Cromulent. Embiggens. Quantamental. They all sound made up, right? Strangely, some investors might be more familiar with the first two terms (Simpsons references all the way from the 1990s) than the last. Quantamental is a portmanteau, combining quantitative and fundamental asset management styles, for the best that both strategies have to offer.
Quantitative finance is complex, but generally, it seeks to take large quantities of data and make sense (and profit) off it. A firm with a quantitative finance arm likely employs a lot of Ph.Ds, data scientists and engineers to take extremely complex data to find signals (hints that a stock or group of stocks may outperform). Successful quantitative shops, like Renaissance Technologies, Bridgewater Associates and D.E. Shaw, are famous for providing solid returns over the past decades,although the recent quant shock proved challenging for even the best to navigate (some firms have already begun recovering from the drop).
Quantitative techniques use massive computing power to take data from different sources and (ideally) find predictive power within that data. Financial data, specifically, is hard to pin down. Whereas a computer can beat a human in chess because there are a finite number of moves and parameters to the game, financial data moves quickly, is subject to once in a lifetime shifts like Covid-19, and can be hard to compare (AAPL’s market cap may make it hard to get valuable data against a smaller tech start-up). It takes a lot of time and resources (not to mention no guarantee) to hopefully get a good output from quantitative finance, but the ones that are successful dominate the market.
(A visualization of artificial intelligence in chess from Thinking Machine 6 – AI can think of every move in existence at lightning fast speed)
The Problem With Fundamental Analysis
More people are aware of fundamental analysis. That is the old-school method of studying a firm’s balance sheet and macro economic factors to make decisions on which companies an institutional investor might want to invest in (or go short). Fundamental analysis is a skill, honed over years or decades of real-world training in capital markets, but it is very time consuming. Comparing, say, every stock in the S&P 500’s P/E Ratio to one another is something a fundamental manager could do with enough time, but it might take weeks. Even more realistically, comparing every tech stock in the NASDAQ, or every financial stock in the Russell 1000, is unfeasible for most fundamental analysts. They lose out on potential gains by not having the time to study everything in their universe.
Other risks to fundamental asset managers include cognitive biases that may make them hold onto losing stocks longer than normal (Enron, WorldCom, and Nortel are all examples), ones that make them cling to investment strategies (investors that will only buy dividend stocks, or only tech stocks, or only momentum stocks because of some past success), and more.
Quantamental Investing Is The Future
It makes sense then that quantamental investing is the way of the future for active managers. It can take the positives of artificial intelligence (ultra-fast computing, ability to compare scores of data points against one another, reduce bias) and pair it with a fundamental analyst’s life of experience in the field. A quantitative model can (and has in the past) melt down if human eyes aren’t there to spot something that looks amiss or hit the pause button on trades. A quantitative model may suggest a stock that the fundamental manager can glean other insights from. In the March 2020 stock market fall, our machine learning models showed some retail stocks had higher and lower correlations to Coronavirus. Combining the AI insights with their own experience, a fundamental manager may have found first-mover advantage in the stocks our artificial intelligence surfaced.
Consider this quote from Mark Wiseman, chair of the Alberta Investment Management Corporation (AIMCo) and ex-President and CEO of the Canada Pension Plan Investment Board, from his tenure at BlackRock in the New York Times: “The old way of people sitting in a room picking stocks, thinking they are smarter than the next guy – that does not work anymore.”
The Harvard Business Review says that while AI and machine learning are exciting new avenues for asset managers to pursue, “The bottom line is that while ML can greatly improve the quality of data analysis, it cannot replace human judgment. To utilize these new tools effectively, asset management firms will need computers and humans to play complementary roles.” The same article says that the largest investment managers, BlackRock and Fidelity, are leading the charge, while a CFA report on AI says most sit on the sidelines – it says only 10% of asset managers surveyed had made any use of AI in the last 12 months.
It is a time of upheaval for the asset management industry. Fee compression, shifting investor sentiment towards passive investing and technological advances are just some of the hurdles all active managers must jump to survive and thrive going forward. Making use of AI techniques – combined with their fundamental analysis skills – can help these new quantamental managers find a way forward.