How Might Quantum in Securities Actions Be Affected by the COVID-19 Pandemic?

"Only when the tide goes out do you discover who's been swimming naked." These wise words from Warren Buffett, the Sage of Omaha, could hardly be more relevant than at the present time, with business activity across most sectors and regions decimated by the effects of the COVID-19 pandemic.

Companies that have been pushing on the boundaries of acceptable reporting - revenue or profit recognition, valuations of illiquid assets, for example - in the hope that strong growth or a supportive market would justify their approach may now find themselves in a position where what was considered acceptable previously will now be exposed as inappropriate. Corrective disclosures, reputational damage and, almost inevitably, securities litigation are sure to follow.

But how should one separate the signal, the damages due to such a corrective disclosure, from the noise, the overall impact to a company's business from the pandemic, when it is arguable that the noise is so great that the signal is imperceptible?

The standard approach to assessing damages from a corrective disclosure in the United States is an event study, a statistical analysis of how a stock's price responded to a market announcement - the event in question. In the current circumstances, there are some concerns about how appropriate this approach is. Alternative approaches should be considered, potentially as the basis for assessing damages, at a minimum as a cross-check and validation.

Everything but the kitchen sink

As Winston Churchill said following World War II, "Never let a good crisis go to waste." Given the scale of bad news that companies are likely to be announcing regardless, it would be surprising if many did not also take the opportunity to review balance sheet valuations, provisions, bad debts and profit recognition - every possible area in order to reset to a solid foundation on which to build their recovery as the pandemic abates.

If a corrective disclosure has to be made, combining it with other bad news may reduce its impact on a company's share price while, at the same time, making it harder (if not impossible) to reliably isolate the effect of the corrective disclosure from the other news, referred to as "confounding events" in this context.

Crash!

Some corrective disclosures are so deep-rooted or complex in nature that the market cannot possibly assess their implications on company value in a matter of a few hours or days. A sharp fall in a company's share price immediately after a disclosure is often followed by a gradual recovery over the subsequent weeks as the market fully digests the implications of the disclosure.

Much has been written about the need to separate the price drop directly caused by the disclosed actions of the company on its the fundamental, underlying value from the excess price drop, the crash component or market over-reaction which combines to make up these sharp price declines.

With stock markets at levels seemingly at odds with the current economic fundamentals, market over-reaction to corrective disclosures may be significantly greater than in more tranquil, pre-pandemic times.

Somebody saw that coming

How often do you see press reports that something doesn't add up with a company's figures, or a contrarian analyst calling out potential issues, or a short seller who goes public with concerns, or a whistleblower who is dismissed as a malcontent, months - even years - before the company makes a corrective disclosure? Inevitably, all that noise draws the attention of professional investors who start to look closer and draw their own conclusions. The net effect is likely that, even prior to the corrective disclosure, the company's stock has underperformed in response to this background noise, rather to any specific event. When a corrective disclosure follows, confirming the issues previously flagged, it is clearly arguable that an investor who purchased prior to the unconfirmed reports emerging may have been harmed by price underperformance prior to the corrective disclosure. However, an approach to quantum-based on an event study will struggle to capture this underperformance given the absence of any specific event.

A more fundamental approach

There is no question that an event study is a useful and powerful tool for analysing the effect of corrective disclosures. However, in these strange times, there is a strong argument for looking to fundamental analysis of a company's long term value to disentangle the signal of a corrective disclosure from the background noise from the pandemic.

Two fundamental valuation approaches, the income approach - discounted cash flow modelling - and the market approach - comparison of financial ratios against peer companies - are widely used in company valuations for mergers and acquisitions. In this context, the potential acquirer is typically interested in the long term value which can be realised from the company being targeted for acquisition, its fundamental value, to determine whether the market price which would have to be paid makes the acquisition attractive.

In the context of a corrective disclosure, the same fundamental valuation approaches could be applied to the relevant modelling inputs for the actual, corrected post-disclosure scenario and the hypothetical, uncorrected pre-disclosure scenario to assess a range for the effect of the disclosure on the fundamental value of the company, and thus the damages incurred by shareholders caused by the company's actions.

In post-M&A disputes relating to the valuation of unlisted companies, fundamental analysis using the income and/or market approaches is the default method for assessing damages because there is no publically quoted and traded market for the shares, so no market "opinion" on the value of damages from a corrective disclosure. Where there is a public market for shares, there may be an argument in favour of using the share price as a guide to damages, to accepting the wisdom of the crowd - but there is also an argument against. As the Sage of Omaha also said, "You are neither right nor wrong because the crowd disagrees with you. You are right because your data and reasoning are right."