In my current role, I routinely liaise with asset management firms to understand the challenges they face in their day to day business. This interaction gave me an insight into an important trend - a gradual shift from active to passive investments to achieve benchmarking. Here are some stats to back up the trend. In the US, 86.7% of active funds have underperformed their benchmark. Similarly, 85.4% of actively managed European equity funds underperformed their benchmark. With the underperformance of active managers, passive asset management has gained significant popularity. It now represents a USD 6 trillion industry globally. This trend is expected to continue with Moody’s predicting that passive funds will constitute over 50% of assets under management (AUM) in the US in the next 4 to 7 years, up from 28.5% at present.
Moreover, high management fee charged by active managers compared to their passive counterparts is further fueling the shift. It’s easy to see why. Investors, whose primary concern is investment returns, cannot withstand the underperformance of active managers on a continual basis. If this is so, are we faced with a situation where active asset managers are being pushed out of the asset management industry business?
The simple answer is ‘no’. Cutting edge technologies like Artificial Intelligence (AI) can help leverage voluminous investment data to turbocharge their alpha. Here’s how.
Digital revolution is transforming the asset management industry and digitized data is generating the fuel needed to feed the AI computing engine. This is good news for asset management firms looking for ways to make better use of this data to serve their clients. Big investment data appears impossible to scale to the human eye, even with the help of complex spreadsheets and sophisticated Visual Basic for Applications (VBAs). However, this critical mass of data is exactly what AI needs in order to truly “learn” and develop insights into investment decision making.
Industry players can reap substantial benefits through the adoption of AI and machine learning (ML) as these technologies help provide real-time actionable insights and facilitate superior portfolio management decisions.
- Automated insights: Reading historical financial transcripts of securities to assess management sentiment.
- Relationship mapping: Identifying non-intuitive relationships between securities and market indicators.
- Alternative datasets: Analyzing alternative data such as news, weather forecast, economic predictions, monitoring search engines for words/trends on specific asset classes - such as equities, fixed income and so on - to structure hedging strategies.
- Growth opportunities: Using corporate website traffic to gauge future growth along with clients’ behavioral patterns.
- Client outreach: Enabling smart client outreach and demand generation via analytics, using alternative data sources such as social media data.
Asset management firms that take the next step and transform their traditional investment processes by leveraging AI/ML with the help of alternative data sets can boost alpha on active investment funds. They can also design completely new investment products that achieve positive alpha over relevant benchmarks.
Accessing sources of information that provide superior insights and leveraging unique methods of extracting such data have always provided an edge in investing. Sustainable alpha generation in the future will ever more be a function of uniquely insightful data-driven investing approaches. Perceptions around data science, alternative data and AI/ML in asset management are changing. Concerns around the effectiveness of AI/ML technologies are giving way to fears of missing the boat - on leveraging them to drive profitability. Clearly, forward looking asset managers (across fundamental and quantitative approaches) that increasingly adopt AI/ML techniques will be better placed to reinforce their value proposition, gain market share, and emerge as the winners.