recentpopularlog in

jerryking : correlations   19

A Tale of Two Metrics
August 7, 2017 | | RetailNext | Ray Hartjen, Director, Content Marketing & Public Relations.

Traffic can’t alone measure the effectiveness of demand creation efforts, but some well-placed math can show retailers strong correlations over a myriad of relevant variables. More over, as my colleague Shelley E. Kohan pointed out in her post earlier this summer, “Expanding the Scope of Metrics,” Traffic is foundational for meaningful metrics like Conversion and Sales Yield (Sales per Shopper), key measurements that help managers make daily decisions on the floor from tailoring merchandising displays to allocating staffing and refining associate training.
With metrics, it’s important to remember there’re different strokes for different folks, with different measurements critical for different functions, much like financial accounting and managerial accounting serve different masters. Today’s “big data” age allows retailers to inexpensively collect, synthesize, analyze and report almost unbelievable amounts of data from an equally almost unbelievable number of data streams. Paramount is to get the right information in front of the right people at the right time.
Sometimes, the right data is Sales per Square Foot, and it certainly makes for a nice headline. But, not to be outshined, other instances call for Traffic. As Chitra Balasubramanian, RetailNext’s Head of Business Analytics, points out in the same Sourcing Journal Online article, “Traffic equals opportunity. Retailers should take advantage of store visits with loyalty programs, heightened customer service, and a great in-store experience to create a long-lasting relationship with that customer to ensure repeat visits.”
metrics  sales  foot_traffic  retailers  inexpensive  massive_data_sets  data  creating_demand  correlations  experiential_marketing  in-store  mathematics  loyalty_management  the_right_people  sales_per_square_foot 
august 2017 by jerryking
Mapping Where Torontonians Bike and Run
FEBRUARY 2, 2015 | Torontoist | BY DAVID HAINS

Developers map out the world's most popular spots for walking, jogging, and cycling—and reveal where in this city Torontonians like, and don't like, to get outside and get active.

....the maps show pieces of a larger story. The most popular trails might seem simply like fun places for a run or merely the result of individual choices, but they’re part of a larger context that governs how the city works—how the built and natural environment, a community’s land-use mix, housing affordability, community health options, and other factors affect the way we relate to and use different parts of the city.
mapping  Toronto  running  cycling  ravines  parks  neighbourhoods  community_health  public_policy  correlations  diabetes  health_outcomes  healthy_lifestyles  cardiovascular  land_uses  self-selection 
january 2017 by jerryking
The humbling of Valeant’s Michael Pearson - The Globe and Mail
TIM KILADZE
The humbling of Valeant’s Michael Pearson
SUBSCRIBERS ONLY
The Globe and Mail
Published Tuesday, Mar. 22, 2016

What we’re left with: A “Canadian” company we should happily disown, and critical reminders that certain business rules should never be broken. Chief among them: Debt is never a problem until, suddenly, it is; markets will love you until, suddenly, they don’t; and the roll-up game, driven by endless acquisitions, is nearly impossible to sustain.....By slashing R&D spending costs, Mr. Pearson freed up cash flow to buy more companies – whose R&D departments were then gutted to repeat the same trick. To juice earnings, he acquired Ottawa-based Biovail in 2010, which came with a Barbados-based subsidiary. Valeant started ushering U.S. profits to offshore tax domiciles – marking the first-ever pharmaceutical tax inversion and sending its corporate tax rate to the mid-single digits.

To fuel acquisitions, Mr. Pearson borrowed tens of billions of $ of incredibly cheap debt. By mid-2015, Valeant had $31-billion (U.S.) in debt and paid over $1-billion a year in interest.

There were warning signs these bold acts would backfire. Last March, Warren Buffett’s inner circle started to inflict damage. At an investor meeting, Charlie Munger, one of the value investor’s best friends, said he was “holding his nose” by looking at Valeant, adding that the company “wasn’t moral.”

That cautionary message did little to deter two of Valeant’s top investors: the Sequoia Fund – which has ties to Mr. Buffett – and Bill Ackman’s Pershing Square Capital Management. Whatever criticisms were hurled at the drug maker, they stood by it, repeatedly stressing that they believed in Mr. Pearson. Their faith in him seemed nearly biblical. And because they showed resolve, hedge funds kept piling in – momentum investing at its very worst. By the end of June, nearly 100 of them had stakes in the drug maker........One of the best lessons from the global financial crisis was that everything became correlated when the U.S. housing market crashed. The same is true for Valeant. Investigations into its pricing policy made investors worry about revenue; worries about the income statement morphed into fears about balance-sheet debt; leverage woes prevented Valeant from borrowing more to fund future acquisitions.

The cynicism turned investors’ momentum strategy on its head.
Valeant  Bay_Street  CEOs  pharmaceutical_industry  Charlie_Munger  Warren_Buffett  M&A  boards_&_directors_&_governance  correlations  hedge_funds  Pershing_Square  William_Ackman  debt  R&D  cash_flows  roll_ups 
march 2016 by jerryking
How to Avoid the Innovation Death Spiral | Innovation Management
By: Wouter Koetzier

Consider this all too familiar scenario: Company X’s new products developed and launched with great expectations, yield disappointing results. Yet, these products continue to languish in the market, draining management attention, advertising budgets, manufacturing capacity, warehouse space and back office systems. Wouter Koetzier explores how to avoid the innovation death spiral....
Incremental innovations play a role in defending a company’s baseline against competition, rather than offering customers superior benefits or creating additional demand for its products.
Platform innovations drive some market growth (often due to premium pricing rather than expanded volume), but their main function is to increase the innovator’s market share by giving customers a reason to switch from a competitor’s brand.
Breakthrough innovations create a new market that the innovator can dominate for some time by delivering new benefits to customers. Contrary to conventional wisdom, breakthrough innovations typically aren’t based upon major technological inventions; rather, they often harness existing technology in novel ways, such as Apple’s iPad.......A recent Accenture analysis of 10 large players in the global foods industry over a three-year period demonstrates the strategic costs of failure to innovate successfully. Notably, the study found little correlation between R&D spending and revenue growth. For instance, a company launching more products than their competitors actually saw less organic revenue growth. That’s because the company made only incremental innovations, while its competitors launched a balanced portfolio of incremental, platform and breakthrough innovations that were perceived by the market as adding value.
attrition_rates  innovation  howto  life_cycle  portfolios  Accenture  breakthroughs  platforms  LBMA  Mondelez  product_development  new_products  product_launches  kill_rates  incrementalism  R&D  taxonomy  disappointment  downward_spirals  baselines  marginal_improvements  correlations  moonshots 
march 2016 by jerryking
Google Correlate: Your data, Google's computing power
Google Correlate is awesome. As I noted in Search Notes last week, Google Correlate is a new tool in Google Labs that lets you upload state- or time-based data to see what search trends most correlate with that information.

Correlation doesn't necessarily imply causation, and as you use Google Correlate, you'll find that the relationship (if any) between terms varies widely based on the topic, time, and space.

For instance, there's a strong state-based correlation between searches for me and searches for Vulcan Capital. But the two searches have nothing to do with each other. As you see below, the correlation is that the two searches have similar state-based interest.

For both searches, the most volume is in Washington state (where we're both located). And both show high activity in New York.

State-based data

For a recent talk I gave in Germany, I downloaded state-by-state income data from the U.S. Census Bureau and ran it through Google Correlate. I found that high income was highly correlated with searches for [lohan breasts] and low income was highly correlated with searches for [police shootouts]. I leave the interpretation up to you.

By default, the closest correlations are with the highest numbers, so to get correlations with low income, I multiplied all of the numbers by negative one.

Clay Johnson looked at correlations based on state obesity rates from the CDC. By looking at negative correlations (in other words, what search queries are most closely correlated with states with the lowest obesity rates), we see that the most closely related search is [yoga mat bags]. (Another highly correlated term is [nutrition school].)

Maybe there's something to that "working out helps you lose weight" idea I've heard people mention. Then again, another highly correlated term is [itunes movie rentals], so maybe I should try the "sitting on my couch, watching movies work out plan" just to explore all of my options.

To look at this data more seriously, we can see with search data alone that the wealthy seem to be healthier (at least based on obesity data) than the poor. In states with low obesity rates, searches are for optional material goods, such as Bose headphones, digital cameras, and red wine and for travel to places like Africa, Jordan, and China. In states with high obesity rates, searches are for jobs and free items.

With this hypothesis, we can look at other data (access to nutritious food, time and space to exercise, health education) to determine further links.

Time-based data

Time-based data works in a similar way. Google Correlate looks for matching patterns in trends over time. Again, that the trends are similar doesn't mean they're related. But this data can be an interesting starting point for additional investigation.

One of the economic indicators from the U.S. Census Bureau is housing inventory. I looked at the number of months' supply of homes at the current sales rate between 2003 and today. I have no idea how to interpret data like this (the general idea is that you, as an expert in some field, would upload data that you understand). But my non-expert conclusion here is that as housing inventory increases (which implies no one's buying), we are looking to spiff up our existing homes with cheap stuff, so we turn to Craigslist.

Of course, it could also be the case that the height of popularity of Craiglist just happened to coincide with the months when the most homes were on the market, and both are coincidentally declining at the same rate.

Search-based data

You can also simply enter a search term, and Google will analyze the state or time-based patterns of that term and chart other queries that most closely match those patterns. Google describes this as a kind of Google Trends in reverse.

Google Insights for Search already shows you state distribution and volume trends for terms, and Correlate takes this one step further by listing all of the other terms with a similar regional distribution or volume trend.

For instance, regional distribution for [vegan restaurants] searches is strongly correlated to the regional distribution for searches for [mac store locations].

What does the time-trend of search volume for [vegan restaurants] correlate with? Flights from LAX.

Time-based data related to a search term can be a fascinating look at how trends spark interest in particular topics. For instance, as the Atkins Diet lost popularity, so too did interest in the carbohydrate content of food.

Interest in maple syrup seems to follow interest in the cleanse diet (of which maple syrup is a key component).

Drawing-based data

Don't have any interesting data to upload? Aren't sure what topic you're most interested in? Then just draw a graph!

Maybe you want to know what had no search volume at all in 2004, spiked in 2005, and then disappeared again. Easy. Just draw it on a graph.

Apparently the popular movies of the time were "Phantom of the Opera," "Darkness," and "Meet the Fockers." And we all were worried about our Celebrex prescriptions.

(Note: the accuracy of this data likely is dependent on the quality of your drawing skills.)

OSCON Data 2011, being held July 25-27 in Portland, Ore., is a gathering for developers who are hands-on, doing the systems work and evolving architectures and tools to manage data. (This event is co-located with OSCON.)

Save 20% on registration with the code OS11RAD

Related:

Data science democratized
Dashboards evolve to meet social and business needs
A new focus on user-friendly data analysis
Social data is an oracle waiting for a question
causality  Data  Future_of_Search  analytics  datatool  googlecorrelate  via:moon  house  LBMA  OPMA  correlations  time-based  geographic_sorting  tools  digital_cameras 
july 2015 by jerryking
Wealth Managers Enlist Spy Tools to Map Portfolios - NYTimes.com
AUG. 3, 2014 | NYT | QUENTIN HARDY.

Karen White, Addepar’s president and chief operating officer, says a typical customer has investments at five to 15 banks, stockbrokers or other investment custodians.

Addepar charges based on how much data it is reviewing. Ms. White said Addepar’s service typically started at $50,000, but can go well over $1 million, depending on the money and investment variables involved.

And in much the way Palantir seeks to find common espionage themes, like social connections and bomb-making techniques, among its data sources,[jk: traffic_analysis] Mr. Lonsdale has sought to reduce financial information to a dozen discrete parts, like price changes and what percentage of something a person holds.

As a computer system learns the behavior of a certain asset, it begins to build a database of probable relationships, like what a bond market crisis might mean for European equities. “A lot of computer science, machine learning, can be applied to that,” Mr. Lonsdale said. “There are lessons from Palantir about how to do this.”
wealth_management  software  valuations  Quentin_Hardy  Addepar  Palantir  money_management  social_connectivity  machine_learning  correlations  portfolio_management  investment_custodians  tools 
august 2014 by jerryking
A 25-Question Twitter Quiz to Predict Retweets - NYTimes.com
JULY 1, 2014 | NYT | Sendhil Mullainathan.

how “smart” algorithms are created from big data: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed — not too differently from how we might learn what our co-workers find funny....one of the miracles of big data: Algorithms find information in unexpected places, uncovering “signal” in places we thought contained only “noise.”... the Achilles’ heel of prediction algorithms--being good at prediction often does not mean being better at creation. (1) One barrier is the oldest of statistical problems: Correlation is not causation.(2) an inherent paradox lies in predicting what is interesting. Rarity and novelty often contribute to interestingness — or at the least to drawing attention. But once an algorithm finds those things that draw attention and starts exploiting them, their value erodes. (3) Finally, and perhaps most perversely, some of the most predictive variables are circular....The new big-data tools, amazing as they are, are not magic. Like every great invention before them — whether antibiotics, electricity or even the computer itself — they have boundaries in which they excel and beyond which they can do little.
predictive_analytics  massive_data_sets  limitations  algorithms  Twitter  analytics  data  data_driven  Albert_Gore  Achilles’_heel  boundary_conditions  noise  signals  paradoxes  correlations  causality  counterintuitive  training_beds  test_beds  rarity  novelty  interestingness  hard_to_find 
july 2014 by jerryking
If you ever wondered how math class could help you later in life, here’s your answer - The Globe and Mail
Jun. 18 2014 | The Globe and Mail | ERIN ANDERSSEN

Jordan Ellenberg’s new book, How Not to Be Wrong: The Power of Mathematical Thinking.

In a world brimming with information, math is an important tool to help spot statistical glitches and everyday fallacies, but it’s being lost. “Math is the science of not being wrong about things,” he writes. “Knowing math is like wearing a pair of X-ray specs that reveal hidden structures underneath the messy and chaotic surface of the world.”....Mathematical amateurs have all kinds of reasons to use math. It helps them learn the difference between correlation and causation, to see the flaw in statistics, to spot a sneaky sell.

“Math is the science of not being wrong.” Ellenberg writes. In the real world, it doesn’t just find the right answers – it teaches us to ask the right question in the first place.
mathematics  books  messiness  correlations  anomalies  numeracy  mistakes  sleaze  questions  tools  ratios  asking_the_right_questions  causality  statistics  in_the_real_world 
june 2014 by jerryking
Art Makes You Smart - NYTimes.com
November 23, 2013 | NYT | By BRIAN KISIDA, JAY P. GREENE and DANIEL H. BOWEN.

FOR many education advocates, the arts are a panacea: They supposedly increase test scores, generate social responsibility and turn around failing schools. Most of the supporting evidence, though, does little more than establish correlations between exposure to the arts and certain outcomes. Research that demonstrates a causal relationship has been virtually nonexistent.... we can conclude that visiting an art museum exposes students to a diversity of ideas that challenge them with different perspectives on the human condition. Expanding access to art, whether through programs in schools or through visits to area museums and galleries, should be a central part of any school’s curriculum.
art  correlations  museums  students  education  evidence  cognitive_skills  creative_renewal  value_propositions  the_human_condition 
november 2013 by jerryking
Big Data should inspire humility, not hype
Mar. 04 2013| The Globe and Mail |Konrad Yakabuski.

" mathematical models have their limits.

The Great Recession should have made that clear. The forecasters and risk managers who relied on supposedly foolproof algorithms all failed to see the crash coming. The historical economic data they fed into their computers did not go back far enough. Their models were not built to account for rare events. Yet, policy makers bought their rosy forecasts hook, line and sinker.

You might think that Nate Silver, the whiz-kid statistician who correctly predicted the winner of the 2012 U.S. presidential election in all 50 states, would be Big Data’s biggest apologist. Instead, he warns against putting our faith in the predictive power of machines.

“Our predictions may be more prone to failure in the era of Big Data,” The New York Times blogger writes in his recent book, The Signal and the Noise. “As there is an exponential increase in the amount of available information, there is likewise an exponential increase in the number of hypotheses to investigate … [But] most of the data is just noise, as most of the universe is filled with empty space.”

Perhaps the biggest risk we run in the era of Big Data is confusing correlation with causation – or rather, being duped by so-called “data scientists” who tell us one thing leads to another. The old admonition about “lies, damn lies and statistics” is more appropriate than ever."
massive_data_sets  data_driven  McKinsey  skepticism  contrarians  data_scientists  Konrad_Yakabuski  modelling  Nate_Silver  humility  risks  books  correlations  causality  algorithms  infoliteracy  noise  signals  hype 
march 2013 by jerryking
What Data Can’t Do - NYTimes.com
By DAVID BROOKS
Published: February 18, 2013

there are many things big data does poorly. Let’s note a few in rapid-fire fashion:

* Data struggles with the social. Your brain is pretty bad at math (quick, what’s the square root of 437), but it’s excellent at social cognition. People are really good at mirroring each other’s emotional states, at detecting uncooperative behavior and at assigning value to things through emotion.
* Data struggles with context. Human decisions are embedded in contexts. The human brain has evolved to account for this reality...Data analysis is pretty bad at narrative and emergent thinking.
* Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation.
* Big data has trouble with big (e.g. societal) problems.
* Data favors memes over masterpieces. Data analysis can detect when large numbers of people take an instant liking to some cultural product. But many important (and profitable) products are hated initially because they are unfamiliar. [The unfamiliar has to accomplish behavioural change / bridge cultural divides]
* Data obscures hidden/implicit value judgements. I recently saw an academic book with the excellent title, “ ‘Raw Data’ Is an Oxymoron.” One of the points was that data is never raw; it’s always structured according to somebody’s predispositions and values. The end result looks disinterested, but, in reality, there are value choices all the way through, from construction to interpretation.

This is not to argue that big data isn’t a great tool. It’s just that, like any tool, it’s good at some things and not at others. As the Yale professor Edward Tufte has said, “The world is much more interesting than any one discipline.”
massive_data_sets  David_Brooks  data_driven  decision_making  data  Nassim_Taleb  contrarians  skepticism  new_graduates  contextual  risks  social_cognition  self-deception  correlations  value_judgements  haystacks  narratives  memes  unfamiliarity  naivete  hidden  Edward_Tufte  emotions  antifragility  behavioral_change  new_products  cultural_products  masterpieces  EQ  emotional_intelligence 
february 2013 by jerryking
Data Is the World
Aug 1, 2005 | Inc.com | Michael S. Hopkins.

Use your data. "Companies aren't taking advantage of the data they generate, Levitt says. "Often, data generated for one purpose is useful for another. Freakonomics describes the case of an entrepreneur selling bagels in corporate offices who kept meticulous records to track profits and loss—data that eventually yielded insights about white-collar crime and the effects of office size on honesty.
Ask different questions. The abortion-crime link revealed itself when Levitt thought to stop asking what made crime fall and try asking why it had risen so much in the first place. That led him to justice system practices in the 1960s, which led him to a statistical understanding of which individuals were likeliest to commit crimes, and ultimately to the question of why a large segment of that population seemed to have vanished.
Don't mistake correlation for causality. Innovative policing and a drop in crime happened simultaneously, but data proved the one didn't cause the other. (Be mindful of the feudal king who, having learned disease was greatest in regions with the most doctors, figured that reducing doctors would reduce disease.)
Question conventional wisdom. An idea that is both easy to understand and a source of comfort (such as the credit quickly given to innovative policing for cutting crime) should be especially suspect.
Respect the complexity of incentives. "You can't imagine, says Levitt, "all the ways humans will connive to beat a system.
Employ data against cheating. Just as companies don't sufficiently capitalize on the data they have access to, they aren't exploiting what Levitt calls "opportunities to think about fraud or theft in their businesses.
'60s  bank_shots  causality  cheating  conventional_wisdom  correlations  data  data_driven  exhaust_data  Freakonomics  gaming_the_system  incentives  insights  justice_system  massive_data_sets  metadata  oversimplification  questions  skepticism  social_data  Steven_Levitt  theft  think_differently  white-collar_crime 
january 2013 by jerryking
The Trouble with Big Data
May 5, 2012 | | What's The Big Data?| GilPress

“With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real… On net, having a degree in math, economics, AI, etc., isn’t enough. Tool expertise isn’t enough. You need experience in solving real world problems, because there are a lot of important limitations to the statistics that you learned in school. Big data isn’t about bits, it’s about talent.”.....The “talent” of “understanding the problem and the data applicable to it” is what makes a good scientist: The required skepticism, the development of hypotheses (models), and the un-ending quest to refute them, following the scientific method that has brought us remarkable progress over the course of the last three hundred and fifty years.
in_the_real_world  massive_data_sets  blogs  skepticism  challenges  problems  problem_solving  expertise  statistics  talent  spurious  data_quality  data_scientists  haystacks  correlations  limitations 
june 2012 by jerryking
A Shout-Out for Segmentation Data - BusinessWeek
March 15, 2011, BusinessWeek By G. Michael Maddock and
Raphael Louis Vitón .Quit yawning and start seizing on the wealth
within segmentation data. Every department should demand to see this
information. a simple, three-part formula:

Step 1. Define success. Get as specific as possible. Step 2. Define the
characteristics you want your segment to have. Step 3. "Simply" find
what predicts/correlates with these variables.

Having decided whom you want to go after, find the variables that will
lead you to these people. Asking Lots of Questions

Having identified this market, you go out and ask the potential
customers within it as many questions as you can think of—how much they
weigh, what snacks they eat, whether they have kids or a pet. Then you
sort through the data and look for commonalities (Step 3).
segmentation  market_segmentation  market_research  questions  JCK  sorting  correlations  predictive_analytics  ethnography  think_threes 
march 2011 by jerryking
Google’s 8-Point Plan to Help Managers Improve - NYTimes.com
March 12, 2011 |NYT| By ADAM BRYANT. IN early 2009,
statisticians at Google began a plan code-named Project Oxygen. Their
mission was to devise a way to build better bosses. So, as only a
data-mining giant like Google can do, it began analyzing performance
reviews, feedback surveys and nominations for top-manager awards. They
correlated phrases, words, praise and complaints. Later that year, the
“people analytics” teams at the company produced what might be called
the Eight Habits of Highly Effective Google Managers. ...H.R. has long
run on gut instincts more than hard data. But a growing number of
companies are trying to apply a data-driven approach to the
unpredictable world of human interactions.
“Google is really at the leading edge of that,” says Todd Safferstone,
managing director of the Corporate Leadership Council of the Corporate
Executive Board, who has a good perch to see what H.R. executives at
more than 1,000 big companies are up to.
Google  Octothorpe_Software  human_resources  data_driven  data_mining  analytics  gut_feelings  correlations  praise  complaints 
march 2011 by jerryking
For innovation success, do not follow the money
07-Nov-2005 | Financial Times | By Michael Schrage "There is
no correlation between the percentage of net revenue spent on R&D
and the innovative capabilities of an organisation – none,"...Just ask
General Motors. No company in the world has spent more on R&D over
the past 25 years. Yet, somehow, GM's market share has
declined....R&D productivity – not R&D investment – is the real
challenge for global innovation. Innovation is not what innovators
innovate, it is what customers actually adopt. Productivity here is not
measured in patents granted but in new customers won and existing
customers profitably retained...A successful innovation policy is a
competition policy where companies see innovation as a cost-effective
investment to differentiate themselves profitably. If a 1 % R&D
intensity buys market leadership, more power to them; if 15 % is what it
takes to keep up with the competition and satisfy customers, that is
fine, too.
Michael_Schrage  innovation  innovation_policies  R&D  productivity  measurements  metrics  ROI  customer_acquisition  correlations  customer_adoption  GM  decline  competition_policy 
october 2010 by jerryking
For Today’s Graduate, Just One Word - Statistics - NYTimes.com
Aug. 5, 2009 | NYT | By STEVE LOHR. “We’re entering a world
where everything can be monitored and measured,” said Erik Brynjolfsson,
an economist and director of MIT’s Center for Digital Business. “But
the big problem is the ability of man to use, analyze and make sense of
the data.”" The rich lode of Web data has its perils. Its sheer vol. can
easily overwhelm statistical models. Statisticians caution that strong
correlations of data do not necessarily prove a cause-and-effect link.
E.g., in the late 1940s, before there was a polio vaccine, public health
experts noted that polio cases increased in step with the consumption
of ice cream and soft drinks, says David A. Grier, a historian and
statistician at GWU. Eliminating such treats was recommended as part of
an anti-polio diet. It turned out that polio outbreaks were most common
in the hot mths of summer, when people ate more ice cream, showing only
an association. The data explosion magnifies longstanding issues in
statistics.
Steve_Lohr  Hal_Varian  statistics  career_paths  haystacks  analytics  Google  data  Freshbooks  information_overload  data_scientists  Erik_Brynjolfsson  measurements  sense-making  massive_data_sets  correlations  causality 
june 2010 by jerryking
Hedge Funds Join Grains Rally - WSJ.com
FEBRUARY 26, 2007 | The Wall Street Journal | by TOM POLANSEK

Hedge funds may start taking a bigger role in the booming grains markets seeking investments that aren't correlated to traditional stock and bond markets. "Starting in the late 19th century, companies like General Mills began buying local grain elevators and building facilities as a way to maintain better control of their supplies, says Bruce Selyem,
founder of the Country Grain Elevator Historical Society."
hedge_funds  grains  food_crops  research  agribusiness  agriculture  farming  commodities  correlations  19th_century 
march 2009 by jerryking

Copy this bookmark:





to read