of characteristics of firms in which managers a priori have greater incentives and/or ability to misreport.

At this point, we'll point out that we also have skin in the game, which is why the conclusion of this paper attracted our attention. Political Calculations has tool based on the F-Score fraud detection model: Using the F-Score to Detect Accounting Fraud. Meanwhile, a tool based on Beneish's M-Score model is also freely available in both spreadsheet and online tool formats.

Aside from having built a tool based on one of these potential accounting fraud prediction models, we'll recommend using either or both. It's hard enough as an investor to do proper due diligence to choose which companies you might invest in. If the potential for fraud is a concern, it's worth the time and effort to use the most effective tools to either rule it in or out of your portfolio.

References

Messod D. Beneish and Patrick Vorst. The Cost of Fraud Prediction Errors. The Accounting Review. DOI: 10.2308/TAR-2020-0068. [SSRN Preprint]. 30 December 2021.

Image Credit: Stable Diffusion DreamStudio beta "A lonely accountant uses advanced internet based analytical models to detect fraud at a publicly-traded firm."

Maths on the Back of an Envelope

Cover of Maths on the Back of an Envelope by Rob Eastaway

How powerful was the first atomic blast? How many cats are there in the world? How much is China's coronavirus epidemic impacting its economy?

These are three very unrelated questions. If you were tasked with responding to them, how could you possibly come up with reasonable answers to any of the three?

Each of these questions are examples of what might be called "Fermi problems", which are named after Italian-American physicist Enrico Fermi. In addition to his pioneering work in nuclear physics, Fermi was also known for doing back-of-the-envelope calculations to come up with estimates that made real world sense even when nobody had any idea of what the right answers might be.

In Rob Eastaway's latest book, Maths on the Back of an Envelope (more affordably available in the UK), which discusses 'clever ways to (roughly) calculate anything', he tells the story of how Fermi estimated the force generated by the first-ever atomic bomb blast using confetti and a few quick calculations just after it happened:

The story goes that Fermi and others were sheltering from the explosion in a bunker about six miles from ground zero. When the bomb went off, Fermi waited until the wind from the explosion reached the bunker. He stood up and released some confetti from his hand, and when it had landed, he paced out how far the confetti had traveled. He then used that information to make an estimate of the strength of the explosion. We don't know for certain how Fermi did this, but it probably involved him estimating the wind speed and working out how much energy was required to push out a 'hemisphere' of air from the centre of the explosion.

Fermi's estimate of the bomb's strength was 10 kilotons. Later, more rigorous calculations revealed that the real strength had been nearer to 18 kilotons, in other words Fermi's answer was out by a factor of nearly two. Anyone submitting an answer that far out in a maths exam would probably get no marks, yet Fermi got huge credit for the accuracy of his back-of-envelope answer. The important thing was that his answer was in the right order of magnitude, and gave scientists a much better understanding of the potential impact of the weapon they were now dealing with.

At the time, the power of an atomic blast was such an unknown that other scientists working on the project had been concerned enough before the test to do some pretty sophisticated math just to provide the reassurance to themselves that the atomic bomb they built would be unlikely to set off a chain reaction that would ignite the Earth's atmosphere and destroy the planet. Getting the order of magnitude of the actual event correct under such uncertainty is what makes Fermi's quick math such a big deal.

So what does all that have to do with the other two questions?

It has everything to do with how you can take information you know and logically chain it together with reasonable guesswork so that you can use it to extrapolate an estimate for the answer you're trying to reach. In his book, Rob Eastaway describes how he once estimated the global population of cats while speaking to a school assembly (we've added the image as an illustration because, well, cats):

Jari Hytönen - Cats in a Basket, via Unsplash

Let's assume that most cats are domestic.

Some people have more than one cat, but usually, a household has only one cat, if any at all.

In the UK, and thinking of my own street as an example, it seems reasonable to suppose that there might be one cat in every five households.

And, if a household contains on average two people, that means there is one cat for every 10 people.

So, with 70 million people in the UK, let's say that there are, perhaps, seven million cats in the UK.

So far, so good. But what about the number of cats in the rest of the world? It seems unlikely that cats are as popular in countries like India or China as they are in the UK (although what would I know? Remember, this is purely guesswork on my part), therefore, I'd expect the ratio across the world to be smaller than it is in the UK - maybe one cat for every 20 people?

So, with eight billion people in the world, that suggests there are maybe:

8 billion ÷ 20 = 400 million cats

It doesn't seem an outrageous number.

A member of Eastaway's audience searched the question on Google and it returned 600 million as the answer, which Eastaway takes as a sign that the math he did was on the right track, or rather, that whoever came up with that larger estimate likely went about working up their estimate using similar methods.

And that brings us to China, which is where economist David Tufte recently did some back-of-the-envelope math to assess how hard China's economy has been hit by the COVID-19 coronavirus epidemic that has killed thousands and sickened tens of thousands in the nation.

This may be the best real time estimate yet on what COVID-19 has done to the Chinese economy. China’s power plants run mostly on coal. China’s coal consumption appears to be down between 20 and 45%.

Daily coal consumption around the Chinese new-year period at six generating companies reporting daily data, in 10,000 tonnes per day. X-axis shows days before and after Chinese new year eve, which falls on various dates in the second half of January or in February. Source: Analysis of data from WIND Information.

This is measured in days since the Chinese New Year, which fell on January 25 this year. So, they’re usually down for about 10 days after that, and this slowdown has stretched on for almost a month now.

To get that to GDP we need to know China’s energy elasticity. A plausible value for any country is around one, estimates from 15 years ago suggest 1.5 is more suitable for China. Here’s the back of the envelope calculation:

  • Choose a round number for China’s GDP like $20,000B/yr
  • Coal consumption is down 20% to 45%
  • The elasticity suggests a hit of 30% to 70% for GDP
  • That’s $6,000B/yr to $14,000B/yr if it’s a discrete jump. It isn’t, so looking at that typical slope showing recovery by about day 25 in most years, that slope suggests effects so far that are perhaps half of that as China built up to a sustained shortfall.
  • This shortfall is new and gradual, let’s say it’s about 1/25th of a year so far (about 2 weeks). That converts to a GDP loss of between $120B and $280B so far, or –0.6% to –1.4% of annual GDP in total.
  • China’s economy in 2020 is roughly the size of the U.S. economy in 2008-9. During the worst parts of that recession, the U.S. economy was off $20B in 2008 III, $85B in 2008 IV, and $45B in 2009 I.

All of these numbers are sketchy, but the suggest that the effect of COVID-19 on China over a few weeks is already comparable to what a large recession did to the U.S. in a few quarters.

Given China's role in global supply chains, both as producers and as consumers, that magnitude of economic impact will affect other national economies, bringing a global recession into view, which is contributing to why the world's stock markets are plunging.

It will be a long time before all the actual damage has been tallied, but until then, the kind of estimations we can do using back-of-the-envelope maths will give us the best indication of how things are going. It's also why we can recommend Maths on the Back of an Envelope as entertaining reading material that you can actually put to use to answer questions that, on first glance, may appear to be unanswerable.

Previously on Political Calculations

Image credit: unsplash-logoJari Hytönen

Black Friday Sale Calculator

Sale! - Source: Unsplash

Black Friday is another uniquely American tradition that arose as many Americans have the day off work following the national Thanksgiving holiday, which about a month before Christmas, provides an opportunity to shop for gifts for that upcoming holiday.

With so many shoppers hitting their stores, many retailers looking to claim a larger share of all the cash that will be spent on Black Friday will offer special discounts to their customers, which raises an obvious question. Are their special sale prices worth the hassle of shopping at their stores?

Hanna Pamula built a tool to find out, which Lifehacker profiled earlier this month:

It certainly feels like everything is on sale all the time these days. But not all sales are created equal. It’s especially important to recognize this during the holidays, when shopping holidays feature complex discount schemes that sometimes carry equally complex limitations.

If mental math isn’t your strong suit, check out the Black Friday Calculator. “A lot of these seemingly genuine sales are often math traps,” said Hanna Pamula, a Ph.D. student in Poland who built it for The Omni Calculator Project. She includes nine different discount scenarios —percent off, buy-one-get-one, and a discount for buying multiple items, to name a few—to help you determine whether a discount is worth your time and money.

We took the tool for a test drive, and so can you, since we've embedded it below. If you're accessing this article on a site that republishes our RSS news feed, please click through to our site to access a working version.

Black Friday Calculator

Overall, we would describe the tool as a fun personal finance application. Since it covers a number of different kinds of promotions that retailers offer, from the simple "X% off" sale to "Buy one, get Y% off a second one" to more complicated ("Get an additional W% off our Z%-off sale price") promotions, it will be useful in evaluating many of kinds of discounts you will inevitably encounter while shopping.

On that latter count, it can help you compare competing sale prices, letting you know which retailer's promotion delivers the biggest discount.

One thing we found was a bit of a hassle is the default setting for sales tax, which we would argue should be set to "No" rather than "Yes" for American shoppers, since most sale prices in the U.S. do not roll in the multiple levels of sales taxes that apply in the various jurisdictions that impose them across the nation. That's less a concern for other countries that impose national value-added taxes on sale transactions, but for Americans, it will add a couple of steps between data entry and results.

The Omni Calculator project is a site that features several hundred calculators with tools for doing math that applies across a wide variety of fields and applications. We've been in the online tool building business for a long time, where we're always happy to find new resources that online applications easier. If things slow down enough for you this Black Friday weekend, do check out their other tools!

Image credit: unsplash-logoArtem Beliaikin


Jack Bogle on Investing

John Bogle, the man who made passive, low-cost index investing a real world thing and who, as a result, built Vanguard into one of the world's largest investment management firms, passed away on 16 January 2019 at Age 89.

The idea of index investing that Jack Bogle championed proved to be very a big deal, which is why the index fund made Tim Harford's list of 50 Inventions That Made the Modern Economy (the UK edition is 50 Things That Made the Modern Economy), where we can strongly recommend the 10-minute podcast episode of the related BBC radio series if you want to learn more about its history.

If your available time is shorter than that, Jack Bogle once claimed that he could teach the essentials about investings in just a few minutes. In 2010, the Bogleheads' Ricardo Guerra put him to the challenge, where he distilled a lifetime of learning about successful investing into 3 minutes and 42 seconds....

Back in October 2006, we participated in a chapter-by-chapter review of the original edition of The Bogleheads' Guide to Investing (now in its second edition), where we had the honor of reviewing the final chapter of a book that sought to capture Jack Bogle's wisdom....

That was a lucky break for us, because the final chapter summarized all the lessons presented throughout the book, which gave us the opportunity to further condense Jack Bogle's thoughts on investing into just six lines, although with quite a few links to follow for deeper insights gleaned by the other participants in the project:

Today, millions of people are considerably richer than they might otherwise have been because of what Jack Bogle wrought. That's one hell of a legacy in the financial world!

Jack Bogle on Investing

John Bogle, the man who made passive, low-cost index investing a real world thing and who, as a result, built Vanguard into one of the world's largest investment management firms, passed away on 16 January 2019 at Age 89.

The idea of index investing that Jack Bogle championed proved to be very a big deal, which is why the index fund made Tim Harford's list of 50 Inventions That Made the Modern Economy (the UK edition is 50 Things That Made the Modern Economy), where we can strongly recommend the 10-minute podcast episode of the related BBC radio series if you want to learn more about its history.

If your available time is shorter than that, Jack Bogle once claimed that he could teach the essentials about investings in just a few minutes. In 2010, the Bogleheads' Ricardo Guerra put him to the challenge, where he distilled a lifetime of learning about successful investing into 3 minutes and 42 seconds....

Back in October 2006, we participated in a chapter-by-chapter review of the original edition of The Bogleheads' Guide to Investing (now in its second edition), where we had the honor of reviewing the final chapter of a book that sought to capture Jack Bogle's wisdom....

That was a lucky break for us, because the final chapter summarized all the lessons presented throughout the book, which gave us the opportunity to further condense Jack Bogle's thoughts on investing into just six lines, although with quite a few links to follow for deeper insights gleaned by the other participants in the project:

Today, millions of people are considerably richer than they might otherwise have been because of what Jack Bogle wrought. That's one hell of a legacy in the financial world!