What Is a Quant Fund?
A quant fund is an investment fund that selects securities by utilizing the capabilities of advanced quantitative analysis. In quant funds, managers build customized models using software programs to determine investments for the fund.
- A quant fund makes investment decisions based on the use of advanced quantitative analysis.
- Managers utilize algorithms and custom-built computer models to pick their investments.
- The popularity of quantitative analysis within funds has risen in recent years, due in part to the rising availability of market data.
- Although quant funds utilize state of the art technology, the use of quantitative analysis dates back eight decades.
How a Quant Fund Works
A quantitative fund relies on algorithmic or systematically programmed investment strategies. Quantitative funds can be one of many investment offerings supported by a large asset manager. However, it can also be the central management focus of a specialized investment manager.
Quant fund offerings have been growing, and the business has become established in the industry with quant fund managers reportedly responsible for a quarter of all U.S. stock trades as of 2017, said the Tabb Group.
Large asset managers have looked to increase their investment in quantitative strategies as fund managers to struggle in consistently beating 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.
Quant funds are often known 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. However, the basis for quantitative analysis 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 strategy, such as Joel Greenblatt’s “The Little Book that Beats the Market” and James O'Shaughnessy’s “What Works on Wall Street.”
Quantitative strategies are often referred to as a “Black Box” due to the high level of secrecy surrounding the algorithms they use.
Requirements for Quant Funds
Fueling the growth of quant funds has been increasingly higher access to a broader range of market data, as well as 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.
Because of this, 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.
Quant funds are often classified as alternative investments since their management styles differ from those of more traditional fund managers. As such, they can be known to charge relatively higher management fees than funds with more traditional investing strategies. Their offerings are also more complex than standard market investment funds. In some cases, they may target investors with a higher net worth or have high fund entrance requirements. Investors will find these strategies in regulated mutual funds and exchange-traded funds. Hedge funds are also known for offering quantitative investment offerings with less regulated management requirements.