by Tom Roseen.
After suffering a meltdown between 2007 and 2011, quantitative investment strategies have come back into vogue. In May 2016 Alpha Magazine highlighted that six of the eight highest earning U.S. hedge fund managers are “quant jocks,” relying heavily on computer-driven investment strategies to produce the lion’s share of their investment decisions. Quant strategies use proprietary models to tease out market inefficiencies as they try to outperform the market. They generally follow a disciplined research-driven process that uses mathematical underpinnings, along with raw computing power, to identify pure alpha (or excess return) opportunities.
Using strict rules-based strategies that rely on mathematical principles and the study of history to create well-diversified portfolios is not a new concept. In fact, when we look back to the forefathers of quantitative analysis, we see them setting the stage for the next evolution in financial analysis and trading strategies. In 1934 Benjamin Graham and David Dodd published their seminal work, Security Analysis, which set the foundation for what is now called value investing and which many believe was the genesis of modern financial analysis.
In the 1950s Harry Markowitz’s work in modern portfolio theory (MPT) was probably the true start to quantitative investing, where mathematics are used to create efficient diversification within a portfolio, showing investors they shouldn’t put all their eggs into one basket. Markowitz proposed that by using expected return, standard deviation, and mean variance optimization, one can find an efficient portfolio that maximizes expected return for an acceptable amount of risk.
MPT was improved by William F. Sharpe and other academicians in the 1960s; they put forward the capital asset price model (CAPM), which introduced the concepts of diversifiable risk and nondiversifiable risk (also known as systematic risk or market risk and often represented by the quantity of beta). CAPM’s premise was that only nondiversifiable risk should be rewarded by the market; the other portion of risk should become very small with a well-diversified portfolio. CAPM foretold that the return of a security, less the risk-free rate, would be related to its beta.
Using the related mathematics, one is able to plot a security’s expected return on the security market line (SML), given the market-risk premium. Stocks that lie on the SML are considered fairly priced, while stocks that lie above the line offer higher returns than are foretold by CAPM and are considered underpriced. The opposite is true for stocks lying below the SML. And therein lies the foundation for both active managers and quantitative models: the discovery of market inefficiencies.
Between 1977 and 1981 researchers stumbled on three primary examples of market inefficiencies based on yield, capitalization, and book-to-price ratios, each showing returns higher than CAPM would have predicted. Firms with high earnings yield and high book-to-price ratios (no surprise to Graham and Dodd fans) and small-cap firms offered returns that on average were in excess of what CAPM would have forecasted. These influences—accompanied by ever-increasing access to computer-based financial market data—were the catalysts for quantitative asset management and optimized portfolio construction.
Over the next 20-plus years quantitative strategies exploded, with researchers uncovering more market inefficiencies and creating multi-factor models that identified market-beating strategies. Between 2000 and 2007 quantitative strategies experienced strong acceptance by asset managers and fund gatekeepers. Back-tested models produced disciplined research-driven processes that appeared to lower risk and provide strong risk-adjusted returns supported by academic research. In the mutual fund industry assets under management in quant funds grew from $38.9 billion for 2000 to a little under $106.8 billion for 2007.
However, as with every winning strategy discovered in business, it didn’t take long for other practitioners to replicate the process, bringing returns back to average and crowding out the source of the excess returns. As a result of the 2007 financial crisis, many quantitative funds witnessed rapid declines in assets under management because of sudden liquidations, margin calls, and deleveraging. For 2008 the average quantitative fund lost 37.32%. Investors soured on quantitative strategies as they began to realize those strategies are subject to the same market risks as other strategies.
The culling of poorly performing quantitative strategies and funds during the market meltdown gave rise to new and improved algorithms for the quant funds that were left standing, with less concern of crowding. In addition to including formal risk management tools, most quant funds began looking for unique methodologies and trading strategies that can identify proprietary alpha-generating ideas—perhaps by identifying factors that are not as easy to replicate as the early inefficiencies that were found.
Quant fund managers are now looking at unique data sets—such as news-based analytics, analytics tied to behavioral biases, dynamic factor rotation, unstructured data, credit-risk data, and the like—to uncover unique market inefficiencies. Managers have come to realize that their models are in constant need of evolution; refining their data has become an ongoing event using Monte Carlo simulation, machine learning algorithms, and refinements to traditional regression analysis. But, refining explanatory data is not the only way quant funds are gaining competitive advantages. Quants also review their deployment and trading methods to gain a competitive advantage; those that can process information and trades faster gain a competitive advantage. They use high-frequency trading techniques to shoulder out their competitors; they use microwaves instead of slower fiber-optics, they locate servers at the site of the exchange, and they design more efficient trading algorithms—anything to exploit inefficiencies and gain the competitive edge.
As with any investment strategy there are strengths and weaknesses to quantitative methodologies. On the plus-side: by using a strict rules-based quantitative technique one removes emotions from the equation and embraces a very disciplined process. Most quantitative processes have been vetted academically and vigorously back-tested and now carefully keep track of risk. Through lightning-fast computers inefficiencies can be identified simultaneously on very large datasets. And some pundits believe quant strategies can typically run at a lower cost because automation takes over the role of some portfolio team members.
On the flipside, however, when quant funds fail they tend to do so in spectacular fashion. Looking back to 2000 when Long-Term Capital Management went bankrupt, we find its model did not take into account the Russian government’s defaulting on its own debt, causing a leveraged domino effect that impacted world markets. This highlights one of the major drawbacks of quant strategies: historical events that have no relationship to future “black swan” events; quant strategies can be overwhelmed by above-average and unanticipated volatility. Because many quant funds use long, short, and leveraged positions in their portfolios, black swan events can have outsized impacts, so caveat emptor! Last but not least, computer-driven trading can lead to higher-than-average portfolio turnover and related commission costs and tax inefficiencies.
Looking at the one-, three-, five-, and ten-year relative returns (fund return less the benchmark return) for both quant and non-quant funds, we see that for the periods ended August 31, 2016, more than 25% of the quant funds outpaced their benchmark for the three- and five-year periods but significantly underperformed at the ten-year mark. A total of 25% and 24%, respectively, of the non-quant funds outperformed their benchmark at the one-year and ten-year marks, while they lagged for the three- and five-year periods.
So, is there a place for quantitative funds in clients’ portfolios? The answer is a definite maybe. Quantitative techniques are constantly evolving and appear to do well if there are no major changes in the market. Many of the traditional fundamentals shops are adding quant options to their lineup and creating a hybrid process that uses both fundamentals and quantitative techniques, trying to capitalize on the best practices of both. We believe—much as we do about the passive versus active strategies debate—that there are benefits from using both techniques, and that it is best to treat quant funds as an investment style rather than as an end-all, be-all. Studies have shown that quantitative strategies can be used as a complement to fundamental management strategies and may even have different risk-return tradeoffs that can enhance a portfolio’s overall return and (hopefully) reduce risk.