Who misbehaved in the previous crisis?

Emma, a software engineer with 17 years of experience, takes her investment portfolio very seriously.

Her portfolio is dominated by sectors she believes hold immense future potential, including biotech, clean energy, and technology. Recognizing the volatility of these themes and their potential for significant drawdowns, she balances these investments with other less risky ones.

Sitting at the end of 2021, seeing the myriad of funds launching in the wake of the COVID meltdown of March 2020, a question comes to Emma’s mind : how would these funds have performed during the crash had they existed? How can she know? With asset prices surging, the perceived risk of these new funds seemed low. And yet they are risky.

Emma recalls the financial crisis of 08. Just two years into her career then, her portfolio was far smaller than its current size. Emma mused that any financials fund launched after 2008 would surely have had a severe drawdown, similar to those funds that did exist back then and traded the same assets.

Risk and loss

Emma is no stranger to financial risks. To her, risk means “potential for large loss”. With her financial future riding on these decisions, she wants to balance the overall risk of her portfolio and won’t assume a fund is low risk just because it hasn't yet shown its colors. 

A relatively new fund, “GINN”, focused on tech innovation pops up on her radar. Examining its one-year performance, she observes a 30% growth. The data indicate the fund has a 16% volatility and a 12% drawdown. But Emma knows that the tech sector is much more risky than this and wants more information.

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Though GINN's track record is short, Emma identifies QQEW, a long-standing, equally weighted tech fund to benchmark against and provide insights into how GINN might have performed historically. After all, some historical context is always better than flying blind.

Plotting GINN alongside QQEW, things look clearer, but no surprises. Both funds display similar volatility during their overlapping periods. Yet, this volatility is considerably lower than QQEW's overall historical volatility and that of the broader tech sector. More importantly, Emma recalls instances when tech investments plummeted by up to 80%. So clearly, risk metrics over the overlapping brief period do not reflect the actual risk potential. Is she to treat this new fund as having had a 12% drawdown or an 80% drawdown when thinking about the potential for loss? The future will not look like the past, but it holds valuable insights. Emma wants to incorporate some of these insights so that her allocation strategy is grounded in realistic expectations.

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Time consuming analysis

Fast forward to 2023. Emma plots the two funds, including two years of QQEW before GINN was introduced. The unfolding paths, impossible to predict, didn’t surprise her. She is now happy she didn’t buy GINN in 2021, as it would have added to her already tech heavy allocation : the earlier comparison with QQEW told her this. 

Furthermore, Emma knows that QQEW may not even be the best fund to compare GINN against. Her engineering background tells her that more sophisticated methods she can apply. She has the technical ability, but is this a good use of her time? Is the individual investor expected to do this themselves? Are they to spend time interpreting such analysis and incorporating it into their decisions? Even without doing the rigorous analysis, investors often caution about certain sectors being risky, indicating that, intuitively, they're already thinking like this. This shows the importance of these analyses in portfolio construction.

Despite all that, where are there tools that automatically undertake these evaluations and present the conclusions in a digestible and actionable format for the users?

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How far back?

Emma finds another fund, ANEFX, plotted along the overlapping history with GINN. It has mirrored GINN’s performance over its lifetime. It is not a big surprise the data are close because ANEFX also seeks growth through investing in innovation. The fund ANEFX though was incepted in 1983, which means it has much more history to look at. Recognizing the importance of backwards risk assessment, Emma wonders: how far back should her analysis span?

Emma knows she cannot assume that because they are so similar over the period where GINN existed that it is a perfect proxy for how GINN might have behaved. The goal is not to penalize all shorter dated funds, after all, they have to start sometime. But with her money at stake, she feels it a wise idea to take a peek at how a fund with a clearly strongly related theme, which exhibited such a close performance, did in the past. Whether it is over crises like 08, the dot-com tech bubble, and further back, or how things looked through one, two and three cycles back – even if we are in a new era today and she certainly doesn’t want to incorporate all that information when assessing a newer fund.

None of this deters Emma from investing, it simply helps her be comfortable with her allocations to a point she feels in control. It also gave her an appreciation for those funds who have visible scars from having been around a long time, but who have done well for their investors over long periods.

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Help is on hand

Few investors have the time or inclination to delve deep as Emma did, let alone deploy advanced methods. With DAYOSS, there's no need. The platform integrates risk extrapolation by default and seamlessly throughout its system. Risk extrapolation is embedded in all risk analysis across the entire DAYOSS system. When you encounter a risk number, rest assured by knowing it has accounted for the past in a fair and scientifically accurate way. No more complicated outputs or analysis. Instead, just one holistic number. Save time by ensuring that when evaluating performance and associated risk metrics, your preferences for historical depth and the balance between overall versus recent data are also taken into consideration.