Each year, for the concluding 25 years, I receive got spent the showtime calendar week playing Moneyball, amongst fiscal data. I assemble accounting in addition to marketplace information on all publicly traded companies, listed globally, in addition to and thus attempt to extract whatever lessons that I tin from the data, to utilization inwards investing, corporate finance in addition to valuation for the residuum of the year. I study the data, classified yesteryear manufacture grouping in addition to yesteryear country, on my website, inwards the promise that others mightiness let out it useful. While, similar concluding year, I volition hold out summarizing what I run into inwards the information inwards a serial of posts over the residuum of January, I decided to utilization this 1 to both furnish approximately perspective in addition to cautionary notes non only on my information but on numbers, inwards general.
The Number Cruncher's Delusions
In an before transportation service on narrative in addition to numbers, I confessed that I am to a greater extent than naturally a release cruncher than a story teller in addition to that I receive got learned through experience that focusing alone on the numbers tin atomic number 82 you lot astray inwards valuation in addition to investing. In fact, as you lot read my posts on what the numbers expect similar at the start of 2017, it is as good worth noting that I am, similar all release crunchers, susceptible to iii delusions almost data:
In an before transportation service on narrative in addition to numbers, I confessed that I am to a greater extent than naturally a release cruncher than a story teller in addition to that I receive got learned through experience that focusing alone on the numbers tin atomic number 82 you lot astray inwards valuation in addition to investing. In fact, as you lot read my posts on what the numbers expect similar at the start of 2017, it is as good worth noting that I am, similar all release crunchers, susceptible to iii delusions almost data:
- Numbers are precise: I say, only one-half jokingly, that when a release cruncher is inwards doubt, his or her reaction is to add together to a greater extent than decimals, inwards the promise that making a release expect to a greater extent than precise volition expire far so. The truth is that numbers are only as precise as the procedure that delivers them in addition to inwards business, that makes them imprecise. Thus, when you lot peruse the returns on uppercase or costs of uppercase that I volition hold out estimating in addition to reporting for both companies in addition to manufacture groups, delight exercise recognize that the quondam is an accounting number, where discretionary choices on expensing in addition to depreciation tin interpret into large changes inwards returns on capital, in addition to the latter is marketplace number, making it non only a moving target (as involvement rates in addition to adventure premiums change) but as good a locomote of my estimation choices as good as estimation fault inwards estimating adventure premiums in addition to adventure parameters.
- Numbers are objective: One of the resentments that release crunchers receive got almost story tellers is that the latter indulge inwards flights of fancy in addition to are unashamed almost bringing their biases into their stories in addition to through them into pricing in addition to investing. The problem, though, is that numbers tin hold out simply as biased as stories, amongst the caveat that it is easier to enshroud biases amongst numbers. To laissez passer on 1 example, 1 of the datasets that I volition hold out updating has revenue enhancement rates paid yesteryear US companies inwards 2016 in addition to I furnish iii measures of effective revenue enhancement rates, ranging from a uncomplicated average of effective revenue enhancement rates across all companies inwards a sector, yielding the lowest values, to a weighted average effective revenue enhancement charge per unit of measurement that is computed only across money-making firms, which yields much higher values. If you lot are dead-set on making a instance that US companies don't pay their fair part inwards taxes, you lot volition study only the showtime release in addition to non cite the rest, whereas if you lot desire to demo that US companies pay their fair part in addition to to a greater extent than inwards taxes, you lot volition expire amongst the latter. It is for this argue that I volition non claim to hold out unbiased (since no 1 is) but I volition attempt to furnish multiple measures of widely used variables in addition to exit it to you lot to create upwards one's heed which 1 best fits your preconceptions.
- Numbers set you lot inwards control: It is human nature to attempt to hold out inwards command in addition to numbers serve us well, inwards that pursuit. As inwards other aspects of life, nosotros seem to mean value that attaching a release to a volatile or uncontrollable variable brings it nether control. So, at the adventure of stating the obvious, permit me tell that mensuration your render on invested uppercase is non going to plough bad projects into proficient ones, simply as estimating your involvement coverage ratio is non going to expire far easier for you lot to brand your involvement payments.
Don't acquire me wrong! I remain, at heart, a release cruncher but I receive got a to a greater extent than complicated, in addition to healthier, human relationship amongst information than I used to have. My organized religious belief inwards information has been tempered yesteryear my experiences amongst data, in addition to peculiarly thus amongst the ease amongst which I receive got seen it bent to reverberate the agenda of the user. I trust numbers, but only afterward I verify them, in addition to I promise that you lot volition exercise the same amongst the information that you lot let out on my site.
A Big Data Skeptic
It is my experience amongst information that brand me skeptical almost ii of the hottest concepts inwards business, large information in addition to information analytics, at to the lowest degree as a footing for making money. It is truthful that companies are collecting to a greater extent than information than e'er before on almost every aspect of our lives, amongst the intent of using that information to brand to a greater extent than coin off us. In a capitalist society, I rest doubtful that large information volition hold out monetized, for iii reasons.
- Data is non information: Not all information is created equal. Data that is based on what you lot exercise is worth a lot to a greater extent than than what you lot tell volition do; a tweet that you lot are bullish on Apple, Twitter or the entire marketplace is less useful information than a tape of you lot buying Apple, Twitter or the entire market. This is a indicate worth remembering as the rush is on to contain social media information (from Twitter in addition to Facebook) amongst fiscal information to create super information bases. In addition, as nosotros collect in addition to shop to a greater extent than data, it is worth noting that information is non information. In fact, if information analytics does its job, converting information to information volition rest its focus, rather than generating neat looking graphs in addition to obscure statistics.
- If everyone has it (data), no 1 has it: For information to receive got value, you lot receive got to approximately storey of exclusivity inwards access to that information or a proprietary border on processing that data. It is 1 of the reasons that investors receive got been unable, for the most part, to convert increased access to fiscal information into investing profits.
- Not all information is actionable: , To convert that information to profits, you lot bespeak to hold out able to let out a means to monetize whatever information border you lot receive got acquired. For companies that offering products in addition to services, this volition accept the shape of modifying existing products/services or coming upwards amongst novel products/services to what you lot receive got learned from the data.
As you lot expect at these iii factors, it is slowly to run into why Netflix in addition to Amazon receive got expire illustrative examples for the benefits of large data. They acquire to detect us (as consumers) inwards action, Amazon watching what nosotros purchase in addition to Netflix observing what nosotros scout on our devices, in addition to that information is non only proprietary but tin hold out used to non only modify production offerings but to as good nudge us to human activity inwards ways that volition hold out beneficial to the companies. By the same token, you lot tin as good run into why using large information as an investing wages will, at best, furnish a transitory advantage, in addition to why I experience no qualms almost sharing my data.
Data Details
If you lot direct to utilization whatsoever of my data, it behooves me to accept you lot through the procedure yesteryear which I collect in addition to analyze the information in addition to offering approximately cautionary notes along the way.
- Raw Data: The showtime pace inwards the procedure is collecting the raw information in addition to I am deeply thankful to the information services that allow me to exercise this. I utilization S&P Capital IQ, Bloomberg in addition to a host of specialized services (Moody's, PRS etc.). For company-specific data, the only criteria that I utilization for including a companionship is that it has to receive got a non-zero marketplace capitalization, yielding a total of 42678 firms on Jan 1, 2017. The information collected is as of Jan 1, 2017, amongst marketplace information (stock prices, marketplace capitalization in addition to involvement rates) existence as of that information but accounting information reflecting the most recent twelve months (which would hold out through September 30, 2016 for calendar yr companies).
- Classification: I kind out these companies showtime yesteryear geographic grouping into 5 groups - the United States, Japan, Developed Europe (including the European Union in addition to Switzerland), Emerging Markets (including Eastern Europe, Asia, Africa in addition to Latin America) in addition to Australia/New Zealand/Canada, a somewhat arbitrary grouping that I am stuck amongst because of history. I as good kind out firms into 96 manufacture groups, built loosely on raw service industrial grouping in addition to SIC codes. The release of firms inwards each manufacture group, broken downwards farther yesteryear geographic grouping, tin hold out constitute at this link and you lot tin let out the companies inwards each manufacture grouping at this link.
- Key numbers: I to a greater extent than oftentimes than non don't study much macroeconomic information (interest rates, inflation, gross domestic product growth etc.), since in that location are much ameliorate sources for the data, amongst my favorite remaining FRED (the Federal Reserve information site inwards St. Louis). I update equity adventure premiums non only for the US but for much of the globe at the start of every year in addition to volition update them in 1 lawsuit again inwards July 2017. Using the companionship data, I study on dozens of metrics at the manufacture grouping in addition to geographic levels on profitability, cost of capital, relative adventure in addition to valuation ratios in addition to you lot tin let out the entire listing here.
- Computational details: One of the lessons that I receive got learned from wrestling amongst the information is that computing fifty-fifty uncomplicated statistics requires making choices, which, inwards turn, tin hold out affected yesteryear your biases. Just to furnish an example, to compute the PE ratio for US steel companies, I tin accept a uncomplicated average of the PE ratios of companies but that volition non only weight tiny companies in addition to real large companies as but volition as good eliminate whatsoever companies that receive got negative earnings from my sample (causing bias inwards my estimates). To eliminate this problem, for most of the manufacture average statistics, I aggregate values across companies in addition to and thus compute ratios. With the PE ratio for US steel companies, for instance, I aggregate the internet income of all steel companies (including money-losing companies) in addition to the marketplace capitalizations for the same companies in addition to and thus split upwards the quondam yesteryear the latter to acquire the PE ratio. Think of these averages in addition to thus as weighted averages of all companies inwards each manufacture group, peradventure explaining why my numbers may hold out dissimilar from those reported yesteryear other services.
- Reporting: I receive got wrestled amongst how best to study this data, thus that you lot tin let out what you lot are looking for easily. I receive got non constitute the perfect template, but hither is how you lot volition let out the data. For the current information (from Jan 2017), expire to this link. You volition run into the information classified into risk, profitability, uppercase construction in addition to dividend policy measures, reflecting my corporate finance focus, in addition to and thus into pricing groups (earnings multiples, majority value multiples in addition to revenue multiples). I as good keep archived information from prior years (going dorsum to 1999) at this link. Unfortunately, since I receive got had to switch raw information providers multiple times inwards the concluding xx years, the information is non perfectly comparable over time, as both manufacture groupings in addition to information measures modify over time.
- Usage: There are ii ways you lot tin acquire the data. For the US data, I receive got html versions that you lot tin run into on your browser. For all of the data, I receive got excel spreadsheets that you lot tin download for the data. I would strongly encourage you lot to utilization the latter rather than the former, since you lot tin in addition to thus manipulate in addition to piece of work amongst the data. If you lot receive got questions almost whatsoever of the variables in addition to how precisely I define them, try this link, where I summarize my computational details.
In Closing
I am a one-man performance in addition to I am certain that in that location are datasets that I receive got non updated or where you lot let out missing pieces. If you lot let out whatsoever of these, delight permit me know, in addition to I volition attempt to laid them. I as good don't run into myself as a raw information provider, peculiarly on a real-time footing in addition to on private companies. So, I don't project design to update this information over the course of study of the year, partly because manufacture averages should non receive got dramatic changes over a few months in addition to partly because I receive got other materials that I would rather do.
YouTube Video
Data Links
YouTube Video
Data Links
Data 2017 Posts
- Data Update 1: The Promise in addition to Perils of Big Data
- Data Update 2: The Resilience of US Equities
- Data Update 3: Cracking the Currency Code - Jan 2017
- Data Update 4: Country Risk in addition to Pricing, Jan 2017
- Data Update 5: Influenza A virus subtype H5N1 Taxing Year Ahead?
- Data Update 6: The Cost of Capital inwards Jan 2017
- Data Update 7: Profitability, Excess Returns in addition to Corporate Governance- Jan 2017
- Data Update 8: The Debt Trade off inwards Jan 2017
- Data Update 9: Dividends in addition to Buybacks inwards 2017
- Data Update 10: The Pricing Game
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