Tuesday, 15th October 2019 By Victor Chicha
Why smart analytics are now a must have

When many economists and analysts on Wall Street were predicting a recession for 2020 earlier last month, the economic outlook was rather bleak. Whilst the outlook has slightly improved since then – not least thanks to the Fed's rate cut – strong downside risks remain. Even if 30-year bonds were yielding 2.26% mid-October, the probability of a recession remains high.

Source: DailyFX.com

One of the options that investors have is to keep buying negative yield bonds, and there are many reasons for that, according to Tim Clift, Chief Investment Strategist at Envestnet.

The other option is to find issuers that offer positive yield pick up. But with the possibility of a recession, the risks are high. Many companies who currently offer a significant positive yield pick up may be downgraded during a recession, putting investors in challenging situations.

When we look at fixed income investments, there are two critical issues related to an economic downturn: 1) an issuer downgrade from Investment Grade to High Yield ("Falling Angels") and 2) an issuer default. 

From investment grade to high yield

As mentioned in a previous article, when a corporate gets downgraded from investment grade (IG) to high yield, it affects all the portfolio managers managing rating-based investment mandates (as those usually focus on the IG segment).

Such a downgrade forces these portfolio managers to sell their position within a couple of weeks, resulting in a high supply and often a drop in price. This is called fire sales.

These fire sales are bad news for portfolio managers who can lose millions within a couple of days. Therefore, the secret is to anticipate rating downgrades and to sell their positions before the downgrade happens.

From high yield to default

The second challenge concerns high yield portfolio managers who need to pay close attention to the risk of default. The major fear of high yield bond holders is the risk of bankruptcy. 

Let me put this in numbers with a very simplified example:

Think about a portfolio of 50 high yield bonds (let’s assume $1,000 bonds with a yield of 5%). With no default, a portfolio would have a 5% performance by the end of the year.

Now, let’s assume that one of these issuers defaults and the bond holder can only recover 30% of the face value. In that case, instead of a total portfolio performance of 5%, the portfolio would have a performance of 3,5%. Defaults in fixed income portfolios have drastic impacts on its performance. 

This explanation shows why high yield portfolio managers must have a close eye on the risk of default. In fixed income, the risk profile is asymmetric. And in the current environment, this asymmetry is growing.

When technology changes the game

How can we anticipate a downgrade or even default? Which data or information must we consider?

Of course, financial reports and news can bring a lot of valuable information, but in today’s data overload it can be difficult to find the right signals. Usually analysts can only follow 10 to 50 issuers at the same time even though they should follow hundreds in order to be more competitive in their picking.

There are smart solutions though: big data analysis and interpretation is now one of the most common applications of artificial intelligence, called smart analytics. In the financial industry, hedge funds are one step ahead compared to the rest of the industry when it comes to technology adoption.

“Use of AI is playing out across a wide spectrum of investment managers from pure AI-driven specialists, to large quant-driven shops, to traditional fundamental investors looking for an edge” says Peter Salvage in his article about AI in hedge funds. And this type of technology is spreading in the industry.

Machine Learning (ML) allows portfolio managers to boost their analysis and to set up intelligent alerts to be more efficient. Of course, this is already their day to day job, but the technology will help them to augment their capacity to cover a wider range of issuers. In fact, technology can have a hugely positive impact on the analyst’s work and Machine Learning can help to distinguish between good and bad issuers in a blink.

Whilst equity analysts are well ahead in the use of ML tools to analyze market data, it seems that offerings for credit analysts are lagging behind and smart analysis tools have not been as widely adopted.

However, in the current low yield environment and in light of margin cost pressure, fixed income fund managers only have two strategies to remain competitive: cut the costs by reducing the size of their analyst team, or invest in new technologies to increase the potential of generating alpha. The race for AI tools has started and the question to ask yourself is: do you want to be an early adopter or a follower?