At a recent conference in the International Conference Centre, Dublin, Arne Staal, Head of Multi-Asset Quant Strategies at Standard Life Investments, spoke about the increased use of data and technology in investment management. This data and technology provides real investment insights and increases the potential for discovery of false signals. Passive or algorithmic investment strategies are growing in popularity and are based on big datasets and machine learning.
This can be a force for good as quantitative approaches help investors understand the true drivers of their portfolios and bring hope of better investment decisions and new sources of returns. However, the easier it is to access ‘big data’ the easier it is to be misled. The rise of investment algorithms have coincided with a nine-year bull run on equity markets. Passive funds work best when markets rise and for the past nine years almost all equities rose in value.
What happens if 2018 turns out to be the year of the bear?
The global financial crisis should have reminded investors of the limits of models. Economists failed to predict the crisis and political decisions mattered more than economics in bringing an end to the downturn. Politicians change the rules in ways that models cannot capture. There is a long history of market failures and market closures that were all ultimately resolved by human interaction. Examples are the Asian Financial crisis of 1997/8; the world financial crisis in 2008 and Cyprus in 2013.
That interaction owes a lot to the network of personal interconnections that often underpin even the most global and impersonal of markets. The massive failure of the hedge fund, Long-Term Capital Management and the collapse of the Irish and other banking systems were mitigated by these networks by helping to keep banks open and preventing a stop to the financial system.
Quantitative techniques have become vital tools for today’s investors, but they are tools, not solutions. Successful investing requires an understanding of both the numbers and the context in which they are used. This requires judgement and while data scientists have a role to play, investment still needs investors.