Morgan Stanley
  • Research
  • Dec 1, 2017

Will Big Data Be Increasingly Fundamental to Stock Picking?

After a record year of inflows, factor- and quant-based investing is a $1.5 trillion market that is only gaining influence. Should fundamental investors be paying attention?

Proponents of bottom-up stock picking say that dispassionate numbers alone cannot predict the success of a stock or asset class; a spreadsheet doesn't capture the talent of a management team or the power of a cyclical trend.

Proponents of quantitative investing—using complex mathematical models to pinpoint opportunities—contend that human emotions, biases and limitations handicap performance. Instead, it’s better to identify the factors associated with superior returns and build models around them.

With data more widespread and complex, the lines between fundamental investing and quantitative investing are blurring.

But while both styles have their champions, a new Morgan Stanley Research report finds that a combination of the two approaches could be advantageous for investors in either camp.

In fact, according to Martin Leibowitz, Vice Chairman of Morgan Stanley Research and Chair of the firm’s Quantitative Council, there will likely be a broader convergence of fundamental and quantitative investment processes over the next five years.

“Fundamental investors will need to pay greater attention to quant and factor investing, whether as a driver of markets or a complement to their investment process. By extension, for quant investors, combining fundamental and factor approaches—what’s called quantamental—could produce superior returns.”

Quant and Factor: A Rapidly Growing Market

Assets in factor and quant-based strategies have increased at a compound annual growth rate of 17% over the past six years, with an estimated $1.5 trillion just in retail funds and hedge funds alone.

While the growth has been impressive, Leibowitz says there is still room to run. “Just a 1% shift in global assets under management each year away from traditional assets—which is not hard to imagine in the current yield-starved environment—translates into a 12% compound annual growth rate for factor assets.”

Investors are paying attention. The Quantitative Council that Leibowitz chairs at Morgan Stanley is comprised of senior quantitative specialists from across the firm with the goal of ensuring that the Morgan Stanley’s quant resources are aligned with the needs of clients and the fundamental strategists and analysts in the Research Department.


Total Smart Beta/Quant/Factor Based AuM ($B)

Source: Morningstar, Hedge Fund Research (HFR), Morgan Stanley ETF trading desk, Morgan Stanley Research; Note: Hedge fund data through 6/30/2017, mutual fund data through 7/31/2017 and smart beta ETF data through 9/20/2017

Quant and Factor Investing Explained

Although investors are becoming more educated on quant and factor investing, it’s worth taking a moment to distinguish between them. Quantitative investing can be defined as a form of active management. Instead of legacy fundamental stock-picking, quant uses complex mathematical models to detect investment opportunities. Like fundamental human analysts, quant can utilize a multitude of strategies under its umbrella, including event-driven, trend-following (momentum), economic data-driven or price inefficiency seeking.

One form of quantitative investing, factor investing, is an investment strategy that seeks to enhance returns, improve portfolio diversification or reduce risk by targeting exposure to specific drivers of risk and return.  These drivers, or factors, are based on valuation, momentum, volatility, quality, and other traits of stocks that investors have traditionally scrutinized.

In factor investing, stocks with favorable combinations of factor exposures, as determined by statistical tests, receive higher weights than do stocks with disfavored combinations of factor exposures. Investment managers can parse and weigh these factors any number of ways to achieve desired investment outcomes, depending on their universe of stocks, investment horizon or other criteria.

So in simplest terms, quant and factor strategies use vast amounts of data to discern the relationships between stock characteristics and overall returns.

The Rise of Quantamental

While factor investing is often associated with rule-based index investing (aka smart beta), active managers are increasingly using factor-based algorithms to reduce risk and maximize returns in the fundamental arena. “We see evidence that combining fundamental and factor approaches—or 'quantamental' investing—can produce superior returns," says Brian Hayes, Global Head of Quantitative Equity Research.

This is particularly true in the era of big data, where public companies can be evaluated not just through company and economic specific data, but also non-traditional data sources like satellite images, internet traffic and logistics data. Fundamental investors must grapple with how to systematically collect, analyze and garner insights from all of this data, while quantitative investors may need fundamental sector expertise to identify reliable patterns.

The upshot: “We see fields of opportunity to boost returns by bridging the fundamental and quantitative divide and combining the investment processes," Hayes says. “This could yield new and greater insights, and enhance alpha generation for those with the technology, tools and process to harness and make sense of it."

Quantamental investing can also improve cross-asset investment decisions. “Factor investing is the next iteration in the evolution of asset allocation, as diversity of return drivers becomes increasingly important in a low-return world," Leibowitz says.

Surprise Results

To test the validity of factor investing—and quantamental investing—the report’s authors systematically constructed factor strategies, both at the individual stock level (micro) and asset class level (macro), globally.

For individual stocks, they created global models for forecasting single-stock performance for 5,400 stocks across developing markets and emerging countries; they studied more than 70 factors classified into four broad categories: valuation, growth and investor sentiment, capital use and profitability, and capital structure and financial leverage.

They then paired those models with Morgan Stanley fundamental analyst ratings. Between 2003 and 2010, a period that included a major equity rally, a quant crisis and one of the worst bear markets in decades, stocks that screened well on the factor-based model and analyst ratings had better historical performance than either approach on a stand-alone basis.


Performance of Various Approaches to Investing (2003 to 2010)

Source: Haver Analytics, Morgan Stanley & Co. Research as of Feb. 27, 2016

Late Stage or Early Stage?

One question some investors have is that although the growth in factor and quant-based strategies is impressive, how much runway is left?  According to the report, quite a bit.

The report finds that as investor education grows, articulating the advantages of solutions-based investing combined with the shift towards AI and big data techniques to enhance alpha generation versus legacy stock-pickers, the trend will boost factor/quant strategies and accelerate growth into the end of the decade and beyond.

Layer these trends with the structural challenges that traditional active management faces from passive investing, the forecast is that technology-driven investing has a large upside going forward.

Morgan Stanley recently held its 5th Annual “Quantitative Equity Research and Investment Forum,” which featured presentations by renowned academics, quant practitioners, equity analysts and thought leaders in data visualization. The event also featured an “Innovation Expo” that brought together managers and analysts, data product experts, and leaders in quant and factor research.

For more Morgan Stanley Research on quantitative and factor investing, ask your Morgan Stanley representative or Financial Advisor for the full report, “Quant Investing – Bridging the Divide" (Oct. 1, 2017). Plus, more Ideas.