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The alternative measure yielded a relatively modest price-to-earnings ratio of , rather than the mind-boggling 1, This suggests that unofficial measures may be a better representation of earnings. The danger, however, is that alternative measures are usually idiosyncratic. Investors and analysts should continue to exercise great caution in interpreting unofficial earnings measures and should look closely at corporate explanations that might depend on the use or abuse of managerial judgment.

Some 25 years ago, before the rise of the internet, corporate financial statements relied on the former, which has the important virtue of being easily verifiable. Today, however, companies use fair value for a growing number of asset classes in the hope that an examination of balance sheets will yield a truer picture of current economic reality.

As the financial crisis took hold in , a myriad of adjustments to the methods of applying fair value were adopted by the U. The goal was to guide auditors on how to verify fair value, but the result has been more confusion, not less. The measurement process has proved difficult, often highly subjective, and controversial.

Consider the accounting treatment of Greek bonds by European banks in , during one of a seemingly endless stream of crises involving government debt in Greece. On that basis, RBS noted that market prices had dipped by just over half the price paid for those bonds when they were issued. If such difficulties arise with tradable securities, imagine how difficult it is to apply fair value principles consistently to intangibles such as goodwill, patents, earn-out agreements, and research and development projects.

Making matters worse, disclosures about how intangible assets are valued must offer only basic information about the assumptions that generated the estimates. If these reports included full disclosure of the assumptions behind fair value estimates—were such a thing even possible—the length of reports would be overwhelming.

Managers may, for instance, choose to overprovision—that is, deliberately overstate expenses or losses, such as bad debts or restructuring costs—to create a hidden cookie-jar reserve that can be released in future periods to artificially inflate profits. Or a company might underprovision, deliberately delaying the recognition of an expense or a loss in the current year. In that case, profit is borrowed from future periods to boost profit in the present.

Recent changes in GAAP and IFRS rules have made such activities less egregious than they once were, although overprovisioning will most likely always be with us. Managers want the accounting flexibility that comes from having hidden reserves, and external auditors will let them get away with it within limits because companies are unlikely to be sued for understating profits.

Managers goose the numbers by manipulating operations, not reports. A study published in the Journal of Accounting and Economics surveyed more than senior executives on how their companies managed reported earnings. The researchers asked the executives to imagine a scenario in which their company was on track to miss its earnings target for the quarter.

Within the constraints of GAAP, what choices might they make to reach the target? The study revealed that managers tend to manipulate results not by how they report performance but by how they time their operating decisions. Managers also goose the numbers by manipulating production. If a company has substantial excess capacity, for instance, mangers can choose to ramp up output, allowing fixed manufacturing costs to be spread over more units of output.

The result is a reduction in unit cost and, therefore, lower costs of sales and higher profits. But this practice also leads to high finished-goods inventories, imposing a heavy burden on a company in return for that short-term improvement in margins, as one study of the automobile industry shows. When huge numbers of unsold cars sit on lots for extended periods, bad and costly things can happen to them: Windshields and tires may crack, wipers break, batteries wear down, and so on.

So investors and directors will have to demand more disclosure on those operating decisions that are most susceptible to manipulation in order to determine whether they are being made for sound business reasons or to artificially boost financial results.

Of course, that will create practical problems in terms of the sheer volume of information being reported and will still involve hard-to-verify assumptions. In fact, regulatory requirements that produce ever more lengthy reports may be an exercise in diminishing returns.

What we need, perhaps, are smarter approaches to analyzing the data available. The good news is that new techniques are increasingly being applied by analysts and investors. The law has been around for a long time, but only recently has it been applied in accounting and in the financial sector: Insurance companies have started using it to detect false claims, the IRS to detect tax fraud, and the Big 4 accounting firms to detect accounting irregularities. In fact, the distribution holds even if the figures are converted from one currency to another.

If an unusually high number of first digits in the accounting data are 7s, 8s, or 9s, it may indicate a conscious effort by managers to finesse the numbers to achieve desired financial results. Verbal cues. Another tool for detecting unscrupulous practices has emerged from the research of two accounting academics who analyzed the transcripts of nearly 30, conference calls by U.

For example, in companies that were later forced by the SEC to make major restatements of key financials, deceptive bosses displayed the following patterns: They referred to shareholder value relatively seldom perhaps to minimize the risk of a lawsuit. They used obscenities more frequently. Of course, the problem is that managers who intend to deceive can be taught to avoid those markers. But in the meantime, verbal cues can be a useful tool for board members and other interested parties to ferret out dishonest practices.

The first years. In order for financial statements to fulfill their important social and economic function, they must reveal the underlying economic truth of a business. To the extent that they deviate from that truth, scarce capital will continue to be misallocated and wealth—and jobs—will be destroyed. Of course, we will never reach a world in which all reports are perfectly and reliably true, but an understanding of their shortcomings and the availability of new tools to detect manipulation can help us continue to strive for that ideal.

As companies increasingly use the timing of operating decisions to artificially boost performance numbers—a practice that is harder to detect and regulate—vigilance becomes vital. A version of this article appeared in the July—August issue pp. Read more on Accounting or related topics Finance and investing and Financial statements H.

David Sherman h. Being the first stablecoin, its growth has benefited from a user base built up early on, which has attracted new adopters seeking ease of trading network externalities. The mechanism for assuring a stable value varies across different designs. In the case of CeFi stablecoins, a designated intermediary manages issuance and redemption as well as the reserve assets backing the stablecoins.

Some of these assets are bank deposits or their close substitutes. Other assets may comprise short-term securities — such as Treasury bills, certificates of deposit and commercial paper — as well as cryptoassets themselves. To the extent that DeFi relies on such stablecoins, it remains dependent on CeFi and traditional finance. DeFi stablecoins record all transacting histories directly on-chain, without the involvement of centralised intermediaries.

They rely on an overcollateralised pool of cryptoassets, ie the underlying assets are worth more than the stablecoins in circulation. Since crypto collateral has a very high price volatility, as measured in the reference fiat currency, DeFi stablecoins incentivise users to actively monitor the collateralisation ratio. They do so by adjusting the supply of stablecoins to match their demand. So far, no purely algorithmic stablecoin has been widely adopted.

In sum, stablecoin issuers receive assets collateral in exchange for their own liabilities stablecoins. While this mechanism looks superficially similar to how banks operate, there are fundamental differences. Issuers lack public backstops, such as deposit insurance, and rely on private backstops collateral to ensure that stablecoins maintain a steady value and are suitable as mediums of exchange. As such, the expansion of the balance sheets of stablecoin issuers, at least currently, is driven more by the appetite of investors to hold the stablecoins than by any desire of the issuers to acquire more assets.

In other words, this growth is liability-driven, while the expansion of bank balance sheets is commonly asset-driven McLeay et al The former are structured around the same principles as their conventional counterparts. CEXs maintain off-chain records of outstanding orders posted by traders — known as limit order books.

By contrast, DEXs work in substantially different ways, by matching the counterparties in a transaction through so-called automated market-maker AMM protocols. AMMs follow mathematical formulas to determine prices based on transaction volumes. Box A discusses how AMMs incentivise liquidity provision; it also looks at their susceptibility to market manipulation.

In addition, trading on DEXs incurs execution costs when transactions are validated on the blockchain. These stem from so-called gas fees, which are designed to compensate validators. Gas fees increased markedly as cryptoassets gained popularity and blockchains such as Ethereum became more congested compare left- and right-hand panels. Although transaction costs are higher in DEXs, some traders still prefer these platforms, in part due to their greater anonymity and interoperability with other DeFi applications the so-called "DeFi Lego".

The reason is similar to that underpinning the overcollateralisation of DeFi stablecoins — the inherent lack of trust in anonymous transactions, together with the high volatility of the cryptoassets used as collateral. To protect the lender, loans can be automatically liquidated when the collateralisation ratio falls below a threshold. At present, the need for crypto collateral stands in the way of lending to households and businesses, eg for house purchases or productive investment.

Rudimentary forms of unsecured lending, known as "credit delegation", are available on some platforms. This often involves entities with established off-blockchain relationships, making collateral unnecessary. DeFi lending platforms also offer a unique financial instrument, typically referred to as flash loans. These allow arbitrageurs to act without their own capital by taking out a loan for the entire arbitrage trade and then repaying the loan.

Such loans are of zero duration and are essentially risk-free requiring no collateral , as they are granted only if the arbitrage trade ensures the repayment of both principal and interest. Crucially, this is possible as all legs of the transaction can be attached to the same block ie settled simultaneously on the blockchain. The growth of DeFi lending platforms has also encouraged the development of applications similar to investment funds in traditional finance.

These decentralised portfolios follow pre-determined investing strategies, eg aggregating funds from investors and automatically shifting them across crypto lending platforms to profit from the best yields. The "decentralisation illusion" in DeFi DeFi purports to be decentralised.

This is the case for both blockchains and the applications they support, which are designed to run autonomously — to the extent that outcomes cannot be altered, even if erroneous. A key tenet of economic analysis is that enterprises are unable to devise contracts that cover all possible eventualities, eg in terms of interactions with staff or suppliers. Centralisation allows firms to deal with this "contract incompleteness" Coase and Grossman and Hart In DeFi, the equivalent concept is "algorithm incompleteness", whereby it is impossible to write code spelling out what actions to take in all contingencies.

This first-principles argument has crucial practical implications. All DeFi platforms have central governance frameworks outlining how to set strategic and operational priorities, eg as regards new business lines. This element of centralisation can serve as the basis for recognising DeFi platforms as legal entities similar to corporations. While legal systems are in the early stages of adapting, decentralised autonomous organisations DAOs , which govern many DeFi applications, have been allowed to register as limited liability companies in the US state of Wyoming since mid In addition, certain features of DeFi blockchains favour the concentration of decision power in the hands of large coin-holders.

Transaction validators need to receive compensation that is sufficient to incentivise them to participate without committing fraud. Blockchains based on proof-of-stake, which are expected to improve scalability, allow validators to stake more of their coins so that they have a higher chance of "winning" the next block and receiving compensation. Since the associated operational costs are mostly fixed, this setup naturally leads to concentration Auer et al Concentration can facilitate collusion and limit blockchain viability.

It raises the risk that a small number of large validators can gain enough power to alter the blockchain for financial gain. Furthermore, large validators could congest the blockchain with artificial trades between their own wallets "wash trades" , steeply raising the fees that other traders pay them. Another concern is that validators can front-run large orders for higher trading profits see Box A. Although front-running also occurs in traditional finance, it incurs punitive measures from regulators.

These rent-seeking behaviours are detrimental to investors and may erode DeFi's appeal going forward. Discussions about changes to governance protocols, in particular to rein in collusion, have gained momentum in the DeFi community.

Vulnerabilities and spillover channels While DeFi is still at a nascent stage, it offers services that are similar to those provided by traditional finance and suffers from familiar vulnerabilities. The basic mechanisms giving rise to these vulnerabilities — leverage, liquidity mismatches and their interaction through profit-seeking and risk-management practices — are all well known from the established financial system.

Some features of DeFi could make them particularly destabilising, though. In this section, we focus first on the role of leverage and run-risk in stablecoins due to liquidity mismatches, before covering spillover channels to the conventional intermediaries. Leverage DeFi is characterised by the high leverage that can be sourced from lending and trading platforms.

While loans are typically overcollateralised, funds borrowed in one instance can be re-used to serve as collateral in other transactions, allowing investors to build increasingly large exposure for a given amount of collateral. Derivatives trading on DEXs also involves leverage, as the agreed payments take place only in the future.

The maximum permitted margin in DEXs is higher than in regulated exchanges in the established financial system Graph 4 , left-hand panel. And unregulated crypto CEXs allow even higher leverage. High leverage in crypto markets exacerbates procyclicality. Leverage allows more assets to be purchased for a given amount of initial capital deployed. But when debt eventually needs to be reduced, eg because of investment losses or depreciating collateral, investors are forced to shed assets, putting further downward pressure on prices.

Such procyclicality can be amplified by the trading behaviour typical of markets at an early stage of development — eg the outsized role of momentum trading, which can add to price swings. In addition, the built-in interconnectedness among DeFi applications can also amplify distress, since the system's stability hinges on the weakest links.

Financial intermediation in DeFi relies exclusively on private backstops, ie collateral, to mitigate risk and enable transactions when participants cannot trust each other. Thus, there are no shock absorbers in DeFi that can cut in during stress periods. By contrast, in traditional finance, banks are elastic nodes that can expand their balance sheets extending loans or purchasing distressed assets via the issuance of bank deposits, which are a widely accepted medium of exchange.

The ability of banks to do so rests on their access to the central bank balance sheet Borio The destabilising role of leverage came to the fore in the latest cryptoasset crash in September Forced liquidations of derivatives positions and loans on DeFi platforms accompanied sharp price falls and spikes in volatility Graph 4 , centre and right-hand panels. Liquidity mismatches and run-risk in stablecoins Stablecoins are inherently fragile.

They are designed to target a fixed face value using various types of reserve asset. This arrangement gives rise to mismatches between the risk profiles of these assets the underlying collateral and the stablecoin liabilities.

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