Friday, January 12, 2018

January 2018 Data Update 3: Taxing Questions on Value

If you have read my prior posts on taxes, you already know my views on the US tax code, especially as it relates to corporate taxes. Without mincing words, the US corporate tax code, as it existed in 2017, was an abomination, a carry over from a prior century where the US was the center of the global economy and companies would do anything demanded of them, to preserve their US incorporation. I was therefore predisposed to favoring tax reform and Congress delivered its version towards the end of 2017. While the process was messy and partisan, it represents the most significant change in corporate taxation in the United States in the my lifetime, and as with all tax reform, it is a mix of the good, the bad and the ugly, with your political priors determining which one you believe dominates. No matter what you think about the tax reform package, there is the one thing that is not debatable: it will impact equity value and affect corporate behavior in the coming year. 

The 2017 Tax Reform: Key Changes
The tax reform package that passed Congress is more than a 1000 pages long and it is easy to get lost in the details. While it makes changes in individual, private business and corporate tax law, I will focus this post on the corporate tax law changes. In my view, there are four big changes embedded in this packet that deserve attention:
  1. Corporate Tax Rate: The federal corporate tax rate on the income that corporations generate ion the United States has been lowered from 35%, at the federal level, to 21%. This is the portion of the bill that attracted the most media attention, primarily because of the magnitude of the drop, bringing corporate taxes in the United States down to levels not seen in the country since the second world war.
  2. Treatment of Foreign Income: The other big change in corporate taxation that attracted less attention but my be just as consequential in the long term is that the US has now joined the rest of the world, replacing its global tax with a regional tax model. Put simply, until 2017, US companies were required to pay the US tax rate on all of their global income, though the differential tax on foreign income does not have to be paid, until repatriated to the US.  Starting in 2018, US companies will have to pay only the foreign taxes due on foreign income and will be free to bring the money back, when they want. There are two ancillary changes that the package makes to foreign income. First, it tries to clean up for past sins by imposing a one-time tax to un-trap cash that companies are holding in foreign locales. As I noted in this earlier post, the trapped cash is a predictable side effect of the global tax model, and not surprisingly, companies with global revenues have built up more than $2 trillion in foreign cash cash balances. The one-time tax rate will be 15.5% on cash invested in liquid assets and 8% on harder-to-sell assets. Second, the tax code also tries to put in disincentives for companies moving intangible assets to tax havens, by imposing a minimum tax rate of 13.1% (rising to 16.4% in 2025)  on excess profits (over and above a 10% cost of capital) earned in foreign subsidiaries. This seems to be specifically directed at technology and pharmaceutical companies that have found ways to create foreign subsidiaries for intangible assets.
  3. Limitation on Interest Deductibility: For the first time, the US tax code will put a limit on the deductibility of interest expenses, restricting it to 30% of the "adjusted taxable income" (with taxable income defined as EBITDA through 2022 and EBIT thereafter). To provide a cushion for companies that may have cyclical income, the lost (non-tax deductible) interest expense deductions can be carried forward and used in future years, with no expiration date.
  4. Capital Expensing: US companies will be allowed to deduct their investments in tangible assets in the year of the investment, for taxable income calculations, rather than have to depreciate it over time. This provision will remain intact until 2023 and be phased out by 2027.
The two best features of the tax reform package, in my view, are the changes in the taxation of foreign income and in the treatment of debt, and I will trace out the consequences for value in the next section. There are three features of the tax reform that I do not like. First, the package does little to reduce the complexity in the code, and in some cases, adds to that complexity. In particular, I don't like either the capital expensing rule change or the way in which it deals with intangible assets overseas. Second, I don't believe that tax codes are good instruments to do economic engineering and I don't think that the provisions that are in the changed code to encourage companies, especially in old-economy sectors, to reinvest more will make a significant difference. Third, by increasing the divergence in tax rates between individual income, pass-through business income and corporate income (the highest marginal federal tax rates will be 37%, 29.6% and 21% respectively), it is going to encourage tax gaming on the part of those who have a choice.

The Value Change
As I read the many assessments of how the tax reform bill will affect stock prices and values, I am reminded of the old parable of the seven blind men and the elephant, where each one after feeling a different part of the elephant's body gives a very different description of the animal. Analysts seem to be picking either one aspect of the tax code (lower tax rates, debt interest restrictions, foreign income taxation) or one dimension of value (cash flows, risk or growth) to arrive at a conclusion that reflects their political biases. Thus, I have seen supporters of the bill zero in on the drop in the tax rate from 35% to 21%, assume that this will increase after-tax income proportionately and extrapolate to a value increase of more than 20%. At the other end of the bias spectrum, there are pessimists who argue that the loss of the tax benefits from debt, from both lower tax rates and interest deductibility restrictions, will push up the after-tax cost of debt and capital for firms, and lower value. Both analyses are incomplete because they are focused on pieces of the valuation puzzle, rather than the entire valuation. The tax code, after all, affects every dimension of value, as can be seen in the picture below:
To assess the impact of tax reform on overall equity value, we have to move through each dimension of value. In making these assessments, I will focus on non-financial service firms, partly because the tax effects on debt and value are cleaner and more transparent.
  1. The Cash Flow Effect: The cash flows that a firm generates on operations are after taxes, but the relevant tax rate is not the statutory tax rate but the effective rate. It is true that the reduction of the statutory tax rate from 35% to 21%, will reduce taxes paid, but the reduction will be from the aggregated effective tax rate that companies paid in 2017, not the marginal rate. Based upon my estimates, in 2017, US non-financial service companies reported $330.8 billion in taxes on taxable income of $1,342.1 billion, translating into an effective tax rate of 25.19%. Since this tax rate includes state and local taxes and taxes on global income, these companies were paying an effective federal tax rate of closer to 23% on all of their taxable income in 2017. With the drop in the US corporate tax rate and the shift to a regional tax model, we would expect this tax rate to drop, but the magnitude of the decline is likely to be far smaller than optimists are assuming. My guess is that the effective tax rate next year will be about 20%, including state and local taxes, after the tax rate changes, resulting in an increase in after-tax operating earnings of approximately 6.67% [(1-.20)/(1-.2519)] in the next year. 
  2. The Cost of Capital Effect: The cost of capital is a weighted average of the cost of equity and the after-tax cost of debt. In computing the after-tax cost of debt, the tax rate that matters is the marginal tax rate on US income, since even companies that have low effective tax rates, like Apple, have found it in their best interests to borrow money in the US and set off interest expenses against their highest-taxed income. The marginal tax rate for a US company in 2017 was close to 38%, with state and local taxes added to the US federal tax rate of 35%. Moving that tax rate down to 24% (my estimate of the marginal corporate tax rate, with state and local taxes, in 2018) will increase the after-tax cost of debt. In 2017, US non-financial service firms collectively reported a debt to capital ratio, in market value terms, of 23.5% and faced a cost of equity of 7.85% and a pre-tax cost of debt of 3.91%. With a 38% marginal tax rate, that would have resulted in an after-tax cost of debt of 2.42% and a cost of capital of 6.57%. Keeping the pre-tax cost of debt and debt ratio fixed, and reducing the marginal tax rate to 24% will increase the cost of capital to 6.70%. 
  3. The Growth Effect: The growth effect is the trickiest one to assess, since the value of growth is a function of both how much companies reinvest but also how well they reinvest, measured as the return they earn on investments over and above their cost of capital. We do know that the incentive to reinvest has increased, especially at companies with physical and depreciable assets, because of the capital expensing provision and we also know that excess returns will change, as after-tax earnings and the cost of capital go up. In 2017, non-financial service companies in the US collectively reinvested 59.27% of their after-tax operating income back into their businesses and earned a return of 12.76% on their capital employed; the sustainable growth rate, if those numbers are maintained, is 7.56%. Increasing the return on capital to reflect the growth in after-tax earnings yields 13.65%, and assuming that reinvestment increases marginally to 65% of the after-tax earnings, because of the capital expensing rule change, yields an expected growth rate of 8.87%.
With these inputs in place, we can value US companies collectively, pre and post tax reform,  and the effect on firm value is captured in the table below:
Download spreadsheet
In making my estimates, I have assumed that the revenues and Note that this is the estimated increase in firm value, but equity value will rise proportionately, if the debt ratio remains unchanged. Does this mean that stock prices will rise 9.70% over the next year? No, and here is why. This tax reform package has been floating around for almost a year now and investors have had a chance to not only read it but incorporate its effects into prices. While the final package contained some surprises, the final version of the bill preserved the key ingredients that we introduced in April 2017. The strong returns posted by US stocks last year already include some of the value effects of the tax law. Note that this does not mean that the effects of the new tax code have already worked their way into prices since we still do not know how companies or the US economy will respond to the changes. This analysis is static, insofar as it does not allow for the changes in investing, financing and dividend behavior that we will see, as a consequence of the tax change. For instance, firms may decrease how much they borrow, since the tax benefit to debt has decreased, and that will lower debt ratios and change the cost of capital further.

Value Redistribution
While much of the discussion about the tax reform has been about its impact on the overall economy and equity values, the bigger effect of the changes to the code will be redistributive, with some sectors gaining and other losing. To identify the winners and the losers across sectors, we can use the same framework that we used to assess the value change and isolate the value effect on a sector to three variables:

VariableEffect on ValueProxy
Effective tax rateCompanies that are currently paying high effective tax rates (>23%) will benefit the most from the tax reform. Companies that are paying low effective tax rates under existing law may pay higher taxes, if their tax deductions /credit have been removed or restricted.Effective Tax Rate
Reinvestment in fixed assetsCompanies that invest large amounts in tangible assets (that are capitalized under existing law) will benefit the most from the capital expensing provision. Companies that invest in R&D or intangible assets, which are already expensed, will benefit less.Capital Expenditures as % of Sales
Debt RatioCompanies that have high debt ratios will see bigger increases in costs of capital, and value decreases, as the tax benefits from debt are reduced. Companies with little or no debt will see little change in the cost of capital.Debt/ (Debt + Equity), in market value terms
Put simply, companies (sectors) that are currently paying high effective tax rates, invest large amounts in tangible (depreciable) assets and have little or no debt will benefit the most from the tax code changes. Companies  (sectors) that are currently paying low effective tax rates, invest little or nothing in tangible (depreciable) assets and have high debt will be hurt the most by the tax code changes. To identify the sectors that will benefit the most or will be hurt the most by the tax reforms, I looked at effective tax rates, capital expenditures/sales and debt ratios across all non-financial service sectors in 2017 and used the market aggregate value as the comparison to identify which side of the divide (higher or lower than the market aggregate) each sector fell. The full list is at the link at the end of this post, but the sectors that delivered the benefit trifecta (high effective tax rate, high cap ex as a percent of sales and low debt ratio) and cost trifecta are listed below:
Download full sector spreadsheet
All the caveats apply, insofar as we are using effective tax rates and capital expenditures for one year (2017) to make the comparisons. There is one sector, real investment trusts (REITs) that showed up the loser trifecta but it's special tax treatment (where its income is not taxed, but passed through) led to its removal from the lists. Again, this should not be taken as an indication that the market will look favorably on the benefited sectors and punish the hurt sectors, since market prices have had time to adjust to the expected tax code changes. In a later post on how the pricing varies across the sectors, we will revisit this question.

It would be hubris to argue that we know what will happen over the next year, as a result of the tax code, but we know what we should be watching out for:
  1. Taxable income and tax rates:  Facing a more benign domestic tax environment, will companies be more expansive in their measurement of taxable income?  How much of this income will they pay out in effective taxes? 
  2. Capital Expenditures in tangible asset sectors: The capital expensing provision should make investing in depreciable assets more attractive, but will that be sufficient to induce companies to reinvest more? If so, how much?
  3. The Untrapping of Cash: How much of the trapped cash will companies bring back home, paying the one-time tax penalty? Will they reinvest this cash or return it (in the form of dividends and buybacks)?
  4. The Debt Shift: Will highly levered businesses react to the reduction in tax benefits from debt by retiring debt? What effects will a system-wide delevering have on bond default spreads?
On top of these company-level concerns are questions about how the economy will react to the tax changes, how much of the benefit will be redirected to employees and what effect there will be on interest rates. It is going to be an interesting year!

YouTube Video

Data/Spreadsheet Links
Data Update Posts
  1. January 2018 Data Update 1: Numbers don't lie, or do they?
  2. January 2018 Data Update 2: The Buoyancy of US Equities!
  3. January 2018 Data Update 3: Taxing Questions on Value
  4. January 2018 Data Update 4: The Currency Question
  5. January 2018 Data Update 5: Country Risk 
  6. January 2018 Data Update 6: Cost of Capital - A Global Update
  7. January 2018 Data Update 7: Growth and Value - Investment Returns
  8. January 2018 Data Update 8: Debt and Value
  9. January 2018 Data Update 9: The Cash Harvest - Dividend Policy
  10. January 2018 Data Update 10: The Pricing Prerogative

Tuesday, January 9, 2018

January 2017 Data Update 2: The Buoyancy of US Equities

If you were an investor in US stocks, 2017 was a very good year for you. Faced with a wall of macro economic and political worries, the US equity market proved more than up to the challenge and delivered good returns, proving the experts wrong again. Looking back at the year, the word that I used to describe US equities at the start of last year, which was "resilient", best described US stocks in 2017 as well. As we enter 2018 with US stocks at historical highs, worries remain, but stocks are on a healthier footing now, than a year ago, in terms of fundamentals. At the same time, the long promised surge in T.Bond rates that the Fed watchers promised us would happen in 2017 was nowhere to be seen, which raises interesting questions about whether we should waste our time listening to either stock market prognosticators and Fed watchers. But then again, without them, how would CNBC fill all its time?

The Year that Was
The best way that I can think of mapping out the year is to look at how stocks and bonds performed on a month by month basis through the entire year. In the table below, I look at returns on the S&P 500 and on bonds, through the year:

Start of monthS&P 500Price Appreciation in MonthT.Bond RateMonthly return
Dividend Yield2.22%-
Total Return21.65%2.80%
The return on the S&P 500 for the year was 21.65%, with price appreciation accounting for 19.43% in returns and dividend yield representing the remaining 2.22%. In fact, the S&P 500 increased in ten of twelve months, with August representing the only significant down month; stocks were barely down in April. The T.Bond rate stayed within a tight bound for much of the year, with rates dropping to 2.12% at the start of September, from 2.45% at the start of the year, before rebounding to end the year little changed at 2.41%. Given that rates changed so little over the course of the year, the return on a 10-year T.Bond, with coupon and price change included, was 2.80%. 

Putting 2017 in perspective, adding the 2017 returns for stocks, T.Bonds and T.Bills to the historical data yields the following historical annual average returns for the three asset classes:
Download historical returns spreadsheet
For devotees of mean reversion (and I am not one), this table becomes the basis for estimating equity risk premiums, with the geometric average returns pointing to an equity risk premium of 4.77% over the 10-year T.Bond rate, i.e., the difference between the geometric average return on stocks (9.65%) and the geometric average return on bonds (4.88%).

When stocks have as good a year as they did in 2017, you would normally expect the fundamentals to weaken, at least relative to prices, but stocks ended the year in a healthier state than at the start. That can be seen by comparing the earnings, dividends and cash returned in 2017, by the S&P 500 companies, relative to 2016:

20162017% Change for year10-Year Average
Dividends + Buybacks108.02109.891.73%82.28
Payout Ratio43.01%39.80%42.05%
Cash Return Ratio101.66%87.95%89.35%
Note that earnings almost kept track with stock prices for the year, but the change is in the cash returned, where you saw a leveling off in the buyback boom. While that would normally be a negative for stocks, the draw back in buybacks left stocks looking healthier by reducing the cash returned as a percent of earnings from an unsustainable 101.66% in 2016 to 87.95% in 2017. 

To evaluate whether the T.Bond rate is at a level that can be justified by fundamentals, I fall back on an approach that I have used before, where I compare the T.Bond rate to an intrinsic risk free rate that I compute by adding the inflation rate for the year to real growth rate in the economy (GDP real growth rate). While those numbers are still not final for 2017, using the most recent values for both allows for an update of my intrinsic interest rate chart:
Download spreadsheet with data
The intrinsic risk free rate, using the estimated numbers as of January 1, 2018, is 4.50%, 2.09% higher than the US treasury bond rate of 2.41%, suggesting that there will be upward pressure on the US treasury bond rate over the next year.

Looking Forward
While it is tempting to continue to dissect last year's numbers, it is healthier to turn our attention to the future. It is why I have increasingly moved away from using historical risk premiums, like the 4.77% premium that I computed by looking at the 1928-2017 return table, and towards implied equity risk premiums, where I back out what investors are demanding as a premium for investing in stocks by looking at how much they pay for stocks and what they expect to generate as cash flows. (Think of it as an IRR for stocks, analogous to the yield to maturity on a bond). At the start of 2018, putting this approach into play, I estimated an equity risk premium of 5.08% for the S&P 500:
Download spreadsheet
It is instructive to look at how the inputs have changed since the start of 2017, when my estimate of the implied ERP was 5.69%. The S&P 500 has risen 19.43%, while cash returned has remained stable; the drop in buybacks has been offset by an increase in dividends. Analysts have become more optimistic about future earnings growth, partly because US companies had a healthy earnings year and partly because of the expected drop in corporate tax rates.  It is true that there are judgment calls that I had to make in estimating the implied premium, including using the analyst estimates of earnings growth for the S&P 500 (7.05%), but the resulting error pales in comparison to the standard error in the historical risk premium estimate. 

While I take this implied equity risk premium as a market price for risk, and will use it in my individual company valuations in January 2018, there are some who like playing the market timing game. If you are so inclined, the question that you are asking is whether 5.08% is a high, low or reasonable number. If you believe that the current implied premium is too low (high), you also have to believe that stocks are over priced (under priced), and to help you make that judgment, I have graphed the implied equity risk premium for the S&P 500 from 1960 to 2017 in the graph below:
Historical Implied ERP spreadsheet
There is a reason why those who are intent on claiming that the market is in a bubble have a tough sell. Unlike the end of 1999, when implied equity risk premiums were at historical lows (close to 2%), the current implied ERP is well within the bounds of historic norms. It is only if you read this graph, in conjunction with the earlier one on risk free rates, that you should be concerned, since one reason that the premium is at 5.08% is because the US treasury bond rate is 2.41%. If the T.Bond rate moves towards 4.50%, and nothing else changes, the implied ERP will drop below comfort levels. 

Worried about Equities? 
There has never been a time in the last three decades where I have felt sanguine about equity markets and I am thankful for that, since that is a sure sign of denial about the risk that is always under the surface, with stocks. That said, my worries shift from year to year and in this new year, I will continue to watch how the changing tax code will play out in both earnings and cash flows, since both are likely to be significantly affected, the former, because a lower tax rate should raise after-tax earnings, and the latter, because of the release of hundreds of billions of trapped cash. My macro crystal ball is always hazy but I expect T. Bond rates to rise, but if those higher rates go with a more robust economy, the market will take it in stride. There is the very real possibility that the economy stumbles, while rates rise, in which case US equities will be hard pressed to repeat their 2017 performance next year.

YouTube Video

Data Links
  1. Historical Returns on Stocks, Bonds and Bills: 1928-2017
  2. T.Bond and Intrinsic Interest Rates: 1960-2017
  3. Implied Equity Risk Premium, S&P 500 (Jan 1, 2018)
  4. Historical Implied Equity Risk Premiums, 1960-2017
Data Update Posts
  1. January 2018 Data Update 1: Numbers don't lie, or do they?
  2. January 2018 Data Update 2: The Buoyancy of US Equities!
  3. January 2018 Data Update 3: Taxing Questions on Value
  4. January 2018 Data Update 4: The Currency Question
  5. January 2018 Data Update 5: Country Risk 
  6. January 2018 Data Update 6: Cost of Capital - A Global Update
  7. January 2018 Data Update 7: Growth and Value - Investment Returns
  8. January 2018 Data Update 8: Debt and Value
  9. January 2018 Data Update 9: The Cash Harvest - Dividend Policy
  10. January 2018 Data Update 10: The Pricing Prerogative

Friday, January 5, 2018

January 2018 Data Update 1: Numbers don't lie, or do they?

Every year, since 1992, I have spent the first week of my year, paying homage to the numbers gods. I collect raw accounting and market data from a variety of raw data providers, and I am grateful to all of them for making my life easier, and I summarize the data on many dimensions, by geography, by industry and by market capitalization. That summarized data, for the start of 2018, can be found on my website, as can the archived data from prior years

The What?
My dataset includes every publicly traded firm that has a market price available for it, in my raw dataset, and at the start of 2018, it included 43,848 firms, up from the 42,678 firms at the start of 2017. To the question of why I don't restrict myself to just the biggest, the most liquid or the most heavily followed firms, my answer is a statistical one. Any decision that I make on screening the data or sampling will create biases that will color my results, and while I will not claim to be bias-free (no one is), I would prefer to not initiate it with my sampling.

There are 135 countries that are represented in the data, though many have only a handful of firms that are incorporated there. That said, it is worth noting that while the companies are classified by country of incorporation, many have operations in multiple countries. I have classified my firms into five "big" groups: the United States, Europe (EU, UK), Emerging Markets, Japan and Australia/Canada/New Zealand. The pie chart below provides the breakdown:
Download spreadsheet
Since the emerging market grouping includes firms from Asia, Latin America, Africa and Eurasia, I also have the data for sub-groups including India, China, Small Asia (other than India, China and Japan), Latin America, Africa & MidEast and Russia/Eurasia. That is pictured in the second pie chart above.

Within each geographic group, I break the companies down into 94 industry groupings and the numbers in each grouping are summarized at this link. While some would prefer a finer breakdown, I prefer this coarser grouping because it allows for larger sample sizes, especially as I go to sub-groups. Finally, I compute a range of numbers for each grouping, reflecting my corporate finance biases, and classify them into risk, profitability, leverage and cash return measures in the table below:

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
The links in the table will lead you to the html versions of the US data, but you can find the excel versions of this data and for the other groupings on my webpage. Since I report more than 150 data items, you may have to work to find what you are looking for but it (or a close variant) should be available somewhere on the site. Since there can be variations on how metrics are computed (like EV/EBITDA or even PE), I summarize my definitions at this link.

The Why?
Much as I would like to claim that my data sharing is driven by altruism and making the world a better place, the reasons are more prosaic. I do this for myself. I enjoy analyzing the data for many reasons:
  1. Perspective: As our access to data increases, partly because of increased information disclosure on the part of firms, and partly because technology has made it easier to download data, it is ironic that we are more likely to develop tunnel vision now than before we had access to this data. When valuing individual companies, I find that knowing the industry and geographic averages gives me perspective on the numbers that I use for the company. Thus, when valuing Indofoods, an Indonesian food processing company, I can look at typical profit margins for food processing companies in South East Asia, in making my estimates for inputs, and compare my valuation to the pricing of other South East Asian food companies, when I am done.
  2. Rules of Thumb: Investing is full of rules of thumb that we devised in a different time for a different market, but still are used by investors, often without question. The notion that a stock that trades at a PEG ratio less than one or at a price less than its book value is cheap is deeply engrained in value investing books, but is it true? Looking at the cross sectional distributions of PEG and Price to Book ratios across all companies should give us the answer and allow us to eliminate the rules of thumb that no longer work.
  3. Curiosity: There are questions that all of us have about companies that the numbers can help answer. Do US companies pay less in taxes than their foreign counterparts? Does growth create or destroy value at companies? The answers to these questions are in the numbers and I find that they provide an antidote to experts who try to pass off opinions as facts.
  4. Trends and Shifts: Companies change over time, albeit slowly, and these changes have consequences not just for investors, but for governments, taxpayers and workers. One reason that I do not make jarring changes in the way that I classify and report my numbers is to see how these numbers change over time.
In the next two weeks, I will try to summarize what I learn from the data about corporate investment, financing and dividend policy in a series of posts that I have tentatively listed at the end of this post, starting with an update on US equities (and risk premiums) and ending with the a look at market pricing multiples at the end of 2017. Along the way, I will grapple with the rise of crypto currencies and what they might or might not mean for valuation. The motivations for creating these datasets are selfish but I find it pointless to keep them to myself. After all, there is no secret sauce in this data that will lead me to riches, and nothing that someone else with access to the raw data could not generate themselves. If, in the process, a few people are able to use my data in their analyses, I consider them deposits in my "good karma" bank.

The Quirks
Each year that I update the data, there are four challenges that await me. The first relates to data timing, where I try to put myself in the shoes of an investor making investment choices on January 2, 2018. The second is how best to deal with missing data, par for the course since my dataset includes some very small companies in under developed markets. The third is to clean up after the accountants, who are not always consistent in their rules across sectors and geographies. The fourth and final challenge is to find and correct mistakes in the data.
  1. Timing: All of the data that I have used in my analysis was collected after the close of trading on the last trading day of 2017 (December 29 for most markets) and reflects the most updated data, as of that day. That said, it is worth noting that not all data gets updated at the same rate, with market-set numbers (risk free rate, stock prices, risk premiums) being as of close of trading at the end of the year, but accounting numbers reflecting the most recent financial reports (from October, November and December of 2017). The accounting numbers that I use to compute my financial and pricing ratios are therefore trailing 12-month numbers, if they are updated every quarter, or even 2016 numbers, if they are not updated. 
  2. Missing Data: Information disclosure requirements vary widely across markets and since my dataset spans all markets, there are some items that are available in some markets and not in others. Rather than eliminate companies with missing data, which will both decimate and bias my sample, I keep them in the sample and deal with them the best that I can.  For instance, US companies report stock based compensation as an expense item but many non-US companies do not. I report stock based compensation as a percent of total revenues in every market but they are close to reality only in the US data.
  3. Accounting inconsistencies: I have argued in prior posts that accountants are inconsistent in their treatment of capital expenditures and debt across companies, treating the biggest capital expenditures (R&D) at technology and pharmaceutical companies as operating expenses and ignoring the primary debt (leases) at retail and restaurant companies. Rather than wait for accounting rules to come to their senses, which may take decades, I have capitalized both R&D and lease commitments for all companies and that has consequences for my earnings, invested capital and debt numbers.
  4. Data mistakes: Working with a spreadsheet with 43,848 companies and 150 data items, I am sure that there are mistakes that have found their way into my summaries, notwithstanding my attempts to catch them. Some of these mistakes are mine but some reflect errors in the raw data. The datasets that are least likely to be affected by mistakes are the US and Global dataset, where I have a combination of the law of large numbers and good disclosure backing me up. Needless to say, if you do find mistakes, please draw my attention to them.
The Caveats
If you find my data useful in your investing, valuation or corporate finance analysis, you are welcome to partake of it. That said, as a number cruncher who both loves numbers and views them with caution, here are a few things to keep in mind.
  1. Numbers ≠ Facts: While the numbers, once reported, look precise, they are not facts. Thus, when you look at the debt ratios that I report for a sector, it is worth emphasizing that I have capitalized lease commitments and added them to all interest bearing debt (short and long term) to arrive at total debt, yielding a different number than what you may see on a different service. I have tried to be as transparent as I can in making my estimates but they reflect my judgment calls. 
  2. Past is not always prologue: There are some numbers where I report historical trend lines and averages. That is not because I am a die-hard believer in mean reversion,  the  driving force in many investment philosophies. I believe that knowing history is useful in investing, but trusting it to repeat itself is dangerous.
  3. Just because everyone does it does not make it right: As you look at the datasets, you will see patterns in investment, financing and dividend policy in sectors. Some sectors, such as telecommunications, are more debt funded than others, say pharmaceuticals, and other pay more dividends (utilities) than others (technology). While there are often good reasons for these differences, there are also bad ones, with inertial on top of that list. The reality is that there are established corporate finance policies in many sectors that no longer make sense, because the sectors have changed fundamentally over time.
As you browse through the numbers, you will notice that I report almost no numbers at the company level. While I do have that data, I am constrained from sharing that data, because I risk stepping on the toes and the legal rights of my raw data providers. 

At the end of my data week, I am both exhilarated and exhausted, exhilarated because I can now analyze the data and exhausted because even a number cruncher can get tired of working with numbers. There is information in this data but it will take more care than I have given it so far, but I have the rest of the year to spend looking for those nuggets. 

YouTube Video

    1. January 2018 Data Update 1: Numbers don't lie, or do they?
    2. January 2018 Data Update 2: The Buoyancy of US Equities!
    3. January 2018 Data Update 3: A New Tax Code - Value Consequences? 
    4. January 2018 Data Update 4: The Currency Question
    5. January 2018 Data Update 5: Country Risk 
    6. January 2018 Data Update 6: Cost of Capital - A Global Update
    7. January 2018 Data Update 7: Growth and Value - Investment Returns
    8. January 2018 Data Update 8: Debt and Value
    9. January 2018 Data Update 9: The Cash Harvest - Dividend Policy
    10. January 2018 Data Update 10: The Pricing Prerogative