We were remiss in not explaining why there is no apparent interest cost for the zero coupon convertible bonds mentioned in the Vignette. In normal times when interest rates are above zero, such coupon bonds are issued at a discount. Under both US GAAP and IFRS, when bonds are issued at a discount, the discount is amortized over the life of the bond at the effective interest rate. Hence an interest expense is recognized each year for zero coupon bonds when they are issued at a discount.

For bonds issued at a discount under both US GAAP and IFRS, the book value of the bond is transferred to equity at the conversion date But there is no additional recognition of the loss incurred from issuing shares at less than the market price. Further, since the book value of the bond when it is converted depends on the amount of interest amortized in previous years, the accounting credits equity with the interest expense recognized in previous years, effectively eliminating the cumulative interest cost from the book value of equity: Again, borrowing without affecting shareholders’ equity.

The Vignette refers to bonds issued in an unusual year, 2021, when interest rates were close to zero and at times even negative. Hence, the bonds were issued at (close to) par with no discount. Therefore, under both the US GAAP and IFRS accounting described above, no interest expense would be recorded for zero coupon bonds issued at par (the effective interest rate is zero).

Technically, IFRS is a little different from US GAAP in its general treatment of the convertible component of a convertible bond. IFRS requires the separation of an equity component representing the conversion option.  (In rare cases US GAAP also requires the recognition of an equity component. This happens when there is a so-called beneficial conversion feature – or BCF – when the conversion price is less than the equity market price at issuance.) However, the equity component is only defined as the residual obtained after subtracting the liability (straight bond) component from the issue proceeds. When interest rates are zero, e.g., in 2021, the liability equals the proceeds and there is no residual to be recognized in equity due to the conversion option. Therefore, there would still be an off-balance sheet liability with no interest cost.

Altman Z-score:

Altman, E. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.  Journal of Finance 23: 589–609.

Olson O-score:

Ohlson, J.  1980.  Financial ratios and the probabilistic prediction of bankruptcy.  Journal of Accounting Research 19: 109–131.

A review of these and other models are in

Beaver, W., M. Correia, and M. McNichols. 2010. Financial statement analysis and the prediction of financial distress. Foundations and Trends in Accounting 5: 99-173.

See also a review of bankruptcy prediction models at

https://epublications.marquette.edu/cgi/viewcontent.cgi?article=1025&context=account_fac

Some models employ market price data for prediction with the insight that a firm’s equity can be viewed as a call option on its debt. See, for example, the KMV-Merton model at

• Firms can delay payments to generate cash flow
• Firms can sell receivables to generate cash flow
• Firms can reduce advertising expenditures to generate cash flow
• Firms can reduce R & D expenditures to generate cash flow
• Non-cash transactions are not in reported cash flows
• Structured financing can make borrowing look like cash from operations
• Capitalization policy shifts cash outflows from cash from operations to cash investment

You can see that the firm, while generating positive net income, generated negative cash flow from operations in 1999. Its operations are absorbing cash, not generating cash; it has potential liquidity problem. At the same time, it is liquidating a net $682 million in debt assets by $682 million to meet the shortfall. But this is treated as a reduction in cash investment in operations. That makes free cash flow lower than it should be, so seemingly operations are using less cash. In 1998 and 1997, it goes the other way.

In 2023, Meta Platforms (Facebook) spent $27.7 billion investing in its business but sold off debt assets for a net $3.2 billion to make the investments. The latter was included in cash investment to report a total of $24.5 billion rather than the $27.7 billion.

In its December 2024 quarter, Apple Inc. reported a positive number, $1,927 million for cash investment in operations, looking as if investing activities were generating cash rather than using it. However, the cash investment section of the cash flow statement included a net $4,603 million from liquidating debt assets. The actual cash expenditure in operations was $2,676 million. As these and other firms invest heavily AI, watch the cash investment number: They might be liquidating their big debt asset holdings to do so.

A factor model projects the expected return from holding a stock by multiplying the expected return for a set of “factors” by the stock’s sensitivity to that factor return and summing for all factors:

The ellipse (…) indicates their can be additional factors, 4, 5, 6, and so on. The factors are   usually seen as risk factors with the  the sensitivity of the given investment to that risk. For a factor model to be legitimate, Alpha is zero if the market is efficiently pricing factors or non-zero if mispricing risk. So a factor model is both a model to capture the risk of investing but also for identifying abnormal returns (alphas).

The Capital Asset Pricing Model (CAPM) is such a model (with only one factor):

where the sole Factor₁ is the expected return on the market. That model was shown to not work^2M“beta is dead”^2Mthus an attempt to identify additional factors in a “multifactor model.”

Now, what are these additional factors? There have been attempts to identify them with theory but the main approach has been empirical: Search in the data for measures that predict returns, then form factors with these measures. This is data mining with no theory or even explanation of why these are the factors we want. At last count, over 400 potential factors have been “discovered” by dredging data! When factors are “discovered,” there remains the additional task to get the expected return to the factors. The tricky matter of getting the expected return on the CAPM is compounded with additional factors. Then there remains the problem of estimating the betas on the factors.

The book has been quite skeptical about the CAPM, and your authors are more so with multifactor models. They appear to work well in explaining returns in the cross section. However, applying them out of sample (in real time) they don’t work and it’s in real time that the investor works. So, when you see an investment firm touting returns from factor investing, remain skeptical. They are probably working with doubtful models or doubtful estimates of factor returns. They may be reporting in-sample correlations or reporting real-time returns in periods when the strategy just got lucky Or, if the model happens to capture some aspect of risk, the returns are just reward for risk exposures.

The most ubiquitous model is the Fama and French model in its various forms. Here is an excerpt from an interview with Fama:

WALKER: In your opinion, what’s the current best asset pricing model? Is it the five-factor model?

FAMA: I don’t know. I wouldn’t claim that. It does well on the things it was designed to explain, both nationally and internationally. But there are contradictions of it. So it’s like every other model. There are things that it can’t explain. So I would say it explains the things it was designed to explain, and they’re really important. A lot of money is managed based on those things. But is it the best model? I hope not.

I would like to see… I don’t want more factors. I want less. I want simpler models that work, not more complicated models. So I’m still hoping that it’ll last—I will last—to the point where something good comes along that says, “I don’t need five; here are two that’ll do the trick.”

WALKER: Yeah. More parsimonious.

FAMA: Yeah, right, exactly.

WALKER: Because the models with more factors feel like you’re just overfitting to the data.

FAMA: Right. You’re just data dredging.

WALKER: Data dredging, yeah. Have you developed any theories behind any of the factors that you added to the CAPM?

FAMA: Well, so the three-factor model basically added a size factor, small stocks versus big stocks, and a value-growth factor, value versus growth being the second factor. And there was a little bit of intuition in those, in the sense that everybody would think that small stocks are more risky than big stocks. Everybody would kind of agree that value stocks tend to be poorly performing companies. Maybe the market requires higher expected returns for those.

But multifactor asset pricing requires something in people’s tastes that make them have negative attitudes that will persist. So if you tell me that after this discovery of these things, value factor, small stock factor, people pile into them because they’re really not concerned that the stocks are small or that they’re poorly performing companies, they only care about the expected return. Well, then I get a problem because I think that will erase it. I think that’ll nullify the model on its own.

The problem is you won’t know if that happened or not. So those models have not done as well in the last 15 to 20 years of data. But that’s a drop in the bucket as far as model testing goes. That’s the reality of it. You basically need a lifetime of data to test an asset pricing model

https://josephnoelwalker.com/eugene-fama-156/?ref=the-joe-walker-podcast-newsletter

For reasons why factor investing fails, see

Arnott, R., C. Harvey, V. Kalesnik, and J. Linnainmaa. 2019. Alices’s Adventures in Factorland: Three Blunders that Plague Factor Investing. Journal of Portfolio Management 45 (4), 18-36.

Recognizing anticipated losses is good conservative accounting. However, there is something else to watch out for: If and when the planes are delivered and revenues recognized, cost of goods sold will be lower and profit margins higher because production costs have been written off. Those margins will not be because of reduced production costs but because of the earlier write-off. In 2020, although production proceeded with costs incurred, Boeing reported that production costs in inventory were near zero because of the write-off. So the cost of planes produced that year will have zero cost of goods sold when sold.

Up to 2002, firms would book an estimate of the total cost of a restructuring as a liability obligation then reduce the liability as costs were incurred. As explained in chapter 10, that led to the abuse of overestimating the liability than bleeding back any estimated cost not incurred to boost income in the future⸺the big bath problem. FASB Statement 146 and IAS 37 checked this practice by requiring restructuring charges to be booked mainly as they were incurred. That, however, had the effect of losing the information in the estimated liability for restructuring charges to be recognized over several periods in the future. And initial charges recognized as incurred might not be one-time items, leaving open the question of whether there will be further charges subsequently.

A case in point is Procter & Gamble (PG). By 2001, the firm had booked restructuring charges as they were incurred over seven quarters, totaling $1.3 billion, with indications the total would later go to $4.0 billion. With so many consumer products succeeding or failing, P&G’s restructuring charges are likely to be recurring. So are they really unsustainable items? As of 2024, the company was conducting an annual restructuring program “to maintain a competitive cost structure,” with annual restructuring charges ranging from $250 to $500 million. For the fiscal quarter ending September 2024, a restructuring charge of $886 million was booked. Better to see these charges as repeating costs of doing business?

By 2025, Xerox had reported impairments of goodwill in three out of the four previous years.

Amazon did the same in 2022, reducing depreciation expense by $3.6 billion and increasing net income by $2.8 billion. In early 2024, it said it would again reduce depreciation by further extending useful lives, increasing operating income before tax by $3.1 billion for the 2024 year.

That was partly explained by higher export growth. However, in the last quarter of 2024, Chinese firms were fast tracking their shipments to the U.S. to avoid the anticipated high tariffs on Chinese imports that the expected next U.S. president, Donald Trump was promising for 2025. That is channel stuffing. Look for a decrease in Chinese income from exports in 2025 as the inventory buildup from the channel studying reduces the demand for Chinese goods (as well as from lower exports because of the tariffs). That will have a negative effect on Chinese GDP growth for 2025, forecast to be 5% once more.

Beneish, M. 1999. The Detection of Earnings Management. Financial Analysts Journal 55 (5), 24-36.

The following sites give for more detail on the score and its implementation:

https://www.affaridiborsa.com/articoli/123-m-score-8-indici-in-uno-per-scovare-le-frodi-contabili.html

https://en.wikipedia.org/wiki/Beneish_M-score