Latepost Numbers Time! Information Update For 2014

In 1992, I had only finished a spreadsheet that contained the average PE ratios for companies inwards dissimilar sectors inwards the United States. There was piddling of heart inwards it, but I decided that since I had it, I mightiness every bit good portion it. I posted that spreadsheet for students inwards my degree to download as well as made it available to others who visited my website (more hopeful thinking than an actual plan, since at that topographic point were relatively few people looking for information online). Each twelvemonth since, I receive got added to the information collection, initially expanding my listing of information items for US companies, as well as inwards the final decade, adding to the collection past times looking at non-US companies. It is my outset chore each twelvemonth as well as it takes upwards the outset calendar week of the year, as well as I only uploaded the information today for the 2014 update. 

I never imagined that my initial foray into information sharing that started amongst i spreadsheet of a unmarried statistic would expand to embrace 285 spreadsheets inwards 2014, amongst to a greater extent than than a chiliad information items as well as that my universe of stocks would include 40,906 listed companies inwards 131 markets.

While y'all tin notice them all past times going to the data department on my website, I won’t bore y'all amongst the details inwards this post, but focus instead on the what, why as well as what side past times side of data.

The “what”: It starts amongst raw data!

In the final 3 decades, nosotros receive got witnessed a revolution inwards information access that nosotros require to stride dorsum to appreciate. In the 1980s, unless y'all worked at a academy or an investment bank, your access to information was non only express but often non-existent. I recollect trekking to the library (yes, the house amongst existent books as well as reference stacks as well as the Dewey decimal system) to review Value Line summary sheets for private companies as well as the manufacture averages that S&P published at the start of every year. I had access to Compustat through the academy but it was accounting-focused (with real few marketplace numbers) as well as dated. 

The outset glimmers of the information revolution were inwards the 1990s as well as for me, it began amongst Value Line offering an electronic version of the data, delivered on a CD every calendar month past times mail. That was the ground for my outset information updates as well as Value Line information remains my base of operations for US data, to a greater extent than because of my familiarity amongst it as well as its history than whatever especial characteristics. In fact, at that topographic point are databases that receive got richer detail, non only inwards terms of having to a greater extent than information items for US companies, but inwards bringing inwards listings inwards other markets. My determination to expand my information updates from US to global companies was triggered past times my access to Bloomberg terminals that were installed at the Stern School of Business most a decade ago. About v years ago, I started tapping into Capital IQ, an S&P product, that is i of the to a greater extent than comprehensive databases for global companies today. In add-on to accounting data, it includes marketplace information as well as corporate governance information on private companies as well as an tardily interface for screening as well as downloading data.

My focus inwards information analysis is to consolidate the information into a shape where it non solely less overwhelming but also to a greater extent than usable inwards valuation as well as corporate finance endeavors. To that effect, I compute averages on primal statistics (profitability measures, adventure measures as well as fiscal leverage measures) across industries as well as geographical groupings. I also role the raw information to seat my spin on corporate finance measures (cost of capital, excess returns) for private companies.

The why: It is purely self-interest!

While I am gratified that at that topographic point are roughly out at that topographic point who role my information inwards their analyses, I desire to hold upwards clear that at that topographic point is real piddling that is altruistic most my efforts. So, inwards instance y'all are curious, hither are the reasons why I intend that the calendar week that I pass at the start of each twelvemonth is good spent.

1. Anchor Angst: Behavioral economists, starting amongst Kahnemann as well as Tversky, receive got noted that investors as well as analysts expect for anchors, starting points for making judgments, when making decision. They also noted that these anchors are often either skewed (by an investor's ain experiences as well as history) or based on fiction, leading to bad decisions. So, what is a depression PE ratio inwards today’s marketplace or a high revenue multiple? Rather than brand those judgments based on bad information, I notice it useful to expect at the information each twelvemonth as well as allow it inform my assessments. It is this theme that I used for my update final year, where I used i of my favorite books/movies, Moneyball, to illustrate the powerfulness of data.
2. It is a fourth dimension saver: This may seem similar an strange claim to make, afterward I receive got spent a calendar week collecting as well as processing the data, but I am convinced that the cyberspace number of my efforts during the final calendar week volition hold upwards a fourth dimension saving over the course of didactics of the year.  As roughly of y'all are aware, I non solely learn a valuation degree but I also value companies frequently, both inwards the context of the degree as well as to satisfy my curiosity. While the starting information for my valuations comes from the company’s fiscal statements, the primal inputs inwards valuation are often industry-wide adventure as well as profitability measures. The manufacture averages that I computed this calendar week volition often hold upwards the numbers that I provide to over as well as over again, during the course of didactics of this year.
3. Go global: It is tardily to beak “global” but it remains truthful that nosotros are most comfortable amongst staying “local”. This is non solely truthful for investors, who transcend along to receive got a habitation bias inwards investing (over investing inwards their domestic markets) but it also applies to businesses as well as academics. In fact, much of finance research, spell paying lip service to the global market, continues to receive got a US focus. One argue that I receive got extended as well as deepened my analysis of global companies over fourth dimension is to fill upwards inwards the empty spots inwards my noesis on listed companies inwards many of the smaller markets. It is telling that 80% of the fourth dimension that I spent inwards the final calendar week was on non-US data, a meaning bound from the cursory efforts I made a decade agone when I started reporting global numbers.

The what next: Caveat emptor!

If y'all exercise create upwards one's heed to download as well as role whatever of the information on my website as well as role it, hither are a few things that I promise that y'all volition maintain inwards mind:

1. Data tin hold upwards subjective: Contrary to the widely held sentiment that numbers are objective, the statistics that y'all volition encounter inwards my datasets reverberate my judgments as well as points of view, roughly of which y'all may concur with, but roughly that y'all may disagree with, perchance vehemently. Thus, my estimates of equity adventure premiums for private countries are largely based upon sovereign ratings as well as CDS spreads, both bond marketplace measures of default risk. Similarly, my estimates of costs of working capital missive of the alphabet for private companies are built on my estimates of relative adventure (beta) for these companies, which are inwards plow estimated from the sectors that they operate inwards as well as their policies on debt.
2.  Bludgeon, non scalpel: One of the primal differences betwixt analyzing i fellowship as well as trying to assess tens of thousands of companies is that y'all cannot receive got also much nuance inwards the estimation approaches that y'all role for the latter. For example, for an private company, I volition attempt to justice the cost of debt, based on an actual or synthetic bond rating. With multitudes of companies, I role a much looser approximation, where I necktie the cost of debt to the variability inwards the stock price. Bottom line: If y'all are valuing an private company, acquire to the origin (the annual study as well as fiscal filings) as well as non the draw information that y'all encounter for that fellowship on my information set. If y'all are analyzing an entire sector, y'all tin role my approximated information inwards your analysis.
3. There volition hold upwards mistakes inwards the raw data: I am incredibly grateful to Value Line, Bloomberg as well as S&P for giving me access to the raw information on companies, but it is also truthful that at that topographic point is potential for human error at the appointment input stage. While I run my ain tests to attempt as well as grab information input errors , I volition lady friend a few. Thus, if y'all exercise notice a fellowship inwards my information base of operations that has a provide on equity of 20,000% or a PE ratio of 0.1, odds are that at that topographic point is something incorrect inwards the raw information of the company. 
4.  The outlier conundrum: Even if the raw information is accurate, the ratios as well as multiples computed from that information tin sometimes yield absurd values. Thus, the PE ratio for a fellowship amongst earnings fading towards naught tin converge on infinity. With private companies, you notice these absurdities as well as either conform for them or expect for option statistics. With large samples, though, that oversight is in i lawsuit to a greater extent than hard as well as spell I could receive got arbitrarily laid limits (ignore PE ratios greater than 200, for instance), I was reluctant to seat my imprint on the data. So, if y'all encounter foreign numbers for roughly statistics, it is what came out of the data.
5. The constabulary of large numbers is your ally: The other side of large samples is a positive one, since the payoff of having real large samples is that the outliers receive got less of an comport on on your statistics. Thus, I am comforted past times knowing that I receive got hundreds of firms inwards each sector, when I compute my averages as well as that foreign numbers on the part of a few companies will have solely a modest comport on on the averages.

I know that at that topographic point is piddling earth-shattering that I receive got said most what I learned from the 2014 information update, but I intend those lessons volition hold upwards amend covered inwards a serial of posts that I excogitation to brand inwards the side past times side  couple of weeks.

P.S: As always, at that topographic point are dozens of links as well as information sets inwards my information page as well as I am certain that I receive got screwed upwards on roughly of them. If y'all notice whatever missing links or receive got issues amongst the data, delight allow me know as well as I volition gear upwards them every bit shortly every bit I can. 

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