Francesca CCO at Axyon AI
Since the outset of the pandemic, the global economy has experienced much uncertainty. Although many people expected this year to be another good year for the stock market as the world economy continues recovering from the crisis, fears over the recent developments of the Omicron variant, together with unclear messages from central banks, have caused more instability. This market volatility presents fund managers with significant operational and performance-related challenges and a potential retention issue in the face of further volatility due to new variants and unexpected developments. So, how can AI and machine learning help investors prepare for this volatility and demonstrate resilience to their clients?
AI’s transformative role for asset management firms
Ultimately, AI and machine learning enable fund and asset managers to gain valuable time to adjust risk and protect investments with the intrinsic value of predictive analytics. It can also help businesses navigate challenging conditions by detecting anomalies in the market before any crisis occurs. By implementing AI, fund and asset managers can also monetise data and improve automation from the front to the back office.
AI vs. traditional models
While nobody can predict the unpredictable, market disruption is often on the cards. Many investors have lost confidence in asset managers who managed portfolios using traditional quantitative models, as they struggled to keep up with volatile market conditions. As such, funds need to find a way to better mitigate the risks with powerful predictive analytics of fast-moving markets and avoid losing investor confidence. In today’s world, relying on traditional portfolio management models when a market crisis occurs can result in the investments becoming severely impaired and can push a large amount of chaotic data into quantitative models. Therefore, asset managers need a system that can account for volatility and manage expectations more accurately.
Traditional portfolio models are built around strong assumptions on the behaviour of underlying assets, measuring normal distribution patterns on linear scales. As a result, they find it difficult to cope with the flood of chaotic data into their systems caused by high levels of volatility and fund managers’ ability to accurately analyse and predict where the market would go next and navigate through the crisis was significantly limited. Throughout the COVID-19 pandemic and consequent volatility, businesses with these models have had their limitations exposed.
Advanced AI systems are unrivalled from portfolio management to risk management for example detecting anomalies in the market. AI models can handle large and chaotic sets of data learning from the past the actual relationship among variables. Moreover, the application of advanced analytics to these data sets may also provide more real-time insight into the risks related to these shocks for the stock market.
Unlike these traditional models, AI systems are completely agnostic about markets and their associated risks, meaning that they can be trained to sound the alarm when the structure in the data is anomalous, and therefore could be a sign of an upcoming unpredictable event. These AI-powered tools can be also used to read the reality of the situation at hand, without any pre-ordered rules.
By strengthening a model against chaotic data, AI allows fund managers to see non-linear, complex patterns in asset behaviours that can be captured, and make market view predictions at a higher level, no matter how changeable conditions become.
Turning Point: The pandemic as an opportunity for change
The pandemic is still an opportunity for new technologies to prove their merits and show that AI and machine learning can offer a better way to use data and quickly adapt to the ever-changing ‘new normal’. By modelling the potential repercussions of major geopolitical, financial, or environmental events, businesses will be better placed to adapt, reposition, and overcome the obstacles the pandemic presents.
We have seen that investment in technology and data infrastructure is working its way up asset managers’ agendas, and AI and machine learning has by no means reached its limits. Advancements in technology will mean improvements to business performance will continue, and due to the wealth of data already stored in most financial institutions, there is great potential to build on the success of previous solutions. Businesses who are late to harness the superior analytical power of AI will likely find themselves trailing behind the competition. Implementing these innovative machine learning technologies will undoubtedly be a powerful solution to the problem of meeting and exceeding investors’ expectations of mitigated risk and higher returns.
