Quantitative, or quant, investing has exploded onto the scene and soared in popularity and usage in recent years. However, it’s not just for institutional fund managers and those with advanced degrees in math, statistics, and data analytics—it can be easily integrated into anyone’s investing process. Let’s discover how we can effectively harness the power of quant investing in our F.I.R.E. world!
If you’re investing for financial independence and/or early retirement (aka F.I.R.E.), then chances are you have probably pondered how you can tap into the power of quant analysis to increase your long-run investing success. Well, it’s not as difficult as you may have thought!
Whether you are focused on dividend-growth investing, dividend-value investing, or a mix of both, you can reap the benefits of quantitative analysis by integrating it into a comprehensive passive-income investing process!
By leveraging factor exposure and quant analysis and combining it with a qualitative check and balance, you can improve your investing and boost your long-term success.
In this article, we’ll explore the history of quant investing, examine its strengths (aka the why) and weaknesses, and discover how we can integrate this powerful value-add tool into our passive-income investing…
At its core, quantitative (or quant) investing is an approach to investing that is predicated on objective, quantitative analysis of financial metrics.
In other words, it is an attempt to construct a rational and systematic approach to investing—one that is objective (i.e., eliminates human subjectivity and bias) and predicated on the analysis hard data. As such, it seeks to avoid the ad-hoc and subjective (aka seat-of-the-pants) approach employed by most retail investors.
As Steve Forester, professor of finance at the University of Western Ontario’s Ivey Business School, aptly notes:
“A lot of work on behavioral economics suggest many investors, left to their own devices, will make poor decisions because of the biases we tend to have… The spirit of quantitative models is to step aside from the emotions and have some logical basis for making investment decisions.”
Now, when most people think about quant investing and these “quantitative models,” they picture an automated, algo-based system—one predicated on complex algorithms that employ massive data-mining, advanced math, and Ph.D. level statistics. To be fair, this does reflect one side—or extreme—of the quant spectrum—the so-called black-box world of quant.
However, there is a much larger portion of that quant sphere to consider—one that is highly applicable to the average retail investor. You don’t have to be a large institutional hedge-fund or high-frequency trader to tap into the power of quantitative investing!
It is this slice of the quant spectrum (viz., quant analysis in retail investing) that we want to focus our attention on in this article. After all, what you and I really want to know is: (1) Can we leverage quant analysis in our investing and, if so, (2) how do we do it?
If the overarching goal of quant investing—one shared by most strategies—is to add value (or alpha) to your portfolio, how do we effectively and efficiently capture that potential value add?
Let’s press on and find out!
Despite its meteoric rise in interest, quant investing didn’t surface overnight. Rather, it has evolved over decades of practical experience and academic research.
The foundations of this strategy can be traced all the way back to Louis Bachelier and his seminal paper The Theory of Speculation, which he published in 1900.
Over the years since, it the concept has evolved and branched out—experiencing explosive growth and development as computing power has gone hyperbolic. It forms the basis of portfolio diversification (predicated on modern portfolio theory or MPT) and has also contributed to the development of the Black-Scholes option pricing formula.
However, from a retail investor perspective, the real foundation of quantitative analysis began with the father of value investing—Benjamin Graham. He authored the two books that remain the gold-standard for the quant approach to value investing: Security Analysis (1934), co-authored with David Dodd, and The Intelligent Investor (1949).
These works introduced many of the concepts we espouse on Wicked Capital, including but certainly not limited to: having a proper investing mindset, buy-and-hold, fundamental analysis, margin of safety (MOS), concentrated (intelligent) diversification, and a contrarian approach.
This quant value philosophy was chronicled by Jim O’Shaughnessey in his book What Works on Wall Street (1996)—which quickly became a classic guide to quantitative value investing.
Furthermore, the quantitative approach lies at the heart of Joel Greenblatt’s investing strategy—as discussed in his books You Can Be a Stock Market Genius: Uncover the Secret (1997), The Little Book that Beats the Market (1999), The Little Book that Still Beats the Market (2007), and The Big Secret for the Small Investor: The Shortest Route to Long-Term Investing Success (2011).
While quantitative analysis can be applied to all genres of investing (including growth), it is best suited and most widely applied to value investing.
As such, it seeks to avoid cognitive biases that produce bad investing decisions by developing a systematic approach to value investing—one predicated on analyzing fundamental data in a regimented process.
This embodies the rules-based approach of Benjamin Graham—one focused on constructing a cohesive portfolio based on a relatively limited set of objective fundamental financial factors.
As such, quantitative value investing is guided by a set of rules designed to exploit short-term market inefficiencies—caused by behavioral errors made by market participants (e.g., emotion-based decisions).
This approach is predicated on a belief that markets are not perfectly efficient.
The capital asset pricing model (CAPM) asserts that expected returns are exclusively dependent on a security’s relationship to the market—based on the assumption that the market (i.e., the aggregate of market participants) have all available information and price securities appropriately.
But you know what they say about assuming! And this model is predicated on two really big assumptions: (1) all market participants have access to all relevant information and (2) they act on that knowledge—price securities—appropriately.
The biggest problem with this is that reality does not support the assertion—in the short-term—that the market always prices securities appropriately. In fact, the market frequently overreacts to short-run catalysts in both directions. It has an emotional component—no matter how much one tries to deny it.
Quantitative value investing provides a framework to capitalize on that emotion by employing a quant-approach that leverages a time-arbitrage edge.
It is a contrarian approach that seeks to strip emotion from the decision-making equation by objectively and systematically identifying stocks that are undervalued in the short-run and exploiting that alpha opportunity over the long run.
If you’re interested in the efficient market hypothesis, I recommend reading our article Here’s Why an Efficient Market is Irrelevant to Long-Term Investors, where we drill down into the subject from a long-term investing perspective and discuss the time-arbitrage value edge.
Before we dive into the details, it’s important to recognize that there are some powerful reasons you should consider adding a measure of quant to your passive-income investing—whether you utilize a dividend-growth strategy, dividend-value strategy, or a blend of both.
As we’ve noted, one of the greatest strengths of quant investing is that it aids us in avoiding common behavioral pitfalls and biases. To learn more about these investing landmines, I encourage you to read our articles 4 Perilous Pitfalls to Avoid with Dividend-Growth Investing and 11 Toxic Investing Biases You Must Guard Against.
Furthermore, integrating a quant component into your investing process enables you to greatly reduce the impact of emotion on your investment decision-making process.
Finally, quantitative analysis enables you to construct an investing process that is repeatable. This is critical to our long-run success. We want to filter out luck and be able to replicate actions that add real value to our portfolio.
These strengths produce tremendous benefits when it comes to our long-term investing success.
While quant investing has its strengths, it is—like all things—not without its weaknesses.
First, a quant approach provides us with a limited depth of analysis. There is more to the long-run performance of a company than just financial metrics (e.g., sustainable competitive advantages or moats, management, industry outlooks, etc.). Without some qualitative dimension, we are only seeing part of the picture. It’s akin to viewing a three-dimensional world in two dimensions. No matter how high the resolution is on our big-screen TV, we are still missing a perspective that we can only get in real life.
Second, quantitative investing is highly dependent on historical data. This creates two sub-problems.
The quality of our decision-making results will be limited by the quality of the data we analyze. The old adage “garbage in, garbage out” definitely applies to quant analysis! This is particular important in terms of the metrics we use—especially if we are calculating them ourselves. Using metrics that are not a significant driver of the result(s) we are striving for (or calculating them wrong) will result in failure—or at least a degraded level of success.
Furthermore, quant analysis is rearward-facing. It is predicated on historical data and—as we stress all the time—past performance is not indicative of future performance! This is a definite limitation.
The past is known and, therefore, we can be very analytical and objective with it.
However, the future is unknowable and, therefore, requires a degree of qualitative and subjective consideration.
This is supported by chaos theory. Tiny changes in variables or parameters can cause massive variances in outcomes over longer periods of time. Stated differently, quant analysis can provide forward-looking accuracy over very short time periods or broad context over longer periods—but not specificity over longer periods. This is a drawback for long-term investors making investing decisions for long time-horizons.
The third weakness inherent in quant analysis is the human tendency to over-optimize (aka over-complicate) things—getting too cute. This can lead to both decreased effectiveness and efficiency.
Finally, because quantitative analysis deals with high volumes of data (even for retail investors), it can lead to a micro focus that misses macro factors—meaning, we can get lost in the minutia of the data and miss the forest for the trees.
While a quant approach to investing is not without its weaknesses, these can be easily mitigated and the strengths—in my humble opinion—greatly outweigh any potential snares that must be avoided.
So, we’ve seen that there are significant advantages to integrating a quant approach to your investing process—but how exactly do we do that?
I recommend taking a comprehensive approach—one that incorporates a factor framework, a quant process, and a qualitative capstone. This will enable you to harness the power of quant investing, while mitigating some of those potential weaknesses we discussed above.
It’s akin to a rocket. With a rocket, our goal is to reach space and a safe orbit. However, getting there requires a rocket with stages. With our investing process, we need three stages: a factor framework (booster stage), a quant-based process (second stage), and a qualitative dimension (final stage).
This provides us with a process that is both comprehensive and systematic. Let’s take a look at each of these stages in detail and discover how we can harness the power of “quant” with our own long-term, passive-income investing…
The first stage we need to develop for our comprehensive investing process is a factor foundation or framework.
As we noted earlier, quant investing is predicated on the notion that the market is not perfectly efficient.
However, the problem is that the CAPM fails to explain why certain characteristics (factors) are statistically tied to alpha. Again, CAPM asserts that an efficient market exists and, therefore, it is impossible to consistently outperform the market over time.
In other words, stock returns are driven exclusively by two factors: the market and company-specific information.
Yet, these anomalies (factors) deviate from this hypothesis and demonstrate that statistically-significant alpha exists.
In an effort to improve the CAPM and explain these anomalies, Fama and French introduced their 3-factor model in 1992. In addition to the CAPM’s original market factor (beta), they found size and value to be statistically-significant factors for explaining excess returns.
In 2015, they further expanded their model to include five factors—adding quality and investment.
While this narrowed the existing gap in fully explaining stock returns, it remains an imperfect solution. As a result, the search for additional factors has raged on, with academics around the globe joining the search. Hundreds of potential factors have been proposed, but most can either (1) be explained by the original five factors or (2) have questionable statistical significance.
This highlights two important keys for retail investors: factors matter… but they can become a distraction.
First, there are reliable factors that produce alpha—factors we want exposure to in our portfolios.
Thus, the first component of a comprehensive investing process is to develop a factor framework.
At Wicked Capital, we employ an investing framework that seeks to maintain exposure to size, value, quality.
Second, it is easy to overcomplicate things when it comes to factor exposure!
Institutional fund managers Willem van Dommelan and Stan Verhoeven share some valuable wisdom when they assert, “We believe the vast majority of so-called factors are a result of data-mining. We stick to the true, well-researched and documented factors and are less inclined to short-term overhauls.”
Sam Benstead reinforces this point in his article when he states that the two “prefer to build key factors that have a long track record of academic approval and proven investment results into their systematic [factor-based] strategies.”
By simply sticking to size, value, and quality factors, we can (1) tap into the value-growth spread and (2) capture the bulk of potential factor-alpha in the market—thereby maintaining both the effectiveness and efficiency of our overarching investing process.
Spend your time on your quantitative and qualitative analysis—not chasing the latest, greatest factor!
Want to learn a bit more about the CAPM and how factors play into it? Here are two videos—the first looks at the CAPM and the second addresses the Fama and French 3-factor model.
One last note: paying dividends—while it has tremendous benefits—is not a factor in and of itself. However, as passive-income investors, this is clearly a fundamental filter that forms the bedrock of our entire process. But have no fear, we discuss all the benefits of dividends in our article Busting the Dividend-Irrelevance Myth… Yet Again!
If you’re interested in learning more about the value-growth spread, I encourage you to read our article What Does the Value-Growth Spread Really Mean for Long-Term Investors?
To learn more about the importance of the value factor, checkout our article Value Consistently Trumps Growth for Long-Term Investing.
To learn more about the size factor, I recommend reading our article Does Size Matter When It Comes to Dividend-Growth Investing?
Now that we have a factor framework to guide our stock selection and overall portfolio allocations, we need to turn our attention to integrating a quant investing process—the second component of our comprehensive investing process.
Again, the point is to develop a rules-based system for identifying stocks to invest in.
This entails selecting fundamental financial metrics to analyze. It is critical that we select metrics that are drivers for the long-term performance we want!
A great example of this is utilizing an F-score approach. In 2000, Joseph Piotroski, a Stanford accounting professor, published a ground-breaking study entitled Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.
In essence, it provides a quantitative rules-based system for identifying quality stocks. The details of his model are outside of the scope of this article; however, I highly encourage you to read our article Can F-Scores Really Improve Your Dividend-Growth Investing, where we dive into the details and discuss how you can develop your own F-score system (or variation of it).
This is just one method—using a point-based system. However, the possibilities are endless!
Unfortunately, like factors, this leads to the issue of focus and efficiency. Quant analysis can be a very deep rabbit hole—one we don’t want to fall too deeply into!
This leads to a critical caveat: When using a quant-based investment strategy—stick to the KISS (Keep It Simple Stupid) principle!
This is particularly true for those of us that love crunching numbers and performing statistical analysis.
Van Dommelan and Verhoeven echo this sentiment when they assert that “investors who are more analytical are more inclined to tweak their data because they might believe it is beneficial. However, academic research points to the opposite: that excess bells and whistles don’t point to excess returns.”
Keeping your quant analysis simple and streamlined is something that even Benjamin Graham advocated for in his later years.
Despite literally writing the book (or books) on applying quant analysis to value investing, in 1976 he asserted, “I am no longer an advocate of elaborate techniques of security analysis in order to find superior value opportunities.”
Regarding Graham’s change in approach in 1976, Wesley Gray writes:
Developing a solid quant process and integrating it into your comprehensive passive-income investing process is critical to your long-term success—but always keep it simple!
The final stage of our comprehensive investing process is to add a qualitative capstone.
This should be a simple go or no-go vote—like you would get from mission control before launch. (Or thumb-up/thumb-down if you prefer)
This part of the process is where we consider qualitative factors, such as management, sustainable competitive advantages (moats), and the outlook for the industry.
If we have a good feeling about the outlook for the company, then it’s a green light to go based on our quant results. If we don’t have a good feeling, then we should consider pumping the brakes and holding off. If we’re agnostic (neither hot nor cold), then it may be an opportunity to start a small developmental position based on the quant analysis—but not go all in building a core position.
Always remember that—as Warren Buffett so eloquently points out—there are no called strikes in investing! We don’t have to swing at pitches we don’t like or aren’t comfortable with.
What’s critical is to get the order right! You don’t fire the third stage rocket before the first and second stage have done their work—that would be a bad day for the astronauts! The same holds true for investors… and we don’t want bad days!
We don’t base our quant analysis on our qualitative feelings. Let the data speak for itself and then—once you have identified potential opportunities—render a qualitative verdict.
For example, you may formulate a qualitative opinion on the outlook for the oil or mining industries. While some may believe the outlook is bleak, you may feel more optimistic about the next decade or two—the long game.
However, you don’t want to invest in an oil or mining stock simply based on your broad, qualitative outlook. Rather, when your quantitative process identifies an opportunity in those industries, you may give it a green light based on your qualitative outlook or underlying investing thesis.
The key to success is to limit your qualitative opinions to a tight range of options—you either feel good, agnostic, or bad. You don’t want to be all over the place and overly subjective—that makes it nearly impossible to consistently repeat your process.
Again, if you don’t have a strong opinion, limit your exposure (not a core position) and give it time to see if your qualitative thesis strengthens or weakens.
You want your quant analysis to be the heart and soul of your process—where the proverbial sausage is made. However, you can think of your qualitative process as executive oversight—with veto authority.
Qualitative analysis is—by its very nature—subjective and emotional. We don’t want to be emotional investors. Let that final step simply serve as a check and balance to keep you between the lines—or on the right trajectory to stick with the rocket analogy.
Quant investing has developed over a long period of time—but has seen an explosion in popularity and usage in recent years. However, it should not be relegated to the realm of institutional fund managers and black-box developers with advanced degrees in mathematics and statistics.
Quantitative analysis is highly relevant and applicable to retail investors too!
However, we’ve also addressed some of the weakness or drawbacks it can pose for retail investors:
Because of these potential weaknesses, it is important that we integrate our quant analysis into a comprehensive investing process—one that is balanced.
I recommend a three-stage approach—one that combines the powerful advantages of factor exposure, quantitative analysis, and qualitative checks and balances.
Remember, it is important to always apply the KISS principle—keep it simple stupid!
More doesn’t always equal better. In fact, when it comes to investing, the least analysis needed to produce a desired outcome is usually the best approach. Like anything, the more moving parts something has, the greater the likelihood of a failure!
Plus, the more variables you introduce into your process, the harder it will be to determine which ones add value, which ones are irrelevant, and which ones subtract value from your investing process.
The bottom line is that integrating some degree of quant analysis into the investing process is something we should all do. It’s a simple and effective way to increase our long-run investing success as passive income investors—whether we’re focused on dividend-growth, dividend-value, or a mix of both!
Build your three stages and you’ll be ready for liftoff—liftoff towards long-term passive-income success with the power of quant investing!
Finally, if you’re interested in dividend-growth investing predicated on a value framework—you’re in the right place! We focus exclusively on helping others be as successful as possible with this passive-income approach to investing and we hope you’ll continue to return to our site to learn, grow, sharpen your skills, and find effective and positive ideas and motivation!
Soak it all in, take and use what you want, modify it to fit your unique situation, and keep building that portfolio with a solid process and winning mindset!
We also encourage you to follow along with our public Wicked Capital Passive-Income Portfolio (PIP) through our monthly updates on the website and by viewing the portfolio on M1 Finance at https://m1.finance/1zUclN2JL
It’s a great way to learn from a real-world example of building and managing a dividend-growth portfolio predicated on a value investing framework!
If you’re interested in starting your own portfolio using the M1 Finance platform (which we highly recommend), please consider using our referral link https://mbsy.co/sZVS3 and we’ll both get some free cash to invest!
That’s just one more reason to start your dividend-growth investing today! It’s never too soon to start working towards your financial freedom!
Benstead, S. (2019, July 22). Why the old ways are the best in quant investing. Retrieved from Citywire Selector.
Bouw, B. (2019, October 10). Quant strategies are reducing risk and taking emotions out of investing. Retrieved from The Globe and Mail.
Graham, B. (1976). A conversation with Benjamin Graham. Financial Analysts Journal, 32(5), 20-23. doi:10.2469/faj.v32.n5.20
Gray, W. (2014, October 7). The quantitative value investing philosophy. Retrieved from Alpha Architect.
Gray, W. (2017, April 22). ‘Alternate’ facts about formulaic value investing. Retrieved from Alpha Architect.
Piotroski, J. D. (2002). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, The University of Chicago Graduate School of Business.
Always remember, investing involves substantial risk of loss and is not suitable for everyone. The valuation of investments may fluctuate, and, as a result, you may lose substantial amounts of money. No one should make any investment decision without first consulting his or her own financial adviser and conducting his or her own research and due diligence.
You should not treat any opinion expressed on the Wicked Capital website as a specific inducement to make a particular investment or follow a particular strategy, but only as an expression of opinion for entertainment purposes.
The opinions are based upon information we consider reliable, but neither Wicked Capital nor its affiliates, partners and/or subsidiaries warrant its completeness or accuracy, and it should not be relied upon as such.
Past performance is not indicative of future results. Wicked Capital does not guarantee any specific outcome or profit. You should be aware of the real risk of loss in following any strategy or investment discussed on this website.
As noted above, strategies or investments discussed may fluctuate in price or value. Investors may get back less than invested.
Investments or strategies mentioned on this website may not be suitable for you. The material presented does not take into account your particular investment objectives, risk tolerance, financial situation, or needs and is not intended as recommendations appropriate for you. You must always make an independent decision regarding investments or strategies mentioned on this website. Before acting on information provided on this website, you should consider whether it is suitable for your particular circumstances and strongly consider seeking advice from your own licensed financial or investment adviser.