Why it’s time for the active fund industry to adapt or die

In the late 20th century and early 2000s, it’s fair to say that star fund managers ruled asset management.

The main challenge for investors was to find the right stock picker at the right time in their performance cycle, as it was virtually impossible to find a fund manager who could consistently outperform.

Since the start of this century, the star of the stars has started to fade, as tracking funds and exchange-traded funds (ETFs), which come with much lower fees, have proven to be more successful and have seen their assets under management skyrocket as they become more and more popular.

The active versus passive debate remains passionate to this day.

Adaptive fund management

But over the past 5-10 years, a threat to the entire active / passive duopoly has emerged, a threat that could see them both relegated to the dustbin of fund management history – potentially by the end of it. of the decade.

It is a new form of ‘adaptive’ fund management that leverages advanced data analysis and machine learning technology, and uses ‘predictive’ data strategies in its quest to outperform. the market, or “finding alpha” as those in the sector refer to it. to that.

Adaptive fund management is based on the fact that while humans can make the right choices (“these are” investment decisions), technology can make the right ones much more often because it is not emotionally sensitive. human but rather deeply empirical and data-driven.

In short, adaptive fund management relies on a massive amount of hard facts, not hunches and instincts.

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Data mining

The technology behind adaptive fund management explores masses of data on a scale never seen before and identifies buying or selling opportunities hidden in real time, or well ahead of traditional managers.

It ingests and analyzes billions of data points every second of every day across a multiplicity of traditional and alternative data sources, ranging from social media platforms and consumer spending trends to newswires and earnings reports. enterprises.

The bottom line is that today’s adaptive machine learning and predictive data technology can detect new trends and patterns at a speed and on a scale that no human can. It is not subject to any particular discipline such as growth or value, small, medium or large caps, domestic or foreign, or even a particular asset class.

With such a large set of opportunities, adaptive management can be much more responsive to changing market conditions and relative risk / reward scenarios. He can then reallocate funds on the spot, adjusting and changing as economies, markets and sentiment adjust and change.

It’s more responsive than a human could ever be and won’t follow the market down like a tracking fund does. It is passive only when it has to be, not because it has to be. It is unprecedented and offers investors unprecedented opportunities.

Pressure to act

For now, ‘adaptive’ fund management techniques are still used primarily by niche investment managers, especially well-established hedge funds that have the size, expertise, foresight and discipline to have invested in building this technological capacity.

But their ability to consistently find alpha and outperform the market is increasingly being noticed by the established fund management industry, which is facing a double whammy of asset exits and advisory fees. falling.

For now, ‘adaptive’ fund management techniques are still used primarily by niche managers (Photo: Shutterstock)

The pressure to act has recently increased with the proliferation of the discount broker and the execution-only investor, mostly millennials and Gen Xers. Oh, and let’s not forget the growing appeal of the newcomer to the block. , crypto.

More and more, traditional fund managers are incorporating adaptive technology into their portfolios, carving out slices of capital to test it in a way that doesn’t rock the boat. And so far, they like what they see. It is agile investment management, always active and always looking for alpha, whatever the market, from the most bullish to the most bearish.

Gradual transition

The transition from passive to active to adaptive won’t happen overnight, of course. There is still a significant training gap that needs to be closed, and not just for fund managers, but also for their often disillusioned investors who desperately want more consistent returns on their investments. The transition should be stable and strategic, not thoughtless and instinctive.

Likewise, adaptive managers always look for more reliable data sources that don’t just jump on buzzword trends, but add real value to the investment. Data means nothing if it isn’t proven to add value and drive returns consistently.

But what is now very clear is that, in the not-so-distant future, the role of the human fund manager will not be to ‘pick stocks’ but to select and mix strategies based on data which meet the risk profile and objectives of their funds.

Given the sheer volume of data available – several quintillion bytes of data are produced every day by some estimates – there is a lot of scope to outperform.

But given the speed at which machine learning technology is advancing, few will deny that the once-active fund manager is on borrowed time, even if they won’t say so yet.

It is time for the active fund management industry to adapt or die.

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