Quantitative Strategy

Quantitative strategy involves the use of data, statistics and computational algorithms to identify persistent trends and anomalies in the market. As it is grounded in observable facts and repeatable procedures, quantitative strategy is immune to the psychological biases that can affect other styles of market analysis.
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Adam Gould, CFA

Adam Gould is the Head of Quantitative Research at Fundstrat Global Advisors. He has nearly 20 years of experience on Wall Street, focusing on quantitative equity research and strategy. Immediately prior to joining Fundstrat, Adam worked at S&P Dow Jones Indices, designing, developing and marketing indices built around machine learning and natural language processing. Prior to S&P, Adam held roles in quantitative equity research at Nomura, Morgan Stanley and Empirical Research Partners. Adam earned Bachelor of Science and Master of Engineering degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. He is also a CFA charterholder.
Quantitative research involves collecting and analyzing data, identifying patterns and relationships in that data, and finally drawing conclusions and making predictions. One way to think about it is that quantitative research applies a disciplined, scientific approach to investing. Because it is systematic and rules-based in nature, techniques used in quantitative research can be generalized to different groups of assets across multiple time horizons.
The data used in quantitative research ranges from well-structured, highly-available information like price and volume, to less structured data, like social media posts and text documents. A wide variety of techniques are employed, including regression analysis, machine learning and artificial intelligence.
Quantitative research has been used in investing for many decades and underpins many of the modern approaches we take for granted in investing today. Benjamin Graham, widely regarded as the father of value investing, can be viewed as an early quant investor. Concepts relevant to portfolio construction, such as risk, correlation, and diversification, are a key focus of quantitative research.

Why is Quantitative Strategy Important?

Since quantitative strategy involves a systematic and rules-based approach, it is removed from the biases and emotions inherent to other investment approaches. While some investments claim to be systematic, quantitative strategy applies objective techniques to historical data to produce repeatable strategies. A quantitative strategy will not panic when the market sells off, nor will it get a case of FOMO when the market rallies.
Since quantitative strategy provides repeatable, objective methods for analyzing markets, assets and individual stocks, we can apply the techniques to larger numbers of securities. For example, our quantitative stock selection approach analyzes all members of an index at a given point in time, providing a cross-sectional comparison for a large number of stocks.
Quantitative strategy also provides methods through which we can break down the performance of a portfolio. Obviously, it’s great when a portfolio does well, but by breaking down performance, we can get an understanding of whether the portfolio is likely to continue to make money going forward.
For example, if a portfolio did well because it took on more risk, it may suffer when the market sells off. On the other hand, if the portfolio did well while taking on similar risk to the market, we can be more confident in seeing gains in the future.
The vast increase in data available to investors today presents a new realm for the application of quantitative investing approaches. Many techniques borrowed from statistics and artificial intelligence can be applied to these large datasets. Investors today are applying these new data sources and techniques to supplement traditional areas of finance.

How to Use Our Quantitative Strategy Research

The quantitative research section of our website will have several subsections, each with a different area of focus.

Each month, we will produce updates on the performance of our custom strategies, such as our dynamic multi-factor strategy and our stock selection strategies. These updates can be found in the Factor Strategy subsection.

Developments around new “alternative” data sources can be found in the Social AI subsection. Here, we will publish findings around strategies and outcomes related to the use of non-structured datasets, with a focus on social media.

We will also occasionally publish tools and commentary on how the market looks from a quantitative perspective. These notes can be found in the Quant Commentary subsection. Topics of interest will include (but not be limited to) market valuation, asset class correlations and earnings season performance. This section can be thought of as an ever-growing toolbox to give unique perspectives on the market.

The signals produced by our quantitatively driven models can be used in conjunction with some of the other products we have to offer on our FSInsight platform. For example, by combining our quantitatively derived stock lists with Mark Newton’s Technical Strategy suggestions, you can search for attractive entry/exit points. Also, some of our quantitative signals aim to measure short-term market sentiment. Combining these signals with Mark’s short-term technical view on a particular market can uncover attractive opportunities. Also, the quantitatively driven market valuation work can be used to supplement the latest views on the market we express through our First Word publication.

Our Brian’s Dunks list can be combined with our quantitative stock selection lists to allow an investor to narrow down a potential set of investments. Stocks that rate favorably on both lists represent interesting opportunities and are likely worth further investigation.

Factor Strategy

Factor investing involves targeting specific drivers of return across asset classes. For years, leading investors have utilized factor investing to improve portfolio outcomes, reduce volatility, and enhance diversification. The two main factors are: macroeconomic and style. Macroeconomic factors capture broad risks across asset classes, while style factors help to explain returns and risk within asset classes.

Further, factor investing focuses on persistent, rules-based sources of excess return. Examples of factors are value (the tendency for cheap stocks to outperform expensive ones) and size (smaller stocks performing better than larger stocks). Most factors have roots in academic research, and today, a variety of ETFs exist for investors to access factors.

Macroeconomic factors include economic growth, real rates, and inflation. Style factors include value, momentum, size, and quality. One widely misunderstood belief around factor investing is that it must be used instead of indexed or active investments. That’s not true: Factor-based strategies, including Factor ETFs, can be used to replace and to complement traditional index or active investments in the portfolio.

What is the Factor Strategy and how to apply it to your portfolio?

We track a dynamic multi-factor portfolio that applies a tilting mechanism to a standard, static multi-factor portfolio consisting of six factors (growth, quality, low-volatility, momentum, size and value). The dynamic portfolio tilts weight toward the factors with the best recent performance, and away from the factors with the worst recent performance.

Profits to the Brave! Lets get started!

Factors used on this strategy


The iShares S&P 500 Value ETF tracks the investment results of an index composed of large-cap U.S. equities that exhibit value characteristics, which include mature businesses, steady growth rates, relatively stable earnings and long-term growth potential. Value stocks trade at a lower price relative to fundamentals. Berkshire Hathaway (BRK.B), Procter and Gamble (PG), and Johnson & Johnson (JNJ) are classic value stocks because they trade for relatively cheap valuations relative to their earnings.


The Invesco S&P 500 Quality ETF tracks an index of U.S. large-cap stocks selected by return on equity, changes in net operating assets, and financial leverage. Think profitable industry leaders with strong balance sheets. Examples include Pfizer (PFE), Visa (V), and JPMorgan (JPM).


The iShares S&P 500 Growth ETF tracks an index composed of large-cap U.S. equities that exhibit growth characteristics, namely high earnings growth, a higher price-to-earnings (PE) ratio than the broader market, and increased volatility and risk. Stocks include Apple (AAPL), Microsoft (MSFT), Tesla (TSLA), Amazon (AMZN), Alphabet (GOOGL), Nvidia (NVDA), and Home Depot (HD).

Low Volatility

The Invesco S&P 500 Low Volatility ETF tracks stocks with the lowest volatility, meaning they experience smaller swings during bull and bear markets. Many pay dividends and function as cornerstones of a more defensive investment approach, which is conversative and diversified. Examples include Verizon (VZ) and PepsiCo (PEP).


The Invesco S&P 500 Momentum ETF tracks stocks with a high “momentum score,” and is rebalanced twice a year. Comprising mostly large-cap stocks, the ETF includes a wide range of companies, from Chevron (CVX) to Exxon (XOM) to UnitedHealth (UNH).


The iShares Russell 2000 ETF tracks small-cap stocks, such as those ranked 1,000-3,000 by market cap. IVM is for investors looking for exposure to smaller U.S. companies, diversify a U.S. stock allocation, and seek long-term growth. Holdings include BJ’s Wholesale Club (BJ), Avis Budget Group (CAR), and Chesapeake Energy (CHK).