Quant investing is growing market share, with cloud-based platforms and cutting-edge analytics meaning portfolio managers no longer need a team of highly skilled data scientists to benefit.
Since the 1980s the buy-side has increasingly used computers in its investment process. Initially, only a handful of investors were able to utilize the tools that were available as they required programming expertise and a familiarity with statistics.
These “quants” were able to screen large universes of stocks for those that met their investment criteria.
They were also able to build models to rank stocks for their potential future performance, measure the risks inherent in their portfolios, and understand what was driving portfolio performance relative to their benchmarks.
Their success in deploying these analytics led to a dramatic market share shift as investors moved more money to quantitatively driven strategies.
Quantitative hedge funds are now responsible for 27 percent of all U.S. stock trades by investors, up from 14 percent in 2013, according to the TABB Group, a research and consulting firm in New York.
It’s also led to an increasing number of “fundamental” investors looking to incorporate these systematic tools into their workflows as they’ve become more accessible with the advent of cloud computing and user interfaces that don’t require programming skills.
What are the benefits of a quant’s approach?
- Potentially improves performance
Building models that reflect a fundamental portfolio team’s approach enables them to analyze thousands of companies instead of the few hundred that they would otherwise research.
This could lead to identifying more opportunities for stocks that will outperform the market.
In backtesting their fundamental models, investors gain greater insight into the relationships between the drivers of stock performance as well as identifying inherent risks in portfolios.
- Increases scalability
By incorporating quantitative tools, portfolio managers can effectively oversee larger pools of money. They’re also able to provide more strategies for their investors, with the largest growth coming in the form of smart beta funds.
Quantitative tools also enable portfolio managers to more easily provide an array of funds tailored to the specific needs of their investors.
- Improved capabilities for fulfilling regulatory and fiduciary requirements
The systematic nature of using factors for helping with stock selection and models to measure performance and risk provides increased transparency into how decisions are made.
Watch video — Introducing Thomson Reuters QA Point
Is it black box investing?
The term “black box” refers to a system where investment decisions are made based on a computer model without an understanding of what is driving the decision. Most investors would never use such a system for obvious reasons.
The idea is for an investor to use computers to provide more insight into their investment decisions.
By building models based on fundamentals (which refers to earnings, profits, growth, valuation, etc.), the portfolio manager can increase their understanding on how companies are performing.
The use of risk models can also improve their understanding of the risks they are taking in their portfolios.
And by using analytics tools, they gain better insight into the factors driving their performance relative to the market.
Meeting buy-side needs
Investors are expected to continue incorporating quantitative tools and disciplines into their workflows as they strive to outperform their benchmarks, cope with thinning margins, provide more transparency, and offer more types of funds.
As long as these drivers continue to exist there are expanding opportunities for service providers that have the tools and content that can meet these buy-side needs.
Thomson Reuters QA Point is a cloud-based platform for backtesting systematic investment models that allows professional investors to introduce cutting-edge quantitative research and analytics to fund management.
It simplifies workflows, making it easy to construct, validate, and deploy multi-factor quantitative investment strategies — in a fraction of the time it takes using traditional quant tools.