It’s indisputable that technology has changed the world in every direction, and the investment landscape is no exception. Simply put, technological innovation has meant sophisticated algorithms working at an unprecedented speed and scale – predicting financial returns, processing company data, identifying patterns and efficiently grouping stocks according to defined characteristics.
Some might say that traditional stock picking has started to look slightly antiquated in recent times, but we say that nothing could be further from the truth. Working on the assumption that financial data has been ‘squeezed dry’ by computer-powered, algorithm-driven quantitative investing, we need to find new and better ways of predicting financial performance – new insights to deliver alternative sources of alpha.
Enter intangible – or non-financial – data. This is the human, intellectual, social and environmental capital that gives great businesses their competitive edge. In practical terms, it could be a company’s culture, its relationship with its customers, suppliers and the community, the ability to attract and retain talent or a culture of innovation. These intangible assets can have a short-term financial cost and are rarely found in annual reports and on balance sheets, yet they are a powerful predictor of long-term financial performance.
Underappreciated extra-financial factors create an exploitable inefficiency
Source: RBC GAM.
Alternative alpha sources can play a valuable part in portfolios. Not only do they lead to 1) investments in better businesses with a better ESG footprint, 2) lower portfolio turnover and 3) reduced transaction costs but, crucially, the outperformance derived from non-financial sources is idiosyncratic in nature and has a low correlation to more traditional systematic, quantitative sources. They are a different flavour of outperformance, or true alpha, and so including these alpha sources can diversify the return stream of a portfolio.
In our full piece below, we look at the challenges in generating and capturing alpha, and discuss why a philosophy, process and team that are sensitive to this non-traditional information is key.