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After months of market volatility and challenging conditions, fund managers are starting to see a light at the end of the Covid-19 tunnel. Countries are starting to relax their lockdown measures and restart economies, with stock markets reacting positively. However, there are still challenges facing managers, namely a low in investor confidence and reticence amongst consumers to start investing again amidst ongoing uncertainty about the real economic impact of the crisis.
Unfit for service
Many funds were exposed to the full extent of the crisis, such as passive funds tracking equity and bond indexes. These had been highly popular options with investors in recent years but were unable to adjust their exposure as the markets reacted to the onset of the virus.
Funds managed with traditional quantitative methods experienced a similar struggle. These funds typically work on the assumption that patterns can be found in historical data and then used to inform investment choices. However, this meant most were badly equipped to make decisions that mitigated risk, as the crisis pushed a large amount of chaotic data into the quantitative models, leading to inaccurate predictive views of market changes. The results have been stark; in the first quarter of 2020, only 17% of U.S. large-cap quant mutual funds outperformed their benchmarks after fees.
On a tightrope
Without significant structural reform, these funds could remain vulnerable to another market fall, which may occur if a second peak or wave of the coronavirus occurs. The COVID-19 crisis has shown, quite starkly, that traditional quantitative models aren’t able to cope with the sudden onset of volatility and deluge of chaotic data that results from these ‘Black Swan’ events. Although ‘Black Swan’ events are unpredictable it is possible to mitigate risk with a better usage of data that AI techniques provide.
While fund managers are now eager to press ahead and look to what the future holds after this crisis, they remain in a delicate position. Central banks have already injected almost $100bn to prop up investment funds during the COVID-19 crisis, after the IMF warned further outflows from vulnerable bond funds risked running down managers’ cash buffers. To mitigate this risk, managers will ned to make every effort to rebuild confidence amongst investors and encourage them to return to the market.
Bringing them back
Some asset managers are reviewing current strategies and re-evaluating portfolios to adapt to the ‘new normal’, with high levels of volatility expected in the months ahead. Some have begun to look beyond traditional risk management tools, with AI-based systems increasingly seen as a way forward. These systems have the capability to better detect and handle anomalous data, something that was lacking in passive and quantitative funds when the crisis first struck.
Deep learning is the most sophisticated form of artificial intelligence, particularly well-suited to capturing more complex, non-linear patterns in asset behaviour, that can benefit largely by the usage of alternative data and allowing algorithms to adapt to changing market conditions. Through this, fund managers can take advantage of well-constructed advanced portfolios that look beyond historical data.
Implementing these innovative technologies can serve as a powerful tool to retain investors showing evidence about a structural change in their strategies introducing for example an alternative source of alpha, a better and efficient usage of available data and new tools to be prepare to unpredictable events. This will help to boost their confidence about the preparedness of a fund, while prospective investors may well reward funds that have responded strongly to the impact of Covid-19.
The asset management sector, browbeat by the initial impact of the crisis, is now starting to look forward to the future, but structural issues remain that will need to be addressed to ensure they are ready for the next crisis. These issues have contributed to investors losing faith, but AI and deep learning solutions are tools that managers can use to predict and prepare for future volatility, helping to shore up investor confidence and ensure managers are better positioned for the future.