I am not a market prognosticator for a simple reason. I am just not good at it, and the first six months of 2023 illustrate why market timing is often the impossible dream, something that every investor aspires to be successful at, but very few succeed on a consistent basis. At the start of the year, the consensus of market experts was that this would be a difficult year for markets, given the macro worries about inflation and an impending recession, and adding in the fear of the Fed raising rates to this mix made bullishness a rare commodity on Wall Street. Markets, as is their wont, live to surprise, and the first six months of 2023 has wrong-footed the experts (again).
The Start of the Year Blues: Leading into 2023
As we enjoy the moment, with markets buoyant and economists assuring us that the worst is behind us, both in terms of inflation and the economy, it is worth recalling what the conventional wisdom was, coming into 2023. After a bruising year for every asset class, with the riskiest segments in each asset class being damaged the most, there were fears that inflation would not just stay high, but go higher, and that the economy would go into a tailspin. While this may seem perverse, the first step in understanding and assessing where we are in markets now is to go back and examine where things stood then.
In my second data update post from the start of this year, I looked at US equities in 2022, with the S&P 500 down almost 20% during the year and the NASDAQ, overweighted in technology, feeling even more pain, down about a third, during the year.
Looking across company groupings, returns on stocks in 2022 flipped the script on the market performance over much of the prior decade, with the winners from that decade (tech, young companies, growth companies) singled out for the worst punishment during the year.
While stocks had a bad year (the eighth worst in the last century), the bond market had an even worse one. In my third post at the start of 2023, I looked at US treasuries, the long-touted haven of safety for investors. In 2022, they were in the eye on the storm, with the ten-year US treasury bond depreciating in price by more than 19% during the year, the worst year for US treasury returns in a century.
The decline in bond prices was driven by surging interest rates, with short term treasuries rising far more than longer term treasuries, and the yield curve inverted towards the end of the year.
The rise in US treasury rates spilled over into the corporate bond market, causing corporate bond yields to rise. Exacerbating the pain, corporate default spreads rose during the course of 2022:
While default spreads rose across ratings classes, the rise was much more pronounced for the lowest ratings classes, part of a bigger story about risk capital that spilled across markets and asset classes. After a decade of easy access, translating into low risk premiums and default spreads, accompanied by a surge in IPOs and start-ups funded by venture capital, risk capital moved to the sidelines in 2022.
In sum, investors were shell shocked at the start of 2023, and there seemed to be little reason to expect the coming year to be any different. That pessimism was not restricted to market outlooks. Inflation dominated the headlines and there was widespread consensus among economists that a recession was imminent, with the only questions being about how severe it would be and when it would start.
The Market (and Economy) Surprises: The First Half of 2023
Halfway through 2023, I think it is safe to say that markets have surprised investors and economists again, this year. The combination of high inflation and a recession that was on the bingo cards of some economists at the start of 2023 did not manifest, with inflation declining sooner than most expected during the year:
It is true that the drop in inflation was anticipated by some economists, but most of them also expected that decline to come from a rapidly slowing economy, i.e., a recession and to be Fed-driven. That has not happened either, as employment numbers have stayed strong, housing prices have (at least up till now) absorbed the blows from higher mortgage rates and the economy has continued to grow.
It is true that economic activity has leveled off and housing prices have declined a little, relative to a year ago, but given the rise in rates in 2022, those changes are mild. If anything, the economy seems to have settled into a stable pattern, albeit at the high levels that it reached in the second half of 2021. I know that the game is not done, and the long-promised pain may still arrive in the second half of the year, but for the moment, at least, markets have found some respite.
During the course of 2023, the Fed was at the center of most economic storylines hero to some and villain to many others, with every utterance from Jerome Powell and other Fed officials parsed for signals about future actions. That said, it is worth noting that there is very little of consequence in the economy or the market, in 2023, that you can attribute to Fed activity. The Fed has raised the Fed Funds rate multiple times this year, but those rate increases have clearly done nothing to slow the economy down and inflation has stabilized, not because of the Fed but in spit of it. I know that there are many who still like to believe that the Fed sets interest rates, but here is what market interest rates (in the form of US treasury rates) have done during 2023:
If there is a Fed effect on interest rates, it is almost entirely on the very short end of the spectrum, and not on longer term rates; the ten-year and thirty-year treasury bond rates have declined during the year. That does not surprise me, since I have never bought into the “Fed did it” theme, and have written multiple posts about why it is inflation and economic growth that drive interest rates, not central banks. As inflation has dropped and the economy has kept its footing, the corporate bond market has benefited from default spreads declining, as fears subside:
As in 2022, the change in default spreads is greatest at the lowest ratings, with the key difference being that spreads are declining in 2023, rather than increasing, though the spreads still remain significantly higher than they were at the start of 2022.
Stock Markets Perk Up: The First Half of 2023
I noted that risk capital retreated from markets in 2022, with negative consequences for risky asset classes. To the extent that some of that risk capital is coming back into the markets, equity markets have benefited, with benefits skewing more towards the companies and markets that were punished the most in 2022. To understand the equity comeback in 2023, I start by looking at the increase in market capitalizations, in US $ terms, across the world in the first six months of the year, with the change in market capitalizations in 2022 to provide perspective:
In US dollar terms, global equities have reclaimed $8.6 trillion in market value in the first six months in the year, but the severity of last year's decline has still left them $14.4 trillion below their values from the start of 2022. Looking across regions, US equities have performed the best in the first six months of 2023, adding almost 14% ($5.6 trillion) to market capitalizations, regaining almost half of the value lost in last year's rout. In US dollar terms, China was the worst performing region of the world, with equity values down 1.01% in the first six months on 2023, adding to the 18.7% that was lost last year. The two best performing parts of the world in 2022, Africa and India, performed moderately well in the first half of 2023. In US dollar terms, Latin America was flat in the first half of 2023, though there were a couple of Latin American markets that delivered stellar returns in local currency terms, albeit with high inflation eating away at these returns. It is currency rate changes that explains that contrast between local currency and dollar returns, and in the graph below, I look at the US dollar's performance broadly (against other currencies) as well as against emerging market currencies , between 2020 and 2023;
After strengthening in 2022, the US dollar has weakened against most currencies in 2023, albeit only mildly.
US Equities in 2023: Into the Weeds!
The bulk of the surge in global equities in 2023 has come from US stocks, but there are many investors in US stocks who are looking at their portfolio performance this year, and wondering why they don't seem to be sharing in the upside. In this section, I will start by looking with an overall assessment of US equities (levels and equity risk premiums) before delving into the details of the winners and losers this year.
Stocks and the Equity Risk Premium
I start my assessment of US equities by looking at the performance of the S&P 500 and the NASDAQ during the first half of this year:
As you can see, why the S&P has had a strong first half of 2023, increasing 15.91%, the NASDAQ has delivered almost twice that return, with its tech focus. One reason for the rise in stock prices, at least in the aggregate, has been a dampening of worries of out-of-control inflation or a deep recession, and this drop in fear can be seen in the equity risk premium, the price of risk in the equity market. In the figure below, I have graphed my estimates of expected returns on stocks and implied equity risk premiums through 2022 and the first six months of 2023:
After a year for the record books, in 2022, when the expected return on stocks (the cost of equity) increased from 5.75% to 9.82%, the largest one-year increase in that number in history, we have had not just a more subdued year in 2023, but one where the expected return has come back down to 8.81%. In the process, the implied equity risk premium, which peaked at 5.94% on January 1, 2023, is back down to 5% at the start of July 2023. Even after that drop, equity risk premiums are still at roughly the average value since 2008, and significantly higher than the average since 1960. If the essence of a bubble is that equity risk premiums become "too low", the numbers, at least for the moment, don't seem to signaling a bubble (unlike years like 1999, when the equity risk premium dropped to 2%).
Sector and Industry
The divergence between the S&P 500 and the NASDAQ's performance this year provides clues as to which sectors have benefited the most this year, as risk has receded. In the table below, I break all US equities into sectors and report on performance, in 2022 and in the first half of 2023:
As you can see, four of the twelve sectors have had negative returns in 2023, with energy stocks down more than 17% this year. The biggest winner, and this should come as no surprise, has been technology, with a return of 43% in 2023, and almost entirely recovering its losses in 2022. Financials, handicapped by the bank runs at SVB and First Republic, have been flat for the year, as has been real estate. Communication services and consumer discretionary have had a strong first half of 2023, but remain more than 20% below their levels at the star of 2022.
Breaking sectors down into industry-level details, we can identify the biggest winners and losers, among industries. In the table below, I list the ten worst performing and best performing industry groups, based purely on market capitalization change in the first half of 2023:
The worst performing industry groups are in financial services and energy, with oilfield services companies being the worst impacted. The best performing industry group is auto & truck, but those results are skewed upwards, with one big winner (Tesla) accounting for a large portion of the increase in market capitalization in the sector. There are several technology groups that are on the winner list, not just in terms of percentage increases, but also in absolute value changes, with semiconductors, computers/peripherals and software all adding more than a trillion dollars in market capitalization apiece.
Market Capitalization and Profitability
The first six months of the year have also seen concentrated gains in a larger companies and this can be seen in the table below, where I break companies down based upon their market capitalizations at the start of 2023 into deciles, and then break the stocks down in each decile into money-making and money-losing companies, based upon net income in 2022:
Again, the numbers tell a story, with the money-making companies in the largest market cap decile accounting for almost all of the gain in market cap for all US equities; the market capitalization of these large money-making companies increased by $5.3 trillion in the first six months of 2023, 97.2% of the $5.45 trillion increase in value for all US equities.
Value and Growth
Over the last decade, I have written many posts about how old-time value investing, with its focus low PE and low price to book stocks, has lagged growth investing, with high growth stocks that trade at higher multiples of earnings and book value delivering much higher returns than old-time value stocks (low PE ratios, high dividend yields etc.). In 2022, old-time value investors felt vindicated, as the damage that year was inflicted on the highest growth companies, especially in technology. That celebration has not lasted long, though, since in 2023, we saw a return to a familiar pattern from the last decade, with the highest price to book stocks earning significantly higher returns than the stocks with the lowest price to book ratios:
As you can see from the chart, almost all of the value increase in US equities has come from the top two deciles of stocks, in terms of price to book ratios. Looking at value and growth go back and forth between the winning and losing columns in 2023, I believe that this is a pattern that will continue to play out for the rest of the decade, with no decisive winner.
An Assessment
I know that one of the critiques of this market rise is that it has been uneven, but almost all market recoveries are uneven, with some groupings of companies always doing better than others. That said, there are lessons to be learned from looking at the winners and the losers in the first half of 2023 market sweepstakes:
Big tech: There is no doubt that this market has been largely elevated not just by tech companies, but by a subset of large tech companies. Seven companies (Apple, Microsoft, NVIDIA, Amazon, Tesla, Meta and Alphabet) have seen their collective market capitalization increase by $4.14 trillion in the first half of 2023, accounting for almost 80% of the overall increase in equity values at all 6669 publicly traded US equities. If these stocks level off or drop, the market will have trouble finding substitutes to keep the market pushing higher, simply because of the size of the hole that will need to be filled.
With a profitability skew: While this does seem like a reversion to the tech boom that drove markets prior to 2022, the market seems to be more inclined to rewarding money-making tech companies, at the expense of money-losers. If risk capital is coming back in 2023, it is being more selective about where it is directing its money, and it is therefore not surprising that IPOs, venture capital and high yield bond issuances have remained mired in 2022 (low) levels.
And an economic twist: One reason that these big and money-making tech companies may be seeing the return of investor money is that they have navigated the inflation storm relatively unscathed and some have emerged more disciplined, from the experience. The two best cases in point are Meta and Google, both of which have not only reduced payrolls but also seem to have shifted their narrative from a relentless pursuit of growth to one of profitability.
It is true that as market rallies lengthen, they draw in more stocks into their orbit, and it is possible that the market rally will broaden over the course of the year. That said, this has been a decade of unpredictability, starting with the first quarter of 2020, where COVID ravaged stocks, and I don't think it makes much sense to take charts from 2008 or 2001 or earlier and extrapolating from those.
The Rest of the Year: What's coming?
The market mood is buoyant, as investors seem to be convinced that we have dodged the bullet, with inflation cooling and a soft landing for the economy. The lesson that I have learned not just from the first six months of 2023, but from market performance over the last three years, has been that macro forecasting is pointless, and that trying to time markets is foolhardy. If I were to make guesses about what the rest of the year will bring, here are my thoughts:
On inflation, the good news on inflation in the first half of the year should not obscure the reality that the inflation rate, at 3% in June, still remains higher than the Fed-targeted value (of 2%). That last stretch getting inflation down from 3% to below 2% will be trench warfare, and we will be exposed to macro shocks (from energy prices or regional unrest) that can create inflationary shocks.
On the economy, notwithstanding good employment numbers, there are signs that the economy is cooling and it is again entirely possible that this turns into a slow-motion recession, as real estate (especially commercial) succumbs to higher interest rates and consumers start retrenching.
On interest rates, I do think that hoping and praying that rates will go back to 2% or lower is a pipe dream, as long as inflation stays at 3% or higher. In short, with or without the Fed, long term treasury rates have found a steady state at 3.5% to 4%, and companies and investors will have to learn to live with those rates. I have never attached much significance to the yield curve inversion as a predictor of economic growth, but that inversion is unlikely to go away soon, as near term inflation remains higher than long term expectations.
On equities, the one certainty is that there will be uncertainties, and it is unlikely that the market will repeat its success in the second half of 2023. I did value the S&P 500 at the start of the year, and and argued that it was close to fairly valued then. Updating this valuation to reflect updated perspectives on both dimensions, as well as an index price that is about 16% higher, here is what I see:
Note that I have used the analyst projections of earnings for the index for 2023 to 2025, and adjusted the cash payout over time to reflect reinvestment needed to sustain growth in the long term (set to 3.88%, after 2027). After the run up in stock prices in the first six months, stocks look fairly valued, given estimated earnings and cash flows, and assuming that long term rates have found their steady state. (Unlike market strategies who provide target levels for the index, an intrinsic value delivers a value for the index today; to get an estimate of what translates into as a target level of the index, you can apply the cost of equity as the expected return factor to get index levels in future time periods.)
It goes without saying, but I will say it anyway, that the economy may still go into a recession, analysts may be over estimating earnings and inflation may make a come back (pushing up long term rates). If you have concerns on those fronts, your investing should reflect those worries, but your returns will be only as good as your macro forecasting abilities. Mine are not that good, and it is why I steer away from grandiose statements about equities being in a bubble or a bargain. While uncertainties abound, there is one thing I am certain about. I will be wrong on almost every single one of these forecasts, and there is little that I can or want to do about that. That is why I demand an equity risk premium in the first place, and all I can do is hope that it large enough to cover those uncertainties.
A Time for Humility
If the greatest sin in investing is arrogance, markets exist to bring us back to earth and teach us humility. The first half of 2023 was a reminder that no matter who you are as an analyst, and how well thought through your investment thesis is, the market has other plans. As you listen to market gurus spin tales about markets, sometimes based upon historical data and compelling charts, it is worth remembering that forecasting where the entire market is going is, by itself, an act of hubris. In the spirit of humility, I would suggest that if you were a winner in the first half of this year, recognize that much of that can be attributed to luck, and what the market gives, it can take away. By the same token, if you were a loser over the course of the last six months, regret should not lead you to try to load up on the winners over that period. That ship has sailed, and who knows? Your loser portfolio may be well positioned to take advantage of whatever is coming in the next six months.
Starting in the early 1990s, I have spent the first week or two of every new year playing my version of Moneyball, downloading raw market and accounting data on publicly traded companies and using that data to compute operating, pricing and risk metrics for them. This year, I got a later start than usual on January 6, but as the week draws to a close, the results of my data exploration are posted on my website and will be the basis for a series of posts here over the next six weeks. As you look at the data, you will find that the choices I have made on how to classify companies and compute metrics affect my findings, and I will use this post to cast some light on those choices.
The Data
Raw Data: We live in an age when accessing raw data is easy, albeit not always cheap, and the tools to analyze that data are also widely available. My raw data is drawn from a variety of sources, ranging from S&P Capital IQ to Bloomberg to the Federal Reserve, and there are two rules that I try to follow. The first is to be careful about attributing sources for the raw data, and the second is to not undercut my raw data providers by replicating their data on my site, if they have commercial interests.
Data Analysis: Broadly speaking, I would categorize my data updates into three groups. The first is macro data, where my ambitions tend to be modest, and the only numbers that I update are numbers that I need and use in my valuation and corporate financial analysis. The second is business data, where I consolidate the company-level data into industry groupings, and report statistics on how companies invest, finance their operations and return cash (dividends and buybacks). The third are my data archives, where you can look at trend lines in the statistics by accessing my statistics from prior years.
A. Macro Data
I am not a market timer or a macro economist, and my interests in macro data are therefore limited to numbers that I cannot easily look up, or access, on a public database. Thus, there is no point in my reporting exchange rates between major currencies, when you have FRED, the Federal Reserve site , that I cannot praise more highly for its reach and its accessibility. I do report and update the following:
Risk free rates in currencies: The way in which currencies are dealt with in valuation and corporate finance leaves us exposed to multiple problems, and I have written about both why risk free rates vary across currencies and why government bond rates are not always risk free. At the start of every year, I update my currency risk free rates, starting with the government bond rates, and then netting out default spreads and report them here. As risk free rates in developed market currencies hit new lows, and central banks are blamed for the phenomenon, I also update an intrinsic measure of the US dollar risk free rate, obtained by adding the inflation rate to real GDP growth each year, and report the time series in this dataset.
Equity Risk Premiums: The equity risk premium is the price of risk in equity markets and plays a key role in both corporate finance and valuation. The conventional approach to estimating this risk premium is to look at history, and to compare the returns that you would have earned investing in stocks, as opposed to investing in risk free investments. I update the historical risk premium for US stocks, by bringing in 2019 returns on stocks, treasury bonds and treasury bills in this dataset; my updated geometric average premium for stocks over US treasuries. I don't like the approach, both because it is backward looking and because the risk premium estimates are noisy, and have argued for a forward looking or implied ERP. I estimate the implied ERP to be 5.20% at the start of 2020 and report the year-end estimates of the premium going back to 1960 in this dataset.
Corporate Default Spreads: Just as equity risk premiums measure the price of risk in equity markets, default spreads measure the price of risk in the debt markets. I break down bonds into bond rating classes (S&P and Moody's) and report my estimates of default spreads at the start of 2020 in this spreadsheet (and it includes a way of estimating a bond rating for a firm that does not have one).
Corporate Tax Rates: Ultimately, companies and investors count on after-tax income, though companies are adept at keeping taxes paid low. While I will report the effective tax rates that companies actually pay in my corporate data, I am grateful to KPMG for going through tax codes in different countries and compiling corporate tax rates, which I reproduce in this dataset.
Country Risk Premiums: As companies expand their operations beyond domestic markets, we are faced with the challenge of bringing in the risk of foreign markets into our corporate financial analyses and valuation. I have spent much of the last 25 years trying to come up with better ways of estimating risk premiums for countries, and I describe the process I use in excruciating detail in this paper. At the start of 2020, I use my approach, flaws and all, to estimate equity risk premiums for 170 countries and report them in this dataset.
With macro data, it is generally good practice in both corporate finance and valuation to bring in the numbers as they are today, rather than have a strong directional view. So, uncomfortable though it may make you, you should be using today's risk free rates and risk premiums, rather than normalized values, when valuing companies or making investment assessments.
B. Micro Data
The sample: All data analysis is biased and the bias starts with the sampling approach used to arrive at the data set. My data sample includes all publicly traded companies, listed anywhere in the world, and the only criteria that I impose is that they have a market capitalization number available as of December 31, 2019. The resulting sample of 44,394 firms includes firms from 150 countries, some of which have very illiquid markets and questionable disclosure practices. Rather than remove these firms from my sample, which creates its own biases, I will keep them in my sample and deal with the consequences when I compute my statistics.
While this is a comprehensive sample, it is still biased because it includes just publicly listed companies. There are tens of thousands of private businesses that are part of the competitive landscape that are not included here, and the reason is pragmatic: most of these companies are not required to make public disclosures and there are few reliable databases that include data on these firms.
The Industry Groupings: While I do have a (very large) spreadsheet that has the data at the company level, I am afraid that my raw data providers do not allow me to share that data, even though it is entirely comprised of numbers that I estimate. I consolidate that data into 94 industry groupings, which are loosely based on the industry groupings I created from Value Line in the 1990s when I first started creating my datasets. To see my industry grouping and what companies fall into each one, try this dataset. As you look at individual companies, there are two challenges that I face. First, there are companies that are in many businesses and I classify these companies into the industry groups from which they derive the most revenues. Second, some companies are shape shifters when it comes to industry grouping, and it is unclear which grouping they belong to; for a few high profile examples, consider Apple and Amazon. There is little that I can do about either problem, but consider yourselves forewarned.
The statistics: My interests lie in corporate finance and valuation and selfishly, I report the statistics that matter to me in that pursuit. Luckily, as I described it in my post a few weeks ago, corporate finance is the ultimate big picture class and the statistics cover the spectrum, and I think the best way to organize them is based upon broad corporate finance principles:
If you are interested, you will find more in-depth descriptions of how I compute the statistics that I report both in the datasets themselves as well as in this glossary. The timing: I use a mix of market and accounting data and that creates a timing problem, since the accounting data is updated at the end of each quarter and the market data is updated continuously. Using the logic that I should be accessing the most updated data for every item, my January 1, 2020, updated has market data (for share prices, interest rates etc) as of December 31, 2019 and the accounting data as of the most recent financial statement (usually September 30, 2019 for most companies). I don't view this an inconsistent but a reflection of the reality that investors face.
C. Archived Data
When I first started compiling my datasets, I did not expect them to be widely used, and certainly did not believe that they would be referenced over time. As I starting getting requests for datasets from earlier years, I decided that it would save both me and you a great deal of time to create an archive of past datasets. As you look at these archives, you will notice that not all datasets go back in time to the 1990s, reflecting first the expansion of my analysis from just US companies to global companies about 15 years ago and second the adding on of variables that I either did not or could not report in earlier years.
The Rationale
If you are wondering why I collect and analyze the data, let me make a confession, at the risk of sounding like a geek. I enjoy working with the data and more importantly, the data analysis is a gift that keeps on giving for the rest of the year, as I value companies and do corporate financial analysis.
It gives me perspective: In a world where we suffer from data overload, the week that I spend looking at the numbers gives me perspective not only on what comprises normal in corporate financial behavior, but also on the differences across sectors and geographies.
Possible, Plausible and Probable: I have long argued that the valuation of a company always starts with a story but that a critical part of the process of converting narrative to value is checking the story for possibility, plausibility and probability. Having the global data aggregated and analyzed can help significantly in making this assessment, since you can see the cross section of revenues and profit margins of companies in the business and see if your assessments are out of line, and if so, whether you have a justification.
Rules of thumb: In spite of all of the data that we now have available, investors and companies seem to still rely on rules of thumb devised in a different time and market. Thus, we are told that companies that trade at less than book value, or six times EBITDA, are cheap, and that the target or right debt ratio for a manufacturing company is 40%. Using the global data, we can back up or dispel these rules of thumb and perhaps replace them with more dynamic and meaningful decision rules.
Fact-based opinions: Many market prognosticators and economists seem to have no qualms about making up stuff about investor and corporate behavior and stating them as facts. Thus, it has become conventional wisdom that US companies are paying less in taxes that companies operating elsewhere in the globe, and that they have borrowed immense amounts of cash over the last decade to buy back stock. Those "facts" are now driving political debate and may well lead to change in policy, but these are more opinions than facts, and the data can be arbiter.
If you are wondering why I am sharing the data, let's get real. Nothing that I am doing is unique, and I have no secret data stashes. In short, anyone with access to data (and there are literally tens of thousands who do) can do the same analysis. I lose nothing by sharing, and I get immense karmic payoffs. So, please use whatever data you want, and in whatever context, and I hope that it saves you time and helps you in your decision making and analysis.
The Caveats
The last decade has seen big data and crowd wisdom sold as the answers to all of our problems, and as I listen to the sales pitches for both, I would offer a few cautionary notes, born out of spending much of my life time working with data:
Data is not objective: The notion that using data makes you objective is nonsense. In fact, the most egregious biases are data-backed, as people with agendas pick and choose the data that confirms their priors. Just as an example, take a look at the data that I have in what US companies paid in taxes in 2019 in this dataset. I have reported a variety of tax rates, not with the intent to confuse, but to note how the numbers change, depending on how you compute them. If you believe, like some do, that US companies are shirking their tax obligations, you can point to average tax rate of 7.32% that I report for all US companies, and note that this is well below the federal corporate tax rate of 21%. However, someone on the other side of this debate can point to the 19.01% average tax rate across only money making companies (since only profits get taxed) as evidence that companies are paying their taxes.
Crowds are not always wise: One of the strongest forces in corporate finance is me-tooism, where companies decide how to invest, how much to borrow and what to pay in dividends by looking at what their peers do. In my datasets, I offer them guidance in this process, by reporting debt ratios and dividend payout ratios for sectors, as well as regional breakdowns. The implicit assumption is that what other companies do, on average, must be sensible, but that assumption is not always true. This warning is particularly relevant when you look at the pricing metrics (PE, EV to EBITDA etc.) that I report, by sector and by region. The market may be right, on average, but it can also over price or under price a sector, at times.
I respect data, but I don't revere it. I don't believe that just having data will give me an advantage over other investors or make me a better investor, but harnessing that data with intuition and logic may give me a leg up (or at least I hope it does).
