What Is a Quant Fund?
The term quant fund refers to an investment fund whose securities are chosen based on numerical data compiled through quantitative analysis. These funds are considered non-traditional and passive. They are built with customized models using software programs to determine investments.
Proponents of quant funds believe that choosing investments using inputs and computer programs helps fund companies cut down on the risks and losses associated with management by human fund managers.
- A quant fund makes investment decisions based on the use of advanced mathematical models and quantitative analysis.
- Managers utilize algorithms and custom-built computer models to pick their investments.
- Investors are turning to and sticking with quantitative analysis within funds because of the rising availability of market data.
- Although quant funds utilize state of the art technology, the use of quantitative analysis isn't new.
How a Quant Fund Works
Quant funds rely on algorithmic or systematically programmed investment strategies. As such, they don't use the experience, judgment, or opinions of human managers to make investment decisions. That being said, they use quantitative analysis rather than fundamental analysis, which is why they're also called quantitative funds. Not only can they be one of many investment offerings supported by asset managers, but they may also be part of the central management focus of specialized investment managers.
Greater access to a broader range of market data fueled the growth of quant funds, not to mention the growing number of solutions surrounding the use of big data. Developments in financial technology and increasing innovation around automation have vastly broadened the data sets quant fund managers can work with, giving them even more robust data feeds for a broader analysis of scenarios and time horizons.
Large asset managers have looked to increase their investment in quantitative strategies as fund managers struggle to beat market benchmarks over time. Smaller hedge fund managers also round out the total quant fund offerings in the investment market. Overall, quant fund managers seek talented individuals with accredited academic degrees and highly technical experience in mathematics and programming.
Quantitative strategies are often referred to as a Black Box due to the high level of secrecy surrounding the algorithms they use.
Quant Fund Performance
Quant fund programming and quantitative algorithms have thousands of trading signals they can rely on, ranging from economic data points to trending global asset values and real-time company news. Quant funds are also known for building sophisticated models around momentum, quality, value, and financial strength using proprietary algorithms developed through advanced software programs.
According to a report by Institutional Investor, quant funds attracted a considerable amount of interest and investment because of the returns they paid. But they've been underperforming since the mid-2010s. The report compared the MSCI World index with the equity quant index, which returned 11.6% on an annualized basis compared to 0.88% through November on an annualized basis, respectively. Despite, this, investors remain confident about their performance going forward.
A Brief History of Quant Strategies
The basis for quantitative analysis and, therefore, quant funds, has a history that dates back eight decades, with the publishing of a 1934 book called Security Analysis. Written by Benjamin Graham and David Dodd, the book advocated investing based on the rigorous measurement of objective financial metrics related to specific stocks.
Security Analysis has been followed by further publications related to quantitative investment strategies, such as Joel Greenblatt’s The Little Book that Beats the Market and James O'Shaughnessy’s What Works on Wall Street.
Quant funds are often classified as alternative investments since their management styles differ from more traditional fund managers. They generally charge relatively higher management fees. Their offerings are also more complex than standard funds.
Some of these funds may target high-net-worth investors or have high fund entrance requirements, similar to regulated mutual funds and exchange-traded funds (ETFs). Hedge funds are also known for offering quantitative investment offerings with less regulated management requirements.
Some investors consider quant funds to be some of the most innovative and highly technical offerings in the investment universe. As such, they encompass a wide range of thematic investment styles and often deploy some of the industry’s most groundbreaking technologies. Successful quant funds keep a close eye on risk control due to the nature of their models. Most strategies start with a universe or benchmark and use sector and industry weightings in their models. This allows the funds to control the diversification to a certain extent without compromising the model itself. Quant funds typically run on a lower cost basis because they don't need as many traditional analysts and portfolio managers to run them.
Risks of Quant Fund Strategies
Some have argued that quant funds present a systemic risk and do not embrace the concept of letting a black box run their investments. For all the successful quant funds out there, just as many seem to be unsuccessful. Unfortunately, for the quants' reputation, when they fail, they often fail big time.
Long-Term Capital Management (LTCM) was one of the most famous quant hedge funds, as it was run by some of the most respected academic leaders and two Nobel Memorial Prize-winning economists, Myron S. Scholes and Robert C. Merton. During the 1990s, their team generated above-average returns and attracted capital from all types of investors. They were famous for not only exploiting inefficiencies but using easy access to capital to create enormous leveraged bets on market directions. The disciplined nature of their strategy actually created the weakness that led to their collapse. Long-Term Capital Management was liquidated and dissolved in early 2000. Its models did not include the possibility that the Russian government could default on some of its own debt. This one event triggered events, and a chain reaction magnified by leverage created havoc. LTCM was so heavily involved with other investment operations that its collapse affected the world markets, triggering dramatic events.
In the long run, the Federal Reserve stepped in to help, and other banks and investment funds supported LTCM to prevent any further damage. This is one of the reasons quant funds can fail, as they are based on historical events that may not include future events.
While a strong quant team will be constantly adding new aspects to the models to predict future events, it's impossible to predict the future every time. Quant funds can also become overwhelmed when the economy and markets are experiencing greater-than-average volatility. The buy and sell signals can come so quickly that high turnover can create high commissions and taxable events. Quant funds can also pose a danger when they are marketed as bear-proof or are based on short strategies. Predicting downturns using derivatives and combining leverage can be dangerous. One wrong turn can lead to implosions, which often make the news.