The Trinity of Better Investment Decisions: AI, Big Data and Your Investment Manager
Posted onArtificial intelligence (AI) has begun to kick-start significant changes across many industries. From the development of robotics in manufacturing to driverless cars on the road, the potential for its application is limitless. This begs the question: what can it do for investment and fund management?
Ben Dunn - Head of Quantitative Solutions Group at Eastspring Investments – believes that the potential of AI in investment is in allowing much larger volumes as well as different types of data to be used to inform investment decisions and potentially improving on the interpretation of that data in investment decision-making.
Ben Wicks and Mark Ainsworth - of Schroders' Research Innovation and Head of Data Insights, Investment, respectively - concur. In their white paper Harnessing the Data Science Revolution, they write: "The proliferation of information available for investment research is a profoundly disruptive force… the injection of new, and potentially unique, methods of data analysis into existing investment processes should enhance long-term alpha generation."
But can AI and big data deliver on their promise? The underlying goal of a purely AI-driven investment strategy is essentially no different from that of a traditional quantitative investment strategy - "That is, to find strong relationships between elements of data and future asset returns and exploit them," according to Dunn.
However, Dunn adds: "The AI-driven approach to finding these data relationships differs in that they tend to be entirely driven by data (evidence) and do not require upfront rationale or intuition from the analyst."
Dunn believes that this purely data-led outcome is both the greatest strength and the greatest challenge of AI techniques: "Machine Learning models can derive unique insights from the data that may not have been considered by a quantitative analyst working with the same data. But it can also identify relationships in the data that are spurious or temporary." He adds that this is one of the reasons that the "Man + Machine" approach is more commonly pursued by the investment industry to date.
Traditional quantitative versus machine learning strategies
ource: Eastspring Investments.
Wicks and Ainsworth agree, concluding that an active investment decision is a function of several factors: "A fund manager who has excellent information to hand and who is able to form a coherent but differentiated view, drawing inspiration from a broad range of sources at the appropriate time, is likely to generate good performance."
Hence, it appears investment managers will not be replaced by robots yet. In the new age of AI, investment managers will be able to make use of new and rapidly growing sources of data. Coupled with portfolio construction and risk management skills, this will help active managers add alpha for investors.
Sources: Quantitative Investing Meets Machine Learning by Eastspring Investments and Harnessing the Data Science Revolution by Schroders.