The H-Factor and Unpriced Behavioral Risk

[A novel way to think about portfolio risk is providing a provocative challenge to conventional portfolio management wisdom. It acknowledges that there exists a pricing disconnect between what a company can actually do and what the market believes – and this disconnect is driven by human behavior like overconfidence, herding, storytelling, or fear of missing out. It is not a numbers problem. It is a people problem.

This perspective argues that the real damage to investment performance is not coming from traditional risk measures such as volatility or beta. It comes from belief – from the stories and biases we are buying into. Humans regularly impound assumptions, along with vague and ambiguous information, into stock prices. This leads to prices that make no sense when you look under the hood. It demonstrates how emotion and narrative can drive prices far from reality and this presents risks that investors are not compensated for.

Rather than asking whether a company is overvalued or undervalued, investment managers and investors should ask a more practical question: What is the likelihood that a company will fail to deliver on what the stock price already assumes? Adding this research layer to the investment process would systematically remove stocks most likely to disappoint while maintaining the broader market exposure from an index, mutual fund, ETF, or SMA portfolio. 

To better understand this novel risk management and alpha-generating perspective, we reached out to Julian Koski, Chief Investment Officer of New Age Alpha – a Rye, NY-based investment management firm that developed an innovative investment strategy that avoids systematic behavioral biases in stock pricing through objective, probability-based security selection. This led to the development of his h-factor (human factor) research tool and methodology, which uses actuarial science to actually measure the probability that a company will not deliver the growth its stock price expects.

The firm offers this approach through eight actively managed mutual funds, SMA capabilities, model portfolios, Index Licensing, and their SPACE platform, an advisor-only resource available at no cost that translates their h-factor methodology into a practical research, portfolio management, product development, and client communication tool.

As validated by Nobel laureate Daniel Kahneman in a discussion with Julian, and a University of Missouri working paper by Dan W. French and David Javakhadze, it is clear that the h-factor adds greatly to the field of behavioral finance by not providing another behavioral model, but by offering a first-time quantification tool of behavior’s impact on stock prices. By quantifying the extent to which behavioral biases affect stock prices, investors can systematically avoid unpriced behavioral risk and position portfolios to achieve superior risk-adjusted returns.]

Hortz: What in your experience most led you to the development of your h-factor behavioral risk methodology to challenge conventional portfolio management strategies?

Koski: It’s twofold. Consistently losing money; that is number one. Number two was growing up around people in the actuarial business. During my time in South Africa as an audit clerk at one of the biggest insurance companies in the world, I worked with the chief investment officer who gave me the insight into using actuarial science to pick stocks rather than traditional portfolio management processes, with the focus being on avoiding losing stocks.

I was always fascinated by the idea that people could pick stocks, and I initially bought into the prevailing Kool-Aid. I was reading research reports and doing fundamental analysis, all the traditional things investors do, but I realized very quickly that it was just Kool-Aid – investing that way just did not go anywhere. You ended up losing more money than you won.

I was on Wall Street as an investment banker doing all those things, but the real push came when Armen Arus and I joined forces to start a private equity firm back in 1999. It was the time of the dotcom era. You had firms coming into our offices telling us that they had zero revenue but were worth a billion dollars. Well, how do you underwrite that kind of risk when somebody has nothing but a good idea and tells you they are worth a billion dollars?

In response, we conceived the idea that instead of trying to worry about the valuation, let us give them the billion-dollar valuation and work backwards to figure out what that valuation implies. Instead of building term sheets around valuation, we will build them around the company’s ability to deliver the growth implied by the valuation.

In other words, my view as an investment banker at that time was that I would rather own 1% of a company that actually can deliver returns and growth, than own 50% of something that is worthless. In most cases in investment banking, there is a tension that exists between the client, who is the company, and the investors. As an investment banker, who do I represent? Do I represent the people putting the money in or do I represent the client company that is coming to us for money? There is an inherent conflict of interest there. But by going backwards, you remove that conflict of interest because you basically say, “Look, I’ll give you your valuation, but this is what I need you to do to get that valuation.”

Armen and I built that model, then built a probability score around that, all the while acknowledging the disconnect between human behavior and stock prices and borrowing key principles from an actuarial perspective – learning how to invest in equities using probabilities rather than relying on traditional research and selection processes.

Hortz: Can you explain your stance on the role of human behavior’s impact on stock prices and behavioral finance in investing?

Koski: First off, we are not a behavioral economics company. We are not looking to try to “exploit” human behavior. We believe that behavior influences stock prices, and we, as a company, have developed what we believe is the first way to really measure when behavior is actually impacting stock prices.

Our stance is that you cannot exploit human behavior in the traditional sense, like a traditional factor, but you can avoid it. The problem with exploiting behavior is two-fold. One, you do not really know which behavior is at work. Number two, behavioral economics is often arbitraged away very quickly. The market arbitrages any dislocation in behavior quite quickly. So, to capture the effects of behavioral economics, you have to be very quick, but you also have to know which behavior is working. Our view is that we do not care. We do not care what behavior is working.

We just believe that behavior impacts stocks and it is all there in the stock price. We sweep it up completely in our process and say that we do not need to use this in measuring valuation. We only use the facts that we positively know – the financial statements and the stock price. That is all you need because the rest is assumptions with little hard evidence of whether you are right or wrong. You just do not know, so the outcome is going to be random. We feel that the outcomes of traditional Wall Street and asset management analysis simply lead to random outcomes and that is the problem. We, on the other hand, have figured out a way to quantify and measure human behavior’s effects on stock prices, and, therefore, when you can measure it, you can also avoid it.

Daniel Kahneman recognized in our discussions that we offer a unique first-time tool – what we call the h-factor, or human-factor – that quantifies human behavior and offers a tool and methodology that completely differentiates us from other behavioral investing practitioners.

Hortz: Can you explain how you validated that this approach actually measures behavioral risk and offers a useful tool for capturing performance?

Koski: If you think about information, it is broken down into known information and what we call vague and ambiguous information. Known information in investing, to us, is the stock price and the financial statements. Now, that is the same proxy for life insurance.

Life insurance does not underwrite risk based on what they think they know about you. Insurance companies do not ask you, “Are you going to quit smoking? Are you going to go to the gym?” They underwrite risk based on what they know. You either go to the gym or do not smoke, and they underwrite that risk. When you think about a stock, the same thing is true. There are only two things known about a stock at any point in time – financial statements and stock price. Now, if you give financial statements and stock prices to a group of analysts, generally they will all come back with the same consensus view about the valuation of that company.

But the minute you give them vague and ambiguous information as defined by news, stories, and emotion; then the consensus view is all over the place. It is not systematic anymore. By removing this problem completely, we are basically saying that when we make an investment decision, we look hard at the stock price. We want to make sure that the stock price is not being influenced by vague and ambiguous information.

We feel that a nice word for working with vague and ambiguous information is forecasting. The second nice word for it is predicting, and the worst word for it is gambling. By doing it our way, we are removing this speculative behavior.

When we calculate the h-factor scores, we break stocks down into low h-factor scores, which represent low human behavior effects, and high h-factor scores, which represent high human factor effects. We do not see much human behavior effects when a stock price has a 1% probability of failing to deliver the growth implied by the stock price. Now, if a company stock has a 90% chance of failing, that means that the stock price has been heavily impacted by some kind of human behavior – aggressive forecasting or other speculative behavior. What we know from the years and years of doing this is that historically low h-factor stocks outperform the high h-factor stocks 71% of the time, on a quarterly basis.

While it does not work all the time, imagine a 71% chance that low h-factor stocks will outperform high h-factor stocks. It is about being less wrong. That is what it is. We are never going to be right all the time. That is not how it works. In fact, it is important to acknowledge loss because if you do not have loss, then something is wrong. Then you are Madoff. Something is going on here.

Casinos have loss, but they keep you at the table long enough for you to gamble it all away. Well, the same theory applies here. Over long periods of time, we isolate low h-factor stocks versus high h-factor stocks, and that is it. There are moments in history where high h-factor stocks will be in temporary favor, but we have found that they tend to reverse very quickly.

Hortz: Why is the h-factor an important new portfolio management tool?

Koski: The problem with your portfolio today is not that you have too few winners, but that you have too many losers. This is a key point in understanding where alpha comes from. Does it come from picking winners or does it come from avoiding losers?

We think this is a key investment management problem as Wall Street has focused on alpha by picking winners. But if you say that, then you have to have perceived knowledge of the future by forecasting, predicting, or gambling, choose your word, because you do not know a stock’s true valuation. Whereas insurance companies figured out decades ago that it is better to avoid losers than try to determine future events.

Insurance companies do it with a simple probability approach that we have now adopted into the world of stock picking. It is the same thing. Once you understand this different way to calculate stock risk, you realize that calculating probability offers a different tool than trying to forecast the future. You can calculate a probability based on what is known about a stock at any time – the stock price and financials. There is nothing else.

This is the first time that there is a quantitative tool backed by known facts and simple math for investment managers on how human behavior affects portfolio performance.

Hortz: Can you further explain how this is different from other behavioral models that have been developed?

Koski: Well, there is a family of heuristics that behavioral finance managers have created. They have loss aversion, anchoring, things like that. But if you think about them, they are not mathematically driven. They are behaviorally driven. People look at those things and say, “How is the world behaving?”  You need to be a psychologist to understand what those behaviors are doing. We do not have that issue. We just know that as long as there are humans involved in making this decision, you are best off avoiding that human behavior.

And again, we say just look at the math. The math that applies in casino operations, in insurance operations, and even in fixed income. We use mathematics on the truths we can see versus the notions of a professional talking head telling us where he or she expects the markets to go.

Hortz: How exactly can investment managers and financial advisors use the h-factor tool and your SPACE platform in their investment process?

Koski: There are two ways. One way is they can simply invest in our mutual funds or SMAs or our upcoming ETFs, and we do it for them. Or number two, we give professional investors full access to the h-factor system, which has the h-factor scores for thousands of stocks around the world. It even has the h-factor score for ETFs because they are also subject to owning stock positions that are carrying a great deal of risk and should be removed.

We make the h-factor system available for free to financial advisors and institutional investment professionals. The statement I always make to them is to at least look at the h-factor scores on some of their portfolio companies and stocks they are researching to understand how much of that stock has been influenced by human behavior. There may be a high probability that you are going to lose money.

So that is how financial professionals can use our h-factor system to look at one individual position, one individual stock. They can also use it to look at a mutual fund, ETF, or SMA, and then apply our h-factor methodology to all the stocks in the product or portfolio, which can help you adjust the risk parameters in the client’s portfolio that they may have.

Hortz: How can the h-factor and SPACE platform help build an advisor’s differentiation and competitive positioning?

Koski: It can become a capability and an analysis research tool that advisors can offer to your clients or prospects for free to differentiate how they are analyzing their clients’ top holdings, whether an ETF, an SMA, or mutual fund. You can keep what is good in their portfolio but offer to make it better by removing the losers from their portfolio or investment vehicle. You can build products with “the losers” – the companies most likely to fail to deliver the revenue growth rate indicated by their stock price – being removed from the portfolio. That is what our system allows you to do – to not give up on active alpha in favor of beta and solve for concentration risk problems that come with many ETFs, mutual funds, or SMA products. So yes, if you can use our tool to actively avoid the losers, you have something that can be quite valuable.

The h-factor platform can become a great approach and offering to gain new clients as a prospecting tool because you are talking about something that a client most probably has never heard of before. Everything today is commoditized with thousands of investment products out there with good performance. How do you extend your value proposition?

The only way to win a new client, or you can charge more for what you do, is by extending your value proposition. And for us, that is the h-factor and its differentiated investment process and story. Clients have not heard of this all before – about avoiding the losers, actuarial risk analysis, and the ability to adjust risk exposures they may not have been aware of. And this is all supported by differentiated research tools and real methodology, not just theory.

And we believe that as an asset manager, our work must go beyond product. I do not focus on product. I focus on how we can create value beyond the product. How can I help advisors build their practice? And with Anthropic announcing that they are now going to launch AI advisory AI bots, you better be doing something unique. We have created a toolkit for advisors to go ahead and show their clients and position them to say, “I have a story that you’ve never heard before backed by mathematics and products that work really well.” It is a story few people are talking about.

Hortz: You have compared the h-factor to “odds on the tote board that a racehorse will win” and emphasized that “you don’t make money by picking favorites”. What other ways can you suggest to explain the h-factor methodology and best way to deploy it in a client’s portfolio?

Koski: If you acknowledge that there may be more luck vs skill in traditional investment management, then you want to have odds on your side. You want to be the casino. When we talk about the h-factor, it is about getting the odds of investment performance more in your favor. It is as simple as that. It does not mean we are always going to be right. That is the way luck works. But the h-factor is no different than the on-base percentage that was used in Moneyball. It is no different than the actuarial tables that life insurance actuaries use. It is no different from casino odds or horse racing odds. It is the same odds. But all of those odds that I just spoke about have one thing in common – every single one of them is calculated based on factual known information.

Wall Street has been hijacked by the talking heads and by the media to believe that we should listen to the talking heads. We have to get back to the mathematics of investing. We are giving them the investment tools and methodology to do it and make a compelling case for selecting securities using actuarial science rather than traditional portfolio management processes. Once we create a relationship with an advisor or other financial professional, it becomes a very immersive relationship where we can really engage with them and show them how to deploy our differentiated investment approach in their practice.

This article was originally published here and is republished on Wealthtender with permission.

About the Author

Bill Hortz

Founder Institute for Innovation Development

Bill Hortz is an independent business consultant and Founder/Dean of the Institute for Innovation Development- a financial services business innovation platform and network. With over 30 years of experience in the financial services industry including expertise in sales/marketing/branding of asset management firms, as well as, creatively restructuring and developing internal/external sales and strategic account departments for 5 major financial firms, including OppenheimerFunds, Neuberger&Berman and Templeton Funds Distributors. His wide ranging experiences have led Bill to a strong belief, passion and advocation for strategic thinking, innovation creation and strategic account management as the nexus of business skills needed to address a business environment challenged by an accelerating rate of change.