Monday, January 27, 2020

Data Update 2 for 2020: Retrospective on a Disruptive Decade

My data updates usually look at the data for the most recent year and what I learn from them, but 2020 also marks the end of a decade. In this post, I look back at markets over the period, a testing period for many active investors, and particularly so for value investors, who found that even as financial assets posted solid returns, what they thought were tried and true approaches to "beating the market" seemed to lose their power. In addition, trust in mean reversion, i.e., that things would go back to historic norms was shaken as interest rates remained low for much of the period and PE ratios rose above historical averages and continued to rise, rather than fall back. 

1. It was a great year, and a very good decade, for equities, and a very good year for bonds!
While investing should always be forward-looking, there is a benefit to pausing and looking backwards. If you had US stocks in your portfolio, 2019 was a very good year. The S&P 500 started the year at 2506.85 and ended the year at 3230.78, an increase of 28.88%, and with dividends added, the return for the year was 31.22%. To get a sense of how this year measures up against other good years, I compared it to the annual returns from 1927 to 2019 in this graph:
Download spreadsheet with annual market data
Over the 92 years that are in this historical assessment, 2019 ranked as the sixteenth best year and second only to 2013 (annual return of 32.15%) in this century. While stocks have garnered the bulk of the attention for having a good year, bonds were not slackers in the returns game. In 2019, the ten-year US treasury bond returned 9.64% and ten-year Baa corporate bonds weighing in with a 15.33% return. That may surprise some, given how low interest rates have been, but the bulk of these returns came from price appreciation, as the US treasury bond rate declined from 2.69% to 1.92%, and the corporate bonds also benefited from a decline in default spreads (the price of risk in the bond market) during the year. The year also capped off a decade of gains for stocks, with the S&P almost tripling from 1115.10 on January 1, 2010 to 3230.78 on January 1, 2020, and with dividends included and reinvested, the cumulated return for the decade is 252.96%. To put these returns in perspective, I have compared this cumulated return to the eight full decades that I have data for in the table below, in conjunction with the cumulated returns for treasury and corporate bonds over each decade:
Download spreadsheet with annual market data
While 2010-19 represented a bounce back for stocks from a dismal 2000-09 time period, with the 2008 crisis ravaging returns, it falls behind three other decades of even higher returns (1950-59, 1980-89 and 1990-1999). It was a middling decade for both treasury and corporate bonds, with cumulated returns running ahead of the three decades spanning 1940 to 1969 but falling behind the other decades, in terms of returns delivered. Treasury bills delivered their worst decade of returns, since the 1940s, with the cumulated return amounting to 5.25%. I don’t want to overanalyze historical data, but there are interesting nuggets of information in the data:
a. Historical Risk Premium: The US historical data has been used by many analysts in corporate finance and valuation as the basis for computing historical risk premiums and in the table below, I compute the risk premiums that investors would have earned in this market, investing in stocks as opposed to treasury bills and bonds, over different time periods, and with different averaging approaches:
Download spreadsheet with annual market data
If you go with the geometric average premium from 1927-2019 as your predictor for the equity risk premium in 2020, US stocks should earn about 4.83% more than US treasury bonds for the year:
Expected return on stocks in 2020 = T.Bond Rate + Historical ERP 
= 1.92% + 4.83% = 6.75%
Since a portion of this return will come from dividends, the expected price appreciation in stocks is the difference:
Expected price appreciation on stocks = Expected Return - Dividend yield 
= 6.75%- 1.82% = 4.93%
I am not a fan of historical premiums, not only because they represent almost an almost slavish faith in mean reversion but also because they are noisy; the standard errors in the historical premiums are highlighted in red and you can see that even with 92 years of data, the standard error in the risk premium is 2.20% and that with 10 or 20 years of data, the risk premium estimate is drowned out by estimation error.
b. Asset Allocation: The fact that stocks have beaten treasury and corporate bonds by wide margins over the entire history is often the sales pitch used to push investors to allocate more of their savings to stocks, with the argument being that stocks always win in the long term. The data should yield cautionary notes:
  • First, in three decades out of the nine in the table, stocks under-performed treasury bonds and treasury bills, and if your response is that ten years is not a long enough time period, you may want to check the actuarial tables. 
  • Second, there is a selection bias in our use of the US markets for computing the historical premium. Looking across the globe, the US was one of the most successful equity markets of the last century and using it may be skewing our results upwards. Put bluntly, if you had invested in the Nikkei at the height of its climb in the 1980s, you would still be struggling to get back the money you lost, when the Japanese markets collapsed.
c. Market Timing: It is human nature to try to time markets, and some investors make it the central focus of their investment philosophies. I will not try to litigate the good sense of doing so in this post, but the historical return data gives us a sense of both the upside and the downside of doing so. In terms of pluses, an investor who was able to avoid the doomed decades (when stocks earned less than T.Bills and T.Bonds) would be comfortably ahead of an investor who did not, if he or she stayed fully invested in the remaining decades. In terms of minuses, if the market timing investor failed to stay invested in stocks in the good decades, the opportunity costs would quickly overwhelm the benefits. Between 2010 and 2019, there were many investors who believed that a correction was around the corner, driven by their perception that interest rates were being kept artificially low by central banks and that they would revert to historic norms quickly. When that reversion did not occur, these investors paid a hefty price in returns foregone. All of the historical returns that I have reported in this section are nominal, and to the extent that you are interested in real returns, you may want to download the historical data from my website and check out the results. (Hint: Not much changes)

2. A Low Interest Rate Decade
If there was a defining characteristic for the decade, it was that interest rates, both in the US and globally, dropped to levels not seen in decades. You can see this in the path of the US 10-year treasury bond rate in the graph below:
Download historical treasury rates, by year
Since the drop in rates occurred after the 2008 crisis, and in the aftermath of concerted actions by central banks to bolster weak economies, it has become conventional wisdom that it is central banks that have kept rates artificially low, and that the ending of quantitative easing would cause rates to revert back to historical averages. As many of you who have been reading my posts know, I don't believe that central banks have the power to keep long term market-set rates low, if the fundamentals don't support low rates. In fact, one of my favorite graphs is one where I compare the 10-year treasury bond rate each year to the sum of the inflation rate and real GDP growth rate that year (intrinsic riskfree rate):
Download historical treasury rates, by year
As you can see, the main reason why rates have dropped in the US and Europe has been fundamental. As inflation has declined (and become deflation in some parts of the world) and real GDP growth has been anemic, intrinsic and actual risk free rates have dropped. To the extent that the difference between the two is a measure of central banking actions, it is true that the Fed’s actions kept actual rates lower than intrinsic rates more in the last decade than in prior years, but it is also true that even in the absence of central banking intervention, rates would not have reverted back to historical norms. 

3. It was a tech decade, and FAANG stocks stole the show!
While it was a good decade for stocks,  the gains varied across sectors. Using the S&P 500 again as the indicator, you can see the shift in value over the decade by looking at how the different sectors evolved over the decade, as a percent of the S&P 500:
The most striking shift is in the energy sector, which dropped from 11.51% of the index to 4.60%, in market capitalization terms. Some of this drop is clearly due to the decline in oil prices during the decade, but some of it can be attributed to a general loss of faith in the future of fossil fuel and conventional energy companies. The biggest sector through the entire decade was technology but its increase in percentage terms seems modest at first sight, rising from 19.76% in 2009 to 21.97% in 2019, but that is because two of the biggest names in the sector, Google and Facebook, were moved to the communication services sector; if they had been left in technology, its share of the index would have risen to more than 30%. In fact, five companies (Facebook, Alphabet, Apple, Netflix and Google), representing the FAANG stocks, had a very good decade, with their collective market capitalization increasing by $3.4 trillion over the ten years:

Put in perspective, the FAANG stocks accounted for 22% of the increase in market capitalization of the S&P 500, and any portfolio that did not include any of these stocks for the entire decade would have had a tough time keeping up with the market, let alone beating it. (This is an approximation, since not all five FAANG stocks were part of the S&P 500 for the entire decade, with Facebook entering after its IPO in 2012 and Netflix being added to the index in 2014).

4. Mean Reversion or Structural Shift
One of the perils of being in a market like the US, where rich historical data is available and easily accessible is that analysts and academics have pored over the data and not surprisingly found patterns that have very quickly become part of investment lore. Thus, we have been told that value beats growth, at least over long periods, and that small cap stocks earn a premium, and have converted these findings into investing strategies and valuation practices. While it is dangerous to use a decade’s results to abandon a long history, the last decade offered sobering counters to old investing nostrums.

a. Value versus Growth
The basis for the belief that value beats growth is both intuitive and empirical. The intuitive argument is that value stocks are priced cheaper and hence need to do less to beat expectations and the empirical argument is that stocks that are classified as value stocks, defined as low price to book and low price to book stocks, have historically done better than growth stocks, defined as those trading at high price to book and high price earnings ratios. Looking at the annual returns on the lowest and highest PBV stocks in the United States, going back to 1927:
Raw Data from Ken French
The lowest price to book stocks have historically earned 5.22% more than the highest price to book stocks, if you look at 1927-2019. Broken down by decades, though, you can see that the assumption that value beats growth is not as easily justified:
Raw Data from Ken French
While there are some, especially in the old-time value crowd, that view the last decade as an aberration, the slide in the value premium has been occurring over a much longer period, suggesting that there are fundamental factors at play that are eating away at the premium. If you are a believer in value, as I am, there is a consolation prize here. Assuming that low PE stocks and low PBV stocks are good value is the laziest form of value investing, and it is perhaps not surprising that in a world where ETFs and index funds can be created to take advantage of these screens, there is no payoff to lazy value investing. I believe that good value investing requires creativity and out-of-the-box thinking, as well as a willingness to live with uncertainty, and even then, the payoff 

b. The Elusive Small Cap Premium
Another accepted part of empirical wisdom about stocks not only in the US, but also globally, is that small cap stocks deliver higher returns, after adjusting for risk using conventional risk and return models, than large cap stocks. 
Raw Data from Ken French
Looking at the data from 1927 to 2019, it looks conclusively like small market cap stocks have earned substantially higher returns than larger cap stocks; relative to the overall market, small cap stocks have delivered about 4-4.5% higher returns, and conventional adjustments for risk don't dent this number significantly. Not only has this led some to put their faith in small cap investing but it has also led analysts to add a small cap premium to costs of equity, when valuing small companies. I have not only never used a small cap premium, when valuing companies, but I am skeptical about its existence, and wrote a post on why a few years ago. Again, updating the data by decades, here is what I see:
Raw Data from Ken French
As with the value premium, the size premium had a rough decade between 2010 and 2019, dropping close to zero, on a value weighted basis, and turning significantly negative, when returns are computed on a equally weighted basis. Again, the trend is longer term, as there has been little or no evidence of a small cap premium since 1980, in contrast to the dramatic premiums in prior decades. If you are investing in small cap stocks, expecting a premium, you will be disappointed, and if you are still adding small cap premiums to your discount rates, when valuing companies, you are about four decades behind the times.

5. New buzzwords were born
Every decade has its buzzwords, words that not only become the focus for companies but are also money makers for consultants, and the last decade was no exception. At the risk of being accused of missing a few, there were two that stood out to me. The first was big data, driven partly by more extensive collection of information, especially online, and partly by tools that allowed this data to be accessed and analyzed. The other was crowd wisdom, where expert opinions were replaced by crowd judgments on a wide range of applications, from restaurant reviews to new (crypto) currencies.

a. Big Data
Earlier in this post, I looked at the surge in value of the FAANG stocks, and how they contributed to shaping the market over the last decade. One common element that all five companies shared was that they were not only reaching tens of millions of users, but that they were also collecting information on these users, and then using that information to improve existing products/services and add new ones. Other companies, seeking to emulate their success, tried their hand at “big data”, and it became a calling card for start-ups and young firms during the decade. While I agree that Netflix and Amazon, in particular, have turned big data into a weapon against competition, and Facebook’s entire advertising business is built on using personal data to focus advertising, I personally believe that like all buzz words, big data has been over sold. In particular, I noted, in a post from 2018 ,that for big data to create value,
  1. The data has to be exclusive: For data to be valuable, there has to be some exclusivity. Put simply, if everyone has it, no one has an advantage. Thus, the fact that you, as a business, can trace my location has little value when two dozen other applications and services on my iPhone are doing exactly the same thing. 
  2. The data has to be actionable: For value conversion to occur, the data that has been collected has to be usable in modifying and adapting the products and services you offer as a business. 
Using these two-part test, you can see why Amazon and Netflix are standouts when it comes to big data, since the data they collect is exclusive (Netflix on your viewing habits/tastes and Amazon on your retail behavior) and is then used to tailor their offerings (Netflix with its original content investments and offerings and Amazon with its product nudging). Using the same two-part test, you can also see why the claims of big data payoffs at MoviePass and Bird Scooters makers never made sense.

b. Crowd Wisdom
One consequence of the 2008 crisis was a loss in faith in both institutional authorities (central banks, governments, regulators) but also in experts, most of whom had been hopelessly wrong in the lead up to the crisis. It is therefore not surprising that you saw a move towards trusting crowds on answers to big questions right after the crisis. It is no coincidence that Satoshi Nakamoto (whoever he might be) posted the paper laying out the architecture of Bitcoin in November 2008, a proposal for a digital currency without a central bank or regulatory overlay, where transactions would be crowd-checked (by miners). While Bitcoin has been more successful as a speculative game than as a currency during the last decade, the block chains that it introduced have now found their way into a much wider range of businesses, threatening to replace institutional oversight (from banks, stock exchanges and other established entities) with cheaper alternatives. The crowd concept has expanded into almost every aspect of our lives, with Yelp ratings replacing restaurant reviewers in our choices of where to eat, Rotten Tomatoes supplanting movie critics in deciding what to watch and betting markets replacing polls in predicting election outcomes. I share the distrust of experts that many others have, but I also wary of crowd wisdom. After all, financial markets have been laboratories for observing how crowds behave for centuries, and we have found that while crowds are often much better at gauging the right answers than market gurus and experts, they are also prone to herding and collective bad choices. For those who have become too trusting of crowds, my recommendation is that they read “The Madness of Crowds”, an old manuscript that is still timely.

The decade to come
It has been said that those who forget the past are destined to relive it, and that is one reason why we pore over historical track records, hoping to get insight for the future. But it has also been said that army generals who prepare too intensely to fight the last war will lose the next one, suggesting that reading too much into history can be dangerous. To me the biggest lesson of the last decade is to keep an open mind and to not take conventional wisdom as a given. I don’t know what the next decade will bring us, but I can guarantee you that it will not look like the last one or any of the prior ones, So, strap on your seat belts and get ready! It’s going to be a wild ride!

YouTube Video

Data Links

  1. Stocks, Bonds and Bills: 1928-2019
  2. Intrinsic and Actual Risk free Rates: 1954-2019
  3. Ken French Data on Value and Size Effects

Monday, January 13, 2020

Data Update 1 for 2020: Setting the table

Starting in the early 1990s, I have spent the first week or two of every new year playing my version of Moneyball, downloading raw market and accounting data on publicly traded companies and using that data to compute operating, pricing and risk metrics for them. This year, I got a later start than usual on January 6, but as the week draws to a close, the results of my data exploration are posted on my website and will be the basis for a series of posts here over the next six weeks. As you look at the data, you will find that the choices I have made on how to classify companies and compute metrics affect my findings, and I will use this post to cast some light on those choices.

The Data
Raw Data: We live in an age when accessing raw data is easy, albeit not always cheap, and the tools to analyze that data are also widely available. My raw data is drawn from a variety of sources, ranging from S&P Capital IQ to Bloomberg to the Federal Reserve, and there are two rules that I try to follow. The first is to be careful about attributing sources for the raw data, and the second is to not undercut my raw data providers by replicating their data on my site, if they have commercial interests. 
Data Analysis: Broadly speaking, I would categorize my data updates into three groups. The first is macro data, where my ambitions tend to be modest, and the only numbers that I update are numbers that I need and use in my valuation and corporate financial analysis. The second is business data, where I consolidate the company-level data into industry groupings, and report statistics on how companies invest, finance their operations and return cash (dividends and buybacks). The third are my data archives, where you can look at trend lines in the statistics by accessing my statistics from prior years. 

A. Macro Data
I am not a market timer or a macro economist, and my interests in macro data are therefore limited to numbers that I cannot easily look up, or access, on a public database. Thus, there is no point in my reporting exchange rates between major currencies, when you have FRED, the Federal Reserve site , that I cannot praise more highly for its reach and its accessibility. I do report and update the following:
  • Risk free rates in currencies: The way in which currencies are dealt with in valuation and corporate finance leaves us exposed to multiple problems, and I have written about both why risk free rates vary across currencies and why government bond rates are not always risk free. At the start of every year, I update my currency risk free rates, starting with the government bond rates, and then netting out default spreads and report them here. As risk free rates in developed market currencies hit new lows, and central banks are blamed for the phenomenon, I also update an intrinsic measure of the US dollar risk free rate, obtained by adding the inflation rate to real GDP growth each year, and report the time series in this dataset.
  • Equity Risk Premiums: The equity risk premium is the price of risk in equity markets and plays a key role in both corporate finance and valuation. The conventional approach to estimating this risk premium is to look at history, and to compare the returns that you would have earned investing in stocks, as opposed to investing in risk free investments. I update the historical risk premium for US stocks, by bringing in 2019 returns on stocks, treasury bonds and treasury bills in this dataset; my updated geometric average premium for stocks over US treasuries. I don't like the approach, both because it is backward looking and because the risk premium estimates are noisy, and have argued for a forward looking or implied ERP. I estimate the implied ERP to be 5.20% at the start of 2020 and report the year-end estimates of the premium going back to 1960 in this dataset. 
  • Corporate Default Spreads: Just as equity risk premiums measure the price of risk in equity markets, default spreads measure the price of risk in the debt markets. I break down bonds into bond rating classes (S&P and Moody's) and report my estimates of default spreads at the start of 2020 in this spreadsheet (and it includes a way of estimating a bond rating for a firm that does not have one).
  • Corporate Tax Rates: Ultimately, companies and investors count on after-tax income, though companies are adept at keeping taxes paid low. While I will report the effective tax rates that companies actually pay in my corporate data, I am grateful to KPMG for going through tax codes in different countries and compiling corporate tax rates, which I reproduce in this dataset.
  • Country Risk Premiums: As companies expand their operations beyond domestic markets, we are faced with the challenge of bringing in the risk of foreign markets into our corporate financial analyses and valuation. I have spent much of the last 25 years trying to come up with better ways of estimating risk premiums for countries, and I describe the process I use in excruciating detail in this paper. At the start of 2020, I use my approach, flaws and all, to estimate equity risk premiums for 170 countries and report them in this dataset.
With macro data, it is generally good practice in both corporate finance and valuation to bring in the numbers as they are today, rather than have a strong directional view. So, uncomfortable though it may make you, you should be using today's risk free rates and risk premiums, rather than normalized values, when valuing companies or making investment assessments.

B. Micro Data
The sample: All data analysis is biased and the bias starts with the sampling approach used to arrive at the data set. My data sample includes all publicly traded companies, listed anywhere in the world, and the only criteria that I impose is that they have a market capitalization number available as of December 31, 2019. The resulting sample of 44,394 firms includes firms from 150 countries, some of which have very illiquid markets and questionable disclosure practices. Rather than remove these firms from my sample, which creates its own biases, I will keep them in my sample and deal with the consequences when I compute my statistics.

While this is a comprehensive sample, it is still biased because it includes just publicly listed companies. There are tens of thousands of private businesses that are part of the competitive landscape that are not included here, and the reason is pragmatic: most of these companies are not required to make public disclosures and there are few reliable databases that include data on these firms. 
The Industry Groupings: While I do have a (very large) spreadsheet that has the data at the company level, I am afraid that my raw data providers do not allow me to share that data, even though it is entirely comprised of numbers that I estimate. I consolidate that data into 94 industry groupings, which are loosely based on the industry groupings I created from Value Line in the 1990s when I first started creating my datasets. To see my industry grouping and what companies fall into each one, try this dataset. As you look at individual companies, there are two challenges that I face. First, there are companies that are in many businesses and I classify these companies into the industry groups from which they derive the most revenues. Second, some companies are shape shifters when it comes to industry grouping, and it is unclear which grouping they belong to; for a few high profile examples, consider Apple and Amazon. There is little that I can do about either problem, but consider yourselves forewarned.
The statistics: My interests lie in corporate finance and valuation and selfishly, I report the statistics that matter to me in that pursuit. Luckily, as I described it in my post a few weeks ago, corporate finance is the ultimate big picture class and the statistics cover the spectrum, and I think the best way to organize them is based upon broad corporate finance principles:
If you are interested, you will find more in-depth descriptions of how I compute the statistics that I report both in the datasets themselves as well as in this glossary.
The timing: I use a mix of market and accounting data and that creates a timing problem, since the accounting data is updated at the end of each quarter and the market data is updated continuously. Using the logic that I should be accessing the most updated data for every item, my January 1, 2020, updated has market data (for share prices, interest rates etc) as of December 31, 2019 and the accounting data as of the most recent financial statement (usually September 30, 2019 for most companies). I don't view this an inconsistent but a reflection of the reality that investors face.

C. Archived Data
When I first started compiling my datasets, I did not expect them to be widely used, and certainly did not believe that they would be referenced over time. As I starting getting requests for datasets from earlier years, I decided that it would save both me and you a great deal of time to create an archive of past datasets. As you look at these archives, you will notice that not all datasets go back in time to the 1990s, reflecting first the expansion of my analysis from just US companies to global companies about 15 years ago and second the adding on of variables that I either did not or could not report in earlier years.

The Rationale
If you are wondering why I collect and analyze the data, let me make a confession, at the risk of sounding like a geek. I enjoy working with the data and more importantly, the data analysis is a gift that keeps on giving for the rest of the year, as I value companies and do corporate financial analysis.
  1. It gives me perspective: In a world where we suffer from data overload, the week that I spend looking at the numbers gives me perspective not only on what comprises normal in corporate financial behavior, but also on the differences across sectors and geographies. 
  2. Possible, Plausible and Probable: I have long argued that the valuation of a company always starts with a story but that a critical part of the process of converting narrative to value is checking the story for possibility, plausibility and probability. Having the global data aggregated and analyzed can help significantly in making this assessment, since you can see the cross section of revenues and profit margins of companies in the business and see if your assessments are out of line, and if so, whether you have a justification. 
  3. Rules of thumb: In spite of all of the data that we now have available, investors and companies seem to still rely on rules of thumb devised in a different time and market. Thus, we are told that companies that trade at less than book value, or six times EBITDA, are cheap, and that the target or right debt ratio for a manufacturing company is 40%. Using the global data, we can back up or dispel these rules of thumb and perhaps replace them with more dynamic and meaningful decision rules.
  4. Fact-based opinions: Many market prognosticators and economists seem to have no qualms about making up stuff about investor and corporate behavior and stating them as facts. Thus, it has become conventional wisdom that US companies are paying less in taxes that companies operating elsewhere in the globe, and that they have borrowed immense amounts of cash over the last decade to buy back stock. Those "facts" are now driving political debate and may well lead to change in policy, but these are more opinions than facts, and the data can be arbiter.
If you are wondering why I am sharing the data, let's get real. Nothing that I am doing is unique, and I have no secret data stashes. In short, anyone with access to data (and there are literally tens of thousands who do) can do the same analysis. I lose nothing by sharing, and I get immense karmic payoffs. So, please use whatever data you want, and in whatever context, and I hope that it saves you time and helps you in your decision making and analysis. 

The Caveats
The last decade has seen big data and crowd wisdom sold as the answers to all of our problems, and as I listen to the sales pitches for both, I would offer a few cautionary notes, born out of spending much of my life time working with data:
  1. Data is not objective: The notion that using data makes you objective is nonsense. In fact, the most egregious biases are data-backed, as people with agendas pick and choose the data that confirms their priors. Just as an example, take a look at the data that I have in what US companies paid in taxes in 2019 in this dataset. I have reported a variety of tax rates, not with the intent to confuse, but to note how the numbers change, depending on how you compute them.  If you believe, like some do, that US companies are shirking their tax obligations, you can point to average tax rate of 7.32% that I report for all US companies, and note that this is well below the federal corporate tax rate of 21%. However, someone on the other side of this debate can point to the 19.01% average tax rate across only money making companies (since only profits get taxed) as evidence that companies are paying their taxes. 
  2. Crowds are not always wise: One of the strongest forces in corporate finance is me-tooism, where companies decide how to invest, how much to borrow and what to pay in dividends by looking at what their peers do. In my datasets, I offer them guidance in this process, by reporting debt ratios and dividend payout ratios for sectors, as well as regional breakdowns. The implicit assumption is that what other companies do, on average, must be sensible, but that assumption is not always true. This warning is particularly relevant when you look at the pricing metrics (PE, EV to EBITDA etc.) that I report, by sector and by region. The market may be right, on average, but it can also over price or under price a sector, at times.
I respect data, but I don't revere it. I don't believe that just having data will give me an advantage over other investors or make me a better investor, but harnessing that data with intuition and logic may give me a leg up (or at least I hope it does).

YouTube Video


Sunday, December 29, 2019

The Market is Huge! Revisiting the Big Market Delusion

For the high-profile IPOs that have reached the market in 2019, with apologies to Charles Dickens for stealing and mangling his words, it has been the best and the worst of years. On the one hand, you have seen companies like Uber and Slack, each less than a decade old, trading at market capitalizations in the tens of billions of dollars, while working on unformed business models and reporting losses. On the other, many of these new listings have not only had disappointing openings, but have seen their market prices drop in the months after. In September 2019, we did see an implosion in the value of WeWork, another company that started the listing process with lots of promise and a pricing to match, but melted down from a combination of self-inflicted wounds and public market scrutiny. While these companies were very different in their business models (or lack of them), they shared one thing in common. When asked to justify their high pricing, they all pointed to how big the potential markets for their products/services were, captured in their assessments of market size. Uber estimated its total accessible market (TAM) to be in excess of $ 6 trillion, Slack’s judgment was that it had 5 million plus prospective clients across the world and WeWork’s argument was that the commercial real estate market was massive. In short, they were telling big market stories, just as PC makers were in the 1980s, dot com firms in the 1990s and social media companies a decade later. In this post, we will start by conceding the allure of big markets, but argue that the allure can lead to delusional pricing. (This post is a not-so short summary of a paper that Brad Cornell and I have written on this topic. You can find it by clicking here.)

The Ingredients
There is nothing more exciting for a nascent business than the perceived presence of a big market for its products and services, and the attraction is easy to understand. In the minds of entrepreneurs in these markets, big markets offer the promise of easily scalable revenues, which if coupled with profitability, can translate into large profits and high valuations. While this expectation is not unreasonable, overconfidence on the part of business founders and their capital providers can lead to unrealistic judgments of future profits, and overly high estimates for what they think their companies are worth, in what I will term the “big market delusion”. That initial overpricing is a common feature of these markets, but results in an inevitable correction that brings the pricing back to earth. In fact, there are three pieces to this puzzle, and it is when they all come together that you see the most egregious manifestations of the delusion.
  1. Big Market: It is the promise of a big market that starts the process rolling, whether it be eCommerce in the 1990s, online advertising between 2010 and 2015, cannabis in 2018 or artificial intelligence today. In each case, the logic of impending change was impeccable, but the extrapolation that the change would lead create huge and profitable markets was made casually. That extrapolation was then used to justify high pricing for every company in the space, with little effort put into separating winners from losers and good from bad business models. 
  2. Overconfidence: Daniel Kahneman, whose pioneering work with Amos Tversky, gave rise to behavioral finance as a disciple described overconfidence as the mother of all behavioral biases, for three reasons. First, it is ubiquitous, since it seems to be present in an overwhelming proportion of human beings. Second, overconfidence gives teeth to, and augments, all other biases, such as anchoring and framing. Finally, there is reason believe that overconfidence is rooted in evolutionary biology and thus cannot be easily countered. The problem gets worse with big markets, because of a selection bias, since these markets attract entrepreneurs and venture capitalists, who tend to be among the most over confident amongst us. Big markets attract entrepreneurs, over confident that their offerings will be winners in these markets, and venture capitalists, over confident in their capacity to pick the winners. 
  3. Pricing Game: We will not bore you with another extended discourse on the difference between value and price, but suffice to say that young companies tend to be priced, not valued, and often on raw metrics (users, subscribers, revenues). As a consequence, there is no attempt made to flesh out the "huge market" argument, effectively removing any possibility that entrepreneurs or the venture capitalists funding them will be confronted with the implausibility of their assumptions.
The end result is that young companies in big markets will operate in bubbles of overconfidence, leading them to over estimate their chances of succeeding, the revenues they will generate if they do and how much profit they can generate on these revenues:

This does not mean that every company in the big market space will be over priced, since a few will succeed and exploit the big market to full effect, but it does mean that the companies will be collectively over priced. As is always the case with markets, there will be a time of reckoning, where investors and managers will wake up to the reality that the big market is not big enough to accommodate all their growth dreams and there will be a correction. In the aftermath, there will be finger-wagging and talk of "never again", but the process will be repeated, albeit in a different form, with the next big market.

Case Studies
We will not claim originality here, since the big market delusion has always been part of market landscapes, and big markets have always attracted overconfident start ups and investors, creating cycles of bubble and bust. In this section, we will highlight three high profile examples:

1. Internet Retail in 1999
The Big Market: As the internet developed and became accessible to the public in the 1990s, the promise of eCommerce attracted a wave of innovators, from Amazon in online retail in 1994 to Ebay in auctions in 1995, and that innovation was aided by the arrival of Netscape Navigator's browser, opening up the internet to retail consumers and PayPal, facilitating online payments. New businesses were started to take advantage of this growing market with the entrepreneurs using the promise of big market potential to raise money from venture capitalists, who then attached sky-high prices to these companies. By the end of 1999, not only was venture capital flowing in record amounts to young ventures, but 39% of all venture capital was going into internet companies.
The Pricing Delusion: The enthusiasm that entrepreneurs and venture capitalists were bringing to online retail companies seeped into public markets, and as public market interest climbed, many young companies found that they could bypass the traditional venture capital route to success and jump directly to public listings. Many of the online retail companies that were listed on public markets in the late 1990s had the characteristics of nascent businesses, with small revenues, unformed business models and large losses, but all of these shortcomings were overwhelmed by the perception of the size of the eCommerce market. In 1999 alone, there were 295 initial public offerings of internet stocks, representing more than 60% of all initial public offerings that year. One measure of the success of these stocks is that data services created indices to track them. The Bloomberg Internet index was initiated on December 31, 1998, with a hundred young internet companies in it, and it rose 250% in the following year, reaching a peak market capitalization of $2.9 trillion in early 2000. Because the collective revenues of these companies were a fraction of that value, and most of them were losing money, the only way you could justify these market capitalizations was with a combination of very high anticipated revenue growth accompanied by healthy profit margins in steady state, premised on successful entry into a big market. 
The Correction: The rise of internet stocks was dizzying, in terms of the speed of ascent, but its descent was even more precipitous. The date the bubble burst can be debated, but the NASDAQ, dominated in 2000 by young internet companies, peaked on March 10, 2000, and in the months after, the pricing unraveled as shown in the collapse of the Bloomberg Internet Index:
The Bloomberg Internet Index
Of the dozens of publicly traded retail companies in existence in March 2000, more than two-thirds failed, as they ran out of cash (and capital access) and their business models imploded. Even those that survived, like Amazon, faced carnage, losing 90% of their value, and flirting with the possibility of shutting down. 

2. Online Advertising in 2015 & 2019
The Big Market: The same internet that gave birth to the dot com boom in the nineties also opened the door to digital advertising and while it was slow to find its footing, the arrival of search engines like Yahoo! and Google fueled its growth.  The advent of social media altered the game even more, as businesses realized that not only were they more likely to reach customers on social media sites, but that social media companies also brought in data about their users that would allow for more focused and effective advertising. The net result of all these innovations was that digital advertising grew in the decade from 2005 to 2015, both in absolute numbers and as a percent of total advertising:

As digital advertising grew, firms that sought a piece of this space also entered the market and were generally rewarded with infusions of capital from both private and public market investors.
The Pricing Delusion: In a post in 2015, I looked at how the size of the online advertising market skewed the companies of companies in this market, by looking at publicly traded companies in the space and backing out from the market capitalizations what revenues would have to be in 2025, for investors to break even. To do this, I made assumptions about the rest of the variables required to conduct a DCF valuation (the cost of capital, target operating margin and sales to capital ratio) and held them fixed, while Ie varied the revenue growth rate until I arrived at the current market capitalization. With Facebook in August 2015, for instance, here is what I estimated:

Put simply, for Facebook's market capitalization in 2015 to be justified, its revenues would have to rise to $129,318 million in 2025, with 93% of those revenues coming form online advertising. Repeating this process for all publicly traded online ad companies in August 2015:
Imputed Revenues in 2025 in millions of US $
The total future revenues for all the companies on the list totals $523 billion. Note that this list is not comprehensive, because it excludes some smaller companies that also generate revenues from online advertising and the not-inconsiderable secondary revenues from online advertising, generated by firms in other businesses (such as Apple). It also does not include the online adverting revenues being impounded into the valuations of private businesses like Snapchat, that were waiting in the wings in 2015. Consequently, we are understating the imputed online advertising revenue that was being priced into the market at that time. In 2014, the total advertising market globally was about $545 billion, with $138 billion from digital (online) advertising. Even with optimistic assumptions about the growth in total advertising and the online advertising portion of it climbing to 50% of revenues, the total online advertising market in 2025 comes to $466 billion. The imputed revenues from the publicly traded companies in August 2015 alone exceeds that number, implying that the companies in were being overpriced relative to the market (online advertising) from which their revenues were derived.
The Correction? The online ad market has not had a precipitous fall from the heights of 2015, but it has matured. By 2019, not only had investors learned more about the publicly traded companies in the online advertising business, but online advertising matured. Using the same process that we used in 2015, we imputed revenues for 2029 using data up through November 2019. Those calculations are presented in the table below:

Imputed Revenues in 2029 in millions of US $
There are signs that the market has moderated since 2015. First, the number of companies shrank, as some were acquired, some failed, and a few consolidated. Second, the market capitalizations had been recalibrated and starting revenues in 2019 are much greater than they were in 2015. As a result, the breakeven revenue in 2029 is $573 billion, only slightly higher than the imputed revenues from the 2015 calculation, despite being four years further into the future. This suggests that the market is starting to take account of the limits imposed by the size of the underlying market. Third, more of the companies on the list have had moments of reckoning with the market, where they have been asked to show pathways to profitability and not just growth numbers. Two examples are Snap and Twitter. For both companies the market capitalizations have languished because of the perception that their pathways to profitability are rocky. In short, if there is a correction occurring in this market, it seems to be happening in slow motion.

3. Cannabis in October 2018
The Big MarketUntil recently, cannabis, in any of its forms, was illegal in every state in the United States in most of the world, but that is changing rapidly. By October 2018, smoking marijuana recreationally and medical marijuana were both legal in nine states, and medical marijuana alone in another 20 states. Outside the United States, much of Europe has always taken a more sanguine view of cannabis, and on October 17, 2018, Canada became the second country (after Uruguay) to legalize the recreational use of the product. In conjunction with this development, new companies were entering the market, hoping to take advantage of what they saw as a “big” market, and excited investors were rewarding them with large market capitalizations.  The widespread view as of October 2018 was that the cannabis market would be a big one, in terms of users and revenues. There were concerns that many recreational cannabis users would continue to use the cheaper, illegal version over the regulated but more expensive one, and that US federal law would be slow to change its view on legality. In spite of these caveats, there remained optimism about growth in this market, with the more conservative forecasters predicting that global revenues from marijuana sales will increase to $70 billion in 2024, triple the estimated sales in 2018, and the more daring ones predicting close to $150 billion in sales.
The Pricing DelusionIn October 2018, the cannabis market was young and evolving, with Canadian legalization drawing more firms into the business. While many of these firms were small, with little revenue and big operating losses, and most were privately owned, a few of these companies had public listings, primarily on the Canadian market. The table below lists the top ten cannabis companies as of October 14, 2018, with the market capitalizations of each one, in conjunction with each company’s operating numbers (revenues and operating income/losses, in millions of US $).
Cannabis Stocks on Oct 14, 2018 ($ values in millions of US$)
Note that the most valuable company on the list was Tilray with a market cap of over $13 billion. Tilray had gone public a few months prior, with revenues that barely register ($28 million) and nearly equal operating losses, but had made the news right after its IPO, with its stock price increasing ten-fold in the following weeks, before subsequently losing almost half of its value in the following weeks. Canopy Growth, the largest and most established company on the list, had the highest revenues at $68 million. More generally, all of them trade at astronomical multiples of book value, with a collective market cap in excess of $48 billion, more than 20 times collective revenues and 10 times book value. For each company, the high market capitalization relative to any measure of fundamental value was justified using the same rationale, namely that the cannabis market was big, allowing for huge potential growth. 
The Correction: In the of the cannabis market, the overreach on the part of both businesses and their investors caught up with them. By October 2019, the assumptions regarding growth and profitability were being universally scaled back, business models were being questioned, and investors were reassessing the pricing of these companies. The best way to see the adjustment is to look the performance of the major cannabis exchange-traded fund, ETFMG, over the period depicted in the figure below:
Note that within a period of approximately one year, cannabis stocks lost more than 50% of their aggregate value. The damage cut across the board. Tilray and Canopy Growth, the two largest market capitalization companies in the October 2019 saw their market capitalizations decline by 80.7% and 38.6% respectively. Given that there was no significant shift in fundamentals, the apparent explanation is that investors came to realize that the “big market” was not going to deliver the previously expected growth rates or the profitability for the expanding group of individual companies.

Common Elements
The three examples that we listed are in very different businesses and have different market settings. That said, there are some common elements that you see in all three, and will in any big market setting:
  1. Big Market stories: In every big market delusion, there is one shared feature. When asked to justify the pricing of a company in the market, especially young companies with little to show in terms of fundamentals, entrepreneurs, managers and investors almost always point to macro potential, i.e., that the retail or advertising or cannabis markets were huge. The interesting aspect is that they rarely express the need to go beyond that justification, by explaining why the specific company they were recommending was positioned to take advantage of that growth. In recent years, the big markets have gone from just words to numbers, as young companies point to big total accessible markets (TAM), when seeking higher pricing, often adopting nonsensical notions of what accessible means to get to large numbers. 
  2. Blindness to competition: When the big market delusion is in force, entrepreneurs, managers and investors generally downplay existing competition, thus failing to factor in the reality that growth will have to be shared with both existing and potential new entrants. With cannabis stocks in late 2018, much of the pricing optimism was driven by the size of the potential market in the United States, assuming legalization, but very few entrepreneurs, managers and investors seemed to consider the likelihood that legalization would attract new players into the market and that illegal sources of supply would maintain their hold on the market.
  3. All about growth: When enthusiasm about growth is at its peak, companies focus on growth, often putting business models to the side or even ignoring them completely. That was true in all three of our case studies. With internet stocks, companies typically based their entire pricing pitch on how quickly they were growing. With social media companies, it took an even rawer form, with growth in users and subscribers being the calling cards for higher pricing. Investors, both private and public, not only went along with the pitch but often actively encouraged companies to emphasize growth at the expense of profits.
  4. Disconnect from fundamentals: If you combine a focus on growth as the basis for pricing with an absence of concern at these companies about business models, you get pricing that is disconnected from the fundamentals. In all three case studies presented in this paper, at the peak of the pricing run up, most of the stocks in each group had negative earnings (making earnings multiples not meaningful), little to show in assets (making book value multiples difficult to work with) and traded at huge multiples of revenues. Put simply, the pricing losing its moorings in value, but investors who look at only multiples miss the disconnect.
The one area where the three case studies diverge is in how the pricing delusion corrects itself. For instance, the dot com bubble hit a wall in March 2000 and burst in a few months, as public markets corrected first, followed by private markets, but the question of why it happened at the time that it did remains a mystery. The online advertising run-up has moderated much more gradually over a few years, and if that trend continues, the correction in this market may be smooth enough that investors will not call it a correction. With cannabis stocks, the rise and fall were both precipitous, with the stocks tripling over a few months and losing that rise in the next few months.

If the big market delusion is a feature of big markets, destined to repeat over time, it behooves us as entrepreneurs, managers, investors and regulators to recognize that reality and modify our behavior.
1. Entrepreneurs and Venture Capitalists
The obvious advice that can be offered to entrepreneurs and venture capitalists, to counter the big market delusion, is to be less over confident, but given that it is not only part of their make up but the driver for exploiting the big market, it will have little effect. Our suggestions are more modest. First, testing out the plausibility of your market size assumptions and the viability of the business model you plan to use to exploit the market on people, whose opinion you value but don't operate in your bubble, is a sensible first step. Second, when you get results from your initial business forays that run counter to what you expected to see, don't be quick to rationalize them away as aberrations. By keeping the feedback loop open, you may be able to improve your business model and adjust your expectations sooner, to reflect reality. Third, build in safety buffers into your model, allowing you to keep operating even if capital dries up (as it inevitably will when the correction arrives), by accumulating cash and avoiding cost commitments that lock you in, like debt and long term cost contracts. Finally, while you may be intent on delivering the metrics that are priced highly, such as users or subscribers, pay attention to building a business model that will work at delivering profits, and if forced to pick between the two objectives, pick the latter.

2.Public Market Investors
The big market delusion almost never stays confined to private markets and sooner or later, the companies in the space list on public markets and are often priced in these markets, at least initially, like they were in private markets. While a risk averse investor may feel it prudent to entirely avoid these stocks, there are opportunities that can be exploited:
  • Momentum investors/traders: The big market delusion is one explanation for the momentum of young, growth stocks. When fascination with a big market like “transportation” takes hold, it can produce momentum in the prices of innovative companies in that space such as Uber and Lyft, and significant profits along the way. The risk, of course, is that the big market delusion fades and the market corrects as has happened in the case of both Uber and Lyft. As we have emphasized, however, there appears to be no way to time such corrections. 
  • Value investors:  The  obvious advice is to avoid young, growth stocks whose value is based on big market stories. But that carries its own risk. In the twelve year stretch beginning in 2007, growth stocks have dramatically outperformed value stocks. As one example, during this period the Russell 1000 growth index outperformed the Russell 1000 value index by an astonishing 4.3% per year. That outperformance was driven in part by stories regarding how technology companies were going to disrupt or invent big markets from housing to entertainment to automobiles. There is a riskier, higher payoff, strategy. Since the big market delusion leads to a collective over pricing, value investors can bet against a basket of stocks (sell short on an ETF like the ETFMG) and hope that the correction occurs soon enough to reap rewards.
In sum, though, young companies make markets interesting and by making them interesting, they increase liquidity and trading.
3. Governments and Market Regulators 
In the aftermath of every correction, there are many who look back at the bubble as an example of irrational exuberance. A few have gone further and argued that such episodes are bad for markets, and suggested fixes, some disclosure-related and some putting restrictions on investors and companies. In fact, in the aftermath of every bursting bubble, you hear talk of how more disclosure and regulations will prevent the next bubble. After three centuries of futility, where the regulations passed in response to one bubble often are at the heart of the next one, you would think that we would learn, but we don't. In fact, over confidence will overwhelm almost every regulatory and disclosure barrier that you can throw up. We also believe that these critics are missing the point. Not only are bubbles part and parcel of markets, they are not necessarily a negative. The dot com bubble changed the way we live, altering not only how we shop but how we travel, plan and communicate with each other. What is more, some of the best performing companies of the last two decades emerged from the debris., a poster child for dot com excess, survived the collapse and has become a company with a trillion-dollar market capitalization.  Our policy advice to politicians, regulators and investors then is to stop trying to make bubbles go away. In our view, requiring more disclosure, regulating trading and legislating moderation are never going to stop human beings from overreaching. The enthusiasm for big markets may lead to added price volatility, but it is also a spur for innovation, and the benefits of that innovation, in our view, outweigh the costs of the volatility. We would choose the chaos of bubbles, and the change that they create, over a world run by actuaries, where we would still be living in caves, weighing the probabilities of whether fire is a good invention or not.

Overconfident in their own abilities, entrepreneurs and venture capitalists are naturally drawn to big markets which offer companies the possibility of huge valuations if they can effectively exploit them. And there are always examples of a few immense successes, like Amazon, to fuel the fire. This leads to a big market delusion, resulting in too many new companies being founded to take advantage of big markets, each company being overpriced by its cluster of founders and venture capitalists. This overconfidence then feeds into public markets, where investors get their cues on price and relevant metrics from private market investors, leading to inflated values in those markets. This results in eventual corrections as the evidence accumulates that growth has to be shared and profitability may be difficult to achieve in a competitive environment. This post is a long one, but if you find it interesting, Brad Cornell and I have a paper expounding a more complete picture here. As always, your feedback is appreciated!

Paper on the big market delusion
Previous posts relating to the big market delusion

Thursday, December 19, 2019

A Teaching Manifesto: An Invitation to my Spring 2020 classes

If you have been reading my blog for long enough, you should have seen this post coming. Every semester that I teach, and it has only been in the spring in the last few years, I issue an invitation to anyone interested to attend my classes online. While I cannot offer you credit for taking the class or much direct personal help, you can watch my sessions online (albeit not live), review the slides that I use and access the post class material, and it is free. If you are interested in a certificate version of the class, NYU offers that option, but it does so for a fee. You can decide what works for you, and whatever your decision is, I hope that you enjoy the material and learn from it, in that order.

The Structure

I will be teaching three classes in Spring 2020 at the Stern School of Business (NYU), a corporate finance class to the MBAs and two identical valuation classes, one to the MBAs and one for undergraduates. If you decide to take one of the MBA classes, the first session will be on February 3, 2020, and there will be classes every Monday and Wednesday until May 11, 2020, with the week of March 15-22 being spring break. In total, there will be 26 sessions, each session lasting 80 minutes. The undergraduate classes start a week earlier, on January 27, and go through May 11, comprising 28 sessions of 75 minutes apiece. 
  1. The Spring 2020 Classses: With all three classes, the sessions will be recorded and converted into streams, accessible on my website and downloadable, as well as YouTube videos, with each class having its own playlist. In addition, the classes will also be carried on iTunes U, with material and slides, accessible from the site. The session videos will usually be accessible about 3-4 hours after class is done and you can either take the class in real time, watching the sessions in the week that they are taught, or in bunches, when you have the time to spend to watch the sessions; the recordings will stay online for at least a couple of years after the class ends. There will be no need for passwords, since the session videos will be unprotected on all of the platforms. 
  2. The (Free) Online Version: During the two decades that I have been offering this online option, I have noticed that many people who start the class with the intent of finishing it give up for one of two reasons. The first is that watching an 80-minute video on a TV or tablet is a lot more difficult than watching it live in class, straining both your patience and your attention. The second is that watching these full-length videos is a huge time commitment and life gets in the way. It is to counter these problems that I created 12-15 minute versions of the each session for online versions of the classes. These online classes, recorded in 2014 and 2015, is also available on my website and through YouTube, and should perhaps be more doable than the full class version.
  3. The NYU Certificate Version: For most of the last 20 years, I have been asked why I don’t offer certificates of completion for my own classes and I have had three answers. The first is that, as a solo act, I don’t have the bandwidth to grade and certify the 20,000 people who take the classes each semester. The second is that certification requires regulatory permission, a bureaucratic process in New York State that I have neither the stomach nor the inclination to go through. The third is, and it is perhaps the most critical, is that I am lazy and I really don't want to add this to my to-do list. One solution would be to offer the classes through platforms like Coursera, but those platforms work with universities, not individual faculty, and NYU has no agreements with any of these platforms. About three years ago, when NYU approached me with a request to create online certificate classes, I agreed, with one condition: that the free online versions of these classes would continue to be offered. With those terms agreed to, there are now NYU Certificate versions of each of the online classes, with much of the same content, but with four add ons. First, each participant will have to take quizzes and a final exam, multiple choice and auto-graded, that will be scored and recorded. Second, each participant will have to complete and turn in a real-world project, showing that they can apply the principles of the class on a company of their choice, to be graded by me. Third, I will have live Zoom sessions every other week for class participants, where you can join and ask questions about the material. Finally, at the end of the class, assuming that the scores on the exams and project meet thresholds, you will get a certificate, if you pass the class, or a certificate with honors, if you pass it with flying colors.
The Classes
I have absolutely no desire to waste your time and your energy by trying to get you to take classes that you either have no interest in, or feel will serve no good purpose for you. In this section, I will  provide a short description of each class, and provide links to the different options for taking each class.

I. Corporate Finance

Class description: I don’t like to play favorites, but corporate finance is my favorite class, a big picture class about the first principles of finance that govern how to run a business. I will not be egotistical enough to claim that you cannot run a business without taking this class, since there are many incredibly successful business-people who do, but I do believe that you cannot run a business without paying heed to the first principles. I teach this class as a narrative, staring with the question of what the objective of a business should be and then using that objective to determine how best to allocate and invest scarce resources (the investment decision), how to fund the business (the financing decision) and how much cash to take out and how much to leave in the business (the dividend decision). I end the class, by looking at how all of these decisions are connected to value.

Chapters: Applied Corporate Finance Book, Sessions: Class session
I am not a believer in theory, for the sake of theory, and everything that we do in this class will be applied to real companies, and I will use six companies (Disney, Vale, Tata Motors, Deutsche Bank, Baidu and a small private bookstore called Bookscape) as lab experiements that run through the entire class.

I say, only half-jokingly, that everything in business is corporate finance, from the question of whether shareholder or stakeholder interests should have top billing at companies, to why companies borrow money and whether the shift to stock buybacks that we are seeing at US companies is good or bad for the economy. Since each of these questions has a political component, and have now entered the political domain, I am sure that the upcoming presidential election in the US will create some heat, if not light, around how they are answered.

For whom?

As I admitted up front, I believe that having a solid corporate finance perspective can be helpful to everyone. I have taught this class to diverse groups, from CEOs to banking analysts, from VCs to startup founders, from high schoolers to senior citizens, and while the content does not change, what people take away from the class is different. For bankers and analysts, it may be the tools and techniques that have the most staying power, whereas for strategists and founders, it is the big picture that sticks. So, in the words of the old English calling, "Come ye, come all", take what you find useful, abandon what you don't and have fun while you do this.

Links to Offerings

1. Spring 2020 Corporate Finance MBA class (Free)
2. Online Corporate Finance Class (Free)

3. NYU Certificate Class on Corporate Finance (It will cost you...)

II. Valuation

Class description: Some time in the last decade, I was tagged as the Dean of Valuation, and I still cringe when I hear those words for two reasons. First, it suggests that valuation is a deep and complex subject that requires intense study to get good at. Second, it also suggests that I somehow have mastered the topic. If nothing else, this class that I first taught in 1987 at NYU, and have taught pretty much every year since, dispenses with both delusions. I emphasize that valuation, at its core, is simple and that practitioner, academics and analysts often choose to make it complex, sometimes to make their services seem indispensable, and sometimes because they lose the forest for the trees. Second, I describe valuation as a craft that you learn by doing, not by reading or watching other people talk about it, and that I am still working on the craft. In fact, the more I learn, the more I realize that I have more work to do.  This is a class about valuing just about anything, from an infrastructure project to a small private business to a multinational conglomerate, and it also looks at value from different perspectives, from that of a passive investor seeking to buy a stake or shares in a company to a PE or VC investor taking a larger stake to an acquirer interested in buying the whole company. 

Finally, I lay out my rationale for differentiating between value and price, and why pricing an asset can give you a very different number than valuing that asset, and why much of what passes for valuation in the real world is really pricing. 

Along the way, I emphasize how little has changed in valuation over the centuries, even as we get access to more data and more complex models, while also bringing in new tools that have enriched us, from option pricing models to value real options (young biotech companies, natural resource firms) to statistical add-ons (decision trees, Monte Carlo simulations, regressions). 

For whom?

Do you need to be able to do valuation to live a happy and fulfilling life? Of course not, but it is a skill worth having as a business owner, consultant, investor or just bystander. With that broad audience in mind, I don't teach this class to prepare people for equity research or financial analysis jobs, but to get a handle on what it is that drives value, in general, and how to detect BS, often spouted in its context. Don't get me wrong! I want you to be able to value or price just about anything by the end of this class, from Bitcoin to WeWork, but don't take yourself too seriously, as you do so.

Links to Offerings
1a. Spring 2020 Valuation MBA class (Free)
1b. Spring 2020 Valuation Undergraduate class (Free)
2. Online Valuation Class (Free)
3. NYU Certificate Class on Valuation (Paid)
III. Investment Philosophies

Class description: This is my orphan class, a class that I have had the material to teach but never taught in a regular classroom. It had its origins in an couple of observations that puzzled me. The first was that, if you look at the pantheon of successful investors over time, it is not only a short one, but a diverse grouping, including those from the old time value school (Ben Graham, Warren Buffett), growth success stories (Peter Lynch and VC), macro and market timers (George Soros), quant players (Jim Simon) and even chartists. The second was that the millions who claim to follow these legends, by reading everything ever written by or about them and listening to their advice, don’t seem to replicate their success. That led me to conclude that there could be no one ‘best’ Investment philosophy across all investors but there could be one that is best for you, given your personal makeup and characteristics, and that if you are seeking investment nirvana, the person that you most need to understand is not Buffett or Lynch, but you.  In this class, having laid the foundations for understanding risk, transactions cost and market efficiency (and inefficiency), I look at the entire spectrum of investment philosophies, from charting/technical analysis to value investing in all its forms (passive, activist, contrarian) to growth investing (from small cap to venture capital) to market timing. With each one, I look at the core drivers (beliefs and assumptions) of the philosophy, the historical evidence on what works and does not work and end by looking at what an investor needs to bring to the table, to succeed with each one.

I will try (and not always succeed) to keep my biases out of the discussion, but I will also be open about where my search for an investment philosophy has brought me. By the end of the class, it is not my intent to make you follow my path but to help you find your own.

For whom?
This is a class for investors, not portfolio managers or analysts, and since we are all investors in one way or the other, I try to make it general. That said, if your intent is to take a class that will provide easy pathways to making money, or an affirmation of the "best" investment philosophy, this is not the class for you. My objective in this class is not to provide prescriptive advice, but to instead provide a menu of choices, with enough information to help you can make the choice that is best for you. Along the way, you will see how difficult it is to beat the market, why almost every investment strategy that sounds too good to be true is built on sand, and why imitating great investors is not a great way to make money.

Links to Offerings

1. Online Investment Philosophies Class (Free)
2. NYU Certificate Class on Valuation (Paid)
  • NYU Entry Page (Coming soon)
I have to confess that I don't subscribe to the ancient Guru/Sishya relationship in teaching, where the Guru (teacher) is an all-knowing individual who imparts his or her fountain of wisdom to a receptive and usually subservient follower. I have always believed that every person who takes my class, no matter how much of a novice in finance, already knows everything that needs to be known about valuation, corporate finance and investments, and it is my job, as a teacher, to make him or her aware of this knowledge. Put simply, I can provide some structure for you to organize what you already know, and tools that may help you put that knowledge into practice, but I am incapable of profundity. I hope that you do give one (or more) of my classes a shot and I hope that you both enjoy the experience and get a chance to try it out on real companies in real time.

YouTube Video