Quantitative-based trading, or simply quant, relies on mathematical or quantitative modeling of the markets. In essence, quant is applied mathematics to the stock market, analyzing all available indicators to calculate both the potential risk and return.
To assess the "true" value of stocks, quantitative investment strategies rely on both historical data and real-time trading prices. This way, the quant approach to portfolio construction aims for so-called alpha returns - ones that beat the market average returns. Suffice to say, this is an enticing prospect for investors, but one should consider downsides as well.
History of Quantitative Methodology for Portfolio Construction
As the financial world became more complex and diversified, a demand for quantifying market patterns emerged. One of the first economists to supply this demand was the Nobel Prize-winning Harry Markowitz. With his book, "Portfolio Selection," first published in the Journal of Finance in March 1952, Markowitz created the concept of portfolio diversification as we know it.
Under the framework of Modern Portfolio Theory (MPT), investors acquired tools for their investment portfolio management and diversification. With MPT, portfolio construction is based on:
- Minimizing risk based on the return ratio or the level of expected risk
- How investments affect the entire portfolio instead of viewing it as a collection of individual investments
Among other measures, MPT relies on the statistical tools of correlation and variance. They are employed to calculate the expected return of the portfolio by calculating the weighted sum of each individual asset. MPT became increasingly useful with the rise of ETFs (Exchange Traded Funds).
As indices of individual assets, MPT gives ETF investors the tools to minimize the risk for a given return. Therefore, the variance of the portfolio would also be reduced.
Robert C. Merton
Building upon Markowitz's MPT, the son of the famous sociologist, Robert K. Morton, is the key contributor to quantitative investment strategies. Robert C. Merton won the Nobel Memorial Prize in Economic Sciences for figuring out how to assess the value of derivatives. Together with two other economists - Fischer Black and Myron Scholes - they developed the Black–Scholes–Merton model.
Also commonly called just the Black–Scholes model, it uses differential equations to create a mathematical model of financial markets holding derivatives as investment mechanisms. The model allows for risk minimization by hedging options. If an underlying asset is traded accordingly, it nearly eliminates the risk.
Such hedging has been dubbed as "continuously revised delta hedging," becoming hugely popular with both banks and hedge funds to this day. Of course, nowadays, there are many variations of the model by tweaking its underlying assumptions.
Who or What Are "Quants"?
Altogether, the application of calculus to quantitative finance made it possible to create quantitative investment strategies that generate excess returns or alpha values. Although the term "quant" was initially reserved for developers of these models, who are usually programmers, mathematicians, and statisticians, it became interchangeable with their models themselves.
This means there are as many quant strategies as there are developers who build them. Following the dot-com bubble in the late 1990s, quant strategies were fully embraced by institutional investors. However, as quants failed to predict the 2008 financial crisis, by not accounting for the effect of mortgage-backed securities, quant limitations became more visible.
Nonetheless, quantitative investment strategies for portfolio construction have proven their merit over the decades. The most time-tested and popular quants were created by James O'Shaughnessy:
- The Trending Value - returned 21.2% annually over a period of 45 years.
- Quality-Adjusted Value Microcap - returned 20.3% annually over a period of 34 years.
More modern quants have proven even more profitable. The Qi Value quant has yielded a 710.7% annual return over 16 years. In the same range of returns is the Free Cash Flow Yield and Price Index 12 Months Momentum quant.
Example of Quant-Based Portfolio Management
As you can see, there are dozens of investment strategies to choose from. Each one is based on an angle. For instance, let's say you want to pursue a quant based on trading volume patterns. Specifically, a quant that establishes a high positive correlation between the asset's price and its trading volume.
Therefore, if an asset's trading volume increases, followed by its price hitting $80 or drops its trading volume when it hits $100, the quant would automatically set a buy at $80.50 and a sell at $100.50. In a similar vein, one can trade with quants based on earnings reports, and any other market dynamic that can be used for algorithmic portfolio construction.
The Rise of Robo-Advisors
Having mathematical models of the market to gain an advantage is one thing, but having them mass-deployed to individual retail traders is another matter entirely. As mentioned previously, before computers became commonplace, quantitative investment strategies were reserved for large financial institutions. After all, it would take teams of analysts to deploy them.
With the dawn of machine learning, it became possible to automate financial strategies and techniques. Hence, the rise of robo-advisors - computer programs that fulfill the role of human financial advisors, including quants. They represent a major shift in how people view finance and how to execute portfolio management.
By replacing humans, robo-advisors drastically reduce the cost of investments - up to 66% in fee reductions alone. From their humble beginnings as online questionnaires to self-learning algorithms, they have evolved significantly within the last two decades.