Prices of securities are affected by human behavior. This creates a risk that investors are not compensated for taking, a risk we call the Human Factor. New Age Alpha’s investment methodology is designed to identify and avoid the Human Factor by using a probability-based approach, seeking better returns with lower total risk. New Age Alpha identifies the risk associated with human behavior and actively seeks to avoid it. The targeted result is lower total risk and better returns. In this paper we describe in detail the Human Factor approach to stock selection. We provide evidence of the efficacy of this approach to investing in high yield corporate bonds, and the resulting outperformance and diversification benefits.
What is the Human Factor?
There is known information and vague or ambiguous information that affects security prices. Known information such as shares outstanding or a company’s industry is priced in without the need for interpretation, but most other information is interpreted by market participants in a systemically incorrect way. This can lead to mispricing. Information, such as new product launches, management changes and competitive shifts, can have multiple interpretations, which leads to market participants making assumptions or guesses about what will happen in the future. This in turn creates uncertainty in the marketplace as various market participants have divergent views on potential outcomes for securities. These behaviors we call the Human Factor and we believe that the Human Factor causes securities to be mispriced.
Calculation of Human Factor
In order to determine the extent to which the Human Factor has affected security prices, we use the stock price and other publicly available information. From this, we can infer the performance that the market is expecting of the company. Knowing this we can calculate the probability that, based on a company’s performance history, the market’s expected performance will be achieved.
We start with the company’s stock price and publicly available information. Using only known information, we calculate the implied revenue growth rate needed to support the current stock price. We then compare this implied revenue growth rate to a distribution of historical growth rates over the past 16 quarters. We find where the implied revenue growth rate falls on the curve and that determines our Human Factor probability. Human Factor is the probability that the company will fail to able to deliver the implied growth rate to support its current stock price. A low Human Factor indicates that the risk of failing to deliver is low. On the other hand, a high Human Factor means the risk of disappointing results is high.
Human Factor for Bond Instruments
The total value of a company is comprised of both debt and equity. Bond prices, like stock prices, are determined by markets and affected by biases described above. We define losers as companies most likely to fail to deliver on the growth implied by their stock price. We avoid these losers by excluding securities of companies with the highest Human Factor. These are the companies we believe are most likely to fail to deliver the growth expected by market participants.
Figure 1 below shows how the Human Factor methodology interacts with securities across the capital structure using the framework of the Merton Model (1974)1 for the valuation of corporate debt. In simple terms, bonds can be viewed as a combination of risk-free rate and a sale of a put option on the company’s assets with a strike piece equal to the value of the debt. For companies with high Human Factor or high probability that investor expectations will not be met, this put option, because it is representative of an overpriced stock, must be underpriced. This also makes the debt overpriced, increasing risk to the investor in the bond. While the Merton Model’s ability to calculate a value requires several unrealistic assumptions, the direction of the relationship is not in question. Given the limited upside of debt in the form of the put option we see in Figure 1, a methodology focused on avoiding losers is well suited for corporate bonds. Particularly in the case of bond investing, avoiding losers is more important to total return than owning winners.
Correlation of Performance Between Equities and Bonds
There is clear empirical evidence of correlation of equity returns and bond returns for specific capital structures, but also the markets overall. (See Blume and Keim (1991) 2 and Shane (1994)3 among many others). As discussed, high yield bond prices are set by market participants and are subject to the same uncertainties as stocks. Further, they are well understood to be closely linked through performance. Figure 2 below shows the relationship between equities represented (S&P 500® Index) and high yield bonds observed in 2020.
While levels vary from period to period, long term averages reflect the strong correlation between bonds and stocks. As Figure 3 below shows, long term correlation of returns of S&P 500® and the high yield bond market is 77%. This number is significantly higher than the 65% correlation between high yield and investment grade bonds, two assets classes that are viewed to be similar as components of the broad corporate bond market. Another interesting fact is that high yield bonds are negatively correlated with the Treasury market. A counterintuitive result for an asset class that is viewed as part of the “fixed income” family.
The high correlation of high yield bond returns with equity returns over the last decade can be explained by the fact that high yield companies’ bonds are more sensitive to spreads driven by corporate fundamentals than they are to interest rates and inflation. Our research confirms this, finding that 73% of monthly changes in the price of high yield bonds over the past 10 years are attributable to spread return, with only 27% attributable to yield changes. That may not be the case in an extremely high rate environment, but it is in the current low yielding world and under most reasonable rate scenarios in the modern economic environment. Analysts and investors follow companies, project earnings and conduct research in similar ways in both equity and bond markets. As a result, human biases become embedded in prices of bonds and stocks. Bond prices react similarly when companies fail to meet investor expectations. The magnitude of these reactions may certainly be different. While that analysis is beyond the scope of this paper, the difference is due to the positions in the capital structure and different potential payoffs.
Public Subset of the High Yield Market
Calculating the Human Factor requires a publicly traded equity in the capital structure. As a result, this approach to high yield investing is applicable only to bond issuers with a publicly traded equity. That subset represents approximately 80% of the US dollar denominated high yield market, or over $1.1 trillion. Bonds issued by public companies are generally more liquid with an average deal size of $730mm versus an average of $567mm for privately held issuers.
Further, these bonds enjoy greater financial transparency with their issuers subject to a stricter reporting and disclosure regime when compared to private companies. In addition, private equity sponsored companies that lack a publicly traded stock often have the most aggressive covenants and therefore present the greatest risk of corporate actions unfriendly to bondholders. These risks may or may not be properly priced by the market and because these securities do not have a public equity counterpart the Human Factor is therefore unable to capture them.
It should be noted that the returns for the public subset are similar to the overall market and this characteristic on its own does not appear to lead to outperformance.
On the other hand, avoiding high (bad) Human Factor issuers and investing in bonds of low (good) Human Factor companies produces impressive results. Looking at the performance of the Human Factor on an equal-weighted unconstrained high yield universe shown in Figure 5 below, we see evidence of strong outperformance for the best quintile vs the bottom quintile.
A more practical approach to constructing diversified bond portfolios involves avoiding the worst companies and investing in average and better issuers. This approach is illustrated in Figure 6 below that shows the difference in performance between the top 60% and the bottom 10% of high yield issuers by the Human Factor.
Another proxy of the Human Factor performance for high yield is the New Age Alpha USD High Yield Corporate Bond Index (HFUHYT). The index is calculated and published by S&P Dow Jones and takes a similar approach in that it avoids the worst Human Factor issuers and also weighs components by the inverse of their Human Factor, increasing allocations to those companies with the lowest Human Factor.
Are there any drawbacks of the strategy?
In distressed situations, the relationship between debt and equity can break down. When equity represents a miniscule portion of a company’s capital structure, the Human Factor loses its effectiveness. The equity-based Human Factor approach is best for core high yield exposure and would need to be reevaluated and likely altered for use in the distressed debt market. Our current strategies avoid bonds of distressed issuers.
There are varying levels of liquidity in the high yield market. Bonds don’t trade continuously, and matching index performance can be a challenge as seen from consistent underperformance of high yield ETF funds. New Age Alpha matches enhanced issuer selection of the Human Factor system with a competent portfolio management and trading team. The HFUHYT index, for example, selects individual bonds for a given issuer using basic criteria to ensure liquidity, because the Human Factor does not tell us anything about specific bonds, only the companies that issue them. Experienced portfolio managers would be able to select the most attractive bonds within a given structure that best meet client requirements. Traders would be able to use their relationships in the high yield market still largely traded over the phone to ensure that their investors benefit from access to opportunities for attractive purchases in new issues and block trades.
The Human Factor methodology is well suited for an approach to security selection that relies on avoiding the losers. Additionally, unlike the equity market where the average active manager does not outperform, the high yield bond market still offers opportunities for active management. New Age Alpha believes in taking advantage of them while benefiting from a unique issuer selection approach. The quantitative actuarial approach to issuer selection ensures that additional resources can be allocated to portfolio management, technology and trading.Disclosures
1 Merton, Robert C. (1974) “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal of Finance 29 (2): 449–470.
2 Blume, M. E., & Keim, D. B. (1991). The risk and return of low-grade bonds: An update. Financial Analysts Journal, 47(5), 85–89
3 Shane, H. (1994). Comovements of low-grade debt and equity returns of highly leveraged firms. The Journal of Fixed Income, 3(4), 79–89.
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Past performance is not indicative of future results. Current and future results may be lower or higher than those shown. Therefore, no current or prospective client should assume that the future performance of any specific investment, investment strategy (including the investments and/or investment strategies recommended and/or purchased by New Age Alpha), or an Index, product, or strategy made reference to directly or indirectly in this material, will be profitable or equal to corresponding indicated performance levels. Any charts, graphs or tables used in this document are for illustrative purposes only and should not be construed as providing investment advice.
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