Traditional factor portfolios have had a tough time before and during the pandemic. This is an excellent opportunity for the industry to accelerate its innovation toward alternative data and AI.
Nestled somewhere between long-term thematic portfolios, and short-term, trend following funds, live the factor premia portfolios of the investment ecosystem. Factor premia, are independent (affordable) drivers of returns, narrated through the lens of fundamental investor terminology, and wrapped into cheap products, they have become common components of many asset owners.
The factor industry now concerns itself with either more efficient combinations of its classic factor library (e.g. value, growth, momentum and quality) or claims it can add through timing its exposures to factors to provide better outcomes for investors.
As an asset owner, you tend to use factor portfolios for their promised long-term returns over a benchmark, or as shorter-term return generation, such as an absolute return premia fund. Both have (likely) failed you over the past three to five years.
The data is clear in its message. Broad indices (e.g. MSCI ACWI Diversified Multi-factor index or the FTSE equivalent) have under-performed over 1, 3 and 5 years, compared to market capitalisation-based indices. If the benchmark was an investor, you would have fired them by now. Many large providers of factor strategies have similarly lagged benchmarks over the past three years, and more.
In a world with exponential availability and production of data, incredible processing power and software, a bustling global industry of data science, the asset management field has been painfully slow to innovate on the fairly traditional stories of factor investing.
Things haven’t got better in the absolute return strategies either. It was over a year ago when AQR’s Cliff Asness said that it is a ‘crappy’ time to be a factor investor. He was right. A number of absolute return funds, both style premia and alternative style premia, posted negative returns for 1, 3 and 5 years.
The industry’s response has been tired and worn, with conversations about value versus growth, or timing versus premia, are long-standing, but don’t appear to have saved the funds from serious questions.
This brings us neatly to a crossroad for the future of the quantitative and factor investing. In a world with exponential availability and production of data, incredible processing power and software, a bustling global industry of data science, the asset management field has been painfully slow to innovate on the fairly traditional stories of factor investing.
In spite of glaring short-comings, the viability of a good story to explain a poor performance holds greater value than the ambition and investment required to repair and improve an investment process.
At the crossroad for most firms are two arguments: wait it out or throw it out.
Needless to say, the investment industry prefers the former: it allows revenues to slide slowly, and through the occasional bursts of performance, still make an (unlikely) comeback. The latter takes serious commitment to new research, career risk and leadership, and an intellectual flexibility within the sales and distribution arms of these companies to pivot the tired factor narratives to more complex, but ultimately better investment processes.
Time is against the factor industry. With challenging performance numbers, and fees and revenues being squeezed, the availability of capital for investment and the pivot required to investigate and launch brand new factor strategies that embrace elements of alternative data and AI, and gain the track record and understanding required to win mandates, are slipping away for most firms.
Businesses that have invested heavily in distribution and sales activities are seeing that the core value proposition of their offerings are under fire, and that in essence, they have leveraged themselves irreversibly into the factor narrative. We have seen factor strategies underperform before, and this alone would not necessitate a radical re-think. However, the combination of low research investment, tired narratives, and enormous opportunity cost of not investing in AI and alternative data paint an industry at breaking point, and ripe for a change.
Michael Kollo is Chief Executive Officer of quantitative research consultancy firm Qurious and host of the podcast The Curious Quant. The opinions expressed in this article are his own and do not necessarily reflect the opinions of the Investment Innovation Institute [i3].