Rethinking How To Approach Value Oriented Equity Investing

By David Kaiser

When institutional investors consider the approaches of growth stock portfolio managers they have long recognized two main established schools of thought — those following subjective, fundamentals-based stock selection and those who are numbers analysis-based followers. The latter group contains what might be described as the old school quant based technical analytics approaches — think candlestick and Elliot Wave — and the new school — think high frequency trading and AI / LLM (large language model) modeling.

But what about when it comes to institutional investors considering the approaches of value stock money managers? What likely comes to mind is the subjective-based, bottom-up fundamental stock picking approach, which value managers have long been known to typically follow; and sometimes there can be a top down asset allocation slant added in. 

Due diligence vetting of qualitative, subjective-based value investing strategies can prove to be challenging. For institutional investors evaluating value equity managers, portfolio manager decision making as to what constitutes a stock being undervalued versus priced low for good reason, and portfolio construction, can be perceived to be an issue. Investors are too often betting on a specific person (the portfolio manager) or people (investment team), rather than a clearly defined investment methodology. If the team changes, so does the expertise, and ultimately the investment process.

Among the sensible investment process due diligence vetting questions investors may ask themselves are whether the portfolio managers under consideration seem to be simply making a mean reversion case for each holding without having ample detail to support their case on a stock by stock basis. Also of concern in evaluating fundamental-based portfolio managers can be the siren call of subjective, behavioral biases — anchoring bias, confirmation bias and recency bias, to name a few. These can steer portfolio managers away from making thoughtful, prudent allocation decisions for their portfolios. Many an institutional investor wants portfolio managers to steer clear of allowing emotional bias to creep into their decision making. Also, are the portfolio managers under consideration seeing and explaining themselves as being stock pickers or portfolio managers? (Allocators often prefer those managers whose focus is on the complexion of their basket of holdings, not stocks as ‘onsies’.)

Could there be a different, non-qualitative way of going about value equity investing that might have the potential to lessen such concerns? We believe there is.

A Data-Driven Approach To Value Oriented Equity Investing

We believe there is a different approach value investors can take; one that offers the potential to remove behavioral biases from portfolio management and spot and take advantage of windows of investment opportunity guided by company-specific data.

We believe that a portfolio of value stocks can be selected systematically, based on quantitative rather than qualitative criteria. Further, we believe such an approach has the potential to deliver higher risk-adjusted returns than an appropriate benchmark as well as the potential to outperform it over time.

For this, a proprietary, data-based fundamental analysis approach to value investing is needed. But run how?

In this day and age, when one hears the phrase data-driven investing LLM AI may be what first comes to mind. Feed a large amount of data into a model and see what it might reveal to act on. As investors have found, many investment firms using such technology do not know what factors, weighted how, are driving their AI models to generate trading signals, or identify securities in which to invest. It is a black box even to many of its users. For allocators who desire to understand and buy into an investment strategy’s methodology, and to have consistency in its application, they may question how such an investment process is able to be repeatable.

It is our view that AI modeling is not required. Instead, for a data-based fundamental analysis approach to value investing to be successful an investor should filter, screen, rank and re-rank companies within the appropriate value universe to identify those possessing what one believes to be the strongest combinations of data-based operating and valuation metrics.

From this, one has the potential to build a long-only equity portfolio of undervalued companies whose stocks may have the greatest price appreciation potential, whether resulting from mean reversion or converting from a value stock to a growth stock.

To achieve this, we believe an investor will need to run a series of muti-factor metrics in order to screen, score, rank and re-rank all of the companies multiple times from multiple perspectives. We understand that even within value investing, different metrics drive price appreciation at different times. That is why a muti-factor metrics analytics approach is required.

By aiming to create a basket of holdings that is attractive by the numbers, we believe this has the potential to keep an investor on target to achieve a satisfactory long-term return objective. For this reason, the aggregate characteristics of the portfolio should be viewed as if they were from those of a one-company portfolio.

Also, while the companies that make up the portfolio are relevant and important, as this style of value investing is more systematic, and subjective-based research and bias are less prevalent, the portfolio should be diversified with enough securities so that a few bad apples do not spoil the bunch. Think about planting an apple orchard. What matters is the health of the orchard and how it relates to the fruit yield. A farmer would not waste time, energy, and other resources to address one dying tree at the expense of keeping the entire orchard healthy.

Due Diligence Recommendations For The Investor

While due diligence vetting of qualitative, subjective-based value investing strategies can prove to be challenging, vetting of data-based value investing strategies may prove to be easier for sophisticated investors to evaluate. Look for clarity and detail in strategy implementation explanations, both for understanding how the manager thinks and finding enough methodology structure to decide whether the process is, indeed, a repeatable one (and thereby have a lesser risk of strategy drift). For that, look for those where the characteristics that drive that strategy are readily available and easily shared with investors. Lastly, while valuation is relative, not absolute, the data metrics of a value equity portfolio as a whole should be clear to see and identify the differentiation between a portfolio, its benchmark and competitors. 

About David Kaiser

David Kaiser is the Managing Partner and CIO of Methodical Investments LLC, which he founded in 2022. He has spent over 20 years at an investment management boutique, where he held roles including research analyst, portfolio manager, and senior investment associate.

David Kaiser
Managing Partner & CIO
Methodical Investments LLC

David Kaiser is the Managing Partner and CIO of Methodical Investments LLC, which he founded in 2022. He has spent over 20 years at an investment management boutique, where he held roles including research analyst, portfolio manager, and senior investment associate.

Methodical Investment, LLC’s intended role within an investor’s total portfolio is to provide capital appreciation potential from exposure to potentially undervalued companies that have, in our opinion, strong operating characteristics.

Our goal in our flagship fund, Method Investments, LP, is to outperform the Russell 2500 Value Index benchmark over a 5-year rolling period, in an attempt to add alpha without adding commensurate risk.