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Many business books have only one good idea. This book has three, which is nice:

1. The business environment is in great shape for early exits – If you want to sell a company for $5-30 million, there are a lot of buyers, they have gotten easier to find, and the deals have gotten simpler. For many more businesses than in the past, an early exit is feasible.
2. Align your exit timeline with your investors to make an early exit possible – If you take traditional venture money, that is a commitment to swing for the fences. It can be very difficult to accept a $5-30 million exit after you have raised even seed money from a VC, because they are depending on your company to consume growth capital and deliver the kind of returns that their funds require.
3. Employ an outside party to manage the exit – The book really drove this point home, presumably because the author is making a career out of this role. It was an interesting perspective to me, not something I hear much about in the blogosphere, and something I’ll have to investigate further.

Unfortunately there were a few problems with the execution of the book:

• Lack of rigor – Peters’s financial models are quite simple, and generally not backed up by broadly sourced data. The traditional-versus-early-exit model that makes the cover of the book and drives home the point about avoiding VC is purely hypothetical numbers, for example.
• Canadian heavy - Writing about what you know is great, but more information about key geographies like the United States would have helped make the book more credible. There is plenty of research and statistics available, and the author only scratched the surface.
• Simplistic Angels versus VCs viewpoint – There are a lot of opinions flying around about what makes someone a super-angel, or what makes a seed-stage VC firm. Suffice to say the book’s black and white distinction between angels and VCs is simplistic. I would have expected more reasoned discussion of investor types, given that investor alignment is a key point.
• Insufficient detail – The later chapters in the book, notably around pricing, are insultingly light on detail. I realized that pricing is complicated and it’s best to work with a professional, but that’s no excuse for introducing net present value of cashflow and then leaving everything else as an exercise for a consult.
• Too much of a commercial – I understand that the point of this book, and many others, is to promote the author’s services. However, there were too many pages dedicated to the need for such services, and too few to the nuts and bolts of what would be done or how value would be realized. That, in the end, is the real problem with this book. I was hoping the author would tell me more of what he knows, since I’ll probably never have the opportunity to hire him directly.

The author has a blog (angelblog.net), and it remains somewhat unclear to me why he put the effort into a book rather than sticking with the blog format. Much of the information in this book is available on his blog and around the blogosphere. I had been hoping for more new work, or more depth. Unfortunately I did not find it.

If you’re considering starting a company with an eye to early exits, it’s easier to read this book than to track down 100 good blog posts on the topic. Unfortunately, you’re going to need the blog posts either way to fill in the gaps.

Nanex makes a few concrete suggestions: NYSE should fix their timestamping logic, HFT firms should be discouraged from what they call “quote stuffing”, and quotes should have a minimum time to live of 50 milliseconds. The last suggestion is the most interesting, and would have a significant impact on market dynamics, but possibly not a significant impact on prices or the availability of liquidity. Many other markets (Brazilian equities, US futures) have some kind of charge for canceling or modifying orders. This kind of restriction or discrimination doesn’t prevent high frequency traders from operating, but it does change their strategies, and it might help to discourage runaway markets.

What I have found myself recommending, rather than these changes to address the immediate problem, is a need for cultural change, to identify potential future system accidents and resolve them before they make headlines. In part 2 of the Nanex report, they identify two previous days on which a similar delay in NYSE quotes occurred without triggering a run on the market. With 20-20 hindsight, one might guess that investigations of these previous events would have been sufficient to prevent or limit the damage on May 6th.

Unfortunately, the culture of American capital markets is hyper-competitive, and that’s something our governments has encouraged through deregulation. Market operators are in competition with one another, brokers are in competition, trading firms (HFT and otherwise) are in competition. A culture of secrecy, and of exploiting weaknesses, makes investigating anomalous market events and other bugs very difficult. Technological transparency is important for the health of the whole financial system, but firms aren’t yet ready for it.

Short of distasteful regulatory enforcement, it’s hard to see how we would get people to participate in any kind of information sharing. But if we want to avoid future instances of market, we’ll need to find a way. There are always going to be bugs in the software we use to operate our financial system. The question is whether we find them among friends, or if we wait for the bugs to make headlines.

Normal Accidents (or as wikipedia calls them “System Accidents“) are major failures caused by unintended and unexpected interactions of many small failures. The term was coined by Yale professor Charles Perrow in his book Normal Accidents: Living with High-Risk Technologies. Complex systems fail for complex reasons. In systems engineered for safety and redundancy the failures that do happen require many contributing factors. Perrow’s focus was on large industrial systems, such as power plants, chemical plants, aircraft, shipping, and military operations. TIme and again we see complex failures in places like Three Mile Island, the Challenger shuttle, or BP’s current oil spill.

In a normal accident, the contributing factors come from many areas and often many organizations. Errors result from poor regulation, lack of training, operator error, specification errors, mechanical failures, lax maintenance, poor morale, organizational structures, economic incentives, and many other areas. Because systems are tightly coupled, many of these factors are able to mutually reinforce one another to lead to systemic failure. The resulting cascade of failures can look like a Rube-Goldberg machine in it’s complexity.

In these tightly coupled systems, potential-normal-accidents are happening all the time. Systems are too complex to be entirely without failures. However, in the common case these partial failures are caught and resolved quietly. In fact, these near misses are an opportunity to understand the unintended failure modes of the system. Rather than build once and deploy, safety must be a continuous process of improvement and understanding. Systems aren’t stable and they are not deployed in a vacuum. As they evolve, failures and near misses must be examined and used to drive improvements.

Software, especially modern networked software, dramatically increases the incidence of normal accidents. As anyone who has ever created, deployed, and debugged software knows, it is common for individual software bugs to have all the characteristics of a normal accident all by themselves. Add together software written by multiple different organizations connecting over a network and it’s a wonder anything works at all.

Getting back to the events of May 6th, the “Flash Crash“, they are best explained as a system accident. People have tried to blame one cause or another, from a fat fingered trader or a faulty brokerage system, to investor agitation over Greek debt and high frequency trading firms going wild, to the NYSE hybrid market system, bugs in other members of the national market system, and outdated circuit breaker regulations. Without going into detail about all these potential causes, I’d like to suggest that the most likely explanation is that all of these causes, together, are what created the exceptional failure of market prices, broken trades, and finger pointing. No one cause is really more precipitating than any other, and apportioning blame is much less important than understanding in detail what happened.

It is impossible to eliminate normal accidents as we increase the complexity of our systems. The best we can do is to learn from accidents, and from near misses, to introduce the kind of slack in our systems that will protect us from the worst accidents. Learning requires transparency. But in systems which cross organizational and regulatory boundaries, with billions of dollars and reputations at stake, transparency is going to be a challenge.

PS: If you’re interest in learning more, I suggest this NASA powerpoint on normal accidents.

In the sphere of financial derivatives, our civilization has recently come to understand that there are a whole class of financial products which look attractive, and perform reasonably well some of the time, but which eventually fail. The most obvious of these are the credit default swaps of the latest crisis. But other examples include portfolio insurance, made famous in the 1987 market crash. The problem with these products is that they are designed to protect the buyer against losses in all circumstances, even when the market is behaving badly. But when the market is behaving badly, it can behave very badly. These products reduce to end of the world insurance. When the world is ending, who is left to pay out the insurance?

I think there is a useful parallel to the halting problem. If your new financial product can be used as end of the world insurance, it probably will be. And since end of the world insurance is fundamentally flawed, it should raise questions about what your product is really accomplishing.

To quote a friend of mine, when asked how we much we should bid on the house:

In a first-price auction, there’s no dominant strategy. However, by the Law of Revenue Equivalence, the seller will, on average, get the second-highest valuation of all the bidders. So, if you think you value the house more than everyone else, all you have to do is guess what the next-best buyer would be willing to pay for it, and bid slightly more than that to win.

Yes, well, that is entirely correct, but unfortunately unhelpful. We are dealing with a non-repeating negotation (so “on average” doesn’t apply). And it’s remarkably unclear that the auction will be run according to any rules at all. Predicting the behavior of the seller and the sellers agent is quite challenging. On Saturday at brunch a different friend recommended The Strategy of Conflict by Thomas Schelling, from the era of game theory research that brought us such gems as “mutually assured destruction.” Unfortunately there wasn’t time to read it.

Obviously (obvious to anyone who hangs around certain kinds of mathematicians) the right thing is to have a Vickrey auction, where the top bidder pays the price of the second highest bid. But this is far from obvious to realtors. They treat making an offer as a very expensive operation. And counteroffers from the seller seem to be very uncommon. There is a lot of concern about “offending” or missing out on an offer. I’m not sure why. If someone wants to buy your house on Monday for $x, they probably still want to buy it on Wednesday. Unless maybe their Realtor is reading something into the delay.

One is left wondering if the world would converge on more optimal auction structure without real estate agents muddying the waters. Or if, as eBay has demonstrated, people would rather keep working in an easy to understand system over an optimal one. Unlike eBay, the current system is quite complicated, with many conventions and few rules. In the age of Redfin, Realtors have an obvious interest in preserving the status quo, since understanding the rules is the only thing they have left. In the past, they at least had priority access to listings and historical sale data. Now they are left understanding the negotiating process.

In the end we offered $410k for the house. There were 5 offers, three of them at a similar price point to ours, $10k over asking. There was one offer at $430k, so the sellers decided to accept that offer, rather than encouraging further bidding. Case closed, transaction completed, Realtor happy. Money might have been left on the table, but getting it would have required both work and risk on the part of the agent.

The Wrong Way

First, let’s start with the wrong way to value stock options. It goes something like this:

1. Wow, I’ve got 10,000 options with a strike price of $0.70
2. The stock price at the last round of investment was $2.50
3. My options are worth 10,000 * $1.80 = $18,000

What’s wrong with this? Several things. First, your options are for common stock, while the investors were buying preferred for their $2.50. If your company did the 409A valuation correctly, then the fair market value of what you have an option on is probably right around what the strike price is. For more on 409A and how strike prices are set, see Brad Feld’s posts on 409A.

Second, the terms of investment probably include some amount of participating preferred, dilution protection, and a variety of other complicated things which protect the people who put in the millions of dollars and paid for the lawyers. If you can get them, the details of these agreements are interesting. To understand them, you’ll probably want to read Brad Feld’s posts on term sheets.

In fact, if you are interested in the details of how startups are financed, you could do worse than just read everything at Feld Thoughts. But my assumption here is you just want to know whether Company A or Company B is making a better offer. So read on.

For a contrarian perspective, you can read Venture Hacks, which disagrees with me and says you should focus on share price. They also have a whole post on analyzing startup job offers. Further evidence there are many ways to think about this.

What Really Gives Your Options Value

If you aren’t a founder or an executive, your options are like lottery tickets. This is because there are three potential outcomes for a startup: a huge win, a weak sale, or total failure. Unsurprisingly, in total failure your options won’t be worth anything. Somewhat surprisingly, you are likely at a deep disadvantage in a weak sale.

All those terms of investment to protect the people who put in millions of dollars mean that they get the lions share of the proceeds. Generally they get their original investment back, and probably a hefty share of whatever is left over. What constitutes a weak sale varies by the terms, but it is probably anything up to three times the amount of money that has been invested. In such a sale even the money that is left over may be complicated with additional terms, like earn outs or management carve outs. In a weak sale, there isn’t enough cash to make everyone happy, so people are going to be counting the pennies and taking care of their own. If you are an engineer who was late to the party, your interests will probably not be protected.

So the only situation in which you will make out well is the big win. In a big win, like an IPO or a big flashy acquisition by a public company, there is plenty of money to make everyone happy. The VCs are having the kind of result they can tell all their friends about. If the term sheets were reasonable, then all of those preferential treatments are gone and everyone is treated equally according to the fraction of the company they own. Even the little guys like you will make out OK.

Because the big win is the only situation in which you make significant money, it’s the only one you need to think about when valuing your options.

The Information You Need

Focusing on the big win, there are three pieces of information you need:

1. What fraction of the company do you own?
2. How much will the company be worth if you win big?
3. How likely is the big win and how far off?

If you knew all these things, some simple multiplication and net present value analysis would tell you what your options are worth. So how do we get this information?

What Fraction of the Company do You Own?

Any option grant is going to tell you how many shares you get. But what is really important is how large a fraction of the company those shares represent. To figure that, you need to know is how many total shares have been approved for issue. The company should be willing to tell you this. Chris Dixon has a great post about this in The one number you should know about your equity grant. That post says it better than I could: you need to know the fraction, and the company must be willing to tell you.

How Much will the Company be Worth If You Win Big?

The second term in the equation is how much the big win is worth. There are a number of ways to look at this, all of them require doing some amount of homework. I recommend collecting a variety of answers, to get a distribution and a sense of what is going on.

The first technique is simply to ask your contacts at the firm how they are thinking about exits. You probably want to preface this with an acknowledgement that you know nothing is certain, and of course they are building a company for the long term and not looking for a quick flip. On the other hand, flipping is good, and this is probably your best opportunity to learn how the management team is thinking about it.

The second is to learn more about the investors. If the company has followed a traditional VC fundraising model, without any down rounds, then you can look at the total capital invested to estimate the expected big win return. In order to invest money, each of the VC partners had to justify the potential for the investment to return five to ten times as much money back in a successful exit. To learn more about the economics behind this, see Fred Wilson’s posts on Venture Fund Economics. You can assume the investors own 50-70% of the company unless you know otherwise. So if you multiply the invested capital by 10, you get a reasonable estimate of where they thing a big win would fall.

The third is to look at historical exits in your space. There are a number of resources here. The first is google. Since you are only interested in big wins, you can look at IPOs and impressive acquisitions. For IPOs, data is easily available. For acquisitions by public companies, you sometimes have to dig to 10-K filings. There are also reports published by organizations like Software Equity Group which can help you understand the merger and acquisition activity and economics.

One possible outcome of these three techniques is that you get wildly different answers. For example, the historical exits and the invested capital might not make sense, indicating the investors are deluded or expecting something improbable. Or the management teams expectations are out of line with historical activities. None of these measurements is accurate enough for a mismatch to be a deal breaker, but it might be something to look into.

How Likely is the Big Win?

The third term is how likely your big win is. If you read the post on venture fund economics, you’ll see that the investors hopefully think it is about 33% likely, or they did when they put money in. As a new employee joining a startup, you should come to your own conclusion here.

You also need to estimate how far off the win is. This is another place where just asking your contacts at the company can be informative. One thing to note is that it takes 7 years on average for a venture-backed company to mature. Of course, there is a lot of variance here.

What to Do With This Information

Given these values, you can calculate the value of your option grant. Multiply your percent ownership by the value of a big win, multiply the result by the likelihood of the win, and then discount by 5% for each year in the future.

For example, if you are being offered 0.1% of a company with a 30% chance of a $400 million exit in 4 years, your value would be:

0.1% × $400 million × 30% × (.95 ^ 4) = $96,000

Plugging in your own numbers should get you a helpful result. If you have a variety of estimates for the values (and you should), do multiple calculations, and get a sense for the variety of results and what impacts them.