Data : Primary markets : Pádraig Floyd

How to spot a bad IPO

Can data be used to sort the wheat from the chaff when a new listing is being sold? Pádraig Floyd reports.

The year 2019 showed so much promise, with high profile New York listings planned for Uber, Lyft and WeWork. In the end, Uber and Lyft both disappointed as share prices dropped below valuation levels, while concerns about transparency of earnings and conduct issues of senior executives resulted in WeWork pulling its initial public offering (IPO). The business had yet to post a profit and was expected to have burnt through all its funding by the end of 2020.

It was not only New York that suffered. Hong Kong saw the $10 billion flotation of Anheuser-Busch InBev’s Asian division pulled due to a perceived lack of interest. This was both the largest IPO of the year and also the largest to be pulled since 2011.

Some say it looked expensive, while others said the investment banks had failed to engage the cornerstone investors who commit to certain levels of share to give the IPO a sound foundation. A year before, Hong Kong had experienced its highest IPO numbers since 2010 when 125 companies raised $36.5 billion, yet the experience of investors in 2018 was that too many companies flopped after listing. Were these ‘failures’ due to poor data, excessive confidence, a combination of the two or just bad luck?

Know your limitations

IPOs may offer investors access to new opportunities, but there are risks. Investors lose money on IPOs after five years in more than 60% of cases, says Mark Hargraves, head of Framlington Global Equities, AXA Investment Managers.

“While being an early investor in a company or technology can offer some initial or short-term upside, not all opportunities will be profitable,” he says. “By waiting to see how well a company can translate innovation into a commercially viable business model and ultimately into profits, you can potentially separate the winners from the losers in any given area.”

Of course, the investment horizon will influence an investor’s position on an IPO. But where do you get the information and who should you believe?

The sell side is still falling over itself to provide research on companies, despite the effect of the revised Markets in Financial Instruments Directive (MiFID II) and the regular paring back of investment on research teams, which was down an average of 20% in 2019, according to data from Euro IRP, the trade body representing independent research companies. The regulatory influence, at least in Europe, and the lack of investment has many – particularly on the buy side – questioning the value of such material.

“They’re employing fewer people and those people tend to be less experienced, but they’re still wanting to cover the same number of stocks and financial instruments,” says Steve Kelly, a special adviser to EuroIRP. “So that spreads the resource thinner than before.”

Investors also have to guard against the danger of research containing bias due to conflicts of interest, says Kelly. Uber offers a good example of this, he says: “There were around 35 analysts covering the Uber IPO, and almost all of them had a buy recommendation. Many of those firms were connected with the IPO in early corporate finance fees and then the fell by almost a half.”

Taking the strain with technology

Portfolio managers have to understand the unique details of the positions they hold in their portfolios and ensure that the factor model they employ can capture such exposures.

While they must take care about relying too heavily on historical figures, no amount of data is going to help if the risk model they use is too simplistic to incorporate the different influences of price fluctuations.

IPOs are not mature stocks and behave differently. Sentiment and market structure are often the driving forces rather than regular market risk. Therefore, a manager’s factor model might assign less exposure to systematic factors and more exposure to idiosyncratic risk.

They must determine their exposure to long- and short-term risks and their ability to tolerate volatility within their portfolios.

Investment platforms offer insights into data and the growing application of artificial intelligence and machine learning is allowing investors to tap into big data or advanced analytics.

These allow PMs the chance of sense-checking their assessment of available data. However, there is growing awareness of the potential for algorithms to contain unintentional biases transplanted from the unconscious minds of those who calculate them or who programmed the system.

Nothing like the personal touch

Chris Bowie, a partner and portfolio manager at TwentyFour Asset Management is sceptical of some of the documentation. It will be true, he says, but the truth the sell-side analysts are looking to present (see box: Aston Martin). As far as Bowie is concerned, there is nothing as revealing as meeting the company and looking into the whites of the executives’ eyes.

“For me, the body language you get from people is as important as the actual numbers,” says Bowie. “My style is to try and ask questions that they won’t have developed prototypes for in their little huddles. And you can usually come up with something that makes them think.”

The search for alternative sources of data is also being applied to the fundamental task of visiting the corporates. But, says Kelly, instead seeing the CEO or CFO, who will be well-rehearsed in their narratives, there is more insight to be had from going directly to the source of knowledge.

“If you meet the guy who is running the factory in Brazil that 25% of the underlying growth is going to come from over the next two years, then that is the place to be doing a deep dive. This is increasingly what we are seeing asset managers doing within their own research teams.”

Everyone likes successful IPO companies, but few are comfortable with letting them run the show. While direct listings may save the IPO company a few quid in adviser fees, the claim that the processes used by Spotify and Slack can offer greater transparency and simplicity of transaction was not supported by those we spoke to. One PM said they were more nervous of direct listings.

“Whatever your view of investment banks,” he says. “They have a reputation to uphold and I think there’s much more opportunity for skullduggery through direct listings. Having an investment bank involved can also highlight any potential problems very quickly. It doesn’t make the IPO risk free, but it does help to weed out any potential elephant traps.”

Ultimately, IPOs, like any other investment have to satisfy risk management processes that exist in any investment house.

Everyone wants to believe they have found a unicorn, but due diligence is key, whatever the company. Because, while you hope it is a unicorn, if it looks like a horse and smells like a horse, it probably is one. ●

Driving down shareholder value

“There are times you can’t trust the numbers,” says one PM, providing the example of the Aston Martin IPO.

In February 2019, Aston Martin saw its shares nosedive when they announced it had made a £68 million loss in 2018 and the IPO had cost £136 million. After posting an £80 million loss in the six months to June 2019, by August, the share price was down around 75%.

“The numbers used a vastly over-inflated multiple, selling the story that they were like Ferrari, whereas it was achieved by stuffing
the sales channel with product. Dealerships haven’t been able to sell it, it has been heavily discounted and existing clients have seen massive falls in second hand values. This is an example of needing to get a specific valuation, pushing out lots of product and worrying about the consequences later.”

They’re not the only ones to sell the IPO and then come clean, says the PM, and of course they then miss their earnings estimates.

“I’m always very nervous of a record pickup in sales for the couple of years before an IPO,” he says. “You have to spend a lot of time in the meetings trying to determine if these are real sales or inventory.”


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