Objectifying Risk / Reward

Risk vs reward – a tale as old as time. From the earliest ages in which we can make our own decisions, humans are forced to ponder this often subjective internal calculation when deciding whether or not to take any action.

Should I try to ride my dog?

Should I stay out past curfew?

Should we go for it on 4^th down?

Should I eat that last slice of cake (yes, always)?

And on, and on, and on. Whether we realize it or not, every day we are faced with these decisions and we act based upon judgement over if the reward from the action will outweigh the associated risks, making it all worthwhile.

Similarly, investors are constantly faced with a simple, albeit dynamic, risk vs reward proposition when evaluating investments. Generally speaking, higher risk investments offer the potential (but no guarantee!) of higher returns as compensation for their uncertainty and volatility; whereas lower risk investments yield lower returns in exchange for their safety and security, some of which may even be legally guaranteed. Our experience investing across asset classes (Fixed Income, Public Equity, Private Equity, etc.) shows that this trade-off doesn’t always map as linearly as an investor would like but understanding the risk vs reward relationship of any investment across the lifecycle of ownership is at the cornerstone of our investment process at Swingbridge. 

Fortunately for investors, unlike the decision around whether or not to eat another slice of cake, we can use actual math to calculate this risk / reward relationship and measure it more objectively.

We define this calculation of upside vs downside relationship as “skew” – a higher skew metric implies that a security has a favorable risk/reward profile offering superior upside reward potential vs its downside risk exposure (i.e., efficient risk taking); a lower skew metric implies the opposite and could result in taking more risk than is warranted (i.e., inefficient risk taking). If we get this directionally right, over and over again, it improves our probability of successfully “buying low and selling high”, which is the end goal for any investor trying to generate strong returns over time - especially if you are seeking to exceed benchmark returns.

In the simplest sense, our skew metrics estimate each unit of return potential relative to each unit of risk exposure for any given security. Or, put another way, at any given point in time, how much upside return potential does a stock/security have measured against its downside risk exposure? To accomplish this, we use financial models to determine what we think are the high (Bull case) and low (Bear case) prices targets, and we then track these on a real-time basis to have a dynamic view of how each stock/security, in addition to the overall portfolio on a consolidated basis, stands at its current valuation. 

To illustrate, let’s assume stock ABC is trading at $100/share, and we estimate a Bull case upside price target of $200/share (100% upside) and a Bear case downside price target of $85/share (15% downside). This would result in a skew estimate of 6.7x per our measurements (100% return potential/15% risk exposure), yielding nearly 7 units of upside return potential for each unit of downside risk exposure. It should be noted that while, mathematically, a stock/security with only 6.7% estimated upside vs. 1% estimated downside would also yield the same 6.7x skew metric, the Swingbridge Public Equity Fund is principally monitoring and targeting names with a minimum profile of 50-60+% upside potential to meet our long-term performance objectives.

Our Private Equity investment process undergoes a similar analytical framework – assessing potential upside case exit valuations relative to downside case down-round valuation resets – though we’ll admit there is more art than science to private-side valuations given the early-stage nature and illiquidity profiles among other things.

To further outline using a more real-world example, let’s review the skew metrics for the largest holding in the fund at the beginning of 2023, which we’ll call “Stock XYZ” (disclaimer: we avoid sharing the actual company name to avoid looking like we’re making any recommendations). At entry, our upside / downside price targets for Stock XYZ were $170 (Bull) and $83 (Bear), respectively. As such, our average initial cost basis of $98 yielded an estimated skew of nearly 5.0x (~74% upside return potential / ~15% downside risk exposure).

As the Company has continued to execute since we first acquired the shares, growing top-line revenues while preserving margins and accelerating free cash flow generation, our current upside / downside price targets for Stock XYZ have been adjusted, increasing to $198 (Bull) and $132 (Bear), respectively. As a result, in effect, we were able to build a position in Stock XYZ at levels ($98/share) significantly below what is now our most recently updated “downside” price target ($132). Though this doesn’t mean the investment is “riskless”, it highlights efficient risk taking when remaining disciplined to our skew assessments at the time of entry.

Today, Stock XYZ trades much closer to our Bull case target near $200, and as such, our estimated measurement of skew has declined materially. Because of this decline in skew, we have actively trimmed the position back to an average-sized holding in the fund and will continue to reduce our exposure if the skew measurement continues to decline and/or our underlying assessment of the qualitative characteristics of the business and its category leading positioning no longer warrant an investment. We are constantly adjusting our upside and downside price targets, and therefore skew metrics, with new information – including updated management forecasts, industry growth rates, capital structure changes, or tweaks to real-time interest rates and equity risk premiums.

Additionally, it is our expectation that price estimates (both Bull and Bear) for our growth-related names should increase over time as management executes, which will continually increase the skew metrics. This is why we continue to own Stock XYZ shares today despite its lower near-term skew estimate. And when that changes, we can rebalance our exposure to new names, or increase exposure to existing names, within the portfolio at more accretive skew measurements to enhance the overall portfolio view.

In the illustration above, we talk about individual securities only. At a portfolio level, we consolidate each individual security’s skew metric and apply the weighting of that security’s size in the aggregate portfolio to measure the weighted average estimated consolidated skew metric of the entire portfolio, which we’ve described before as targeting to remain > 5x (and should always remain superior on a risk-adjusted basis vs. the broader stock market indices like the S&P 500). We strongly believe that over time if we can continue to monitor a growing population of attractive stocks that fit the target Swingbridge criteria, and invest in a concentrated sub-set of the highest quality names at decidedly attractive skew metrics (while preserving a consolidated portfolio skew estimate > 5x), we will continue to preserve an efficient risk/reward profile for the fund and ultimately drive long-term outperformance of the S&P 500 index.

It really is the same concept as daily decision making, only with the ability to use actual numbers and math as a guiding light. We view this as a much better, and more confident, way to invest compared to just trusting our guts. Though we’re perfectly happy to trust our guts when it comes to weighing the risk / reward of eating that last piece of cake.