Friday, January 11, 2019

Back to Class: A Teaching Manifesto!

I am convinced that each of us is granted moments of grace, where, if we are open to the possibility, we find out what we are meant to do with our lives. For me, one of those moments occurred in the second year of my MBA program at UCLA, when, cash poor, I decided to be a teaching assistant for a quarter to earn some money. At the time I made that decision, my plans were typical of many of my MBA cohort, to get a job in consulting or investment banking, and to make my work up the corporate ladder, but the day that I walked in to teach my first session, I knew that I had found my calling. I was going to be a teacher, though I was not sure what I would be teaching, or to whom. As fate would have it, I found myself fascinated by finance, and I ended up as a finance professor at NYU's business school. I have never regretted that choice, but when asked to describe what I do, I still tell people that I am a teacher, not a professor, a researcher or an academic.

Back to the Classroom!
Starting in 1986, I have been teaching almost every semester at Stern, but I have had a break of almost a year and a half from my class room teaching, the first year representing a long-delayed sabbatical and the last half year reflecting a choice that I made to do all my teaching in one semester this academic year (2018-19). During that period, I continued to teach my short-term (2 to 3 day) classes in different parts of the globe, and while I have enjoyed these visits immensely, I have missed my regular classroom. I am therefore looking forward to a new semester and three new classes this spring, a corporate finance class that I teach, primarily to first year MBAs, a valuation class, an elective for mostly second year MBAs, and another valuation class for undergraduates in their sophomore, junior and senior years. If you are not at Stern, you will not be able to sit in the class  but through the wonders of technology, you can still take these classes. With no further ado, let me describe them and offer you the choices.

Corporate Finance (MBA)
If there is one class in finance that everyone, no matter what their paths in life or business may be, should take, it is corporate finance. Corporate finance is a class that covers the first principles that govern how a business should be run and its reach is complete. Every decision that a firm makes is ultimately a corporate finance decision, no matter which functional area (marketing, production, personnel) it originates from, and that is the perspective I take in the class. I teach the class around what I call my big picture page, where I classify business decisions into investing, finance and dividend groupings and frame how to make those decisions with an end objective of increasing the value of the business.
Class webpage
You will notice chapter numbers and sessions under each topic, with the chapters representing chapters in my Applied Corporate Finance book, a book that I loved writing but one that is so hopelessly over priced that I do not require it for my own class, and the sessions showing the sequence of the class through the 26 sessions that start on February 4, 2019 and end on May 13, 2019. The class meets every Monday and Wednesday during this period, barring the break week of May 16-23, and the syllabus for the class can be found at this link. If you cannot be in these classes in person, don't fret since the classes will be recorded and be available for you to watch, not in real time, but about 2-3 hours after each class is done. To follow along with the webcasts (each about 80 minutes long), you can also access the slides that I use for each class, as well as additional material. Finally, I demand a great deal of my class (weekly puzzles, add on videos, exams and a project) and if you want, you can also do the puzzles, take the exams and do the project, though you will have to grade yourself (with a template that I will put online). You can even read the emails I send my class, and I send about a hundred over the course of a semester, at this link. If you prefer your videos on YouTube, you can try the playlist for the class, and if your preference is for an iTunes U version, this link should take you to the site. The good news is that it will cost you nothing (other than your time and perhaps a few relationships) but the bad news is that you will not get any official certification, if that is what you are looking for.

Valuation (Undergraduate and MBA)
I have a fondness for this class, since I created and taught the first full-semester version of it, at any business school, in 1987.  I was told then that there was not enough "stuff" in valuation to fill a class, and while that might have been true at that time, I have found plenty to fill in the gaps since. As the title of the class indicates, this is a class about valuation. Valuing what, you might ask, and my answer would be "just about everything" from stocks to bitcoin to the Kardashians. The picture below captures the broad reach of the class:
Class webpage
As I teach it, this is a class that is not only about valuing assets but also pricing them (I am afraid that you have to sit in on the class to find out the difference) and it looks at valuation/pricing from a variety of perspectives (investors looking at a stock, managers using value to guide decision making and even accountants writing disclosure and accounting rules). As those of you who read my blog know, my fidelity is to intrinsic value, but I try to keep an open mind on different perspective and approaches in this class.

As with the corporate finance class, we will meet every Monday and Wednesday for 14 (15) weeks, starting February 4 (January 28) for my MBA (undergraduate) class. If you are wondering which version to follow, I will save you the trouble, since the classes are identical in content and delivery, since I don't believe that there is any reason why I should challenge a bright 21-year old less than a bright 28-year old; age and work experience can give the latter more perspective but this is often offset by the extra energy and curiosity that youth brings to the table. The links that you can use to follow the class are in the table below for both versions of the class:

Webcast pageYouTube PlaylistiTunes U classStartsEnds# Sessions
MBA Valuation
4-Feb-19
13-May-19
26
Undergraduate Valuation
28-Jan-19
13-May-19
28

With both versions of the valuation class, I will also be posting what I call my valuation of the week, a company that I will value, with links to the excel spreadsheet and the story behind the value. I encourage you, if you are taking the class, officially or unofficially, to take my valuation and make it your own, changing the story and the inputs, and then recording your valuation in a shared Google spreadsheet. In a world where crowds decide what movies are successes (Rotten Tomatoes) and which restaurants we eat at (Yelp reviews), we can create our version of crowd valuations. It is an optional exercise, but the more people who participate, the more fun that we can have.

Other Options
I am under no illusions that you are sitting around, wherever you are in the world, with nothing better to do than watching two long sessions each week from February through May. Watching long lecture videos on my tablet is not my idea of fun and while some of you will start with the objective of sitting in on the class, life will get in the way. There are three options that you can consider, depending upon your constraints:
  1. If time is your constraint: One of the advantages of taking the class or classes online is that you do not have to do finish the class in May 2019. In fact, the webcasts for the class will stay on for at least another year after the class ends. So, if you like the long class format, you can stretch the class out for longer, if all you need is more time.
  2. If format is your concern: If you find your attention lagging or your brain decomposing because the lectures are too long, I have created online versions of both classes (plus a third one on investment philosophies), where I have compressed my 80 minute sessions into 12-15 minutes each. Without giving away any trade secrets, and at the risk of discounting the value of an MBA, it was not difficult to do. As with the regular classes, these are still free, still come with slides and post class quizzes but offer no official certification.
  3. If you want accreditation: Even if you take my classes online religiously, mastering every nook and cranny of the topic, and acing every quiz, I do not have the bandwidth or the authority to hand out accreditation or certificates. Three years ago, I remedied this, with the help of NYU, by creating certificate versions of the online classes (with shorter duration videos). The pluses are that the videos are more polished than the ones I created for the free version, there is more administrative support and an active message board where you can chat with others taking the class and you will get a certificate at the end of the class. I will also, at least for the foreseeable future, also do live hourly WebEx sessions once every two weeks and grade your  projects.  The minus is that NYU does not give away certificates for free and if you get sticker shock, please don't make me your target. The decision on whether the certificate is worth the fee is yours to make, and the links to both the free and the NYU certificate versions are below.

Online classNYU Certificate Online class
Corporate Finance
Valuation
Investment Philosophy
Link

Finally, you are always welcome to pick the parts of each class that interest you and ignore the rest. The end game is learning, and what interests me may not interest you.

Bottom Line
I know that there are some who say that those who can, do, and those who cannot, teach, and I have been told that or variants of it multiple times. I don't mind the insult, since I have a thick skin, but I know that there is nothing else in the world I would rather do. I answer to no one (other than my wife), pick when or where I work (for the most part), get a chance to change how people think and make a decent living. If your desire is to manage other people's money, be an equity research analyst or investment banker, or to start and run your own company, I wish you the very best, but I am lucky to be doing what I love, and I would be foolish to trade it in for more money or prestige. At the risk of recycling a cliche, I have only one life to live!

YouTube Video


Class Links
a. Full Semester Classes (Spring 2019) (Free)
  1. MBA Corporate Finance Class (Spring 2019) (Free)
  2. MBA Valuation Class (Spring 2019) (Free)
  3. Undergraduate Valuation Class (Spring 2019) (Free)
b. Online Classes (Free)
  1. Online Corporate Finance Class (Free)
  2. Online Valuation Class (Free)
  3. Online Investment Philosophies Class (Free)
c. NYU Online Certificate Classes (Not free!)

  1. NYU Online Corporate Finance Certificate Class
  2. NYU Online Valuation Certificate Class

Wednesday, January 9, 2019

January 2019 Data Update 3: Playing the Numbers Game!

Every year, for the last three decades, I have spent the first week of the year, looking at numbers. Specifically, as the calendar year ends, I download raw data on individual companies and try to decipher trends and patterns in the data. Over the years, the raw data has become more easily accessible and richer, but ironically, I have become more wary about trusting the numbers. In this post, I will describe, in broad terms, what the data for 2019 looks like, in terms of geography and industry, and spend the next few posts eking out as much information as I can out of them.

The Data: Geography
My sample includes all publicly traded firms with a market capitalization greater than zero and all of the information that I get from my data providers is in the public domain. Put differently, for an individual firm, you should be able to extract all of the information that I have for the firms in my sample, and compute the statistics and ratios that I do, if you are so inclined. If you are wondering why I don't screen out firms that have small market capitalizations or are in markets where information disclosure is spotty, it is because any sampling choices that I make to restrict my sample will create biases that may skew the statistics.

For my 2019 data update, I have 43,846 firms in my sample. While these companies are incorporated in 148 countries, I classify them broadly into five geographical groups:

Geographical Grouping
Includes
Rationale
Australia, NZ and Canada
Australia, New Zealand and Canada
Share a reliance on natural resources.
Developed Europe
EU, UK, Switzerland and Scandinavia
Includes riskier EU countries, but reflects European company pricing and choices.
Emerging Markets
Asia other than Japan, Africa, Middle East, Latin America, Eastern Europe & Russia
A really mixed bag of countries from many regions with different characteristics, with variations in added risk.
Japan
Japanese companies
Different enough from the rest of the world that it still deserves its own grouping.
United States
US companies
Accounts for the biggest chunk of world market capitalization.

I will confess up front that there is an element of arbitrariness to this classification, but no classification will ever be immune to that subjectivity.  The breakdown of my sample both in terms of numbers of firms and market capitalization is below:

US firms are still the leaders in the market capitalization race, accounting for 38% of overall market value. While emerging market firms account for roughly half the firms in my overall sample, their market capitalization is 30% of the overall global market capitalization. The emerging market grouping includes firms from four continents, listed in countries that range in risk from low risk to extraordinarily high risk. The two biggest emerging markets, in terms of listings and market capitalization, are India and China and I will break out companies listed in those countries separately for computing my numbers.

The Data: Industry Groupings
To classify companies into industrial groups, I start with the industry listings provided by my raw data providers but add my own twist to create industry groupings. One reason that I do so is to respect my raw data providers' proprietary classifications and the other is to compare across time, since I have classified firms with my groupings for decades. In making my classifications, I will err on the side of broader classifications, rather than narrower one, for two reasons:
  1. Law of large numbers: The power of averaging gets stronger, as sample sizes increase, and using broader groupings results in larger samples. To illustrate, I have 1148 apparel firms in my global sample, thus allowing for enough firms in every sub grouping. 
  2. Better measures: In both valuation and corporate finance, there is an argument to be made that the numbers we obtain for broader groups is a better estimate of where companies will converge than focusing on smaller groups. 
That said, there will be times where the broad industry classifications that I use will frustrate you, especially on pricing metrics, like PE ratios and EV to EBITDA multiples. I report the industry average PE ratios and EV to EBITDA multiples for specialty retailers collectively, but if you are valuing a luxury retailer, you would have liked to see these averages reported just for luxury retailers. I apologize in advance for that, but the consolation price is that if you want to compute an average across a small sample of companies just like yours, the data to do so is available online and often for free. 

In sum, I break companies down into 94 industries and you can see the numbers of firms and market capitalizations of each industry in this file. The ten biggest industries, at the start of 2019, based upon the number of publicly traded firms and market capitalization are reported below:
Download full list of industries
While I used to provide company level data until 2015, my raw data providers have put restrictions on that and I can no longer do that. If you are interested in finding out which industry grouping a specific company that you are interested in belongs to, you can find out by downloading this file. Finally, I separate financial service firms from the rest of the sample in computing my market-wide statistics, simply because they are so different that including them will skew the numbers. You can see for yourself how much of a difference this makes.

The Data: Statistics
Timing
I download data from both accounting statements and financial markets and in doing so, I do run into a mild timing issue. The accounting data that I have for most firms on January 1, 2019, is as of the third quarter of 2018 (ending September 30, 2018) and I use the trailing 12-month data as of the most recent financial filing. For companies in countries with semi-annual filings, the data will be even mow dated, but there is little that can be done about that. For market data, I use the market prices and rates, as of December 31, 2018. While you may think of that as a timing inconsistency, I do not, since that is most updated information an investor would have had on January 1, 2019.

Adjustments
With the accounting information, I use my discretion to change accounting rules that I believe not only make no sense but skew our perspectives on companies. The first adjustment that I make is to convert lease commitments to debt, which alters operating income and debt numbers, a modification that I have made for more than 20 years. I am pleased to note that accounting will finally come to its senses and try to do the same starting in 2019 and you should be able to get a preview of how margins, debt ratios and returns on capital will change from my computations. The second adjustment is to convert R&D expenses from an operating expense (which it clearly is not) to a capital expense, which it clearly is, again affecting operating income and invested capital. For purposes of transparency, I report both the adjusted and the unadjusted numbers for the statistics that are affected by it.

Statistics and Ratios
Since my interests lie in corporate finance, valuation and investment management, I compute a wide range of statistics, as can be seen in the table below (reproduced from last year). :

Risk MeasuresCost of FundingPricing Multiples
1.     Beta1.     Cost of Equity1.     PE &PEG
2.     Standard deviation in stock price2.     Cost of Debt2.     Price to Book
3.     Standard deviation in operating income3.     Cost of Capital3.     EV/EBIT, EV/EBITDA and EV/EBITDA
4.     High-Low Price Risk Measure4.     EV/Sales and Price/Sales
ProfitabilityFinancial LeverageCash Flow Add-ons
1.     Net Profit Margin1.     D/E ratio & Debt/Capital (book & market) (with lease effect)1.     Cap Ex & Net Cap Ex
2.     Operating Margin2.     Debt/EBITDA2.     Non-cash Working Capital as % of Revenue
3.     EBITDA, EBIT and EBITDAR&D Margins3.     Interest Coverage Ratios3.     Sales/Invested Capital
ReturnsDividend PolicyRisk Premiums
1.     Return on Equity1.     Dividend Payout & Yield1.     Equity Risk Premiums (by country)
2.     Return on Capital2.     Dividends/FCFE & (Dividends + Buybacks)/ FCFE2.     US equity returns (historical)
3.     ROE - Cost of Equity
4.     ROIC - Cost of Capital
You can click on the links to see the US data for the start of 2019, in html, but I would strongly recommend that you download the data in Excel from my data page. You will not only get data that is easier to work with but you can also download the data for the global sample and geographical groups (as well as India and China).

The Data: Use
It would be presumptuous of me to tell you how to use data, since that is a personal choice, but having worked with this data for almost 30 years, I can offer you some caveats:
  1. Don't assume that mean reversion is automatic: A great deal of valuation and investment management is built on the presumption that mean reversion will occur. Thus, low PE stocks will deliver high returns, as the PE converges on the average for the sector. While mean reversion is a strong force, it is not immutable, and when you have structural changes in the economy and sectors, it will break down. 
  2. Trust, but verify: While I would like to believe that my computations of widely used ratios (from accounting ratios like return on equity and ROIC to pricing metrics like EV to EBITDA) are correct, they represent my views and may differ from yours. It is for this reason that I provide a full listing of how I compute my numbers at this link. If you do find a statistic that I report that you are not clear about, and you cannot find the description of how I computed it, please let me know.
  3. The data will age, and some more quickly than others, over the course of the year: I have neither the interest, nor the inclination, to be a full-fledged data service. So, please don't expect daily, weekly or monthly updates of the data. In fact, God willing, the data will be updated a year on January 5, 2020. The only numbers that I plan to update mid year are the country risk premiums.
I hope that you find my data useful in whatever you pursue, and if you do use it, you are welcome to it. I find that sharing data that I will need and use anyway costs me nothing, and the only thing that I will ask of you is that you pass on the sharing.  

YouTube Video

Data links

Monday, January 7, 2019

January 2019 Data Update 2: The Message from Bond Markets!

I must admit that I don't pay as much attention to fixed income markets, as I do to equity markets, other than to use numbers from the markets as inputs when I value companies or look at equity markets. This year, I decided to look at bond market movements, both in the sovereign bond and corporate bond markets for two reasons. First, bond markets offer predictive information about future economic growth and inflation, and since one of the big uncertainties for equities going into the new year is whether the economy could go into recession, it is worth paying attention to what bond investors are telling us. Second, one of the stories in the equity market during 2018 was that the price of risk, in the form of an equity risk premium, rose and became more volatile, and it makes sense to look at whether the price of risk in the bond market, taking the form of default spreads, also exhibited the same characteristics. Bear in mind, though, that the bond market is not my natural habitat and if you are a fixed income trader or an interest rate prognosticator or even a Fed Watcher, you may find my reasoning to be simplistic and perhaps even wrong.

The US Treasury Market
The place to start any assessment of interest rates is the US treasury market, with it range of offerings, both in terms of maturity (from 1 month to 30 year) and form (nominal and real). When valuing equities on an intrinsic value basis, it is the long term US treasury that is your opportunity cost (since your cash flows on equity are also long term in intrinsic value) and the ten-year US treasury bond rate is my input. (The 30-year US treasury may actually be better suited to equities, from a maturity perspective, but has less reliable history, more illiquid and subject to behaving in strange ways). The path of the US 10-year T. Bond on a daily basis is captured in the graph below:

At the start of the year, I had argued that there was a good chance that the 10-year T. Bond would hit 3.5% over the course of the year, but after reaching 3.24% on November 8, the rate dropped back in the last quarter, to end the year at 2.69%.  

Returns on T. Bonds and Historical Premiums
If you bought ten-year treasury bonds on January 1, 2018, the rise in the T.Bond rate translated into a price drop of 2.43%, effectively wiping out the coupon you would have earned and resulting in a return for the year of -0.02%. The consolation price is that you would have still done better than investing in US stocks over the year and generating a return of -4.23%. Updating the historical numbers for the United States, here is the updated score on what US stocks have earned, relative to T.Bonds and T.Bills over time:
Download historical annual returns
There is no denying that historically stocks have delivered higher returns that treasuries, but as we saw in the last quarter this year, it is compensation for the risk that you face. 

The Yield Curve Flattens
The big story over the course of the year was the flattening of the yield curve, with short term rates rising over the course of the year; the 3-month T.Bill rate rose from 1.44% on January 1, 2018 to 2.45%on December 31, 2018 and the 2-year US treasury bond rate rose from 1.92% on January 1, 2018 to 2.42% on December 31, 2018. The yield curve flattening is shown in the graph below:

By December, a portion of the yield curve inverted, with 5-year rates dropping below 2-year and 3-year rates, leading to a flood of stories about inverted yield curves predicting recessions. I did post on this question a few weeks ago, and while I will not rehash my arguments, I noted that the slope of the yield curve and economic growth are only loosely connected.

The TIPs Rate and Inflation
Finally, I  looked at the rate on the inflation protected 10-year US treasury bond over the course of the year, in relation to the US 10-year bond. 

Note that the difference between these 10-year T.Bond rate and the 10-year TIPs rate is a market measure of expected inflation over the next ten years. Over the course of 2018, the "expected inflation" rate has stayed within a fairly tight bound, ranging from a low of 1.70% to a high of 2.18%. In fact, if the return on inflation was on investor minds, the memo seems to have not reached this part of the bond market, with expected inflation decreasing over the course of the year.

What now?
At the start of last year, when investors were expecting much stronger growth in the economy and had just seen a drop in corporate tax rates, the debate was about how much the US treasury bond rate would climb over the course of 2018. As we saw in the section above, the 10-year US treasury bond rate did rise, but only moderately so, perhaps because there was a dampening of optimism about future growth in the last quarter. That said, the Federal Reserve and its chair, Jerome Powell, are still the focus of attention for some investors, obsessed with what the central bank will or will not do next year.  

Intrinsic Riskfree Rates
As some of you have read this blog know well, I am skeptical about how much power the Fed has to move interest rates, especially at the long end of the spectrum, and the economy. To get perspective on the level and direction of long term interest rates, I find it more useful to construct what I call an intrinsic risk free rate by adding together the inflation rate and real GDP growth rate each year. The figure below provides the long term comparison of the actual treasury bond rate and the intrinsic version of it:
Download raw data
There are two versions of the intrinsic risk free rate that I report, one using just the current year;'s inflation and real growth and one using a ten-year average of inflation and real GDP growth, which I will termed the smoothed intrinsic risk free rate. This graph explains the main reasons why interest rates dropped after 2008, very low inflation and anemic growth. As growth and inflation have picked up in the last two years, the treasury bond rate has stayed stubbornly low, and for those who blame the Fed for almost everything that happens, this was a period during which the Fed was raising the Fed Funds rate, the only interest rate it directly controls, and scaling back on quantitative easing. At the end of 2018, the treasury bond rate (2.68%) lagged the contemporaneous intrinsic risk free rate (5.54%) by 2.86% and the smoothed rate (3.58%) by 0.90%.

Reading the Tea Leaves
What does this all mean? I am no bond market soothsayer, but I see two possible explanations. One is that the bond market is right and that expected growth in the next few years will drop dramatically. The other is that bond market investors are being much too pessimistic about future growth, and that rates will rise as the realization hits them.  I believe that the truth falls in the middle. Nominal growth in the US economy will drop off from its 2018 levels, but not to the levels imputed by the bond market today, and treasury bond rates will rise to reflect that reality. In the absence of a crystal ball, I will hazard a guess that the US 10-year treasury bond rate will rise to 3.5%, the smoothed out intrinsic rate, by the end of the year, and that GDP growth will drop by a percent (in nominal and real terms) from 2018 levels. As with all my macroeconomics predictions, this comes with a  money back guarantee, which explains why I do this for free.

The US Corporate Bond Market
If the government bond rate offers signals about future inflation and expected growth in the economy, the corporate bond market sends its own messages about the economy, and specifically about risk and its price. In particular, the spread between a US $ corporate bond and the US Treasury bond of equivalent maturity is the price of risk in the bond market. To see how this measure moved over the course of the year, I looked at the yields on a Aaa. Baa and Can 10-year corporate bonds (Moody's) relative to the US 10-year treasury  bond over the course of the year:

As with the equity risk premium, default spreads widened over the course of the year for all bond ratings classes, but more so for the lower ratings. Also, similar to the pattern in equity markets, all of the widening in the equity risk premium happened in the last quarter of 2018. In fact, the intraday volatility of default spreads increased in October, mirroring what was happening in the equity market. In a later update, I will be looking at country risk, using sovereign default spreads as one measure of that risk. These default spreads also widened in 2018, setting the stage for higher country risk premiums. All in all, 2018 saw the price of risk go up in both the equity and debt markets, and not surprisingly, companies will see higher costs of capital as a consequence.

Bottom Line
For the most part, the bond and stock markets were singing from the same song book this year. Both markets started the year, expecting continued strength in the economy, but both became less upbeat about economic prospects towards the end of the year. For stock markets, this translated into expectations of lower earnings growth and stock prices, and for bond markets, its showed up as lower treasury bond rates and higher default spreads. Investors in both markets became more wary about risk and demanded higher prices for taking risk, with higher equity risk premiums in the stock market and higher default spreads in the bond market. 

YouTube Video

Datasets
  1. Historical Returns on Stocks, T. Bonds and T.Bills - 1928 to 2018
  2. T. Bond Rates, Inflation and Real Growth - 1953 to 2018
  3. Corporate Bond Default Spreads - Start of 2019

Wednesday, January 2, 2019

January 2019 Data Update 1: A reminder that equities are risky, in case you forgot!

In bull markets, investors, both professional and amateur, often pay lip service to the notion of risk, but blithely ignore its relevance in both asset allocation and stock selection, convinced that every dip in stock prices is a buying opportunity, and soothed by bromides that stocks always win in the long term. It is therefore healthy, albeit painful, to be reminded that the risk in stocks is real, and that there is a reason why investors earn a premium for investing in equities, as opposed to safer investments, and that is the message that markets around the world delivered in the last quarter of 2018.

A Look Back at 2018
The stock market started 2018 on a roll, having posted nine consecutive up years, making the crisis of 2008 seem like a distant memory. True to form, stocks rose in January, led by the FAANG (Facebook, Amazon, Apple, Netflix and Google) stocks and momentum investors celebrated. The first wake up call of the year came in February, first as the market responded negatively to macroeconomic reports of higher inflation, and then as Facebook and Google stumbled from self-inflicted wounds. 

The market shook off its tech blues by the end of March and continued to rise through the summer, with the S&P 500 peaking for the year at 2931 on September 20, 2018.   For the many investors who were already counting their winnings for the year, the last quarter of 2018 was a shock, as volatility returned to the market with a vengeance. In October, the S&P 500 dropped by 6.94%, though it felt far worse because of the day-to-day and intraday price swings. In November, the S&P 500 was flat, but volatility continued unabated. In December, US equities finally succumbed to selling pressures, as a sharp selloff pushed stocks close to the "bear market" threshold, before recovering a little towards the end of the year.  

Over the course of the year, every major US equity index took a hit, but the variation across the indices was modest.
The ranking of returns, with the S&P 600 and the NASDAQ doing worse than the Dow or the SD&P 500 is what you would expect in any down market. With dividends incorporated, the return on the S&P 500 was -4.23%, the first down market in a decade, but only a modestly bad year by historical standards:

I know that this is small consolation, if you lost money last year, but looking at annual returns on stocks in the last 90 years, there have been twenty years with more negative returns. In short, it was a bad year for stocks, but it felt far worse for three reasons. First, after nine good years for the market, investors were lulled into a false sense of complacency about the capacity of stocks to keep delivering positive returns. Second,  the negative returns were all in the last quarter of the year, making the hit seem larger (from the highs of September 2018) and more immediate. Third,  the intraday and day-to-day volatility exacerbated the fear factor, and those investors who reacted by trading faced far larger losses.

The Equity Risk Premium
If you have been a reader of this blog, you know that my favorite device for disentangling the mysteries of the market is the implied equity risk premium, an estimate of the price that investors are demanding for the risk of investing in equities. I back this number out from the current market prices and expected future cash flows, an IRR for equities that is analogous to the yield to maturity on a bond:

As with any measure of the market, it requires estimates for the future (expected cash flows and growth rates), but it is not only forward looking and dynamic (changing as the market moves), but also surprisingly robust and comprehensive in its coverage of fundamentals. 

At the start of 2018, I estimated the equity risk premium, using the index at that point in time (2673.61), the 10-year treasury bond rate on that day (2.41%) and the growth rate that analysts were projecting for earnings for the index (7.05%). 
The equity risk premium on January 1, 2018 was 5.08%. As we moved through the year, I computed the equity risk premium at the start of each month, adjusting cash flows on a quarterly basis (which is about as frequently as S&P does it) and using the index level and ten-year T.Bond rate at the start of each month:

While the conventional wisdom about equity risk premiums is the they do not change much on a day to day basis in developed markets, that has not been true since 2008. In 2018, there were two periods, the first week of February and the month of October, where volatility peaked on an intraday basis, and I computed the ERP by day, during the first week of February, and all through October:

During October, for instance, the equity risk premium moved from 5.38% at the start of the month to 5.76% by the end of the month, with wide swings during the course of the month.

After a brutal December, where stocks dropped more than 9% partly on the recognition that global economic growth may slacken faster than expected, I recomputed the equity risk premium at the start of 2019:

The equity risk premium has increased to 5.96%, but a closer look at the differences between the inputs at the start and end of the year indicates how investor perspectives have shifted over the course of the year:

Going into 2019, investors are clearly less upbeat than they were in 2018 about future growth and more worried about future crises, but companies are continuing to return cash at a pace that exceeds expectations.

What now?
I know that you are looking for a bottom line here on whether the numbers are aligned for a good or a bad year for stocks, and I will disappoint you up front by admitting that I am a terrible market timer. As an intrinsic value investor, the only market-related question that I ask is whether I find the current price of risk (the implied ERP) to be an acceptable one; if it is too low for my tastes, I would shift away from stocks, and if it is too high, shift more into them. To gain perspective, I graphed the implied ERP from 1960 through 2018 below:

At its current level of 5.96%, the equity risk premium is in the top decile of historical numbers, exceeded only by the equity risk premiums in three other years, 1979, 2009 and 2011. Viewed purely on that basis, the equity market is more under valued than over valued right now.

I am fully aware of the dangers that lurk and how they could quickly change my assessment and they can show up in one or more of the inputs:

  1. Recession and lower growth: While there was almost no talk about a possible recession either globally or in the US, at the start of 2018, some analysts, albeit a minority, are raising the possibility that the economy would slow down enough to push it into recession, at the start of 2019. While the lower earnings growth used in the 2019 computation already incorporates some of this worry, a recession would make even the lower number optimistic. In the table below, I have estimated the effect on the equity risk premium of lower growth, and  note that even with a compounded growth rate of -3% a year for the next five years, the ERP stays above the historical average of 4.19%.
  2. Higher interest rates: The fear of the Fed has roiled markets for much of the last decade, and while it has played out as higher short term interest rates for the last two years, the ten-year bond rate, after a surge over 3% in 2018, is now back to 2.68%. There is the possibility that higher inflation and economic growth rate can push this number higher, but it is difficult to see how this would happen if recession fears pan out. In fact, as I noted in this post from earlier in the year, higher interest rates, if the trigger is higher real growth (and not higher inflation), could be a positive for stocks, not a negative.
  3. Pullback on cash flows: US companies have been returning huge amounts of cash in the form of stock buybacks and dividends. In 2018, for instance, dividends and buybacks amounted to 92% of aggregate earnings, higher than the 84.60% paid out, on average, between 2009 and 2018, but still lower than the numbers in excess of 100% posted in 2015 and 2016. Assuming that the payout will adjust over time to 85.07%, reflecting expected long term growth, lowers the ERP to 5.55%, still well above historical levels.
  4. Political and Economic Crises: The trade war and the Brexit mess will play out this year and each has the potential to scare markets enough to justify the higher ERP that we are observing. In addition, it goes without saying that there will be at least a crisis or two that are not on the radar right now that will hit markets, an unwanted side effect of globalization. 

Looking at how the equity risk premium will be affected by each of these variables, I think that the market has priced in already for shocks on at least two of these variables, in the form of lower growth and political/economic crises, and can withstand fairly significant bad news on the other two. 

Bottom Line
I have long argued that it is better to be transparently wrong than opaquely right, when making investment forecasts. In keeping with my own advice, I believe that stocks are more likely to go up in 2019, than down, given the information that I have now. That said, if I am wrong, it will be because I have under estimated how much economic growth will slow in the coming year and the magnitude of economic crises. Odds are that I will see the tell tale signs too late to protect myself fully against any resulting market corrections, but that is not my game anyway. 

YouTube Video

Datasets
  1. Historical Returns on Stocks, Bonds and Bills - 1928 to 2018
  2. Historical Implied Equity Risk Premiums for US - 1960 to 2018
Spreadsheets
  1. Implied ERP for January 1, 2019
Data Updates for 2018
  1. January 2019 Data Update 1: A Reminder that Equities are risky, in case you forgot
  2. January 2019 Data Update 2: Fed Up? The Interest Rate Story
  3. January 2019 Data Update 3: A Big Picture Perspective 
  4. January 2019 Data Update 4: Currency Crisis, with a nod to the Cryptos!
  5. January 2019 Data Update 5: Country Risk - The 2019 version
  6. January 2019 Data Update 6: The Cost of Capital
  7. January 2019 Data Update 7: Corporate Investing- The Good, the Bad and the Indifferent 
  8. January 2019 Data Update 8: Debt and Taxes
  9. January 2019 Data Update 9: The Return of Cash (Dividends and Buybacks)
  10. January 2019 Data Update 10: The Pricing Game



Friday, December 7, 2018

Is there a signal in the noise? Yield Curves, Economic Growth and Stock Prices!

The title of this post is not original and draws from Nate Silver's book on why so many predictions in politics, sports and economics fail. It reflects the skepticism with which I view many 'can't fail" predictors of economic growth or stock markets, since they tend to have horrendous track records. Over the last few weeks, as markets have gyrated, market commentators have been hard pressed to explain day-to-day swings, but that has not stopped them from trying. The explanations have shifted and morphed, often in contradictory ways, but few of them have had staying power. On Tuesday (December 4), as the Dow dropped 800 points, following a 300-point up day on Monday, the experts found a new reason for the market drop, in the yield curve, with an "inverted yield curve", or at least a portion of one, predicting an imminent recession. As with all market rules of thumb, there is some basis for the rule, but there are shades of gray that can be seen only by looking at all of the data.

Yield Curves over time
The yield curve is a simple device, plotting yields across bonds with different maturities for a given issuing entity. US treasuries, historically viewed as close to default free, provide the cleanest measure of the yield curve,  and the graph below compares the US treasury yield curve at the start of every year from 2009 to 2018, i.e., the post-crisis years:
The yield curve has been upward sloping, with yields on longer term maturities higher than yields on short term maturities, every year, but it has flattened out the last two years. On December 4, 2018, the yields on treasuries of different maturities were as follows:
The market freak out is in the highlighted portion, with 5-year rates being lower (by 0.01-0.02%) than 2-year or 3-year rates, creating an inverted portion of the yield curve. 

Yield Curves and Economic Growth: Intuition
To understand yield curves, let's start with a simple economic proposition. Embedded in every treasury rate are expectations of expected inflation and expected real real interest rates, and the latter
Interest Rate = Expected Inflation Rate + Expected Real Interest Rate
Over much of the last century, the US treasury yield curve has been upward sloping, and the standard economic rationalization for it is a simple one. In a market where expectations of inflation are similar for the short term and the long term, investors will demand a "maturity premium" (or a higher real interest rate) for buying longer term bonds, thus causing the upward tilt in the yield curve.  That said, there have been periods where the yield curve slopes downwards, and to understand why this may have a link with future economic growth, let's focus on the mechanics of yield curve inversions. Almost every single yield curve inversion historically, in the US,  has come from the short end of the curve rising significantly, not a big drop in long term rates. Digging deeper, in almost every single instance of this occurring, short term rates have risen because central banks have hit the brakes on money, either in response to higher inflation or an overheated economy. You can see this in the chart below, where the Fed Funds rate (the Fed's primary mechanism for signaling tight or loose money) is graphed with the 3 month, 2 year and 10 year rates:
Interest Rate Raw Data
As you can see in this graph, the rises in short term rates that give rise to each of the inverted yield curve episodes are accompanied by increases in the Fed Funds rate. To the extent that the Fed's monetary policy action (of raising the Fed funds rate) accomplishes its objective of slowing down growth, the yield slope metric becomes a stand-in for the Fed effect on the economy, with a more positive slope associated with easier monetary policy. You may or may not find any of these hypotheses to be convincing, but the proof is in the pudding, and the graph below, excerpted from a recent Fed study, seems to indicate that there has been a Fed effect in the US economy, and that the slope of the yield curve has operated as proxy for that effect:
Federal Reserve of San Francisco
The track record of the inverted yield curve as a predictor of recessions is impressive, since it has preceded the last eight recessions, with only only one false signal in the mid-sixties. If this graph holds, and December 4 was the opening salvo in a full fledged yield curve invasion, the US economy is headed into rough waters in the next year.

Yield Curves and Economic Growth: The Data
The fact that every inversion in the last few decades has been followed by a recession will strike fear into the hearts of investors, but is it that fool proof a predictor? Perhaps, but given that the yield curve slope metrics and economic growth are continuous, not discrete, variables, a more complete assessment of the yield curve's predictive power for the economy would require that we look at the strength of the link between the slope of the yield curve (and not just whether it is inverted or not) and the level of economic growth (and not just whether it is positive or negative).

To begin this assessment, I looked at the rates on  three-month and one-year T.Bills and the two, five and ten-year treasury bonds at the end of every quarter from 1962 through the third quarter of 2018:
Following up, I look at five yield curve metrics (1 year versus 3 month, 2 year versus 3 month, 5 year versus 2 year, 10 year versus 2 year and 10 year versus 3 month), on a quarterly basis from 1962 through 2018, with an updated number for December 4, 2018. 
For the most part, the yield curve metrics move together, albeit at different rates. I picked four measures of the spread, one short term (1 year versus 3 month), one medium term (5 year versus 2 year) and two long term (10 year versus 2 year, 10 year versus 3 month) and plotted them against GDP growth in the next quarter and the year after. 
Interest Rate Raw Data
The graph does back up what the earlier Fed study showed, i.e., that negatively sloped yield curves have preceded recessions, but even a cursory glance indicates that the relationship is weak. Not only does there seem to be no relationship between how downwardly sloped the yield curve is and the depth of the recessions that follow, but in periods where the yield curve is flat or mildly positive, subsequent economic growth is unpredictable. To get a little more precision into the analysis, I computed the correlations between the different yield curve slope metrics and GDP growth:

Taking a closer look at the data, here is what I see;
  1. It is the short end that has predictive power for the economy: Over the entire time period (1962-2018), the slope of the short end of the yield curve is positively related with economic growth, with more upward sloping yield curves connected to higher economic growth in subsequent time periods. The slope at the long end of the yield curve, including the widely used differential between the 10-year and 2-year rate not only is close to uncorrelated with economic growth (the correlation is very mildly negative).
  2. Even that predictive power is muted: Over the entire time period, even for the most strongly linked metric (which is the 2 year versus 1 year), the correlation is only 29%, for GDP growth over the next year, suggesting that there is significant noise in the prediction. 
  3. And 2008 may have been a structural break: Looking only at the last ten years, the relationship seems to have reversed sign, with flatter yield curves, even at the short end, associated with higher real growth. This may be a hangover from the slow economic growth in the years after the crisis, but it does raise red flags about using this indicator today.
How do you reconcile these findings with both the conventional wisdom that inverted yield curves are negative indicators of future growth and the empirical evidence that almost every inversion is followed by a recession? It is possible that it is the moment of inversion that is significant, perhaps as a sign of the Fed's conviction, and that while the slope of the yield curve itself may not be predictive, that moment that the yield curve inverts remains a strong indicator. 

Yield Curves and Stock Returns
As investors, your focus is often less on the economy, and more on stock prices. After all, strong economies don't always deliver superior stock returns, and weak ones can often be accompanied by strong market performance. From that perspective, the question becomes what the slope of the yield curve and inverted yield curves tell you about future stock returns,  not economic growth. I begin the analysis by looking at yield curve metrics over time, graphed against return on US stocks in the next quarter and the next year:
If you see a pattern here, you are a much better chart reader than I am. I therefore followed up the analysis by replicating the correlation table that I reported in the economic growth section, but looking at stock returns in subsequent periods, rather than real GDP growth:
As with the economic growth numbers, if there is any predictive power in the yield curve slope, it is at the short end of the curve and not the long end. And as with the growth numbers, the post-2008 time period is a clear break from the overall numbers.

What does all of this mean for investors today? I think that we may be making two mistakes. One is to take a blip on a day (the inversion in the 2 and 5 year bonds on December 4) and read too much into it, as we are apt to do when we are confused or scared. It is true that a portion of the yield curve inverted, but if history is any guide, its predictive power for the economy is weak and for the market, even weaker. The other is that we are taking rules of thumb developed in the US in the last century and assuming that they still work in a  vastly different economic environment. 

Bottom Line
There is information in the slope of the US treasury yield curve, but I think that we need to use it with caution. In my view, the flattening of the yield curve in the last two years has been more good news than bad, an indication that we are coming out of the low growth mindset of the post-2008 crisis years. However, I also think that the stalling of the US 10-year treasury bond rate at 3% or less is sobering, a warning that investors are scaling back growth expectations for both the global and US economies, going into 2019. The key tests for stocks lie in whether they can not only sustain earnings growth, in the face of slower economic growth and without the tailwind of a tax cut (like they did last year), but also in whether they can continue to return cash at the rates that they have for the last few years.

YouTube Video


Data

  1. Raw data on US treasury rates, GDP growth and Stock Returns


Monday, December 3, 2018

Investing Whiplash: Looking for Closure with Apple and Amazon!

In September, I took a look, in a series of posts, at two companies that had crested the trillion dollar market cap mark, Apple and Amazon, and concluded that series with a post where I argued that both companies were over valued. I also mentioned that I was selling short on both stocks, Amazon for the first time in 22 years of tracking the company, and Apple at a limit price of $230. Two months later, both stocks have taken serious hits in the market, down almost 25% apiece, and one of my short sales has been covered and the other is still looking profitable. It is always nice to have happy endings to my investment stories, but rather than use this as vindication of my valuation or timing skills, I will argue that I just got lucky in terms of timing. That said, given how much these stocks have dropped over the last two months, it is an opportunity to not just revisit my valuations and investment judgments, but also to draw some general lessons about intrinsic valuation and pricing.

My September Valuations: A Look Back
In September, I valued Apple and Amazon and arrived at a value per share of roughly $200 for Apple and $1255 for Amazon, well below their prevailing stock prices of $220 (Apple) and $1950 (Amazon). I was also open about the fact that my valuations reflected my stories for the companies, and that my assumptions were open for debate. In fact, I estimated value distributions for both companies and noted that not only did I face more uncertainty in my Amazon valuation, but also that there was a significant probability in both companies that my assessment (that the stocks were over valued) was wrong. I summarized my results in a table that I reproduce below:
Apple Valuation & Amazon Valuation in September 2018
I did follow through on my judgments, albeit with some trepidation, selling short on Amazon at the prevailing market price (about $1950) and putting in a limit short sell at $230, which was fulfilled on October 3, as the stock opened above $230. With both stocks, I also put in open orders to cover my short sales at the 60th percentile of my value distributions, i.e., $205 at Apple and $1412 at Amazon, not expecting either to happen in the near term. (Why 60%? Read on...) Over the years, I have learned that investment stories and theses, no matter how well thought out and reasoned, don't always have happy endings, but this one did, and at a speed which I did not expect:
My Apple short sale which was initiated on October 3 was closed out on November 5 at $205, while Amazon got tantalizingly close to my trigger price for covering of $1412 (with a low of $1420 on November 20), before rebounding. 

Intrinsic Value Lessons
Every investment, whether it is a winner or a loser, carries investment lessons, and here are mine from my AAPL/AMZN experiences, at least so far:
  1. Auto pilot rules to fight behavioral minefields: If you are wondering why I put in limit orders on both my Apple short sale and my covering trades on both stocks, it is because I know my weaknesses and left to my own biases, the havoc that they can wreak on my investment actions. I have never hidden the fact that I love Apple as a company and I was worried that if I did not put in my limit short sell order at $230, and the stock rose to that level, I would find a way to justify not doing it. For the limit buys to cover my short sales, I used the 60th percentile of the value distribution, because my trigger for buying a stock is that it be at least at the 40th percentile of its value distribution and to be consistent, my trigger for selling is set at the 60th percentile. It is my version of margin of safety, with the caveat being that for stocks like Amazon, where uncertainty abounds, this rule can translate into a much bigger percentage price difference than for a stock like Apple, where there is less uncertainty. (The price difference between the 60th and 90th percentile for Apple was just over 10%, whereas the price difference between those same percentiles was 35% for Amazon, in September 2018.)
  2. Intrinsic value changes over time: Among some value investors, there is a misplaced belief that intrinsic value is a timeless constant, and that it is the market that is subject to wild swings, driven by changes in mood and momentum. That is not true, since not only do the determinants of value (cash flows, growth and risk) change over time, but so does the price of risk (default spreads, equity risk premiums) in the market. The former occurs every time a company has a financial disclosure, which is one reason that I revalue companies just after earnings reports, or a major news story (acquisition, divestiture, new CEO),  and the latter is driven by macro forces. That sounds abstract, but I can use Apple and Amazon to illustrate my point. Since my September valuations for both companies occurred after their most recent earnings reports, there have been no new financial disclosures from either company. There have been a few news stories and we can argue about their consequentiality for future cash flows and growth, but the big change has been in the market. Since September 21, the date of my valuation, equity markets have been in turmoil, with the S&P 500 dropping about 5.5% (through November 30) and the US 10-year treasury bond rate have dropped slightly from 3.07%  to 3.01%, over the same period. If you are wondering why this should affect terminal value, it is worth remembering that the price of risk (risk premium) is set by the market, and the mechanism it has for adjusting this price is the level of stock prices, with a higher equity risk premium leading to lower stock prices. In my post at the end of a turbulent October, I traced the change in equity risk premiums, by day, through October and noted that equity risk premiums at the end of the month were up about 0.38% from the start of the month and almost 0.72% higher than they were at the start of September 2018. In contrast, November saw less change in the ERP, with the ERP adjusting to 5.68% at the end of the month.
    Plugging in the higher equity risk premium and the slightly lower risk free rate into my Apple valuation, leaving the rest of my inputs unchanged, yields a value of $197 for the company, about 1.5% less than my $200 estimate on September 21. With Amazon, the effect is slightly larger, with the value per share dropping from $1255 per share to $1212, about 3.5%. Those changes may seem trivial but if the market correction had been larger and the treasury rate had changed more, the value effect would have been larger.
  3. But price changes even more: If the fact that value changes over time, even in the absence of company-specific information, makes you uncomfortable, keep in mind that the market price usually changes even more. In the case of Apple and Amazon, this is illustrated in the graph below, where I compare value to price on September 21 and November 30 for both companies:
    In just over two months, Apple's value has declined from $201 to $196, but the stock price has dropped from $220 to $179, shifting it from being overvalued by 9.54% to undervalued by 9.14%. Amazon has become less over valued over time, with the percentage over valuation dropping from  55.38% to 39.44%. I have watched Apple's value dance with its price for  much of this decade and the graph below provides the highlights:
    From my perspective, the story for Apple has remained largely the same for the last eight years, a slow-growth, cash machine that gets the bulk of its profits from one product: the iPhone. However, at regular intervals, usually around a new iPhone model, the market becomes either giddily optimistic about it becoming a growth company (and pushes up the price) or overly pessimistic about the end of the iPhone cash franchise (and pushes the price down too much). In the face of this market  bipolarity, this is my fourth round of holding Apple in the last seven years, and I have a feeling that it will not be the last one.
  4. Act with no regrets:  I did cover my short sale, by buying back Apple at $205, but the stock continued to slide, dropping below $175 early last week. I almost covered my Amazon position at $1412, but since the price dropped only as low as $1420, my limit buy was not triggered, and the stock price is back up to almost $1700. Am I regretful that I closed too early with Apple and did not close out early enough with Amazon? I am not, because if there is one thing I have learned in my years as an investor, it is that you have stay true to your investment philosophy, even if it means that you leave profits on the table sometimes, and lose money at other times. I have faith in value, and that faith requires me to act consistently. I will continue to value Amazon at regular intervals, and it is entirely possible that I missed my moment to sell, but if so, it is a price that I am willing to pay.
  5. And flexible time horizons: A contrast that is often drawn between investors and traders is that to be an investor, you need to have a long time horizon, whereas traders operate with windows measured in months, weeks, days or even hours. In fact, one widely quoted precept in value investing is that you should buy good companies and hold them forever. Buy and hold is not a bad strategy, since it minimizes transactions costs, taxes and impulsive actions, but I hope that my Apple analysis leads you to at least question its wisdom. My short sale on Apple was predicated on value, but it lasted only a month and four days, before being unwound. In fact, early last week, I bought Apple at $175, because I believe that it under valued today, giving me a serious case of investing whiplash. I am willing to wait a long time for Apple's price to adjust to value, but I am not required to do so. If the price adjusts quickly to value and then moves upwards, I have to be willing to sell, even if that is only a few weeks from today. In my version of value investing, investors have to be ready to hold for long periods, but also be willing to close out positions sooner, either because their theses have been vindicated (by the market price moving towards value) or because their theses have broken down (in which case they need to revisit their valuations).
Bottom Line
As investors, we are often quick to claim credit for our successes and equally quick to blame others for our failures, and I am no exception. While I am sorely tempted to view what has happened at Apple and Amazon as vindication of my value judgments, I know better. I got lucky in terms of timing, catching a market correction and one targeted at tech stocks, and I am inclined to believe that  is the main reason why my Apple and Amazon positions have made me money in the last two months. With Amazon, in particular, there is little that has happened in the last two months that would represent the catalysts that I saw in my initial analysis, since it was government actions and regulatory pushback that I saw as the likely triggers for a correction. With Apple, I do have a longer history and a better basis for believing that this is market bipolarity at play, with the stock price over shooting its value, after good news, and over correcting after bad news, but nothing that has happened  to the company in the last two month would explain the correction. Needless to say, I will bank my profits, even if they are entirely fortuitous, but I will not delude myself into chalking this up to my investing skills. It is better to be lucky than good!

YouTube Video


Blog Posts
  1. Apple and Amazon at a Trillion $: A Follow-up on Uncertainty and Catalysts (September 2018)
  2. An October Surprise: Making Sense of Market Mayhem (October 2018)