Of Dollars And Data

A few months ago, I saw this tweet about the World Sauna Championships and how men will literally push themselves to the brink of death to win them, despite there being no tangible rewards. As the author of the tweet noted: > It was interesting to me because it's another piece of evidence that as soon as you create a ruleset...no matter how ridiculous it is, no matter how small the group of participants, no matter the extreme chance of death, and no matter a total lack of prizes. There will always be men willing to compete to the point of actually killing themselves... > > It's kind of like a hijacking of the programming evolutionary mind, where no incentive makes sense but it happens anyway. You can find a million examples of this for every male interest on the planet. Just the simple act of starting down a path confers it meaning to the person, and the more they are surrounded by other men who care about the same thing, the more they learn and compete, the more entranced by it they are, until their identity is fully subsumed by it and stuff like these sauna deaths happen. They're right, but these kinds of dangerous status games aren't exclusive to men. Competitive female freedivers like Audrey Mestre and Natalia Molchanova both passed away pursuing a sport with little financial reward or media coverage. What they were chasing was status. When we look at competitive freediving or the World Sauna Championships, it's easy to see why such competitions seem so irrational. Both have a negatively asymmetric payoff—the upside is trivial and the downside is everything. But what about those status games where the payoffs aren't so obvious? Where the upsides are there, but the downsides are unknown ahead of time? I’ve thought about this a lot since becoming a father a few months ago. Until that point, my primary sense of fulfillment came from my writing and my career. Everything was focused on more content and more followers. Rinse and repeat. But helping to raise my daughter has been a joy unlike anything I’ve experienced before. I’ve been so fulfilled by it that it made me question whether the game I was playing for the past 10 years still makes sense. You have to understand that, for an online content creator, the ultimate sign of success is a huge platform. People like Tim Ferriss, Andrew Huberman, and Chris Williamson are examples of those who have won the game. These are the creators we look up to. They're supposed to help us live better lives with their accumulated knowledge and wisdom. Yet, as far as I can tell, all of these men are unmarried and childless. I understand that not everyone wants to be married or have children, but now that I have both, I feel a bit deceived. Because the online environment I've operated in for most of my career has been glorifying a lot of people playing the wrong game. It's put some on a pedestal who are missing arguably the most important part of life—a family. And there's no amount of money, success, or influence that can convince me otherwise. For the record, I'm not talking about people who wanted a family and, for reasons outside their control, never got one. I understand that life doesn't always cooperate. My issue is with a system that holds up work-obsessed influencers as the blueprint for a life well-lived while ignoring family altogether. Don't just take my word for it. Consider what famed investor Paul Tudor Jones said on the topic in a recent episode of Invest Like the Best: > My significance is not gonna be from being a trader...My significance is going to be, first and foremost, my family... If I think about the end of my life and I look back, I'm not gonna be thinking about the '87 crash or Bitcoin. I'm gonna be thinking about who I loved and who loved me. Jones is arguably more successful than any influencer on the planet and publicly recognizes the centrality of family to living a great life. So why isn't this message more widespread among content creators? There's no shortage of intricate morning routines, optimized supplement stacks, and must-read book lists. But where's "settle down and start a family"? It's a message I don't often hear. Yes, there are some big-name creators who put family at the center of their content. But they're the exception, not the rule. For every post about being a parent, there are ten on productivity and sleep hacks. Why is this the case? Because we are encouraged to focus so much on ourselves. Men are told to "lock in" and women are told to "girl boss" it up. And there's nothing wrong with these messages by themselves. But when you listen to them for too long you can end up missing out on one of the most meaningful parts of human existence. I know because it was almost me. For many years I was a workaholic bachelor in NYC. Going to parties. Meeting new people. Dating all the time. Creating content. Tweeting constantly. It was addictive in every way. My focus was on having fun and building my brand. The issue wasn't my lifestyle, but the belief that this lifestyle was the ideal. I truly thought that any other path would have made me worse off. I now know that's false. But I only know this because I met my wife and was fortunate enough to experience the joys of fatherhood. As a result, it's completely changed how I use my time. For example, I spend far less time on social media than I used to. Why argue with strangers on the internet when I can make memories with my daughter? It can't compare. I got lucky though. Because I easily could've been the perpetual bachelor in NYC continuing down the "influencer" path. That's not a bad life, but I now understand that it's missing something. The problem with being ultra-focused on your career/content is that the upsides are clear: money, success, and influence. But the downsides aren't readily apparent. Every year I spent focused on myself was a year less with my future family. This was the unintended consequence of maximizing one variable while overlooking others. In that sense, the creators who spend years maximizing their platforms at the expense of everything else are more like the sauna competitors than they realize. They don't know what winning costs them until it's too late. But I do. Because if you focus on yourself too long, the only thing you'll have in the end is...you. Thank you for reading. If you liked this post, consider signing up for my newsletter. This is post 523. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data ---

If you want to understand the wealth of the typical American household, the resources are excellent. Not only are there free public data sources, but there are also many great books that detail how people live across The Wealth Ladder. For example: - For Level 1 (<$10k) there's Dignity by Chris Arnade. - For Level 2 ($10k-$100k) to Level 3 ($100k-$1M), Get Good with Money by Tiffany Aliche. - For Level 3 ($100k-$1M) to Level 4 ($1M-$10M), you could read I Will Teach You to Be Rich by Ramit Sethi or *Your Money or Your Life*by Vicki Robin (or even Just Keep Buying). But the further you go up the wealth spectrum, the worse the data gets. Rich households are rare, hard to survey, and typically have their wealth tied up in illiquid, private businesses. As a result, there isn't a great book that details the wealth of Level 5 ($10M-$100M) and Level 6 ($100M+) of The Wealth Ladder...until now. It's called The Everywhere Millionaire: Who Is Really Rich in America and How They Got There. It was written by Owen Zidar and Eric Zwick, two economists who did specialized research on how much tax American business owners pay. Because of this, they were able to provide insight into the wealth of the upper class and the superrich. Technically, Zidar and Zwick focus on households with a net worth above $5M (the top 4%), but the average net worth in their data is $25M. This provides an incredible glimpse into what Zidar and Zwick call America's "Main Street Millionaires." I already knew from my research in The Wealth Ladder that those in Levels 5-6 get most of their wealth from private businesses. You can see this in the chart below illustrating the asset breakdown by wealth level: But what I didn't know was the details. What kind of businesses? Who are America's Main Street Millionaires? And how did they get there? This is where The Everywhere Millionaire comes in. So let's take a look at the upper echelon of American wealth, starting with the businesses that got them there. ## What Kind of Businesses Do They Own? When most people picture the rich, they think of people like Bill Gates, Ken Griffin, and Warren Buffett—tech founders, hedge fund managers, and longtime investors. These are the ultra-wealthy found inside the Forbes 400. But that's not where most of America's wealth is held. According to Zidar and Zwick, Main Street Millionaires hold over 13x more wealth than the Forbes 400. And where do they hold it? In pass-through entities (like LLCs and S-corps) in various industries. Which industries in particular? Zidar and Zwick note that the top five are: 1. Legal services. This is mostly law firm partnerships. 2. Financial investment activities. This includes venture capital, private equity, and hedge fund managers. 3. Auto dealers. These are the local, independently owned dealerships that sell new and used cars. 4. Professional and technical services and management consulting. This includes independent consulting firms and other specialized professional practices. 5. Oil and gas extraction. This includes fracking companies in North Dakota, Pennsylvania, and other states. There are a few things to note about these industries (and the 20 others fully listed in the book). First, these businesses exist across the nation. You might need a lawyer, a car, or a consultant whether you're in Nebraska or New York. Second, these industries aren't dominated by a few megafirms. There's no Amazon or Walmart of law firms or auto dealers. Instead, each has thousands of independently owned businesses that tend to make their owners wealthy. This is why Zidar and Zwick call these owners "everywhere" millionaires. They aren't located solely in Silicon Valley or Manhattan, but all across the United States. But knowing where they are doesn't tell us who they are. Let's turn to that now. ## Who Are America's Main Street Millionaires? The typical Main Street Millionaire is older, male, married, and White. Ninety percent of decamillionaire ($10M+) business owners are married, and their median age is 62. A lot of this is a function of time. Since it typically takes decades to build a successful business (especially at the decamillionaire level), Main Street Millionaires tend to be much older. Additionally, the demographic breakdown of these business owners reflects who is more likely to found a business in the first place. According to Zidar and Zwick, men are twice as likely to start a business as women, and Asian and White individuals are 7x and 5x more likely, respectively, to own an employer business than Black individuals. And since White Americans make up the majority of the U.S. population, they comprise the majority of Main Street Millionaires as well. But not everything about the Main Street Millionaires is based on demographics. One of the striking things about them is how educated they are. As Zidar and Zwick note: > Some 80 percent of decamillionaire business owners have a college degree, and 40 percent have a postgraduate degree (e.g., MBA, MD, DDS, JD, PhD). > > These decamillionaire business owners are twice as likely to attend college and postgraduate school as the general population. But, interestingly enough, they're less likely to go to college and graduate school than decamillionaires who don't own businesses. In other words, if you want to get rich, get a college degree or start a business. While most successful business owners have degrees, a degree seems to matter less if you own a business than if you don't. I came to a similar conclusion earlier this year, so it's great to see other researchers validate this with additional data sources. Of course, all of these statistics describe the business owners who made it, not the many who tried and didn't. Now that we know who Main Street Millionaires tend to be, let's see how they got there. ## How Did They Get There? Contrary to what you might expect, most Main Street Millionaire firms aren't inherited. As Zidar and Zwick wrote: > In 2007, the most recent year for which the Census collected such information, 46 percent of all Main Street Millionaire firms were owned by the original founder, 32 percent by someone who bought it, 14 percent by someone who received it as a gift or transfer, and 7 percent by someone who inherited it. Counting gifts and other family transfers, they estimate that the overall inheritance rate for these businesses is only about 25%. In other words, the vast majority of these businesses (75%) were not inherited. But not inheriting a business isn't the same as starting from zero. Statistically, founders are more likely to come from wealthy families. According to Zidar and Zwick, people from families in the top 1% of income are 2.4 times more likely to found a firm than those from families at the 90th percentile, and 4.1 times more likely than those at the median. Yet most founders don't come from wealthy families. Seventy percent come from families outside the top 10% of income, and a third come from families in the bottom half. How so? There are far more people in the bottom 90% than the top 10%. So while coming from a wealthy family increases the odds of starting a business, most founders are not from the wealthiest families. Since family background only partially explains where entrepreneurs come from, what else does? Their first job, and, in particular, what industry their first job is in. Zidar and Zwick found that if your first job is in an industry that produces more entrepreneurs, then you're more likely to become an entrepreneur. And if your first job is more entrepreneurial in nature (e.g., computer systems design, accounting, building equipment contracting), then you're more likely to succeed as an entrepreneur. That's because your first job teaches you how an industry works and can help you develop a professional network that is valuable when starting a business. Zidar and Zwick estimate that these differences in early career experience explain 20% to 50% of the gaps in business creation by income, race, and gender. In other words, a meaningful part of why some groups start fewer businesses is that they're less likely to get their start in entrepreneurial industries. So, how did the Main Street Millionaires get there? Most built or bought their businesses (rather than inheriting them), and many got their start in the same industry where they'd eventually found their business. This is useful to know, but if you're further along in your career, what does it mean for you? ## What It Means for You If the Main Street Millionaires can teach us anything, it’s that the path to great wealth runs through business ownership. You can get to Level 4 ($1M-$10M) with a good income, a good savings rate, and consistent investing. But the data suggests that getting to Level 5 ($10M-$100M) and beyond typically requires ownership. You don’t need to start the business either. As Zidar and Zwick mentioned, about one-third of Main Street Millionaire firms were purchased rather than founded. Of course, buying a business requires resources, which take time to build. This brings us to another key ingredient of being a Main Street Millionaire—time. Rome wasn’t built in a day and neither is a successful business. Don’t forget that the median age of decamillionaire business owners is 62. Unfortunately, there are no shortcuts in this game. It’s also worth noting that for every business owner who became a Main Street Millionaire, many more didn’t. According to Zidar and Zwick, only about 5% of founders built a firm worth more than $5 million within ten years, and around half of firms stopped operating as stand-alone entities within five. Yes, this data suffers from survivorship bias. At the same time, many founders who don't strike it rich still land on their feet. Zidar and Zwick found that more than half of founders do just as well as their similarly situated, non-founder counterparts. Business failure doesn’t mean destitution. And not starting a business doesn’t mean settling either. You don’t need to start a business to have a great life. You don’t need to be in Level 5 or 6 to be happy or fulfilled. But if this option appeals to you, then maybe you should consider becoming an Everywhere Millionaire yourself. Special thanks to Owen Zidar and Eric Zwick for their incredible research. And thank you for reading. If you liked this post, consider signing up for my newsletter. This is post 522. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data ---

Imagine joining a startup as employee number 10. Your salary is a bit below what the market pays for your role, but, in exchange, you get equity. How much? 0.25% of the company to be exact. You do the math in your head—if the company sells for $100M in a few years, you'll make $250,000. Not bad, right? Wrong, and for quite a few reasons. Once you take into account how startup equity typically works, that initial equity grant is worth $97,500, not $250,000. Let's walk through why this is the case and then answer an even bigger question: is startup equity a bad deal? ## Why Initial Equity Isn't Exit Equity Startup equity is confusing because most of what determines its value happens behind the scenes (outside of an employee's control). This is why, as an employee, your equity at exit is rarely what your initial grant was. This happens for the following reasons: - Vesting: Most startup equity vests over four years with a one-year cliff. This means you get nothing if you leave within the first year. But if you make it past year 1, 25% vests immediately and then the remaining 75% vests monthly at 1/48th of the total grant (~2.08% per month). So, if an employee with 0.25% of the company leaves after 2 years, they are only entitled to 0.125% (or half) of their original equity. - Dilution: Dilution is a common practice where subsequent financing rounds reduce the total amount of equity owned by everyone in the company. Typical dilution rates are 20% for Series A, 15% for Series B, and around 10% in each round thereafter. So your 0.25% could be 0.15% after a Series A, Series B, and Series C [0.25% * 0.8 * 0.85 * 0.9 = 0.15%]. There are also pool refreshes where new shares are created for future employees, which can further dilute your shares. These can be offset by refresher grants that you receive for staying longer, but this isn't always the case. - Liquidation Preference: Even when a company is sold or acquired, if the amount it exits for is below a certain exit price, then liquidation preferences take hold. For example, imagine a startup raises $10M for 50% of its equity, valuing the company at $20M with a 1x liquidation preference. The liquidation preference means that the venture capitalists (VCs) who gave the company the $10M, get the higher of 50% of the sale price or their $10M upon exit. So, if the company is sold for $15M, the VCs would get their $10M back since 50% of the sale price is less than $10M. This means that the remaining $5M is left for the founder(s) and employees. Liquidation preferences prevent a founder from raising money and then immediately selling the company and pocketing the proceeds. So, even if you own 0.25% of the company, after liquidation preferences, your 0.25% would apply to a smaller pool of funds. - Post-Termination Exercise Period (PTEP): Arguably the biggest issue that prevents employees from realizing their equity isn't related to financing mechanics, but timing. While equity tends to vest over four years, many startups take much longer to exit. For example, PitchBook found that the median time to IPO for tech companies was 11.5 years. Jared Heyman did an analysis on YC companies and found that bigger companies (>$1B) took about 9 years to exit while smaller ones (<$100M) only took 3 years. The issue is that if you leave your startup early, you typically have 90 days to decide whether to exercise your stock options. This is called the post-termination exercise period and it is the biggest risk a startup employee takes once they are no longer with the company. You can exercise these options and pay the tax due, yet you'd still need to wait for a payoff...if it ever comes. - Exercise Cost: Even if you do decide to exercise your options, doing so isn't cheap. Since your strike price is set by the company's valuation when you're hired, later employees face far higher costs than early ones. Their exercise cost can often be five figures or more, all due within 90 days of leaving. That's cash locked away for years, earning nothing, with no guarantee of getting it back. Employees who don't have this spare cash walk away empty-handed. Thankfully, some companies now offer a 5-year to 10-year PTEP window, which reduces the stress over whether to exercise your options immediately upon leaving a startup. - Taxes: When it comes to taxes, your startup equity typically gets taxed twice. The first taxation occurs when you exercise your options where the difference between the strike price and the company's share price gets treated as income (ordinary income for NSOs, or as an AMT adjustment for ISOs). You have to pay this tax in cash immediately though you can't sell the shares yet (and may never be able to). The second tax comes as a capital gain on any further appreciation of the shares in the event of an exit (IPO, acquisition). These costs aren't trivial either. Secfi estimated that taxes made up 73% of the total stock option exercise cost among their clients. Imagine paying out cash to buy these options only for the startup to ultimately fail. Startup equity can be far more complex than this. I've deliberately left out RSUs (which later stage companies use instead of options), harsher liquidation preferences, acqui-hires where the common stock gets wiped out, and secondary sales that can provide employees some liquidity before an exit. Nevertheless, you can see how your equity at exit is likely to be meaningfully less than what was originally granted. Using our opening example, a 0.25% grant could end up diluting down to only 0.17% after two financing rounds [0.25% * 0.8 * 0.85], meaning $170,000 gross on a $100M sale. Net out the cost of your options (assume $7,500 flat) and taxes (40%) and you are left with $97,500 [($170,000 - $7,500) * 0.6]. That's still a good payout, but 61% less than what you initially imagined. Now that we know how employee equity grants can be affected before an exit, let's take a look at how often startups exit in the first place. ## How Often Do Startups Exit? When it comes to startup exits, the vast majority never IPO or get acquired. CB Insights found that only 30% of the 1,119 U.S. seed companies they tracked had an exit. The rest either failed, stagnated, or became profitable enough to not require future financing rounds. Either way, the equity outcome for the employees was the same—no liquidity event or payout. This is where startup equity and the equity in a typical business differ. In a typical business, equity can pay out distributions over time. But with startups, equity payouts rarely come outside of an exit. And not all exits are created equal either. A working paper from Adigital found that more than 80% of U.S. tech startups between 2002 and 2020 were acquired for under $50M. You can see this more clearly in this chart which shows the distribution of exit valuations of U.S. VC-funded companies (acquisitions vs. IPOs): While this data suggests that large IPOs are more common than large acquisitions, you have to keep in mind that there tend to be 30x more acquisitions than IPOs. In 2016, CB Insights reported that there were 3,260 M&A exits globally, but only 98 IPOs (33x more). And the lack of IPOs since 2022 has only widened that gap. From the data shown thus far, I've estimated that: - 70% of startups don't exit - 25% exit for under $100M - 4% exit for $100M-$1B - 1% exit for over $1B Given this, let's walk through how much an early startup employee would make depending on the size of their company's exit. ## How Much Do Early Startup Employees Make Upon Exit? The amount of money a startup employee makes upon exit will be determined by how much equity they have, the deal terms, and the size of the exit. Rather than varying all of these at once, let's look at an employee with a fully vested 0.25% initial equity grant (our hypothetical employee #10 at a startup) and how much they would make across various exit sizes. We will assume a flat 40% tax rate, a fixed $7,500 cost to exercise all shares, and share dilution of 20% in Series A, 15% in Series B, and 10% in each round thereafter. If we were to do this for various exit sizes, here's how much our hypothetical startup employee would make in each scenario. Note that I've used $50M, $300M, and $1.5B as representative values within each exit tier. - No Exit (70% of Startups) = $0 * This assumes a startup that sees no liquidity event. - Small Exit (25% of Startups) = $55,500 * This assumes a startup that exits for around $50M after one funding round which dilutes the equity down to 0.2% [0.25% * 0.8]. This leads to $100,000 in gross proceeds. Net out the $7,500 exercise cost and the 40% tax on the gain and you are left with $55,500. - Big Exit (4% of Startups) = $270,900 * This assumes a startup that exits for around $300M after three funding rounds which dilute the equity down to 0.153% [0.25% * 0.8 * 0.85 * 0.9]. This leads to $459,000 in gross proceeds. Net out the $7,500 exercise cost and the 40% tax on the gain and you are left with $270,900. - Unicorn (1% of Startups) = $1,110,870 * This assumes a startup that exits for $1.5B after five funding rounds which dilute the equity down to 0.124% [0.25% * 0.8 * 0.85 * 0.9 * 0.9 * 0.9]. This leads to $1,858,950 in gross proceeds. Net out the $7,500 exercise cost and the 40% tax on the gain and you are left with $1,110,870. If we do an expected value calculation across all of these outcomes, our hypothetical startup employee would have an expected payout of $35,820. That's not nothing. But when you consider the risk, the four years it takes to vest, and the below market salary, it's much lower than many would hope for. For example, if you were paid $20,000 per year less than your market value to join a startup, that's $80,000 of forgone income (or $48,000 after 40% taxes). Your $35,820 expected payout (which arrives years in the future) wouldn't make up for it. Of course, the devil is in the details. If you used a 0.5% initial equity grant, all the numbers here would be roughly doubled. If you included liquidation preferences or the present value of these payouts, the expected value would be much lower. More importantly, if you left before an exit and couldn't afford to exercise your options within 90 days, your payout would be zero. Either way, in our base case, there's roughly a 5% chance of a six figure (or higher) payout for an early startup employee. Is this worth it to you? I'm not so sure. So far we've looked at an early startup employee's equity experience. But what about the typical startup employee who isn't employee #10 (or earlier)? Are they any better off? Let's take a look. ## What About the Median Startup Employee Payout? In the example above we analyzed how an early startup employee (#10) would fare under different exit scenarios. We assumed that there was a 70% chance that the employee would never realize any of their equity. However, this doesn't imply that 70% of all startup employees never realize their equity. Why? Because of company size. Larger startups employ more people than smaller ones. And since larger startups are more likely to exit than smaller ones, this means that the typical (or median) startup employee is more likely to see an exit as well. For example, let's make the following assumptions about company size based on exit size (which I've estimated from Carta's data here). Note that Carta estimates employee size at funding (not exit), so I've adjusted upward where applicable: - No Exit = 20 employees (on average) - Small Exit = 70 employees (on average) - Big Exit = 250 employees (on average) - Unicorn = 1,000 employees (on average) If we assume that this is roughly accurate, then, for every 100 startups, we would expect a total of around 5,150 total employees (broken down as follows): - 70 No Exits * 20 employees = 1,400 total employees (27% of total) - 25 Small Exits * 70 employees = 1,750 total employees (34% of total) - 4 Big Exits * 250 employees = 1,000 total employees (19.5% of total) - 1 Unicorn * 1,000 employees = 1,000 total employees (19.5% of total) Once we control for company size, roughly 73% of startup employees experience an exit even though 70% of startups don't. If we ordered all of these employees, the median (50th percentile) employee would be in the "Small Exit" bucket. We can assume that they would be the median employee at one of these companies too, which makes them employee #35 (out of a 70 person company). Since they joined later than employee #10, they probably only got a 0.05% initial equity grant (in my estimation). And if we assume a $50M exit after one dilution round, that equates to only $20,000 [0.05% * 0.8 * $50M] in gross proceeds from their shares. After deducting the exercise cost ($1,500) and taxes (40%), the median startup employee in this scenario would receive an $11,100 payout. And this assumes that the employee fully vested and that they exercised all of their options. Unfortunately, this last assumption has become increasingly unlikely. As of late 2024, Carta found that startup employees exercised just 32% of their options that were fully vested and in the money (i.e., the current value of the options was higher than the strike price). As the startup ecosystem has gotten more challenging in recent years, fewer employees are willing to lock up their cash for years with no guarantee of a payoff. As Heather Doshay, a partner at SignalFire, stated: > It's not necessarily a three-year or four-year investment anymore. It could end up being a decade-plus investment. You just don't know. Though equity compensation for the median startup employee doesn't seem like a great deal, there are some ways to make it better. ## How to Make Startup Equity into a Better Deal While the economics of startup equity aren't particularly attractive for the typical startup employee under the typical exit scenario, there's a long right tail that can make it worth it. While this sort of outcome is unlikely (a 5% chance of a six figure payout), no such upside exists for nearly all 9-to-5 jobs. With that being said, the real case for working at a startup isn't always the equity, but the experience and the network that comes along with it. For someone who is young with low expenses, taking a few swings at the startup world can be incredibly rewarding without a major hit to your career if they don't work out. Still, startups are risky because your paycheck and your equity come from the same place. I'd never recommend that someone take on a concentrated, illiquid asset that is correlated with their main source of income, but then again, that's why these outsized rewards exist in the first place. Given this, there are a few ways you can make your startup equity into a better deal for yourself: - Pick trustworthy founders (and coworkers) * The most important decisions about your future equity will be made by the people leading your startup. Selecting trustworthy founders is how you protect yourself from being screwed over in the future. While filtering for trustworthiness is more of an art than a science, if you don't trust the people in charge, don't sign up. - Negotiate your exercise window * Some companies offer 5-10 year post termination exercise periods (PTEP) rather than the standard 90-day windows. Getting your PTEP window extended is the single most valuable thing you can do to lower your future anxiety and give yourself optionality when it comes to your stock options. If the company won't budge on this, that tells you something. - Understand the cap table * Ask leadership about prior financing rounds, total capital raised, and what liquidation preferences exist. Before you sign up, you need to know who gets paid what if the company sells at various prices. This will help you determine how much your equity would be worth based on different future exit scenarios. - Understand Qualified Small Business Stock * While I assumed a 40% tax rate throughout this blog post, the Qualified Small Business Stock (QSBS) provision can dramatically reduce how much you owe on your equity, if you exercise early enough and hold for the required period. While other conditions must also hold (e.g., the shares must be issued from a domestic C-corporation under a certain asset level), QSBS can eliminate the capital gains portion of your tax bill, though not the tax you owe at exercise. This issue is important enough that I recommend consulting a tax professional to see if it applies to you. Ultimately, is startup equity a bad deal? Statistically, for most employees, it will turn out worse than they hope. But if you negotiate the right terms and set the proper expectations around exit, it can still be worth the risk. Thank you for reading! If you liked this post, consider signing up for my newsletter. This is post 521. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data ---

A few months ago, I analyzed U.S. stock returns over the last century. Today, I'm going to determine the best portfolio over the last 50 years among the following asset classes: - U.S. Stocks - International Stocks - Real Estate Investment Trusts (REITs) - U.S. Corporate Bonds - 10-Year Treasury Notes - 3-month Treasury Bills - Gold - Commodities - U.S. Homes To do this, I examined the BullionVault annual return data from 1972-2025 (54 years) to determine the optimal/best portfolio over this time period. But before we look at the optimal portfolio, let's get a better understanding of how these various asset classes performed since 1972. ## How Have Assets Performed Since 1972? Since 1972, investors have experienced a wide range of outcomes including bull markets, bear markets, recessions, inflationary periods, and everything in between. As a result of this, asset class returns have varied widely as well. Below is a chart illustrating this for the nine asset classes listed above: There are a few important things to note about these returns: - Asset with Most Winning Years = Gold * Gold had 13 years (out of 54) where it was the asset class with the highest return. This means that Gold was on top roughly 1 in 4 years. - Asset with Most Losing Years = Commodities/Gold * Both Commodities and Gold were tied for the assets with the lowest return in a given year. Each had 11 years where they had the lowest return. - Best Overall Return = S&P 500 * $1 invested into the S&P 500 in 1972 would've become $35.90 (in real terms) by 2025. This blows every other asset class out of the water. For comparison, here is what $1 invested in 1972 would've become by the end of 2025 (in real terms) for the next three highest growth asset classes: + Gold = $12.89 + REITs = $12.26 + International Stocks = $11.27 - Worst Overall Return = Commodities * Commodities had a negative real return from 1972-2025, as $1 invested in 1972 would've shrunk to $0.42 by 2025. More importantly, this negative return came with a high dose of volatility. The standard deviation on Commodities was 14% annually while still producing no long-term growth. As a result, this is an asset class I make sure to avoid. - Most Volatile Asset = Gold * Gold's best year was +106% in 1979 and its worst year followed shortly thereafter with -37.6% in 1981. Across all 54 years, the standard deviation was 24.7%, the highest among our asset classes. If you own gold, be prepared for big wins and losses along the way. - Least Volatile Asset = 3-month Treasury Bills * Unsurprisingly, 3-month Treasury bills are the least volatileasset since they are basically the same as cash (or the risk-free rate). However, this lower volatility comes with a cost—lower long-term growth. $1 invested in 3-month T-Bills in 1972 would've only become $1.14 (in real terms) by 2025. This pales in comparison to the S&P 500's overall growth of $35.90 or International Stocks which grew to $11.27 (in real terms) over the same time period. - Best Inflation-Hedge = Commodities (and Gold if you're lucky) * When we break out the data into high inflation (>4% CPI) vs. low inflation (<=4% CPI) periods, Commodities tend to perform the best among all assets. Despite its poor long-term returns, the average annual return for Commodities during periods of high inflation was about 2.9 percentage points higher than during periods of low inflation while the median annual return was basically identical. This wasn't true for most of the other asset classes. For example, during periods of high inflation, the S&P 500 had an average return that was 11 percentage points lower (and a median return that was 17 percentage points lower) than during periods of low inflation. Similarly, International Stocks, REITs, U.S. Corporate Bonds, and 10-Year Treasuries all performed worse during periods of high inflation. Whether we use the average or median, returns were about 8 percentage points lower (if not more) during these periods. * Gold is a complicated edge case. Gold's average annual return was 5.6 percentage points higher during periods of high inflation (compared to periods of low inflation). This is the highest among any asset studied. However, Gold's median return was actually 7.7 percentage points lower during periods of high inflation. This demonstrates that Gold's ability to act as an inflation hedge is based upon a few outlier up years (e.g., +106% in 1979, +59% in 1973, +49% in 1974, etc.) that may not repeat again in the future. For example, Gold recently had a +60% year in 2025, but this was 3 years after inflation peaked in 2022. My main takeaway from analyzing this data is that no single asset dominates. Some assets have more growth, some have more risk, and some only perform during certain economic regimes. Whether these asset classes will behave similarly in the future is up for debate. Now that we've looked at how different asset classes have performed (in real terms) since 1972, what would have been the optimal mix to get the highest risk-adjusted return? ## What's In the Optimal Portfolio? When it comes to the portfolio with the highest risk-adjusted returns (Sharpe Ratio) from 1972-2025 (i.e., "Optimal Portfolio"), the answer is: - U.S. Homes = 36% - S&P 500 = 27% - Gold = 18% - 10-Year Treasuries = 16% - REITs = 3% Note that 3-month Treasury Bills are absent because they serve as the risk-free rate, so by definition they can't appear in the Optimal Portfolio. The Optimal Portfolio allocation might surprise you, but a core reason for this is that U.S. homes are less volatile (since they are marked-to-market less often) while still having good returns since 1972. As a result, a U.S. home added a decent amount of return to your portfolio without adding much risk. You can see this in the plot below showing the annual return and risk (standard deviation) of the Optimal Portfolio, the efficient frontier, and a handful of other portfolios and individual asset classes: As you can see, the Optimal Portfolio has the highest return per unit of risk. You can't increase your return from this point without also increasing the amount of risk you take. For example, though the S&P 500 had a real average annual return of about 8.3% (or 77% higher than the Optimal Portfolio's 4.7% per year), it also had a standard deviation of 16.8% per year (or 155% higher than the Optimal Portfolio's 6.6% per year). In other words, owning the S&P 500 would get you 1.77x the average annual return of the Optimal Portfolio, but with 2.55x the risk. Note that these are arithmetic means, which is what the efficient frontier is built from. If we were to use geometric means, the return gap would narrow significantly—6.9% per year for the S&P 500 vs. 4.5% per year for the Optimal Portfolio. The return difference would now be 1.54x (versus 1.77x for the arithmetic return), while the risk difference of 2.55x remains the same. The S&P 500 gives up about 1.4 percentage points a year due to volatility while the Optimal Portfolio only gives up 0.2 percentage points. Not having to recover from big losses is a source of return for the Optimal Portfolio. You can visualize this in the plot below which illustrates the annual drawdowns in each portfolio from 1972-2025: Over this time period, the Optimal Portfolio's maximum drawdown was about 13% (in 2022) compared to 48% for the S&P 500 (in 1974). This difference illustrates why portfolio optimization can be so powerful. In theory, you could take the Optimal Portfolio and lever it up (by borrowing) to generate the same return as the S&P 500 while also taking less risk overall. In practice this is harder than it sounds. Levering the Optimal Portfolio 1.77x only works if you can borrow near the risk-free rate, rebalance without friction, and stomach a possible margin call. As a result, most investors are unable to lever their assets so easily. There is one partial exception though—housing. A 30-year fixed mortgage is the most common leverage available to the typical household. More importantly, this leverage can't be margin called. This means that many U.S. households are already running a partially levered version of the Optimal Portfolio without realizing it. Based on my own calculation of the 2022 Survey of Consumer Finances, the median American homeowner has 51% of their net worth in their home (via home equity). The fact that the Optimal Portfolio suggests a 36% allocation to a home is a convenient result that isn't far off many existing homeowner allocations. Of course, some would argue that your home shouldn't be considered as part of your portfolio since housing is a consumption good. I agree with this sentiment, which is why I re-ran the Optimal Portfolio while excluding U.S. housing as an asset class. Without U.S. housing, the Optimal Portfolio from 1972-2025 would have been: - S&P 500 = 43% - Gold = 26% - 10-Year Treasuries = 22% - REITs = 9% The 36% exposure to U.S. housing got absorbed by the remaining four asset classes (+16% to S&P 500, +8% to Gold, +6% to 10-Year Treasuries, and +6% to REITs). After this change, the new Optimal Portfolio looks like a more aggressive version of the Permanent Portfolio (25% stocks, 25% gold, 25% bonds, 25% cash). Swap the 25% cash for stocks and some REITs and it's remarkably similar. You can see this more clearly if we add the Permanent Portfolio to the efficient frontier plot from earlier: This isn't an argument for (or against) the Permanent Portfolio or the Optimal Portfolio. Both of these portfolios have been great historically. But history is the past—yet we have to invest in the future. And, unfortunately, the Optimal Portfolio is the ideal solution to the world of yesterday, not the world of tomorrow. This is why the "best" portfolio will always be an illusion. ## Why the Best Portfolio is an Illusion Imagine the world 50 years into the future. Will the Optimal Portfolio over those 50 years (2026-2075) look like the one from the prior 54 years (1972-2025)? No chance. How do I know? Because the optimal portfolio in 1998 was quite different from the one today. For example, if we were to solve for the Optimal Portfolio from 1972-1998 (half the years in the data), here's what we would find: - S&P 500 = 68% - Gold = 24% - International Stocks = 6% - U.S. Corporate Bonds = 2% There's some similarity to the Optimal Portfolio from 1972-2025 in that it holds a sizable S&P 500 and Gold allocation, but that's about it. The 1972-1998 Optimal Portfolio adds International Stocks and U.S. Corporate Bonds, but completely drops 10-Year Treasuries, REITs, and U.S. Homes. Yes, U.S. Homes, which were 36% of the 1972-2025 Optimal Portfolio, are nowhere to be found in the 1972-1998 one! When I say that the Optimal Portfolio of 2026-2075 won't look like the one from 1972-2025, this is what I mean. It's not because I'm bearish on U.S. stocks, skeptical of Gold, or convinced REITs are doomed. It's because I don't know the future. Maybe the U.S. will default on its debt. Maybe Gold will fall out of fashion. Maybe demographic transition will send REITs toward a slow demise. I have no clue and neither does anyone else. This is what makes portfolio optimization such a cruel art form. You get the perfect solution to a problem that no longer exists. The world changes and what you knew about it changes with it. This is why the Optimal Portfolio is an illusion. Because you can never have it going forward, only looking backward. Because the future is like the past...until it isn't. This has convinced me that the actual optimal portfolio is the one that you can stick with no matter what the world throws at you. Until next time, happy investing and thank you for reading. If you liked this post, consider signing up for my newsletter. This is post 520. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data ---

A few months ago, I analyzed U.S. stock returns over the last century. Today, I'm going to determine the best portfolio over the last 50 years among the following asset classes: - U.S. Stocks - International Stocks - Real Estate Investment Trusts (REITs) - U.S. Corporate Bonds - 10-Year Treasury Notes - 3-month Treasury Bills - Gold - Commodities - U.S. Homes To do this, I examined the BullionVault annual return data from 1972-2025 (54 years) to determine the optimal/best portfolio over this time period. But before we look at the optimal portfolio, let's get a better understanding of how these various asset classes performed since 1972. ## How Have Assets Performed Since 1972? Since 1972, investors have experienced a wide range of outcomes including bull markets, bear markets, recessions, inflationary periods, and everything in between. As a result of this, asset class returns have varied widely as well. Below is a chart illustrating this for the nine asset classes listed above: There are a few important things to note about these returns: - Asset with Most Winning Years = Gold * Gold had 13 years (out of 54) where it was the asset class with the highest return. This means that Gold was on top roughly 1 in 4 years. - Asset with Most Losing Years = Commodities/Gold * Both Commodities and Gold were tied for the assets with the lowest return in a given year. Each had 11 years where they had the lowest return. - Best Overall Return = S&P 500 * $1 invested into the S&P 500 in 1972 would've become $35.90 (in real terms) by 2025. This blows every other asset class out of the water. For comparison, here is what $1 invested in 1972 would've become by the end of 2025 (in real terms) for the next three highest growth asset classes: + Gold = $12.89 + REITs = $12.26 + International Stocks = $11.27 - Worst Overall Return = Commodities * Commodities had a negative real return from 1972-2025, as $1 invested in 1972 would've shrunk to $0.42 by 2025. More importantly, this negative return came with a high dose of volatility. The standard deviation on Commodities was 14% annually while still producing no long-term growth. As a result, this is an asset class I make sure to avoid. - Most Volatile Asset = Gold * Gold's best year was +106% in 1979 and its worst year followed shortly thereafter with -37.6% in 1981. Across all 54 years, the standard deviation was 24.7%, the highest among our asset classes. If you own gold, be prepared for big wins and losses along the way. - Least Volatile Asset = 3-month Treasury Bills * Unsurprisingly, 3-month Treasury bills are the least volatile asset since they are basically the same as cash (or the risk-free rate). However, this lower volatility comes with a cost—lower long-term growth. $1 invested in 3-month T-Bills in 1972 would've only become $1.14 (in real terms) by 2025. This pales in comparison to the S&P 500's overall growth of $35.90 or International Stocks which grew to $11.27 (in real terms) over the same time period. - Best Inflation-Hedge = Commodities (and Gold if you're lucky) * When we break out the data into high inflation (>4% CPI) vs. low inflation (<=4% CPI) periods, Commodities tend to perform the best among all assets. Despite its poor long-term returns, the average annual return for Commodities during periods of high inflation was about 2.9 percentage points higher than during periods of low inflation while the median annual return was basically identical. This wasn't true for most of the other asset classes. For example, during periods of high inflation, the S&P 500 had an average return that was 11 percentage points lower (and a median return that was 17 percentage points lower) than during periods of low inflation. Similarly, International Stocks, REITs, U.S. Corporate Bonds, and 10-Year Treasuries all performed worse during periods of high inflation. Whether we use the average or median, returns were about 8 percentage points lower (if not more) during these periods. * Gold is a complicated edge case. Gold's average annual return was 5.6 percentage points higher during periods of high inflation (compared to periods of low inflation). This is the highest among any asset studied. However, Gold's median return was actually 7.7 percentage points lower during periods of high inflation. This demonstrates that Gold's ability to act as an inflation hedge is based upon a few outlier up years (e.g., +106% in 1979, +59% in 1973, +49% in 1974, etc.) that may not repeat again in the future. For example, Gold recently had a +60% year in 2025, but this was 3 years after inflation peaked in 2022. My main takeaway from analyzing this data is that no single asset dominates. Some assets have more growth, some have more risk, and some only perform during certain economic regimes. Whether these asset classes will behave similarly in the future is up for debate. Now that we've looked at how different asset classes have performed (in real terms) since 1972, what would have been the optimal mix to get the highest risk-adjusted return? ## What's In the Optimal Portfolio? When it comes to the portfolio with the highest risk-adjusted returns (Sharpe Ratio) from 1972-2025 (i.e., "Optimal Portfolio"), the answer is: - U.S. Homes = 36% - S&P 500 = 27% - Gold = 18% - 10-Year Treasuries = 16% - REITs = 3% Note that 3-month Treasury Bills are absent because they serve as the risk-free rate, so by definition they can't appear in the Optimal Portfolio. The Optimal Portfolio allocation might surprise you, but a core reason for this is that U.S. homes are less volatile (since they are marked-to-market less often) while still having good returns since 1972. As a result, a U.S. home added a decent amount of return to your portfolio without adding much risk. You can see this in the plot below showing the annual return and risk (standard deviation) of the Optimal Portfolio, the efficient frontier, and a handful of other portfolios and individual asset classes: As you can see, the Optimal Portfolio has the highest return per unit of risk. You can't increase your return from this point without also increasing the amount of risk you take. For example, though the S&P 500 had a real average annual return of about 8.3% (or 77% higher than the Optimal Portfolio's 4.7% per year), it also had a standard deviation of 16.8% per year (or 155% higher than the Optimal Portfolio's 6.6% per year). In other words, owning the S&P 500 would get you 1.77x the average annual return of the Optimal Portfolio, but with 2.55x the risk. Note that these are arithmetic means, which is what the efficient frontier is built from. If we were to use geometric means, the return gap would narrow significantly—6.9% per year for the S&P 500 vs. 4.5% per year for the Optimal Portfolio. The return difference would now be 1.54x (versus 1.77x for the arithmetic return), while the risk difference of 2.55x remains the same. The S&P 500 gives up about 1.4 percentage points a year due to volatility while the Optimal Portfolio only gives up 0.2 percentage points. Not having to recover from big losses is a source of return for the Optimal Portfolio. You can visualize this in the plot below which illustrates the annual drawdowns in each portfolio from 1972-2025: Over this time period, the Optimal Portfolio's maximum drawdown was about 13% (in 2022) compared to 48% for the S&P 500 (in 1974). This difference illustrates why portfolio optimization can be so powerful. In theory, you could take the Optimal Portfolio and lever it up (by borrowing) to generate the same return as the S&P 500 while also taking less risk overall. In practice this is harder than it sounds. Levering the Optimal Portfolio 1.77x only works if you can borrow near the risk-free rate, rebalance without friction, and stomach a possible margin call. As a result, most investors are unable to lever their assets so easily. There is one partial exception though—housing. A 30-year fixed mortgage is the most common leverage available to the typical household. More importantly, this leverage can't be margin called. This means that many U.S. households are already running a partially levered version of the Optimal Portfolio without realizing it. Based on my own calculation of the 2022 Survey of Consumer Finances, the median American homeowner has 51% of their net worth in their home (via home equity). The fact that the Optimal Portfolio suggests a 36% allocation to a home is a convenient result that isn't far off many existing homeowner allocations. Of course, some would argue that your home shouldn't be considered as part of your portfolio since housing is a consumption good. I agree with this sentiment, which is why I re-ran the Optimal Portfolio while excluding U.S. housing as an asset class. Without U.S. housing, the Optimal Portfolio from 1972-2025 would have been: - S&P 500 = 43% - Gold = 26% - 10-Year Treasuries = 22% - REITs = 9% The 36% exposure to U.S. housing got absorbed by the remaining four asset classes (+16% to S&P 500, +8% to Gold, +6% to 10-Year Treasuries, and +6% to REITs). After this change, the new Optimal Portfolio looks like a more aggressive version of the Permanent Portfolio (25% stocks, 25% gold, 25% bonds, 25% cash). Swap the 25% cash for stocks and some REITs and it's remarkably similar. You can see this more clearly if we add the Permanent Portfolio to the efficient frontier plot from earlier: This isn't an argument for (or against) the Permanent Portfolio or the Optimal Portfolio. Both of these portfolios have been great historically. But history is the past—yet we have to invest in the future. And, unfortunately, the Optimal Portfolio is the ideal solution to the world of yesterday, not the world of tomorrow. This is why the "best" portfolio will always be an illusion. ## Why the Best Portfolio is an Illusion Imagine the world 50 years into the future. Will the Optimal Portfolio over those 50 years (2026-2075) look like the one from the prior 54 years (1972-2025)? No chance. How do I know? Because the optimal portfolio in 1998 was quite different from the one today. For example, if we were to solve for the Optimal Portfolio from 1972-1998 (half the years in the data), here's what we would find: - S&P 500 = 68% - Gold = 24% - International Stocks = 6% - U.S. Corporate Bonds = 2% There's some similarity to the Optimal Portfolio from 1972-2025 in that it holds a sizable S&P 500 and Gold allocation, but that's about it. The 1972-1998 Optimal Portfolio adds International Stocks and U.S. Corporate Bonds, but completely drops 10-Year Treasuries, REITs, and U.S. Homes. Yes, U.S. Homes, which were 36% of the 1972-2025 Optimal Portfolio, are nowhere to be found in the 1972-1998 one! When I say that the Optimal Portfolio of 2026-2075 won't look like the one from 1972-2025, this is what I mean. It's not because I'm bearish on U.S. stocks, skeptical of Gold, or convinced REITs are doomed. It's because I don't know the future. Maybe the U.S. will default on its debt. Maybe Gold will fall out of fashion. Maybe demographic transition will send REITs toward a slow demise. I have no clue and neither does anyone else. This is what makes portfolio optimization such a cruel art form. You get the perfect solution to a problem that no longer exists. The world changes and what you knew about it changes with it. This is why the Optimal Portfolio is an illusion. Because you can never have it going forward, only looking backward. Because the future is like the past...until it isn't. This has convinced me that the actual optimal portfolio is the one that you can stick with no matter what the world throws at you. Until next time, happy investing and thank you for reading. If you liked this post, consider signing up for my newsletter. This is post 520. Any code I have related to this post can be found here with the same numbering: https://github.com/nmaggiulli/of-dollars-and-data ---