Wednesday, January 19, 2022

Data Update 2 for 2022: US Stocks kept winning in 2021, but…

Leading into 2021, the big questions facing investors were about how quickly economies would recover from COVID, with the assumption that the virus would fade during the year, and the pressures that the resulting growth would put on inflation. In a post at the start of 2021, I argued that while stocks entered the year at elevated levels, especially on historic metrics (such as PE ratios), they were priced to deliver reasonable returns, relative to very low risk free rates (with the treasury bond rate at 0.93% at the start of 2021). At the start of 2022, it feels like Groundhog Day, with the same questions about economic growth and inflation looming for the year, and the same judgment about stocks, i.e., that they look expensive. In this post, I will begin with a historical assessment of stock returns in the recent past, then move on to evaluate the returns that investors can expect to make, given how they are priced at the start of 2022, and end with a do-it-yourself valuation of the index right now.

The year that was.... 

    If equity markets surprised us with their resilience in 2020, not just weathering a pandemic for the ages, but prospering in its midst, US equity markets, in particular, managed to find light even in the darkest news stories, and continued their rise through 2021. Foreign markets, though, had a mixed year, and that divergence is worth noting, since it may provide clues to what may be coming in the next year.

US Equities, in the aggregate

   US equities had a good year, by any measure, with the S&P 500 rising from 3756 at the start of 2021 to end the year at 4766, an increase of 26.90%. While that followed another good year for stocks in 2020, with the index rising 16.25%, from 3231 to 3756, the index took different pathways during the two years:


In 2020, the market was up, but only after it absorbed the after-shocks of the inception of the virus in February and March of 2020. In 2021, the index had a smoother ride, up in nine of twelve months, with only September qualifying a significant drop (with the index down 4.75%). When you augment this price change with the dividends on the index during 2021, the total return on the S&P 500 for 2021 was 28.47%. I report a dataset containing annual returns on stocks, going back to 1928, and to put 2021 in context, I looked at the historical returns on the index:
Download historical returns on US stocks

Looking at the 94 years in this dataset, the returns in 2021 would have ranked 20th on the list, good, but not exceptional. Note, though, that 2021 is the third consecutive year of very good returns on the index, with 2019 delivering 31.21%, and 2020 generating 18.02%, and that the cumulative return over the three years (2019-21) is 98.95%. If you compute cumulative returns, on a rolling three-year time period (1928-30, 1929-31, 1930-32 etc.), the 2019-21 time period would rank 8th on the list of 92 3-year time periods. The table below provides the rankings for returns over 5-year and 10-year periods, and where the most recent three-year, five-year and ten-year cumulative returns would rank on the list:
Download return rankings

In sum, if you have money to invest over the last decade, and you stayed invested, you should count yourself as lucky to have enjoyed one of the great market runs of the last century. Conversely, if you stayed out of the market, for the last decade, you would have committed one of the great investing mistakes of all time, and blaming the Fed or bubble-talk will not bring you absolution.

US Equities, by sub-group

    It has always true that when markets move, up or down, not all sectors and sub-groups are treated equally. I do believe that too much is often made of these differences, as it is generally more the rule than the exception that markets, when they are up strongly, get the bulk of that rise from a small sub-set of stocks or sectors. Using S&P's sector classification, I take a look at how each one did in 2022, looking at the percent changes in market capitalization:

In contrast to 2020, when technology and consumer discretionary firms ran well ahead of the pack, the best performing sectors in 2021 were energy and real estate, two of the biggest laggards in 2020. That can be viewed as vindication, at least in this year, for contrarians, but as a cautionary note for ESG advocates, who assumed that fossil fuel companies were on a death march, based upon their performance over the last decade.

There is no debate more likely to draw heat than the value versus growth debate, and at the risk of being labeled simplistic by value investors, I looked at returns on companies, in 2021, based upon their PE ratios at the start of 2021:


Unlike 2020, where high PE stocks beat low PE stocks decisively, the results in 2021 were mixed, with no clear patterns across the classes. The results are similar if you break stocks down based upon price to book ratios or revenue growth rates. Finally, and in keeping with my fixation on corporate age and life cycles, I broke companies down by company age (measured from the founding year):

Again, unlike 2020, when young companies delivered significantly higher returns than older companies, the best returns in 2021 were delivered by middle aged companies.

For the rest of the world

   While US equities continued to set new highs in 2021, the picture in the rest of the world was not as rosy, as you can see in the table below (with percent returns in US dollar terms):


In US dollar terms, India had the best-performing market in 2021, following a strong 2020, but China, the best performer in the world in 2020 came back to earth in 2021. North America (US and Canada) outperformed the globe, but Latin America was the worst performing region, down more than 20% in US dollar terms. There are many reasons why markets diverge, but here again the contrast with 2020 is worth drawing. In 2020, the COVID crisis played out across markets, increasing the co-movement and correlation across developed markets, with the US, Europe and Japan moving mostly in sync. In 2021, you saw a return to more normal times, with markets in each country affected more by local factors.

The Price of Risk in Equity Markets

    The allure of having the historical data that we do in financial markets, especially in the United States, is that there is information in the past. The danger of poring over this historical data is that a focus on the past can blind us to structural changes in markets that can make the future very different from the past. To get a measure of what equity markets are offering in terms of expected returns, we are better served with a forward-looking and dynamic measure of these returns, and that is the focus of this section.

Implied Equity Risk Premiums

   To understand the intuition behind the implied equity risk premium, it is easiest to start with the concept of a yield to maturity on a bond, computed as the discount rate that makes the present value of the cash flows on the bond (coupons, during the bond's lifetime, and face value, at maturity) equal to the price of the bond. With equities, the cash flows take the form of dividends and buybacks, and in addition to estimating them using future growth rates, you have to assume that they continue in perpetuity. In computing this implied equity risk premium for the S&P 500, I start with the dividends and buybacks on the stocks in the index in the most recent year (which is known) and assume that they grow at the rate that analysts who follow the index are projecting for the next five years. Beyond the fifth year, I make the simplifying assumption that earnings growth will converge on the nominal growth rate of the economy, which I set equal to the risk free rate

If you set the present value of the expected cash flows equal to the index level today, and solve for a discount rate (you may need to use the solver function in Excel, or trial and error), the resulting number is the expected return on stocks, based upon how stocks are priced today, and expected cash flows. This approach is built on the proposition that the intrinsic value of stocks is the present value of the expected cash flows that you generate in perpetuity, from holding these stocks, but it is model agnostic. Put simply, it does not require that you believe in any risk and return model in finance, since it is based on price and expected cash flows. To the critique that analysts can over estimate future earnings and  growth, the response is that even if they do (and there is no evidence that top-down forecasts are biased), it is the price of risk, given expected cash flows.

The Implied ERP - Start of 2022

I have computed the implied equity risk premium at the start of every month, since September 2008, and during crisis periods, I compute it every day.  Over the course of 2021, as the index rose, risk free rates climbed and analysts got much more upbeat about expected earnings for the next three years, the equity risk premium drifted down, to end the year at 4.24%:

Download spreadsheet

Much as I would love to claim that I have the estimated the equity risk premium to the second decimal point, the truth is that there is some give in these numbers and that changing assumptions about earnings and cash flows generates an equity risk premium between 4-5%. The contrast between the behavior f equity risk premiums in 2020 and 2021 are in the picture below, where I show my (daily) estimates of ERP during 2020 on the left, and my (monthly) estimates of ERP for 2021 on the right. 

Download data on ERP by month from 2008-2021

During 2020, the equity risk started the year at about 4.7%, spiraled to almost 8% on March 23, 2020, before reverting back quickly to pre-crisis levels by September 2020. During 2021, you saw equity risk premiums revert back to a more sedate path, with numbers staying between 4% and 5% through the course of the entire year.

Historical Perspective

    As talk of a bubble fills the air, one way to reframe the question of whether stocks are in bubble territory is to ask whether the current implied equity risk premium has become “too low”. If your answer is yes, you are arguing that stocks are over priced, and if the answer is no, that they are under priced. At least on the surface, the current level of the equity risk premium is not flashing red lights, since at 4.24%, it is running slightly above the long term averages of 4.21% (1960 - 2021).

Download past implied ERP

That said, there are two reasons for concern. The first is that the premium is now lower than the average premium since 2008, a period that perhaps better reflects the new global economy. The second and scarier reason is that the 5.75% expected return that is implied in today’s stock prices is close to a sixty-year low:

Download past implied ERP

A pessimist looking at this graph will conclude that expected returns on stocks have become too low, and that we are due for a correction, but that would be more a statement about treasury bond rates being too low than about equity risk premiums. Even if you belong to the camp arguing that low risk free rates are now the norm, this graph suggests that we all need to re-examine how much we, as individuals, are saving for retirement, since the old presumption that you can earn 8-10% investing in stocks will longer hold. Across the United States, defined-benefit pension funds that have set aside funds on this same assumption will face massive funding shortfalls, unless they reevaluate benefit levels or infuse new funds.

A Market Assessment

  If you look at history, it seems difficult to argue against the notion that market timing is the impossible dream, but that has never stopped investors from trying to time markets, partly because the payoff from being right is immense. I have long claimed that I am not a market timer, but that is a lie, since every investor times markets, with the difference being in whether the timing is implicit (with cash holdings in your portfolio increasing, when you feel uneasy about markets, and decreasing, when you feel bullish) or explicit (where you actively bet on market direction). Rather than just give you just my estimate of whether I think the market is under or over valued, I will ask each of you to make your own judgments, while also offering my own. 

    The process of valuing the index starts with an assessment of expected earnings, and with the S&P 500, there is no shortage of either historical data or assessments of the future, on this dimension. Let's start with a look at S&P earnings over time:

Download historical earnings

While COVID wreaked havoc with corporate earnings in 2020, the comeback in earnings in 2021 has been remarkable, with trailing 12-month earnings (October 2020 - September 2021) at 190.34, and estimated earnings for 2021 of 206.38, both significantly higher than the 162.35 that was earned in 2019  (pre-COVID). At the end of 2021, analyst estimates for earnings in 2022 and 2023 reflect their views that the earnings recovery will continue:

I will use the analyst estimates as my expected earnings for the index for the next two years, but assume that earnings growth rate thereafter will move down over the following three years to a stable growth rate (set equal  to the risk free rate). It is true that analysts are often wrong, and in some cases, biased, but the latter is more of a problem with analyst estimates for individual companies, than for market aggregate earnings. However, if you believe that analysts have overestimated earnings, my valuation spreadsheet gives you the option of haircutting those estimates. Conversely, if your contention is that analysts are still playing catch-up, you can increase their estimates by a factor of your choosing.

    Investors don't get value from earnings directly, but do get value from the cash flows flowing from those earnings. As I have noted in prior posts, these cash flows, which used to entirely take the form of dividends, have increasingly shifted, over the last three decades, to stock buybacks. 

While dividends are stickier than buybacks, insofar as companies are more willing to reduce the latter during crisis years like 2009 and 2020, it is also clear, as the comeback in buybacks in 2021 shows. In my base case valuation, I will start with 77.36%, the percent of earnings that companies have returned to shareholders, in dividends and buybacks, in the last twelve months, but I will increase this over time to a cash payout ratio that is consistent with my estimate of stable growth (risk free rate) and the return on equity of 16.10% that S&P 500 companies have earned, on average, over the last decade. (Sustainable Payout Ratio = 1 - g/ ROE; with a 16.10% return on equity and a 2.5% growth rate, the payout ratio in stable growth is 84.47%= 1 - .025/.161).

    On the risk free rate, I start with 1.51%, the 10-year treasury bond rate on January 1, 2022, but I will assume that this rate will drift upwards over the next five years to reach 2.5%. That reflects my view that inflation pressures will push up long term rates in the year to come and has little to do with what the Fed may or may not do with the Fed funds rate. Finally, I build in the expectation that a fair ERP for the S&P 500 should be 5%, higher than the long term historical average of 4.21%, but closer to the average ERP since 2008. On both these macro assumptions, I encourage you to take your own point of view. With these assumptions in place, my valuation for the S&P 500, as of January 1, 2022, is shown below:

Download spreadsheet to value S&P 500 index

Note that even in the two weeks since I did this valuation, there have been material changes in key inputs, with the treasury bond rate rising to 1.87% on January 19, 2022, and the S&P dropping to 4533, down 3.8% from its level at the start of the year. 

In Conclusion

    As with any valuation, I don't believe that I should try to convince you that my valuation is the right one, nor do I have no desire to do so. In fact, I know that my valuation is wrong, with the question being in what direction, and by how much. I would strongly encourage you to take my valuation spreadsheet, change the numbers that I have used on earnings, cash flows, the risk free rate and the equity risk premium to reflect your views, and come up with your own assessment of value. Good investing requires taking ownership of your investment decisions, and trusting this choice to talking heads on TV, market strategists at investment banks or those market gurus who looked good last year is a dereliction of investment duty.    

YouTube Video

Datasets (to download)

  1. Historical returns on stocks, bonds, bills and real estate: 1928 - 2021
  2. Historical implied ERP on the S&P 500: 1960 -2021

Spreadsheet (to value the S&P 500)

  1. Spreadsheet to compute implied ERP on January 1, 2022
  2. Spreadsheet to value the S&P 500 on January 1, 2022


Saturday, January 8, 2022

Data Update 1 for 2022: It is Moneyball Time!

Happy New Year, and I hope that 2022 brings you good tidings! To start the year,  I returned to a ritual that I have practiced for thirty years, and that is to take a look at not just market changes over the last year, but also to get measures of the financial standing and practices of companies around the world. Those measures took a beating in 2020, as COVID decimated the earnings of companies in many sectors and regions of the world, and while 2021 was a return to some degree of normalcy, there is still damage that has to be worked through. This post will be one of a series, where I will put different aspects of financial data under the microscope, to get a sense of how companies are adapting (or not) to a changing world.

The Moneyball Question

When I first started posting data on my website for public consumption, it was designed to encourage corporate financial analysts and investors alike to use more data in their decision making. In making that pitch, I drew on one of my favorite movies, Moneyball, which told the story of Billy Beane (played by Brad Pitt), the general manager of the Oakland As, revolutionized baseball by using data as an antidote to the gut feeling and intuition of old-time baseball scouts.


In the years since Beane tried it with baseball, Moneyball has decisively won the battle for sporting executives' minds, as sport after sport has adopted its adage of trusting the data, with basketball, football, soccer and even cricket adopting sabermetrics, as this sporting spin off on data science is called. Not surprisingly, Moneyball has found its way into business and investing as well.  In the last decade, as tech companies have expanded their reach into our personal lives, collecting information on choices and decisions that used to private, big data has become not just a buzzword, but also a justification for investing billions in companies/projects that have no discernible pathway to profitability, but offer access to data.  Along the way, we have all also bought into the notion of crowd wisdom, where aggregating the choices of tens of thousands of choice-makers, no matter how naive, yields a consensus that beats expert opinion. After all, we get our restaurant choices from Yelp reviews, our movie recommendations from Rotten Tomatoes, and we have even built crypto currencies around the notion of crowd-checking transactions.

Don't get me wrong! I was a believer in big data and crowd wisdom, well before those terms were even invented. After all, I have lived much of my professional life in financial markets, where we have always had access to lots of data and market prices are set by crowds of investors. That said, it is my experience with markets that has also made me skeptical about the over selling of both notions, since we have an entire branch of finance (behavioral finance/economics) that has developed to explain how more data does not always lead to better decisions and why crowds can often be collectively wrong. As you use my data, I would suggest four caveats to keep in mind, if you find yourself trusting the data too much:
  1. More data is not always better than less data: In a post from a few months ago, I argued that we as investors and analysts) were drowning in data, and that data overload is now a more more imminent danger than not have enough data. I argued that disclosure requirements needed to be refined and that a key skill that analysts will need for the future is the capacity to differentiate between data and information, and materiality from distraction.
  2. Data does not always provide direction: As you work with data, you discover that its messages are almost always muddled, and that estimates always come with ranges and standard errors. In short, the key discipline that you need to tame and use data is statistics, and it is one reason that I created my own quirky version of a statistics class on my website.
  3. Mean Reversion works, until it does not: Much of investing over the last century in the US has been built on betting on mean reversion, i.e. that things revert back to historical norms, sooner rather than later. After all, the key driver of investment success from investing in low PE ratio stocks comes from their reverting back towards the average PE, and the biggest driver of the Shiller PE as a market timing device is the idea that there is a normal range for PE ratios. While mean reversion is a strong force in stable markets, as the US was for much of the last century, it breaks down when there are structural changes in markets and economies, as I argued in this post
  4. The consensus can be wrong: A few months ago, I made the mistake of watching Moneyheist, a show on Netflix, based upon its high audience ratings on Rotten Tomatoes, and as I wasted hours on this abysmal show, I got a reminder that crowds can be wrong, and sometimes woefully so. As you look at the industry averages I report on corporate finance statistics, from debt ratios to dividend yields, remember that just because every company in a sector borrows a lot, it does not mean that high debt ratios make sense, and if you are using my industry averages on pricing multiples, the fact that investors are paying high multiples of revenues for cloud companies does not imply that the high pricing is justified.
In short, and at the risk of stating the obvious, having access to data is a benefit but it is not a panacea to every problem. Sometimes, less is more!

The Company Sample for 2022
When I first started my data collection and analysis in 1990, data was difficult to come by, and when available, it was expensive. Without hundreds of thousands of dollars to spend on databases, I started my journey spending about a thousand dollars a year, already hitting budget constraints, subscribing to a Value Line database that was mailed to me on a CD every year. That database covered just 1700 US companies, and reported on a limited list of variables on each, which I sliced and diced to report on about a dozen variables, broken down by industry. Times have changed, and I now have access to extraordinarily detailed data on almost all publicly traded global companies. I am grateful to all the services that provide me with raw data, but I am cognizant that they are businesses that make money from selling data, and I try not to undercut them, or act as a competitor. That is why almost every variable that you will see me reporting on my website represents a computation or estimate of mine, albeit with raw data from a provider, rather than a regurgitation of data from a service. It is also why I report only aggregated data on industries, rather than company-level data.

Regional Breakdown
My data sample for 2022 includes every publicly traded firm that is traded anywhere in the world, with a market capitalization that exceeds zero. That broad sweep yields a total of 47,606 firms, spread across 135 countries and every continent in the world:

The largest slice is Small Asia, where small has to be read in relative terms, since it includes all of Asia, except for India, China and Japan, with 9,408 firms. It is followed by the United States, with 7,229 firms, and then China (including Hong Kong listings), with 7.043 firms. Since many of these firms have small market capitalizations, with some trading at market caps of well below $10 million, the chart below looks at the breakdown of the sample in market capitalization:

The market capitalization breakdown changes the calculus, with the US dominating with $52 trillion in collective market cap, more than 40% of the overall global value, followed by China with $19 trillion in aggregate market capitalization. 

Sector/Industry Breakdown
The most useful way to categorize these 47,606 companies is by industry groupings, but that process does raise thorny questions about what industry groupings to use, and where to put firms that are not easily classifiable. To illustrate, what business would you put Apple, a company that was categorized (rightly) as a computer hardware company 40 years ago, but that now gets more than 60% of its revenues and profits from the iPhone, a telecommunication device that is also a hub for entertainment and services? I started my classification with a very broad grouping, based upon S&P's sector classes:
This should not come as a surprise, especially given their success in markets over the last decade, but technology is the largest sector, accounting for 19.22% of global market capitalization, though industrials account for the largest number of publicly traded firms. One sector to note is energy, which at 4.86% of global market capitalization at the start of 2022, has seen its share of the market drop by roughly half over the last decade. Addressing the legitimate critique that sector classifications are too broad, I created 94 industry groupings for the companies, drawing on the original classifications that I used for my Value Line data thirty years ago (to allow for historical comparisons) and S&P's industry list. The table below lists my industry groups, with the number of companies in each one:
I am sure that some of you will find even these industry groupings to be over-broad, but I had to make a compromise between having too many groupings, with not enough firms in each one, and too few. It also required that I make judgment calls on where to put individual firms, and some of those calls are debatable, but I feel comfortable that my final groups are representative.

The Data Variables
When I first started reporting data, I had only a dozen variables in my datasets. Over time, that list has grown, and now includes more than a hundred variables. A few of these variables are macro variables, but only those that I find useful in corporate finance and valuation, and not easily accessible in public data bases. Most of the variables that I report are micro variables, relating to company choices on investing, financing and dividend policies, or to data that may be needed to value these companies.

Macro Data
If your end game is obtaining macroeconomic data, there are plenty of free databases that provide this information today. My favorite is the one maintained by the Federal Reserve in St. Louis, FRED, which contains historical data on almost every macroeconomic variable, at least for the US. Rather than replicate that data, my macroeconomic datasets relate to four key variables that I use in corporate finance and valuation.
  1. Risk Premiums: You cannot make informed financial decisions, without having measures of the price of risk in markets, and I report my estimates for these values for both debt and equity markets. For debt markets, it takes the form of default spreads, and I report the latest estimates of these corporate bond spreads at this link. In the equity market, the price of risk (equity risk premium) is more difficult to observe, and I start by reporting on the conventional estimate of this measure by looking at historical returns (going back to 1928) on stocks, bonds, bills and real estate at this link. I offer an alternative forward-looking and more dynamic measure of this premium in an implied premium, with the start of 2022 estimate here and the historical values (going back to 1960) of this implied premium here.
  2. Risk free Rates: While the US treasury bond rate is widely reported, I contrast its actual value with what I call an intrinsic measure of the rate, computed by adding the inflation rate to real growth each year at this link
  3. Currency and Country Risk: Since valuation often requires comfort with moving across currencies, I provide estimates of risk free rates in different currencies at this link. I extend my equity risk premium approach to cover other countries, using sovereign default spreads as my starting point, at this link.
  4. Tax Rates: Since the old saying about death and taxes is true, I report on marginal tax rates in different countries at this link, and while I would love to claim that I did the hard work, the credit belongs to KPMG for keeping this data updated over time.
I do update my equity risk premiums for the US at the start of every month on my website, and the country equity risk premiums once every six months. 

Micro Data
I am not interested in reported financial ratios, just for the sake of reporting them, and my focus is therefore on those statistics that I use in corporate finance and valuation. You may find my choices to be off putting, but you could combine my reported data to create your own. For example, I believe that return on assets, an accounting ratio obtained by dividing net income by total assets, is an inconsistent abomination, leading to absurd conclusions, and I refuse to report it, but I do report returns on invested capital and equity. 

Rather than just list out the variables that I provide data on, I have classified them into groups in the table below:
Data on my site

With each of these variables, I report industry averages for all companies globally, as well as regional averages for five groups: (a) US, (b) EU, UK and Switzerland, (c) Emerging Markets, (d) Japan and (e) Australia, NZ and Canada. Since the emerging market grouping is so large (representing more than half my sample) and diverse (across every continent), I break out India and China as separate sub-groups. You can find the data to download on my website, at this link.

Data Timing and Timeliness
Almost all of the data that you will see in my updates reflects data that I have collected in the last week (January 1, 2022- January 8, 2022.  That said, there will be difference in timeliness on different data variables, largely based upon whether the data comes from the market or from financial statements. 
  • For data that comes from the market, such as market capitalization and costs of capital, the current data is as of January 1, 2022.
  • For data that comes from financial statements, the numbers that I use come from the most recent filings, which for most companies will be data through September 30, 2021. 
Thus, my trailing PE ratio for January 1, 2022, is computed by dividing the market capitalization on January 1, 2022, by the earnings in the twelve months ending in September 2021. While that may seem inconsistent, it is consistent with the reality that you, as an investor or analyst, use the most current data that you can get for each variable. As we go through the year, both the market and the accounting numbers will change, and a full-fledged data service would recompute and update the numbers. I am not, and have no desire to be, a data service, and will not be updating until the start of 2023. Thus, there are two potential dangers in using my data later in the year, with the first emerging if the market sees a steep shift, up or down, which will alter all of the pricing multiples, and the second occurring in sectors that are either transforming quickly (disrupted sectors) or are commodity-based (where changes in commodity prices can alter financials quickly).

Estimation Choices
When I embarked on the task of estimating industry averages, I must confess that I did not think much of the mechanics of how to compute those averages, assuming that all I would have to do is take the mean of a series of numbers. I realized very quickly that computing industry averages for pricing and accounting ratios was not that simple. To illustrate why, I present you with a slice of my table of PE ratios, by industry grouping, for US firms, the start of 2022:
PE ratio, by industry, for US companies
Take the broadcasting group, just as an illustration, where there were 29 firms in my US sample. The three columns with PE ratios (current, trailing and forward) represent simple averages, but these case be skewed for two reasons. The first is the presence of outliers, since PE ratios can be absurdly high numbers (as is the case with auto & truck companies), and can pull the averages up. The second is the bias created by removing firms with negative earnings, and thus no meaningful PE ratio, from the sample. The last two columns represent my attempts to get around these problems. In the second to last column, I compute an aggregated PE ratio, by taking the total market capitalization of all firms in the group and dividing by the total earnings of all firms in the group, including money losers. In effect, this computes a number that is close to a weighted average that includes all firms in the group, but if a lot of firms are money-losers, this estimate of the PE ratio will be high. To see that effect, I compute an aggregated PE ratio, using only money-making firms, in the last column. You may look at the range of values for PE ratios, from 7.05 to 24.99 for broadcasting firms, and decide that I am trying to confuse the issue, but I am not. It is the basis for why I take all arguments that are based upon average PE ratios with a grain of salt, since the average that an analyst uses will reflect the biases they bring to their sales pitches. 

The other issue that I had to confront, especially because my large sample includes many small companies, listed and traded in markets with information disclosure holes, is whether to restrict my sample to markets like the US and Europe, where information is more dependable and complete, or to stay with my larger sample. The problem with doing the former is that you create bias in your statistics by removing smaller and risker firms from your sample, and I chose to have my cake and eat it too, by keeping all publicly traded firms in my global sample, but also reporting the averages for US and European firms separately.

Using the Data
I report the data on my website, because I want it to be used. So, if you decide that some  of the data is useful to you, in your investing or analysis, you are welcome to use it, and you don't have to ask for permission. If you find errors in the data, please let me know, and I will fix it. If you are looking for a variable that I do not compute, or need an average for a region that I don't report separately on (say Brazil or Indonesia), please understand that I cannot meet customized data requests. I am a solo operator, with other fish to fry, and there is no team at my disposal. As I mention in my website, this data is meant for real time analysis for those in corporate finance and valuation. It is not designed to be a research database, though I do have archived data on most of the variables going back in time, and you may be able to create a database of your own. If you do use the data, I would ask only three things of you:
  1. Understand the data: I have tried my best to describe how I compute my numbers in the spreadsheets that contain the data, in worksheets titled "Variables and FAQ". On some of the variables, especially on equity risk premiums, you may want to read the papers that I have, where I explain my reasoning, or watch my classes on them. Whatever you do, and this is general advice, never use data from an external source (including mine), if you do not understand how the data is computed.
  2. Take ownership: If you decide to use any of my data, especially in corporate financial analysis and valuation, please recognize that it is still your analysis or valuation. 
  3. Don't bring me into your disagreements, especially in legal settings: If you are in disagreement with a colleague, a client or an adversary, I am okay with you using data from my website to buttress your arguments, but please do not bring me in personally into your disputes. This applies in spades, if you are in a legal setting, since I believe that courts are where valuation first principles go to die.
Conclusion
I would love to tell you that I am driven by altruistic motives in sharing my data, and push for sainthood, but I am not.  I would have produced all of the data that you see anyway, because I will need it for my work, both in teaching and in practice, all year. Having produced the data, it seems churlish to not share it, especially since it costs me absolutely nothing to do so. If there is a hidden agenda here, it is that I think that in spite of advances over the last few decades, the investing world still has imbalances, especially on data access, and I would like it make a little flatter. Thus, if you find the data useful, I am glad, and rather than thank me, please pass on the sharing. 

YouTube

Links

Wednesday, December 15, 2021

Back in the Classroom: Time to Teach!

At the start of every semester for as long as I can remember, I have invited people to sit in informally on my classes at NYU or take the shorter online versions on my website. After thirty six years of teaching, you would think I would be jaded, but I am not. As we get ready for the spring, I am excited, perhaps more so than usual, because I hope to finally be in a real classroom, instead of online, for my classes. 

Spring is here, and the classroom beckons!

    I have always described myself as a teacher, first and foremost, but like many of you, COVID has been a disruptor. For much of the last two years, rather than teach my classes in a classroom, I taught my classes from my home office, making a few low-cost, low-tech investments to improve my set up. 

I know that many of us, especially as we age, take the dystopian view that technology has hurt more than helped, and while I share the concern about the damage that social media has wrought on society, I remain thankful for the good that has come from technological advances. The combination of speedy internet access and delivery platforms (Zoom, Teams, Skype, Blue Jeans etc.) allowed me to deliver my classes from home, with some help. With a M1 MacBook Pro, a 27 inch LG display and my iPad Pro in sidecar mode, I could see everyone in my class, albeit with some work; with Zoom, the limit was 48 students at a time. My Rode Go lav mike and my AirPods Pro, took care of my audio needs, and my Logitech C920 camera supplemented my computer's camera to cover my video requirements, with two extra spotlights for late evening sessions, when natural light failed. To top it all off, and this was priceless, I could see the Pacific Ocean, out of my window, especially when I was able to teach standing, using my Flexispot standing desk to elevate my monitor. (If you are wondering why I have been so specific about my accessories, it is not because I receive sponsorship payments from any of these companies, but because it may help you replicate my set up, other than the view of the Pacific, if you are teaching or working from home. If you have a bigger budget, I would try to emulate Professor Andrew Lo, who described his astounding set up for teaching last year.)

     In these last two years, I have learned a lot about online teaching and I hope that learning makes me a better teacher, both online and in the classroom. 

  • First, with today's technology, online classes get scarily close to physically being in a classroom, a reminder to me, and teachers all over the world, that unless we offer something unique in a classroom setting, disruption is coming for the teaching business. 
  • Second, I learned there is some learning that is better delivered online, than in a physical setting, and I believe that there are some topics that I will continue to deliver online, even after this virus passes on. 
  • Third, while I still loved teaching online, I desperately missed the feeling of being in an actual classroom, looking at a collection of faces, some with eyes closed, some bored and some waiting to ask a question. After all, every teacher is a repressed actor, and actors draw their energy from their audiences, and I have been missing mine!

Each semester, I step into a classroom wanting to teach the “best” class that I ever have, perhaps even the perfect class, knowing fully well that I will fall short, in practice, because there will be things to improve.

Classes

    I am a dabbler, not a specialist, and my teaching reflects that predisposition. During my teaching lifetime, I have taught a wide swath of classes, ranging from banking to equity instruments, but in the last twenty years, my focus has been on three classes, corporate finance, valuation and investment philosophies, with the last one taught only online. My classroom teaching at Stern has been mostly corporate finance and valuation, to both MBAs and undergraduates. With MBAs, the corporate finance class has been a first year elective and the valuation has been an elective in the second year, and with undergraduates, I have alternated between the two classes across the years. I have added shorter online versions of each class, offered on my website, at no cost, but with no credit. Starting a few years ago, Stern has offered certificate versions of each of the three classes, albeit at a price, but with more structure (quizzes, exams, projects) and a certificate, if you make it through. 

Pre-Game Prep

    In all of my regular classes, I have drawn on the assumption that my students come in with an exposure and understanding of three areas, accounting (more in the context of being able to read financial statements than the mechanics of debit and credit), basics of finance (especially the time value of money and an understanding of markets) and statistics (how to make sense of data). Being a control freak, I have created my own versions of what I would like my students to know in each of these disciplines, and you can find my versions on my website. With each of my classes, I am sure that purists in each of these areas would blanch not just at my choice of topics that matter, but also at my sloppiness in description, but I will let you be the judge of content.

    The place to start is with accounting. Much as I abuse accountants in my classroom, I also recognize that almost all of the raw material we use in corporate finance and valuation comes from accounting statements. Put simply, if you cannot tell the difference between operating and net income, or what to consider as debt, you will be lost in any type of financial analysis. In my eleven-session (with each session lasting 15-20 minutes) accounting class, I cover the material that I draw on in my finance classes:

Web page for class

Once you have the accounting basics under your belt, you can turn to the basics of finance. The time value of money is at the heart of almost everything in finance, and understanding the mechanics and intuition of present value is a bedrock on financial analysis. In my introductory finance class, I cover the time value of money, and how risk plays out in that computation, as well as look at three macro variables that we encounter repeatedly in financial analyses - inflation, interest rates and exchange rates.

Web page for class

Finally, we live in the age of data, and it is surprising that we use that data so little, and when we do, so badly. If the role of statistics is to make sense of large and contradictory data, it is a critical skill in every discipline, and especially one, with as many numbers as finance. With the full disclosure that I am a statistical novice, I put together a statistics class, purely as a user of its many tools, and in 13 sessions,  I cover everything from descriptive statistics to multiple regressions and simulations.


Web page for class

If you are well versed in accounting, statistics and the basics of finance, you may find the material in these classes simplistic, but it never hurts to reinforce existing concepts.

The Game

    If you have the pre-game behind you, it is time to turn to the main attractions (or tortures, depending on your perspective), and I will present them in the sequence that I think it makes the most sense to take them, if you want to torture yourself by taking all three. 

a. Corporate Finance

    If you have taken a corporate finance class in your past life, you may be surprised by what I cover, and what I do not, in my corporate finance class. My version of the class should have a different name, since it is not just about corporations and I am not sure that it is all about finance either. It covers the first financial principles that govern how to run a business, and as a consequence, it has the broadest reach and the deepest impact of any of my classes. Whether you are an entrepreneur, starting on the long process of converting an idea into a business, a manager, evaluating how to make business decisions consistently or a consultant, offering advice on what a business should do differently, corporate finance is your go-to class, since it offers guiding principles for all your tasks. I start the class with a one-page summary of the entire class:

Web page for class
As you can see from the coverage, everything that happens in business is fair game in a corporate finance class, from whether ESG adds or detracts from value, why companies are shifting from paying dividends to buying back stock and how corporate tax changes can affect financing decisions. It is also, in every sense of the word, an applied class, with every concept applied to real companies that range the spectrum, across the life cycle, geographies and sectors.

   I will be teaching this class to Stern MBAs, starting on January 31, 2022, meeting every Monday and Wednesday, from 10.30 am - 11.50 am, New York time, through May 9, 2022. If you are a Stern MBA, you are welcome to take the class, but if you are not, you can take the class informally, by watching the recorded sessions at this link, taking the quizzes and exam, if you are up for them, and even tracking the emails that I send the class at this link. Since the 26 sessions of the class are 80 minutes apiece, this will require a substantial investment in time, though no investment in money, albeit with no certification or credit. If that time investment is too much of a burden, I have created an online version of the class here, with 15-minute sessions replacing the longer classroom sessions, and while they will cost you nothing as well, they come with no certification. If certification is your end game, and I understand that it may help augment a resume, you can take the NYU version of the online class in the fall of 2022, with a more polished interface and personal interaction, but the same content, where you will get a certification and NYU will get a portion of your savings. 

b. Valuation 

    My valuation class starts with an ambitious agenda, i.e., to give you the tools and techniques to value or price just about anything, from bitcoin to collectibles to infrastructure projects, and from any perspective, from a potential buyer to an accountant estimating fair value. 

Web page for class

Along the way, I emphasize what I believe to be long standing truths about valuation. First, it is a craft, not an art or a science, and you get better at valuation by doing, not by reading or watching others do valuation. Second, while there are many practitioners and academics who use the words value and price interchangeably, the value and pricing processes are not only driven by different determinants, but also can yield different numbers for the same asset. Third, while valuations ultimately are collections of numbers, those numbers lose their resonance and meaning, if they are not connected to narratives that tie these numbers together.

    This class will be taught to two different audiences, Stern MBAs, many of whom were in my corporate finance class last spring, and Stern undergraduates, mostly juniors. While the first group will meet every Monday and Wednesday, from 1.30 pm - 2.50 pm, from January 31, 2022, to May 9, 2022, and the second will meet every Monday and Wednesday from 3.30 pm - 4.45 pm, from January 24, 2022, to May 9, 2022, the classes are identical in content and delivery. You are welcome to unofficially partake in either of these classes, both in recorded form, but as with corporate finance, you can take an online version of the valuation class, with twenty six shorter (15-minute) sessions, for free, with no certification, on my site, or for a price and a certificate from NYU.

c. Investment Philosophy

    This class has its origins in a seminar class that I was asked to teach more than twenty years ago, where successful investors would come in each session and talk about what they did in investing that made them successful. As we transitioned from technical analysts to value and growth investors to market timers, each of whom was successful, albeit with wildly different views of markets and divergent paths to success, I concluded that there could not be one template for investment success, and started looking at not only differences in investment philosophies, but also what personal qualities made for success, with each one. That led to a book, and then to a class on investment philosophies, where I cover the range of choices.

Web page for class

If there are two lessons that I hope that people take away from this class, it is (a) that no investment philosophy, no matter how storied and successful it has been in the past, has a monopoly on investment virtue and that (b) the right investment philosophy for you is the one that best fits your personality and strengths. 

    While I do not teach this course in a classroom, there are two ways you can take the class. One is online, on my website, where I lead you through a journey through different investment philosophies, weighing not just past successes, but also the combination of factors that you need to have to succeed each one, over the course of 36 sessions. As with the other online classes on my site, it is free, but without certification. If you do want certification, there is the NYU version of the class available here, but for a price (that I do not set or control... so it is not fair to argue its fairness or unfairness with me).

Game Plan

    While I hope that the descriptions of the classes will help you decide which of these courses best fits you, you may still be confused about the choices and the sequence. I hope that the flow chart below provides a little more clarity:

Web page for all classes
As you can see, if your end game is financial decision making within a business, as owner or employee, the corporate finance class will do, whereas if your intent is to learn the skills of appraising value, either for accounting/regulatory or transaction purposes, adding valuation will augment your tool kit. Finally, if you are an investor in companies, and are uninterested in the mechanics of value, you can go directly to the investment philosophies class, or make an intermediate stop, and take a look at valuation.

Conclusion
    Each time I present these choices, I will always have a few people demand to know my investment record, and with respect, I will refuse, for two reasons. First, there is nothing in my track record, whether positive or negative, that will help you assess whether what I talk about has heft, since luck is the dominant factor in any investor's track record, even over long periods. Second, this demand would make complete sense, if I were seeking to manage your money, which I am not, or promising you investment riches, which I am also not. If this absence of proof is a deal breaker for you, I understand, but if it is, trust me when I say that these are not the droids classes that you were looking for. My classes may not make you richer or wiser, but I hope that they give you a fresh perspective on finance and markets and the confidence to question what others contend to be truths. May the force be with you!

YouTube Video

Class List

  1. Accounting for finance and investing (My webpage, YouTube Playlist)
  2. Foundations of Finance (My webpage, YouTube Playlist)
  3. Statistics for finance and investing (My webpage, YouTube Playlist)
  4. Corporate Finance (Spring 2022 MBA Class, Free Online, NYU Certificate in Fall 2022)
  5. Valuation (Spring 2022 MBA Class, Spring 2022 Undergraduate Class, Free Online, NYU Certificate)
  6. Investment Philosophies (Free Online, NYU Certificate)

Friday, December 10, 2021

Managing across the Corporate Life Cycle: CEOs and Stock Prices!

One of the big news stories of last week was Jack Dorsey stepping down as CEO of Twitter, and the market's response to that news was to push up Twitter's stock price by almost 10%. That reaction suggested, at least for the moment, that investors believed that Twitter would be better off without Dorsey running it, a surprise to those in the founder-worship camp. As the debate starts about whether Dorsey's hand-picked successor, Parag Agrawal, is the right person to guide Twitter through its next few years, I decided to revisit a broader question of what it is that makes for a "great CEO" and how there is no one right answer to that question, because it depends on the company, and where it stands in its life cycle. In the process, I will also look at the thorny issue of what happens when there is a mismatch between a company and its CEO, either because the board picks the wrong candidate for the job or because the company has changed over time, and the CEO has not. Finally, I will use the framework to look at the relationships between founders and their companies, and how mishandling management transitions can have damaging, perhaps even devastating, consequences for value.

The "Right" CEO: A Corporate Life Cycle Perspective

   The notion that there is a collection of characteristics that makes a person a great CEO for a company, no matter what its standing, is deeply held and fed into by both academics and practitioner. In this section, I will begin by looking at the mythology behind this push, and why it does not hold up to common sense questioning. 

The Mythology of the Great CEO

    Are there a set of qualities that make for a great CEO? To answer the question, I looked at two institutions, one academic and one practice-oriented, that are deeply invested in that idea, and spend considerable time advancing it. 

  • The first is the Harvard Business School, where every student who enters the MBA program is treated as a CEO-in-waiting, notwithstanding the reality that there are too few openings to accommodate that ambition. The Harvard Business Review, over the years, has published multiple articles about the characteristics of the most successful CEOs, and this one for instance, highlights four characteristics that they share in common: (a) deciding with speed and conviction, (b) engaging for impact with employees and the outside world (c) adapting proactively to changing circumstances and (d) delivering reliably.
  • The second is McKinsey, described by some as a CEO factory, because so many of its consultants go on to become CEOs of their client companies. In this article, McKinsey lists the mindsets and practices of the most successful CEOs in the following picture: 

Given how influential these organizations are in framing public perception, it is no surprise that most people are convinced that there is a template for a great CEO that applies across companies, and that boards of directors in search of new CEOs should use this template. 
    That perspective also gets fed by books and movies about successful CEOs, real or imagined. Consider Warren Buffett, Jack Welch and Steve Jobs, very different men who have been mythologized in the literature, as great CEOs. Many of the books about Buffett read more like hagiographies than true biographies, given how star struck the writers of these books are about the man, but by treating him as a deity, they do him a disservice. The fall of GE has taken some of the shine from Jack Welch's star, but at his peak, just over a decade ago, he was viewed as someone that CEOs should emulate. With Steve Jobs, the picture of an innovative, risk-taking disruptor comes not just from books about the man, but from movies that gloss over his first, and rockier, stint as founder-CEO of Apple in the 1980s.
    The problem with the one-size-fits-all great CEO model is that it does not hold up to scrutiny. Even if you take the HBR and McKinsey criteria for CEO success at face value, there are three fundamental problems or missing pieces. First, even if all successful CEOs share the qualities listed in the HBR/McKinsey papers, not all people or even most people with these qualities become successful CEOs. So, is there a missing ingredient that allowed them to succeed? If so, what is it? Second, I find it odd that there are no questionable qualities listed on the successful CEO list, especially given the evidence that over confidence seems to be a common feature among CEOs, and that it is this over confidence that allows them to take act decisively and adopt long term perspectives. When those bets, often made in the face of long odds, pay off, the makers of those bets will be perceived as successful, but when they do not, the decision makers are consigned to the ash heap of failure. Put simply, it is possible that the quality that binds together successful CEOs the most is luck, a quality that neither Harvard Business School nor McKinsey can pass on. Third, there are clearly some successful CEOs who not only do not possess many of the listed qualities, but often have the inverse. If you believe that Elon Musk and Marc Benioff, CEOs of Tesla and Salesforce, are great CEOs, how many of the Harvard/McKinsey criteria would they possess?

The Corporate Life Cycle

    I believe that the discussion of what makes for a great CEO is flawed for a simple reason. There is no one template that works for all companies, and one way to see why is to bring in the notion that companies go through a life cycle, from start-ups (at birth) to maturity (middle age) to decline (old age). At each stage of the life cycle, the focus in the company changes, as do the qualities that top managers have to bring for success:

Early in the life cycle, as a company struggles to find traction with a business idea that meets an unmet demand, you need a visionary as a CEO, capable of thinking outside the box, and with the capacity to draw employees and investors to that vision. In converting an idea to a product or service, history suggests that pragmatism wins out over purity of vision, as compromises have to be made on design, production and marketing to convert an idea company into a business. As the products/services offered by the company scale up, the capacity to build businesses becomes front and center, as production facilities have to be built, and supply chains put in place, critical for business success, but clearly not as exciting as selling visions. Once the initial idea has become a business success, the needs to keep scaling up may require coming up with extensions of existing product lines or geographies to grow, where an opportunistic, quick-acting CEO can make a difference. As companies enter the late phases of middle age, the imperative will shift from finding new markets to defending existing market share, in what I think of the trench warfare phase of a company, where shoring up moats takes priority over new product development. The most difficult phase for a company is decline, as the company is dismantled and its sells or shuts down its constituent parts, since any one who is put in charge of this process has only pain to mete out, and bad press, to go with it. Have you ever read a book or seen a movie about a CEO who shrunk his or her company, where that person is painted as anything but a villain? In fact, I used "Larry the Liquidator" as my moniker for that CEO, to pay homage to one of my favorite movies of all time, "Other People's Money":

   

As you watch the video, note that the CEO of the company, under activist attack, is played by Gregory Peck (the distinguished gray-haired gentleman who sits down at the start of the video), who presumably embodies all the qualities that Harvard and McKinsey believe embody a great CEO, and Danny DeVito plays "Larry the Liquidator". Talk about type casting, but this company needs more DeVito, less Peck!

Mismatches, Transitions and Turnover

    If you buy into my structure of a corporate life cycle, and how the right CEO for a company will change as the company ages, you can already see the potential for mismatches between companies and CEOs, for three reasons. 

  • A Hiring Mistake: The first is that the board of directors for a company seeking a new CEO hires someone who is viewed by many as a successful CEO, but whose success came at a company at a very different stage in its life cycle. I think Uber dodged the bullet in 2017, when they decided not to hire Jeff Immelt as CEO for the company. Even if you had believed that Immelt was successful at his prior job as CEO for GE, and that is arguable, he would have been a horrifically bad choice as CEO at Uber, a company that is as different from GE as you can get, in every aspect, not just corporate age. 
  • A Gamble on Rebirth: The second is when a board of director picks a mismatched CEO intentionally, with the hope that the CEO characteristics rub off on the company. This is often the case when you have a mature or declining company that thinks hiring a visionary as a CEO will lead to reincarnation as a growth company. While the impulse to become young again is understandable, the odds are against this gamble working, leaving the CEO tarnished and company worse off, in the aftermath. It was the reason that Yahoo! hired Marissa Mayer as a CEO in 2012, hoping that her success at Google would rub off on the company, an experiment that I argued would not end well for either party (and it did not).
  • A Changing Business; The third is a more subtle problem, where a company is well matched to its CEO at a point in time, but then evolves across the life cycle, but the CEO does not. Using the Uber example again, Travis Kalatnick, a visionary and rule breaker, might have been the best match for Uber as a company, in early years, when it was disrupting a highly regulated business (taxi cabs), but even without his personal missteps, he was ill-suited to a company that faced a monumental task of converting a model built on acquiring new riders into one that generated profits in 2017.
In NY case, a CEO/company mismatch is a problem, though the consequences can range from benign to malignant. 
In the most benign case, a mismatched CEO recognizes the mismatch, sets ego aside and finds a partner or co-executive with the skills needed for the company. In my view, and many of you may disagree with me, the difference between the first iteration of Steve Jobs, where he let his vision run riot and almost destroyed Apple as a company, and the second iteration, where he led one of the most impressive corporate turnarounds in history, was his choice of Tim Cook as his chief operating officer in his second go-around and his willingness to delegate operating authority. In short, Jobs was able to continue to put his visionary skills to work, while Cook made sure that the promises Jobs made were delivered as products on the ground. In the most malignant form, a badly mismatched CEO is entrenched in his or her position, perhaps because the board of directors has become a rubber stamp or by tilting voting rules (shares with different voting rights) in favor of incumbency, and continues on a pathway that takes the company to ruin. In the intermediate case, the board of directors, perhaps with a push from activist investors and large stockholders, engineers a CEO change, albeit after some or a great deal of damage has been done.

The Compressed Life Cycle: Implications for Founder CEOs

    It is a testimonial to how much technology companies have changed the economy and the market that some of the best-recognized names in business are those of the founders of successful technology company. I would wager that almost everyone has heard of Bill Gates, Jeff Bezos and Elon Musk, and that very few would recognize the names of Mary Barra (CEO of GM) or Darren Woods (CEO of Exxon Mobil). While there are some who venerate these founders, in what can only be called founder worship, there are others who have a more jaundiced view of them, both as human beings and as CEOs. The corporate life cycle framework provides a useful structure to think about how the technology companies, that dominate the twenty first century business landscape, are different from the manufacturing companies of the last century, and why these differences can create more management tensions at these companies.

Aging in Dog Years?

    While every company goes thought the process of starting up, aging and eventually declining, the speed at which it does will vary depending on the business it is in. More specifically, the more capital it takes to enter a business and the more inertia there is among the existing players (producers, customers) are, the longer it will take for a company to get from start up to mature growth, but the same forces will play out in reverse, allowing the company to stay mature for a lot longer and decline a lot more gradual:

The great companies of the twentieth century took decades to ramp up, facing big infrastructure investments and long lags before expansion, had long stints as mature firms, milking cash flows, before embarking on long and mostly gradual declines. To illustrate, Sears and GE had century-long runs as successful companies, before time and circumstances caught up with them, and GM and Ford struggled for three decades setting up manufacturing capacity and tweaking their product offerings, before enjoying the fruits of their success. In contrast, consider Yahoo!, a company that was founded in 1994, that managed to reach a hundred billion in market capitalization by the turn of the century, enjoyed a few years of dominance, before Google's arrival and conquest of the market, before finally being acquired by Verizon in 2017. This compression of the life cycle has played out in tech company after tech company and the graph below captures the difference:

In short, tech companies age in dog years, with a 20-year tech company often resembling a hundred-year old manufacturing company, with creaky business models and facing disruption.

Implications for Founder CEOs and Management Turnover

    Companies have always had founders, and while the conflict between founders and others in the company have been around for decades, the compressed life cycle has exacerbated these tensions and magnified problems. In particular, the research on founder CEOs has yielded two disparate findings. The first is that in the early stages of companies, founder CEOs either step down or are pushed out at much higher rates than in more established companies. The second is that those founder CEOs who nurse their companies to more established status, and to public offerings, are more entrenched that their counterparts at mature companies.

    To understand the first phenomenon, i.e., the high displacement rate among founder CEOs of very young companies, I will draw on the work of Noah Wasserman at Harvard Business School who has focused intensively on this topic. Using data on top management turnover at young firms, many of them non-public, he concludes that almost 30% of CEOs at these firms are replaced within a few years of inception, usually at the time of new product development or fresh financing. Much of this phenomenon can be explained by venture capitalists, with large stakes, pushing for change in these companies, but a portion of it is voluntary, and to explain why a founder CEO might willingly step down, Wasserman uses the concept of the founder's dilemma, where founders trade off full control of a much less valuable firm (with themselves in control) for lesser control of a much more valuable firm (with someone else at the helm). In the corporate life cycle structure, it is a recognition on the part of founders or capital providers that the skills needed to take a company forward require a different person at the top of the organization, especially as a firm transitions from one stage of the life cycle to the next.

    The founders who do manage to stay at the helm of companies that make it through to early growth status are put on a pedestal, relative to CEOs of established companies. While that may be understandable, in some cases, it can take the form of founder worship, where founders are viewed as untouchable, and any challenge to their authority is viewed as bad, leading to efforts to change the rules of the game to prevent these challenges. In the United States, where prior to 2004, it was unusual to see shares with different voting rights in the same firm, it is now more the rule than the exception in many tech companies. 

    Endowing CEOs with increased powers to fend off challenges seems like a particularly bad idea at tech companies, since their compressed life cycles are likely to create more, rather than less, mismatches between companies and their founder/CEOs, and sooner, rather than later. To see, why consider how corporate governance played out at Ford, a twentieth century corporate giant. Henry Ford, undoubtedly a visionary, but also a crank on some dimensions, was Ford's CEO from 1906 to 1945. His vision of making automobiles affordable to the masses, with the Model T, was a catalyst in Ford's success, but by the end of his tenure in 1945, his management style was already out of sync with the company. With Ford, time and mortality solved the problem, and his grandson, Henry Ford II, was a better custodian for the firms in the decades that followed. Put simply, when a company lasts for a century, the progression of time naturally takes care of mismatches and succession. In contrast, consider how quickly Blackberry, as a company, soared, how short its stay at the top was and how steep its descent was, as other companies entered the smart phone business. Mike Lazaridis, one of the co-founders of the company, and Jim Balsillie, the CEO he hired in 1992 to guide the company, presided over both its soaring success, gaining accolades for their management skills for doing so, and over its collapse, drawing jeers from the same crowd. By the time, the change in top management happened in 2012, it was viewed as too little, too late.

    In my view, the next decade will bring forth more conflict, rooted in the compressed life cycle of companies. If I were a case study writer, and thank God I am not, I would not rush to write case studies or books about successful tech company CEOs, because many of those same CEOs will become case studies of failure within a few years. If I am an investor, I would worry more than ever before about giving up voting power to founder/CEOs, even if they are well regarded, because today's star CEO can become tomorrow's problem. I wonder whether the way Facebook has dealt with its privacy and related problems over the last few years would have been different, if investors had not allowed Mark Zuckerberg to effectively control 57% of the voting rights with less than 20% of the outstanding shares. It is worth noting that Twitter was one of the few social media companies that chose not to split its voting rights across shares, and that may explain the Jack Dorsey departure.

Implications for Investors
   I have long argued that when investing in young tech companies, you are investing in a story about the company, not an extrapolation of numbers.  The compressed corporate life cycle, and the potential for CEO/company mismatches that it creates, adds a layer of additional uncertainty to valuation. In short, when assessing the value of a young company's story, you are also assessing the capacity of the management of the company to deliver on that story. To the extent that the founder is the lead manager, and the narrative-setter, any concerns you have about the founder's capacity to convert that story into business success will translate into lower value. Let me illustrate using three examples, the first being Amazon in my early valuations of the company between 1997 and 2000, the second being Twitter, especially in the context of Jack Dorsey's departure and Paytm, the Indian online company, whose recent IPO was a dumpster fire.
  • I have always liked Amazon, as a company, and one reason for that was Jeff Bezos. Many younger investors are surprised when I tell them that Bezos was not a household name for much of Amazon's early rise, and that it was The Washington Post acquisition in 2013 that brought him into public view. One reason that I attached lofty values to Amazon as a company, even when it was a tiny, money-losing company was that Bezos not only told the same story, one that I described as Field of Dreams story, where if you build it (revenues), they (profits) will come. but acted consistently with that story. He built a management team that believed that story and trusted them to make big decisions for the company, thus easing the transition from small, online book retailer to one of the largest companies in the world. It is a testimonial to Bezos' success in transitioning management that Amazon's value as a company today would be close to the same, with or without him at the helm, explaining why the announcement that he was stepping down as CEO on July 5 created almost no impact on the stock price.
  • I valued Twitter for the first time, just ahead of its IPO in 2013, and built a model premised on the assumption that the company would find a way to monetize its larger user base and build a consistently money-making enterprise. In the years since, I have been frustrated by its inability to make that happen, and in this post in 2015, I laid the blame at least partially at the feet of Twitter's management, contrasting its failure to Facebook's success. I don't know Jack Dorsey, and I wish him well, but in my view, his skill set seemed ill suited to what Twitter needed to succeed as a business, especially as he was splitting his time as Square's CEO, and talking about taking a six-month break in Africa.  In fact, eight years after going public, Twitter's strongest suit remains that it has lots of users, but its capacity to make money of these users is still questionable. One reason why the market responded so positively, jumping 10% on the news that Dorsey was leaving, is indicative of the relief that change was coming, and the reason that it has fallen back is that it is not clear that Parag Agrawal has what the company needs now. He has time to prove investors wrong, but he is on probation, as investors look to him to reframe Twitter's narrative and start delivering results.
  • A few weeks ago, I valued one of India's new unicorns, Paytm, an online payment processing company built on the promise of a huge and growing online payment market in India. In my valuation, I told an uplifting story of a company that would not only continue to grow its user base and services, but also that it would increase its take rate (converting users to revenues) and benefit from economies of scale to become profitable over the course of the next decade.

    I valued Paytm at about 2,200, but in telling that story, I noted one big area of concern with existing management, that seemed to be more intent on adding users and services than on converting them into revenues, and pre-disposed to grandiosity in its statement of purpose and forecasts. In the months since, the company has gone public, and while the offering price, at 2150, was close to my value, the stock price collapsed in the days after to less than 1400 and has languished at about 1600-1700 since. It is always dangerous to try to explain why markets do what they do over short periods, but I do think that the company's founders and spokespeople did not do themselves any favors, ahead of the IPO. Specifically, if you were concerned about Vijay Sharma's capacity to convert the promise of Paytm into eventual profits, before the IPO, you would have been even more concerned after listening to him in the days leading into the IPO. It is still too early to conclude that there is a company/CEO mismatch, but if I were top management of the firm, I would talk less about users and gross merchandise value, and focus more on improving the abysmally low take rate at the firm. 
If there is a general lessons for investors from this post, it is that when a founder CEO leaves his position, or is pushed out, the value change can be positive or negative, and that  good founders will work at making themselves less central to their company's stories over time and thus less critical to its value. It should also be a cautionary note for those investors who have looked the other way as venture capitalists, founders and insiders have fixed the corporate governance game in their favor, in young tech companies that go public, ensuring that mismatches from companies and CEOs, if they occur in the future, will persist. 

Family Group Companies: The Life Cycle Effect
    In much of the world, businesses, even if they are publicly traded, are run by family groups. To the extent that the top management of these businesses are members of the family, these companies are uniquely exposed to company/CEO mismatches, especially as second or third generations of a family enter the management ranks, and these families enter new businesses.

Family Group Companies: The Life Cycle Effect?
    Much of Asian and Latin American business is built around family groups, which have roots that go back decades. Using a combination of connections and capital, these family groups have lived through economic and political changes, and as many of the companies that they own have entered public markets, they have stayed in control. In some cases, this control has come from dominant shareholdings, but more often, it is exercised by using holding company structures and corporate pyramids that effectively leave the family in control, even as the public acquires a larger stake in equity.
    To see how the corporate life cycle structure story plays out in family group companies, it is worth remembering that family groups often control companies that spread across many different business, effectively resembling conglomerates in their reach, but structured as individual companies. Consequently, it is not only possible, but likely, that a family group will control companies at different stages in the corporate life cycle, ranging from young, growth companies at one end of the spectrum to declining companies at the other end of the spectrum. In fact, one of the reasons family groups survived and thrived in economies where public markets were under developed was their capacity to use cash generated in their mature and declining businesses to cover capital needs in their growing businesses. This intra-group capital market becomes trickier to balance, as family group companies go public, since you need shareholder assent for these capital transfers. With weak corporate governance, more the rule than the exception at family group companies, it is entirely possible that shareholders in the more mature and cash-generating companies in a family group are being forced to invest in younger, growth companies in that same group. 

Implications for CEOs and Management
    There is research on CEO turnover in family group companies, and the results are not surprising. In a recent study, researchers looked at 4601 CEOs in companies, classifying them based on whether they were family CEO or outside CEOs, and found the forced turnover was much less frequent in the first group. In other words, family CEOs are less likely to be fired, and more likely to stay around until a successor is found, often within the family. While this is good news in terms of continuity, it is bad news if there is a company/CEO mismatch, since that mismatch will wreak havoc for far longer, before it is fixed. 
    What can family groups do about dealing with the mismatch, especially as the potential for that mismatch increases, as disruption turns some mature and growing businesses into declining ones and capital gets shifted to new businesses in the green energy and technology space? First, power has to become more diffuse within the family, away from a powerful family leader and more towards a family committee, to allow for the different perspectives needed to become successful in businesses at other stages in the life cycle. If, like me, you are a fan of Succession, the HBO series, you don't want to model yourself on Logan Roy, and the Roy family, if you are a family group company. Second, there has to be a serious reassessment of where different businesses, within the family group, are in the life cycle, with special attention to those that are transitioning from one phase to another. Third, if top management positions are restricted to family members, the challenge for the family will be finding people with the characteristics needed to run businesses across the life cycle spectrum. As many family groups enter the technology space, drawn by its potential growth, the limiting constraint might be finding a visionary, story teller from within the family, and if one does not exist, the question becomes whether the family will  be willing to bring someone from outside, and give that person enough freedom to run the young, growth business. Finally, if a mismatch arises between a family member CEO and the business he or she is responsible for running, there has to be a willingness to remove that family member from power, sure to raise family tensions and create fights.

Implications for Investors
   Are family group companies, in general, better or worse investments than investments in other publicly traded companies? The evidence, not surprisingly, is mixed, with some finding a positive payoff, which they attribute to a better alignment of long term investor and management interests at these companies, and others finding negative returns, largely as a result of management succession problems. 
    To address why family control can help in some cases, and hurt in others, it again helps to bring in the corporate life cycle.  In the portions of the corporate life cycle, where patience and a steady hand are required, the presence of a family member CEO may increase value, since he or she will be more inclined to think about long term consequences for value, rather than short term profit or pricing effects. On the other hand, if a family CEO is entrenched in a company that is transitioning from growth to mature, or from mature to declining, and is not adaptable enough to modify the way he or she manages the company, it is a negative for value. Family group companies composed primarily of companies in the former grouping will therefore trade at premiums, whereas family group companies that include a disproportionately large number of disrupted or new businesses will be handicapped. 

Conclusion
    I started this post by talking about Jack Dorsey leaving Twitter, and why the market celebrated that news, but I would like to end on a more general note. If there is a take away from bringing a life cycle perspective to assessing CEO quality, it is that one size cannot fit all, and that a CEO who succeeds at a company at one stage in the life cycle may not have the qualities needed to succeed at another. For boards of directors, in search of new CEOs, my suggestion is that you pay less attention to past track records of nominees, in their prior stints as employees or as CEOs of other companies) and more attention to the qualities that they possess, to see if they match what the company needs to succeed. 

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