Transcript: Equity Values of a Different Flavor
Wednesday, May 22, 2019
4:00 – 5:30 PM, Eastern time
Your panelists:
- Kevin McLaren, Principal, Korn Ferry
- Carly Sanfilipo, Director, Aon Reward Solutions
Moving Beyond Survey & Accounting Values in Plan Design
Balance Value Data With Quality Benchmarks
Beware Blending Equity Value Data From Multiple Sources
Accounting Valuations For Planning & Market vs. Performance Awards
Limitations of Survey Data
A Portfolio Approach
LTI Theory/Outcomes/Leverage
Communicating Value to Employees
Kathleen Cleary, Education Director, NASPP: Good afternoon, everyone. Welcome to “Equity Values of a Different Flavor.” Today, our panel will be discussing considerations and approaches for valuing awards. If you find the multiple approaches for valuing awards across compensation planning, grant guidelines, accounting and other purposes to be a real challenge, you're not alone. Today's panel will help you shed some light and hopefully clear up some of that confusion.
First introductions, my name is Kathleen Cleary and I'm the Education Director for the NASPP. I'm very happy to welcome an exceptional panel of industry experts today, Kevin McLaren, Principal at Korn Ferry and Carly Sanfilipo, Director from Aon Reward Solutions. I will let each of the panelists introduce themselves a little more fully in a minute. But first, I have just a couple of housekeeping items.
The slide presentation for the webcast is posted on Naspp.com and you can download or print it from there. If you're logged into GoToWebinar, you should be seeing the presentation slides move as we go through the webcast. You'll have the opportunity to ask questions throughout the webcast by typing them into your GoToWebinar panel. We will go through the presentation and hold the questions until the end, but feel free to type them in as you think of them. If for any reason we don't have time to answer all your questions during the webcast, I'll reach out to you by email. We will post an archive of today's program within the next day or two, and then a transcript will be posted in the next few weeks.
OK. I'm going to turn it over to Kevin for a brief bio.
Kevin McLaren, Principal, Korn Ferry: Thank you, Kathleen, and good afternoon National Association of Stock Plan Professionals. My name is Kevin McLaren and I'm a principal within Korn Ferry's Executive Pay & Governance practice. My expertise is focused mainly in public companies, private equity and long-term stock plans. And I appreciate the opportunity to speak with you today.
Cleary: Carly?
Carly Sanfilipo, Director, Aon Reward Solutions: Thanks, Kathleen and Kevin. I'm Carly Sanfilipo, a director with Aon Reward Solutions. You may be familiar with our team; we are often referred to as the Radford valuation team. Many of us are—myself included—as we refer to, are recovering pension actuaries. So, we've got a strong actuarial background but we're applying it to equity compensation. I always tell my clients the more complicated your plan design gets, the more excited I get, because it just means I get to build a bigger, more complicated model. I'm excited to talk to you guys today about the ins and outs of equity valuation and how it affects comp.
Return to Index
Moving Beyond Survey & Accounting Values in Plan Design
Sanfilipo: Moving on to the goals for today's session, hopefully, by the end of this hour you should be able to absorb the compensation survey values and apply the survey results in context with how the survey data was collected. You should be able to understand the implications of using the accounting valuation during your plan design process and you should learn innovative ways to move beyond surveys and valuations. Then finally, we'll talk about better ways to communicate award value to employees.
Survey data—it's great and it just keeps getting better, because every year, more and more data is collected on just how specifically companies compensate and reward their employees. Survey data is a great place to start, but just as with any exercise that involves a large data set, it's important to consider what the data represented at the time it was collected.
Here we have a graph showing stock price returns for the three major indices starting in 2007. You can see this graph shows what the stock prices were doing just before and just after the great recession in 2008. Usually there is a six-month lag from the time the survey data is collected to the time the survey data is used. That six-month lag typically wouldn't be too problematic, as you can see in that first bracket. The stock price didn't move all that much between when the survey data was collected versus when it was used.
But you can see when that great recession happened, the survey data that was collected was majorly disconnected from the circumstances under which the survey data would be used. We've got an example on the next slide.
Last year, in that first bracket, we saw that the survey value we're trying to accomplish in our comp planning is $20,000. The share price at the time of use last year was $10. Taking the $20,000 value that we're trying to achieve and dividing it by the $10 current stock price, we have a resulting award size of 2,000 shares.
Now, we move to the next year, 2008. Once again, the survey data was collected. This time it was at $18,000, so slightly down from the previous year but still somewhat in the same range. However, our stock price has dropped way down from $10 to $3. If we take the intended value of $18,000 and divide by our $3 stock price, we have a resulting award size of 6,000 shares, which is three times higher than what we awarded last year.
Is that appropriate? If you end up granting 6,000 shares, you're increasing your burn rate by three times. But conversely, if we were to keep it at 2,000 shares, that is short-changing the executive you're awarding these shares to, because the fact of the matter is that the share price is lower now, so the value of the award is lower. At the end of the day neither of these choices is appropriate, because the disconnected survey data is flawed.
The next slide shows one way to adjust disconnected survey data. In this example, survey data was collected on 15 peer companies. Then we took it one step further by making adjustments based on how the peer company stock price moved from the time of collection to the time of use. You can see after making the adjustments, we look at both the average and the median, and if we had seen the average and the median values be drastically different, let's say negative 20 percent and negative 40 percent, we would have to dig into the data a little bit more to understand the difference between the average and the median.
But using this example, the average and the median are pretty close, so it would be reasonable under the circumstance to adjust any survey data that was collected down by 25 percent. One more thing to note here, you can see that we use an averaging period when making the adjustment, and the purpose of that is just to smooth out any daily volatility.
Return to Index
Balance Value Data With Quality Benchmarks
Sanfilipo: All right, moving on to slide eight, in addition to adjusting survey data, we can also make use of multiple survey data points. Here, we weigh two common methods for determining the number of stock options to grant.
In the first column here, we have the Black-Scholes fair value. That means is that we would take the intended value to be delivered, divide it by the Black-Scholes fair value and the results would be the number of options to grant.
Conversely, we have a quantity shares methodology which is just a flat number of shares that's communicated. As far as ease of use by staff, Black-Scholes fair values can be a little bit complicated. Mostly because it requires the development of Black-Scholes assumptions that can be difficult to determine, particularly things like expected life and how to measure volatility. There's a lot of rigor that goes into those assumptions.
It can be a little bit difficult for companies to handle that appropriately, so quantity of shares is much easier for HR staff to use when determining target shares for options. Moving down to the next row, ease of understanding by employees.
For the same reason as above, the Black-Scholes fair value can be pretty difficult to communicate to employees, because it's so dependent on things like stock price volatility and our assumption of when the options will be exercised.
As far as consistency with accounting and SEC executive total comp disclosures and proxies, the Black-Scholes fair value is a great way to go when determining the number of options to grant because ultimately, the value that's disclosed in your proxy will be based on that Black-Scholes fair value. If you intend to award $10,000 worth of options, and use the Black-Scholes fair value, you'll then disclose that you awarded $10,000 worth of options.
Finally, it's important to consider the organizational maturity of your company. For mature public companies with low volatility, Black-Scholes fair value may be a good way to go for determining grant size because the fair value won't swing too much from year-to-year.
But if you're an immature public company with high volatility, high volatility means your stock option fair value can swing drastically from year-to-year, which would mean that the resulting award sizes would then swing drastically from year-to-year. If you are a company with high volatility, you may be better off just sticking with the flat quantity of shares methodology. Down at the bottom here, we have a little example. For the company assumptions, our current stock price is $15, our Black-Scholes fair value is about 50 percent of face value, which is $7.50, and we have 100 million common shares.
We studied survey data and grabbed the 50th percentile of LTI value, which was $100,000, so using our current Black-Scholes fair value, that would result in 13,333 options being awarded. Second, we studied the 50th percentile survey data for the quantity of options and that was $10,000, so that's just the $10,000 result there. Finally, we studied the 50th percentile for options as a percentage of the company and we found that it was 0.015 percent. If we multiply that against our 100 million common shares, we have resulting options being granted of 15,000. So, you can see we've got three options to consider here. It may be that the best answer is to simply average all three approaches. And that's what we decided on here.
Return to Index
Beware Blending Equity Value Data From Multiple Sources
Sanfilipo: All right, moving on to the next slide. Beware of blending equity value data from multiple sources. Here we have the results of two surveys that we studied, and you can see the methodologies are very different. For example, for option valuation, some companies may use company-specific volatility. Other companies may use a peer group for volatility. Volatility may be calculated as of the current year or it may be calculated as of the prior fiscal year.
For full value shares, some companies may disclose the face value on the grant date and others may disclose some investing provisions associated with it. As you can see, these examples are significantly disconnected. The data as reported should not be simply blended and averaged.
There have been recent efforts to standardize the way these are disclosed, but there is still no consensus among the firms. Ultimately, your company has to consider the data you're collecting and using, then determine whether it needs to be adjusted either from a company specific standpoint or from an aggregate peer group standpoint.
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Accounting Valuations for Planning & Market vs. Performance Awards
Sanfilipo: All right, moving on, should we be using the accounting valuations for compensation planning? We have some arguments in favor. As I mentioned before, if you use the accounting valuation to determine your target buys, your proxy disclosure value will be consistent with the intended value that you meant to grant.
It will flip directly from earnings charges to budgeting and it's also readily available for your peer companies to be able to study their proxies and see what value they use for their awards. However, there are valid arguments against using the accounting valuation when you're planning the target size for your awards.
For example, the accounting objectives we use to value an equity award have to be consistent with ASC 718. And ASC 718 has some pretty strict rules, most notably being we have to value these awards in a risk-free, neutral environment.
But, of course, we know that the real market has risks. So, there is already a disconnect between the accounting objective and the real-world objective. It's important to remember that the compensation objectives are to assess and maintain competitiveness relative to labor market peers. For example, let's say we have two otherwise identical companies granting stock options to their executives. One company, we've noticed that their executives tend to hold their options longer than the other company. If you hold your options longer, the intrinsic value has a longer opportunity to increase. That increases your fair value, but is it necessarily appropriate to use different assumptions and different fair values for these otherwise similar companies because one company's employees held their options longer historically.
The result of that is fewer shares are being delivered to executives in the company that holds their shares longer. Is that fair, is it competitive? There are arguments for and against, and these things need to be considered when using the accounting valuation to plan compensation awards.
Finally, some accounting valuations are counterintuitive. The biggest problem we see with using accounting valuations for performance awards is that performance award fair values can be significantly higher than the stock price. In fact, I saw one relative TSR fair value that was 180 percent of the stock price. That was a little bit jarring when they went to disclose the value of the award. They had intended to grant a certain amount of PSUs, but they didn't use the accounting fair value, so when it came time to disclose the accounting fair value in their proxy, it looked like they had awarded 80 percent additional value than what they had intended.
It's always important to consider the arguments for and against the accounting valuations because there are good reasons on either side. And those reasons may change over time.
Speaking of PSUs, moving on to slide 11, it's important to understand the difference between accounting valuation for market conditions and performance conditions. They are valued very differently, and they are accounted for very differently.
Let’s start with market condition—and I'll put it simply. The valuation for market conditions is hard and the accounting is easy. The valuation is hard because we have to use a Monte Carlo simulation model to value awards with market conditions. One fascinating thing about Monte Carlo simulations is that the underlying mathematics is exactly identical to what Black-Scholes is using.
If we were to use the Black-Scholes formula and the Monte Carlo model with the same exact plain vanilla option that we're trying to model, we would get the same fair value. But the Monte Carlo allows much more flexibility, so for things like stock price hurdles or relative TSR plans, we have to use a Monte Carlo to value that market condition.
In our model, we consider all possible future results, as well as the likelihood of those possible future results. In our models at Aon, we typically start with at least 250,000 simulations to try and predict all of the possible future results of the stock price. We then average all of those simulations, and that is your fair value. You simply take that fair value, multiply it by your target shares and then you have fixed grant date expense. If the market condition pays out at maximum or it doesn't pay out anything, we don't have to change that expense, because the fair value we calculated using Monte Carlo already includes the possibility of a max payout, no payout, target or anything in between.
Once again, the valuation is complicated because we're required to use Monte Carlo, but the accounting is easy because once you have the grant date fair value expense locked in, there are no adjustments. Unless, of course, the participant terminates before the requisite service period is complete. In that case, you do get to claw back all of that expense the same way you would for any other equity forfeiture.
That's market condition, moving on to performance condition, kind of the opposite of market condition. The valuation is easy, and the accounting is hard. The valuation is easy because the fair value is just the stock price on the date of grant. There are some exceptions based on whether or not you include dividend equivalents. We won't get into that nitty-gritty right now, but main rule of thumb is the fair value for performance awards is simply equal to the grant date stock price.
Then you multiply that by the number of awards you expect to vest. Most companies start out assuming target, so it's simply the grant date stock price times the target shares. Then every reporting period, you have to reassess the number of awards you expect to vest and ultimately, the final expense will equal the grant date stock price times the number of awards that actually vest.
Unlike market conditions, if a performance condition doesn't pay out anything, you don't have to expense anything. But conversely, if the performance condition pays out above target, you have to increase your expense above target, whereas you don't have to do that with a market condition.
I'm going to pass it on to Kevin now and he's going to discuss some limitations of survey data.
Return to Index
Limitations of Survey Data
McLaren: Thanks, Carly. Okay, limitations of survey data. Buried in the complexity of different valuation approaches and accounting methodologies, which Carly just provided a fantastic overview of, we often overlook the limited insights that we actually want. We settle for the best information available—survey data, prevalence data, market values, grant date fair value. In reality, this is just the tip of the iceberg. The breadth and depth of the foundation of each LTI plan is often hidden.
When designing these programs, we think about compensation and LTI strategy, the interplay and counterbalancing of other compensation programs, things like perceived value by recipient, the business circumstances, including recent performance, organizational strategy, the growth prospect of the company and the evolving tax accounting and regulatory influences and rules that we've seen over the last few years.
These variables present risk. Risk that our long-term incentive programs will drive poor alignment either with the competition externally or with our own business internally in terms of strategy and performance.
Each practitioner must acknowledge that every company has a different risk tolerance and understand that no single vehicle can properly leverage and/or mitigate that risk—and for the most part we have. The result has been an evolution from solely option-oriented programs in the '90s to more RSUs; differentiated and diversified programs with companies delivering a portfolio of vehicles, trying to drive productive risk and LTI programs in the most efficient way that their business circumstances will allow.
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A Portfolio Approach
McLaren: No portfolio is created equal. The preferred approaches are clear, multiple vehicles and a clear orientation towards performance outcome. Looking at the data right here, we can see that according to our CEO compensation study in the last couple of years, there is a prevalence of these portfolio approaches. All three vehicles—options, restricted stock and performance shares—being used by the majority of companies as well as options and performance shares or restricted shares and performance shares.
You can see over to the right with these different combinations, the different weightings of vehicles within the portfolio, again, seeing a large weighting on performance awards. I realize I just talked about the limitations in survey data and now I'm showing the survey data and I feel guilty.
Let's think about it really quick. This is CEO data. Most of us know that the further down the organization you go, the more the vehicles become time vested, a little more restricted stock in focus as you go further down the organization.
This is also general industry data, reflecting all industries. If you think about your own industry and how it differs in terms of its risk profile, high-tech versus banking versus telecom versus industrial—all very different.
The prevalence of options within specific industries can be wildly different. If you're making plan design decisions based on this kind of data, then please pay attention. The key takeaway here is the dependency of a portfolio approach on unknown outcomes.
This is something that Carly was talking about as well with market conditions and performance conditions and the unknowns. We have to get comfortable with the unknown to understand the ways we are exposing compensation value to the magnitude and probability of those outcomes. In general, more risk, lower probability, more reward potential; less risk, higher probability, less reward potential. As you can see on the first bullet here, our conventional wisdom tells us that RSUs are the least risky, performance shares a little bit more risky with stock options being the riskier, and then performance-based options—some stock options with performance conditions can be even more risky.
The typical company will try to control risk exposure by managing the mix of each vehicle within the portfolio, for example, we will increase the weight of RSUs to protect in a downturn at the expense of a windfall and an upswing or vice versa. We also know that we can play with and customize the risk or leverage within those vehicles, hence the broad adoption of performance shares to the most flexible and customizable of all the vehicles. We can set thresholds, targets, maximums, wherever we want. We can directly cater to the probability of certain or expected outcomes.
You can even tie your payout to relative stock price performance. If you look at what's being listed on this page, again, that target maximum payout level is affected by stock price volatility, the options to restricted stock unit weighting, as well stock pricing, business performance, industry and growth potential.
Return to Index
LTI Theory/Outcomes/Leverage
McLaren: Next slide please. With these limitations, we cannot be bound by one particular approach or set of assumptions defined by economy over a given survey. You can definitely rely on them, but we don't have to be contained by them. We must get out of the surveys and into the outcome.
Let's talk about stock price performance, the next page. This can be daunting. There are an unlimited number of outcomes to account for. You can see by the mess in this chart, stock prices can go anywhere.
If we could predict stock price performance, you probably wouldn’t be listening to this webcast. The volatility of our stock is the biggest uncontrollable piece. It's an element and assumption that is influenced by an unlimited number of business performance and industry factors—some of which Carly described—which are beyond our control. But it has a direct and meaningful impact on accurate pay levels that we can't ignore. It impacts the compensation that actually reaches the pockets of our long-term incentive plan participants.
Benchmarking requires different approaches. Ideally, we want to compare our plans to companies that have exhibited a reasonable or acceptable degree of alignment and stock price performance. We want companies that perform similar to us. This group may be different from or the same as our true or desired market for talent or a cut of data based on revenue size or industry within a given survey.
With this type of approach, we can begin to understand how our plans compare within a range of stock price performance outcomes applied consistently across each company and we can grow our understanding beyond the target or grant date value we're trying to deliver to how much we will actually deliver in similar circumstances.
Let's take a look at this chart on slide 18. What this depicts is realizable pay of a particular equity grant mapped to different changes in stock price relative to a peer group. What you can see here is the green line, the relative realizable pay tracks below median for the most part until about a 15 percent stock price increase. This may be an option grant that kicks in to the money, perhaps a performance share grant that is triggered on the vesting of a share price. But we see at plus 15 percent, the leverage of these types of grants increases to a level beyond what we would consider the peer median. What does this tell us? Do we need to raise go forward grant levels? We're tracking below median in terms of realizable pay. Do we need to temper the leverage at higher stock prices for those plus 15 percent with more RSUs? Should we grant premium price options? Neither of these decisions would be supported by conventional survey data based on best practices.
So, forget benchmarking for a second. This is critical information to have. It exposes the sensitivity in our retention efforts and pinpoints situations where we are not achieving a fundamental objective within our plan and when remedial measures or actions may be needed. It also shows where the return on our LTI investment begins to accelerate, you see plus 15 percent or where we're getting more bang for our buck.
Next page please—then there's goal setting. It's another factor that has a direct and meaningful impact on actual pay level, compensation that actually reaches the pockets of LTI recipients. This one is more controllable. We all aim to set challenging yet attainable targets—when we provide opportunities to outpace our target market positioning, if we exceed those goals, and provide downside risk at the same time, when we fall short.
The human element here is real and it introduces even more volatility into our pay program both intentionally and unintentionally. The volatility of those performance outcomes is easily seen by looking at historical and actual performance trends.
When we assess historical payouts—assessing historical payouts provides us with the insight into a level of rigor that we and our competitors are applying to the goal-setting process—and how that translates into the probability of achievement and ultimately the perceived value to participants.
Ideally, a proper distribution over a five-year period would be the achievement of our own targets. You can see the degree of error in goal setting visible in this type of exhibit, with the dispersion around the target performance level. You look to the far right and you see some nice tight clustering of actual performance around the target, so we're setting expectations and we're achieving them. However, if you look at some of the peers to the left, you can see a very disparate payout structure, where we've been coming in all over the map.
This has a direct impact on perceived value, particularly in performance shares when the goal setting process is weak. Looking at this, the range of potential outcomes gives us better insight into how we're approaching goal setting and ultimately pay outcomes.
Next page. Again, let's get beyond probability weighted accounting measures and understand a range of possible outcomes. Let's statistically assess the volatility of our pay outcomes and determine where the ends of the spectrum are. Target or grant values are only one possibility within that range. Where does our plan top out, where does it bottom out and how does that compare to market? Does the target skew towards the high end or does it skew towards the lower end? What's the balance of upside opportunity and downside protection?
This is what we aim to depict in this chart, with our target values in the green circle in the middle, as well as an outcome on the high end and an outcome on the lower end of our expectations, then tracking that to market and how our leverage compares. I actually believe that private companies are better and more disciplined in this exercise. These are companies that don't have a readily available stock price to base the value of their grants on or a liquid market on which to sell those shares.
I work a lot with private equity firms, and they are notorious for solely option-based programs tied to a liquidity event, sales, IPOs, with tranches that vest based on stock price growth hurdles, et cetera. The principle here is that management gets paid when, and to the extent, that the sponsors do.
The value they try to deliver is not driven by any current data or any stock price, but rather the outcome of a single event. One that generates a return on the sponsor’s investment and creates liquidity to pay out the option holder. They are often forced, at the time that they are making the investment, to look to that event and outcome, and determine a range of pay outcomes based on different performance scenarios.
That performance is mostly defined as a multiple of invested capital, so whatever they're investing in the beginning, to what they actually earn when they sell or IPO, or possibly the internal rate of return, what's the percentage, what's the return on investment over a period of time in terms of IRR.
Why do they do this? Because management is doing it. Think about hiring a new CEO, a new CFO and trying to communicate the value proposition of these grants based on no publicly available information. What is that value proposition based on? That's right, it's based on outcomes.
Next page please. The private equity, I will admit is a different animal. These are shareholders with a high degree of risk tolerance that are willing to make big bets and share potentially outsized growth with the management team if those bets pay off and they'll link it directly to what they make.
Public companies, however, tend to have a lower risk tolerance. They sit on a wider spectrum, so maybe they're private equity or venture capital backed. Maybe they're a controlled or family owned company, maybe they're broadly or institutionally held, all these varying degrees of public float, how many shares are actually owned by the public and how does that influence the risk within our business and within our pay program? Are the growth prospects high or are we paying for value and for yield? Are we firing on all cylinders within our strategy or are we doubling down on a turnaround?
Theoretically, the volatility and overall leverage in a pay program should directionally reflect the potential volatility in a company's controllable business results, thereby realigning pay and business trends. Companies making those bigger bets in the business should theoretically give the higher end of the volatility scale while those pursuing a more conservative strategy should be at the lower end.
I think this chart depicts what we consider conventional knowledge with higher leverage, more performance driven programs with greater weightings of performance shares and stock options, to the very far right, the leveraged and more retentive programs which are 100 percent time vested restricted stock.
You can see the range of everything in between in terms of the mix and how we're managing and shifting the weights of each of the vehicles trying to mirror the risk that we want within our compensation programs.
OK, so we talked in the beginning in terms of making grants, the accounting valuations and how we compare to the survey data. We've now talked about the end or the outcome or the performance outcomes and the pay outcomes.
Well what about now? We're in the middle of vesting cycle, we're in the middle of a performance period, our stock price is rising, our stock price is falling. Keeping participants up to date on the progress and their value of the rewards in real time is an important step, that’s one that Carly will cover now.
Return to Index
Communicating Value to Employees
Sanfilipo: Thanks Kevin. Communicating value to employees is very important because at the end of the day, we're expensing these awards. Ideally the perceived value of the award should be somewhat close to the expense that we're actually putting on our books for the awards.
But communicating grant dates and fair value versus ongoing value, can be quite challenging particularly for certain types of equity vehicles. Here we've got some quick takeaways for the three main types of equity vehicles.
First, stock options or SARs—communicating the grant date value is oftentimes quite difficult, and I discussed this a little bit earlier in this hour. The grant date value is dependent on Black Scholes assumptions such as volatility and expected life, and especially if those assumptions are changing drastically from year to year, the value will change drastically from year to year, which certainly has some of its own communication challenges.
On an ongoing basis, during the vesting period, during the exercisable period and then finally when the option is exercised, the value is actually pretty easy, it's simply the intrinsic value, or in other words the spread between the stock price and the strike price at the exercise.
Moving on to full value shares, there's a reason RSAs and RSUs are as popular as they are, they're easy to value, they're easy to communicate, they're easy to understand, both at the grant date and then on an ongoing basis. The value is simply the face value of the stock on any given day.
Finally, for performance shares, communicating the grant date value can be difficult especially for market condition awards, for the reasons I talked about earlier—even on an ongoing basis, it can be somewhat difficult. For example, if it's a market condition that is dependent on a peer group of say, all 2,000 companies in the Russell 2000, that can be kind of hard to track on a day-to-day basis. Or if it's a performance condition for something like ROIC or EPS, sometimes those values aren't known or even able to be estimated until the second or third year of a performance period, so that communication can be pretty challenging.
Moving on to the next slide, relative TSR awards are one of the few performance awards that actually can be communicated daily, which makes relative TSR awards unique. Oftentimes performance awards can be seen as sort of a black box, a lottery ticket, the executive really doesn't know where the payout is going to land until three years into the performance period when he's told how many options or shares he's going to get. So for relative TSR, being able to put ongoing performance in front of the award holder’s face on a day-to-day basis can really help to increase the perceived value of the award, and hopefully the perceived value starts to reach the expense value that we've been putting on our books.
For relative TSR awards, we know the plan design specifics, that's disclosed in a plan design document. We know which companies are in the peer group. We're able to pull stock prices for all of the companies in a peer group, calculate any averaging periods, calculate TSR and then determine the rank of the company. On any given day, the current value of the plan is target shares times the expected payout as of that day, times the current stock price, and we can determine all of that information from publicly available information.
Moving on to the next slide, this is sample output of one of these ongoing communication trackers. Here, we have a plan that started in 2016 with a three-year performance period. We're about two and a half years into the three-year performance period now, so we know the company stock price at the beginning, the average stock price was $40 and now if we look at the most recent 30 days, the average price is $48, $48 divided by 40 minus 1 is 20 percent. So ABC's TSR, two and a half years into the performance period, is 20 percent.
We did the same exact process for the Nasdaq 100 and we find that the Nasdaq 100's TSR for the past two and a half years is 10 percent. ABC is currently outperforming the Nasdaq 100 by 10 percent, and that tracks to a 150 percent payout based on ABC's index outperformance plan design.
Here, the participant would be able to log on to the site, see that he's on track to earn 150 percent of target, and then he can check it the next day and see how it's moved. And to the extent he has any control over the stock price, he has a better line of sight into how the stock price is affecting his own award and his value earned.
Alright, we got through that pretty quickly, so we've got time to open up to questions, Kathleen?
Cleary: Yes, thank you, Carly, we sure do. Record time for getting through this complex information! We do have a couple of questions that came in. First of all, I just want to comment, we did discuss survey data quite a bit and the NASPP is getting ready to post some really great survey data on our website. That's a great way to benchmark where your company is and may be provide you with information as to what other people are doing and things that you might consider.
Kevin, I believe it was you that mentioned premium priced options, can you talk a little bit about what those are and why you might use them? And maybe who is using them, how common they are?
McLaren: Yes, not very common. They are options with an exercise price set to a level higher than the grant date value—plain vanilla options will have an exercise price equal to the grant date—if we’re granting an option today, it will be today's price. Take for example a company that's perhaps in a downturn or has a depressed stock price where we want to drive the program a little bit by setting an exercise price with a higher hurdle. Awardees won't kick in the money until perhaps maybe a 10 percent growth.
You can set an exercise price higher than today's price, maybe today is 15, we can set it at 20 or 25 and the money will not kick in until that stock price is actually achieved. It will vest, but you still have to increase stock price beyond the hurdle or a premium price in order for it to generate intrinsic value.
I think that does impact the expense values Carly was describing with Black-Scholes; you would have a current stock price of 15 in this case and the higher exercise price would actually reduce the Black-Scholes value. Companies that are in a depressed phase and maybe need to lower P&L impact, but still have that upside opportunity to participate in value creation, premium priced options may be a possibility for them. Even companies that are at the very high end of their growth, setting even higher hurdles, so not necessarily having an in-the-money option value too soon, but having it kick in a little bit later.
Again, not a broadly used vehicle, but it is a type of vehicle that has been used by some companies.
Cleary: Thank you. I don't hear people using those a lot either. I don’t know if the survey data covers that, but there are people doing things outside the norm. In fact, I'll mention, I just recorded a podcast with Fred Whittlesey and it's about equity outliers. It will be posted later this week, so for all of our listeners, if you're not listening to the podcast series, there's some really great information out there and the most current podcast will be about equity outliers. I believe we do talk about premium priced options in the podcast, they're not used very often but they can be utilized for certain purposes.
McLaren: I will make one other comment, I think I mentioned this back in the portfolio approach around the adoption of performance shares, largely with the downturn, with the decline in the use of options and the increase in the use of performance shares, the premium priced options are now almost reflected in performance shares with a stock price hurdle. Think of a performance award that has a certain number of shares vesting at 20, and more shares vesting at 25. Companies are better able to impact or customize those plans and build plans for performance shares in a much less dilutive way than stock options.
Cleary: Thank you for that, Kevin. I have a question that came in about an acronym we used—we are definitely an industry of acronyms—and I apologize, we should always explain when we're using acronyms. Carly, I think you mentioned it, can you tell us what TSR stands for, maybe talk a little bit about that sort of a metric?
Sanfilipo: Yes, and I apologize for not explaining that. I forget that not everyone deals with TSR awards on a daily basis like I do. TSR stands for “total shareholder return” and that is a market condition where the payout varies based on how your stock price performs against the comparative group.
Sometimes that comparative group is just an index, like it was on that last slide I showed you, where ABC company had a TSR of 20 percent and the index had a return of 10 percent. Or they can be as complex as performing against thousands of companies in your peer group, where you then rank your TSR among the thousands of other companies in your peer group. Then whatever your percentile ranking is, will determine your payout.
For example, a very common plan design is if you rank right at the median at the 50th percentile, you earn target. If you rank at the 75th percentile, you earn 200 percent of target. If you rank at the 25th percentile, you only earn 50 percent of target and if you're in the bottom quartile, you don't earn anything, that's a very common relative TSR design.
Also, I wanted to make a point about the premium priced options we were talking about, even though it's not common, I do want to point out it does have additional valuation challenges. For example, for your expected life assumption, you shouldn't use the same expected life as you would for an option that's granted at the money, because all things being equal, we would expect that someone would have hold their option a little bit longer. Since it’s granted at a premium price, you first have to wait for it to be in-the-money.
There are always the valuation nitty gritty details that may be difficult to think about if you're not living, breathing valuation like I do.
Cleary: It's funny, Carly, we have someone who just put in a comment that they have a couple of clients using premium priced options and if you could comment on the valuation—but you just did. I hope that's helpful for this listener, if not, please feel free to contact me with additional questions and I can always get Carly or Kevin involved to make sure you get all the information you need.
I'll also just mention we do have a Q&A form on the website, which is a really great way to get information. If you have questions about anything that you're tackling in your day-to-day administration, post a question out there, and you'll get a lot of answers from peers and other industry professionals. It's really a great tool to utilize and it gives you good information that you can take back to your advisors at your company and say, "Well, this is what others are doing."
I also have another question that came in, “Where do we get a copy of the presentation?” It is posted on the NASPP website on the webcast page, and you're welcome to download it and retain in for future reference. There's a lot of data in here, so you might want to go back and re-digest some of it.
I'll also post an archive of this session, so you can listen back as many times as you'd like and maybe certain areas, you might want to listen to more than once. When it gets very technical, that's not unusual. I know I find myself listening to things over and over again to make sure I absorb the right information.
I did have one more question that just popped in. The example of TSR, how did you arrive at 180 percent premium? Are we talking about this example?
Sanfilipo: Yes, earlier I mentioned that oftentimes the fair value for relative TSR awards can be drastically different from the current stock price on the date of grant and the example I used was a relative TSR's fair value that was 180 percent of face. What that came down to was two different things that were going on. The first was just a really rich plan. That plan, in particular, had a 250 max payout where oftentimes we only see a 200 percent payout, this one had a 250 max.
Additionally, they had granted the award several months into the performance period. I believe they were already five months into the performance period and their stock price performance during that five months was very rich. They had done much better than the majority of their peer group, so they were already at the top of their peer group at month's end and they were so far ahead that the model showed it was not all that likely that they would drop below target in many of the simulations.
If you think about it, it’s like a foot race, right? We have to value the award on the grant date and on the grant date, some of the people in the race were already ahead of some of the other runners. All things being equal, if you're starting the race ahead, they're more likely to end the race ahead; and when you're more likely to have an above target payout, that increases your fair value. Hopefully that answers your question.
Cleary: Yes, that listener said thank you for the explanation, so I think you did. Thanks, Carly. All right, we have just a couple more minutes, if anybody wants to send in another question, as I start to close out the webcast.
I just want to say thank you so much to Carly and Kevin for putting together all the data in this presentation and for taking the time to share their expertise with us today. I hope you all have a better understanding of these valuation methods and when they're used and how they're used and hopefully you can take some concepts back to your company and use them there. I know I learn something in every webcast, so I hope you all did as well. If you have additional questions after the webcast is complete, always feel free to e-mail me. It's just kcleary@naspp.com. We also have a general mailbox, naspp@naspp.com, and someone who sorts through the emails will filter the questions over to me, so I can be sure you get an answer.
If you dialed in late—I saw a few people that came in late—don't worry, we will post up the archive, and you can listen back to it as many times as you like. I'll also be posting a transcript within the next few weeks.
Again, thank you to Kevin and Carly for all their time preparing for and presenting today, I appreciate both of them very much. Thank you to our audience for joining us today and I hope that you will mark your calendar and join us next month for “Ten Global Gotchas” on June 12.
Thank you everyone and that concludes our webcast for today.
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