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How Fund Managers Are Changing The Way They Look At Investments

The Coronavirus crisis has had a dramatic impact on the fund management sector. Businesses have had to adapt to the implications of social distancing and remote working by equipping analysts and fund managers to operate from home. But the crisis has also produced another challenge. Faced with volatile returns through 2020, managers have had to rethink how they assess risk too, in a bid to boost their resilience against future market drops. So how exactly are managers adapting?

Change of plan

The national lockdowns enforced by most countries due to the pandemic caused an almost immediate switch in the traditional and hedge fund sectors to different ways of managing portfolios and interacting with investors. Face-to-face meetings and company visits, traditionally a crucial part of the investment process, were no longer possible or at least severely restricted. Instead, managers have had to switch to digital ways of working to analyse and select investments, as well as to speak to and retain investors.

Inside their organisations, investment and trading teams had to relinquish the familiarity, immediacy of interaction and collegial atmosphere of their offices and trading floors. Instead, they’ve had to embrace working and trading in the quiet, and at times solitude, of their homes. Trading and operations systems had to be re-routed and adapted very quickly as the stay-at-home guidance arose at a time of the highest market volatility in February and March 2020. Funds and organisations which already had implemented remote and digital ways of working were naturally far better placed to adapt to the pandemic, and the transition for many was seamless. Cloud-based technology at many banks and fund houses made home working far easier.

Nowadays, most markets trade electronically: once again, technology did not disappoint. Trade routing and back-office systems that had only experienced wide price variations on paper coped very well with the real thing. Contrary to popular wisdom, often in the form of entertaining fiction presented as facts such as Michael Lewis’s Flash Boys and Robert Harris’s Fear Index, algorithmic traders did not vanish at the first hint of volatility: indeed, machine-driven, algorithmic trading proved its robustness and the technology made new friends, even amongst the most staunchly traditional managers.

The success of remote working processes could see many traditional and hedge fund businesses adopt hybrid business models for the long-term that would have been difficult to imagine on a meaningful scale only a few months ago. Smaller funds may permanently move away from the office and commit to working remotely. Client relations flow has moved online and, while new business acquisition may still require a degree of face-to-face interaction, online client video calls are here to stay.

Flawed approach

While managers have had to adapt to these immediate implications of the virus, COVID-19 will also have a long-term impact on how the sector operates.  

Recent years have seen a move towards traditional “quant” models to assess and manage portfolio and trading risk across the investment industry. Once the preserve of systematic managers, these models are now ubiquitous: they identify and represent risk’s “common factors” on the basis of reassuringly “normal” statistical distributions.

These traditional models are built around strong assumptions on the behaviour of underlying assets and measuring patterns on linear scales, making them effective during times of relative stability. However, their flaws were exposed when they were hit by record levels of market volatility bought on by the pandemic. As a result, many fund managers had difficulty deciding how to navigate their way through the crisis, manage risk effectively and make sure investments were protected.

While measuring and managing risk quantitatively is a meaningful step ahead from ad-hoc arrangements such as “stop losses”, too many users relying on the same types of quant models may truly be “too much of a good thing”, as many will herd for the same exit door during volatile times by liquidating similar, model-driven, “diversifying” positions. This is a lesson that “quants” themselves had learnt the hard way back in the summer of 2007.

The next step

With traditional risk models struggling to deliver during the pandemic, COVID-19’s long-term implications could see fund managers increasingly turning to innovation and evolution. While not alone, AI-powered models for investment and risk management are certainly in the lead pack of the next digital evolution. Rather than relying on linear patterns, advanced AI systems are completely agnostic, simply trained to model what is in the data with no theory behind it. When the structure in the data shows an anomaly, the AI is trained to raise the alarm about the possibility of an upcoming unpredictable event, giving fund managers more time to prepare and adapt.

While recent events confirm Nobel Laureate Niels Bohr’s old adage that “Prediction is very difficult, especially if it's about the future”, AI-based systems can offer a finer mesh through which to filter investment and risk data, and a richer and ultimately more robust framework for investment and risk modelling.

Some evidence of this may already be in the results of funds that had already integrated AI-led processes into their businesses. According to a recent industry article, AI-led hedge funds delivered cumulative returns of 34%, compared to a 12% gain for the global hedge fund industry between May 2017 – May 2020[1]. For these reasons, we confidently expect to see the adoption of these tools increase significantly next year as fund managers look to better prepare themselves against future risks. In fact, according to a recent survey by Greenwich Associates more than half (56%) of hedge funds stating they would be using machine learning in their trading process by 2021[2].

Yet, it’s important that funds choose the right AI-powered tools for their business to get the best results in the long-term. Out of different types of leading AI technology, “deep learning” is the one that fund managers could gain the most from. Through deep learning, they can capture complex, non-linear patterns in asset behaviour and allow algorithms to adapt to changing market conditions continuously. Implementing deep learning could materially help in significantly reducing concerns about whether a model could cope with unusual and very volatile data during times of market uncertainty.

Changing times

It’s clear the COVID-19 pandemic has had a profound impact on the hedge fund sector and will continue to do so in the coming years. The switch to digital has accelerated for many hedge funds, with the lockdown period forcing managers to implement remote working processes to maintain the day-to-day running of the business.

The next step for many hedge funds will be to embrace AI-powered tools such as deep learning to continue their digital transformation. AI can offer increased analytical and predictive capabilities when it comes to market returns and risks, helping to boost their resilience against future market drops. With this technology already proving its ability to cope with the pandemic’s impact, this may well be one impact of COVID-19 that really will affect the way our sector works for the long-term.

Giovanni Beliossi is an Advisor at Axyon AI

[1] https://www.institutionalinvestor.com/article/b1mssrswn1mpr0/AI-Powered-Hedge-Funds-Vastly-Outperformed-Research-Shows

[2] https://towardsdatascience.com/the-best-machine-learning-algorithms-to-learn-for-landing-a-top-hedge-fund-job-in-the-2020s-c58b660ac27f


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