In our Chart for the Curious about Apple Computer, we document the challenges faced by investors who follow forecasters. For those seeking compelling evidence based on robust quantitative methods, please see our White Paper The Persistence of Profits. That piece builds on work done by a legendary professor of behavioral finance Joseph Lakonishok – a founder of LSV – one of the world’s great asset managers.

Get our insights direct to your inbox: SUBSCRIBE

What is Quantamental Investing?

Quant investing management typically employs large portfolios of stocks rebalanced at predetermined intervals. These approaches attempt to exploit alpha from various factors or perceived information advantages gained from alternative data. In contrast, fundamental investors seek to ascertain the quality of a firm based on its current and future cash flows and numerous other qualitative factors.

The term “quantamental” is really just an ugly merger of the words “quantitative” and “fundamental.” Quantamental just means that someone is using quantitative outputs while also pursuing the more traditional fundamental investment process frequently associated with people like Peter Lynch. We will talk more about both in a moment but want to make sure we give the fundamental side of the term its due. In our post on speculative trading we transcribed dialogue from a presentation given by Peter Lynch that hammered home, with a good deal of humor, the tremendous value in understanding what you are investing in.

Quantamental investing merges the use of systematic stock picking from quantitative tools with the oversight and diligence of fundamental investors. As the asset management industry has embraced this approach, quantamental investing has become an omnibus term that defines a wide range of often disparate methods. At one extreme are programmers using big data and machine learning to try and parse alpha from high-speed trading. At the other extreme, traditional fundamental researchers use simple factor-based tools to help ensure they are hewing to their mandates which might hold their positions for over a decade.

Quantitative methods typically benefit from removing of human beings’ unfortunate tendency to be their own worst enemies when it comes to investing. Fundamental methods benefit from human beings’ ability to recognize everything from environmental factors, like a global pandemic, to granular items in footnotes that data cannot capture. By avoiding bias and incorporating rigorous firm-level information, quantamental investors seek to exploit returns that are available from both factors (quantitative) and stock selection (fundamental).

Summarily, combining quantitative and fundamental analysis, a “quant amental” approach, allows users to get the best out of both the fundamental research and quantitative investment processes.

Quantamental Investing Strategies: The KCR Approach

If you look at the biographies of the Research Team’s co-founders, you will see they both spent their entire careers using a “quantamental strategy.” For them, this is a skill set they have been refining and working on for 50 combined years. Our team views the quantitative side as integral to the fundamental research process which, then surfaces improvements in the quantitative models we use.

KCR’s portfolio managers and researchers know no other method to successfully create systematic alpha.

Steeped in behavioral finance, our ranking tools seek to exploit the tremendous pricing errors that emerge due to the inevitable herding, fear, and greed that characterize markets. The financial data used to build our models was hand cleaned at the company’s founding in 2010. This arduous process continues to this day and is why KCR’s models are used by some of the top institutional investors and asset allocation firms in the world.

Spanning over a decade, our published research has used our quantamental approach to successfully identify and exploit bubbles in everything from dividend stocks to pockets of speculative excess akin to that seen in 1999. Similarly, our work has identified areas where undue pessimism has gripped the market and allowed us to buy blue-chip companies trading at significant discounts to intrinsic value.

Please click on a 90-second video tutorial on our tools or scroll down to see a simple history of Apple.

An Example of a KCR Model: Our S&P 500 Large Cap Model

The chart below shows how KCR classifies our bullish, neutral, and bearish zones.

  • When a stock ranks in the top 100 ranked stocks, the model highlights the stocks Kailash Concepts believes, represent mispriced quality
  • When the stock is in the middle of the model, between rank 200 – 400, that means the model effectively has no view
  • When the stock falls into the bottom 100 stocks, the model is warning us that the firm is either grossly overvalued, has balance sheet problems, accounting irregularities, or some combination of those issues

Progression of KCRs View on AAPL Since 1989

Quantamental Investing Tools

Please contact us if you would like a trial of any or all of our various tools.

Remember steer clear of bear trading.

  1. Becoming a Better Investor with a Quantamental Investing Strategy

    One of the biggest benefits of using a quantamental approach to investing is that it helps remove not just your own bias but the bias of the inputs you might otherwise use. A quick, simple and powerful example of this is our piece Stocks Analyst Loathe & Love. In that piece we showed that Wall Street researchers tended to favor lower quality companies that were more expensive than the market. This is a very common reason for underperformance and falls into the category of “herding.”

    People who embrace a quantamental method are, by definition, letting themselves see how the market looks “by the numbers.” And when we say “by the numbers” we simply mean that the quantitative side is dispassionate. A ranking methodology, like ours, does not care what the crowd is doing. Sophisticated tools like the ones we offer give investors simple outputs that tell them what stocks have high quality earnings, strong balance sheets and how cheap (or expensive) they are. There is no room for emotion to enter the process.

    And we think that is a starting point of critical value that quantamental solves. The less emotions investors bring into the investment process the less likely they are to commit behavioral errors. If you are looking for a quick primer on some of the major mistakes that the quantitative part of a quantamental process fixes, check out our free post summarizing the legendary Andre Shleifer’s book Inefficient Markets. As a reminder: KCR is a big believer in investor education and we offer any new or existing subscriber a free copy of any of the books we have written up – like our summary of one of Galbraith’s legendary books Bull Markets and their Consequences. The exception, of course, is Mr. Klarman’s legendary Margin of Safety – we wrote up a summary of our copy but, at $2,000 per book, that one is not offered for free!

    Another reason KCR believes so fiercely in using a quantamental approach stems from the ability of each researcher to balance exactly how much “quant” to employ and how much “fundamental” work to employ. Quantitative ranking engines can miss things that are important to the investment process but not in the data. Great examples of this might be lawsuits against the company. If a CEO is encumbered by a huge amount of legal challenges that could indicate that management is distracted. This problem might be all the more important if the company the CEO runs happens to also be severely overvalued, then those lawsuits, outside the purview of quantitative rankings, might weigh more heavily in your analysis.

    Risks Involved in Quantamental Investing:

    One of the major risks to quantamental investing in our view is a lack of balance or overconfidence. Ideally the quantitative side of the process should create portfolios of stocks that, over the long haul, will generate solid risk adjusted returns. When the quantitative side of the book begins to work you might suffer a bout of attribution bias and start thinking it is your fundamental analysis that is generating all the excess returns and suddenly you are off putting far too much weight on qualitative items. The same can be true in reverse.

    If the quantitative side of the process is performing poorly because the market is in the midst of a growth stock mania you might begin to engage in performance chasing. Instead of realizing that clean tech stocks were a short – something our models were clear in suggesting – you might have ended up buying them. Similarly, temporary dislocations on the quantitative side could have led you to miss buying Devon energy and opting instead to buy a very poorly ranked stock like Snapchat.

    The biggest risk, in our view, can be summarized as follows: when the quantiative side is most valuable it is likely going to feel the most and least comfortable. Whe it is hitting hard, you might drift away from it. Similarly, when it is underperforming you might be tempted to chase performance.