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  Release 2.0

  8 Issue 2.0.8, April 2008

  Jimmy Guterman, from Money 2.0, page 1

“A year after Release 2.0 first looked at what financial markets and

web markets have to teach one another, it can seem like the two

groups are still talking past each other. But we’re seeing early

signs of how Wall Street and Web 2.0 can work together—and

deepening evidence that the two may become inextricable.”

  Release 2.0 Issue 2.0.8, April 2008

  Contents Published six times a year by O’Reilly Media, Inc., 1005 Gravenstein Highway North,

  Sebastopol, CA 95472 By Jimmy Guterman This newsletter covers the world

   of information technology and

  y on how to

  the Internet — and the business

   and societal issues they raise.

   executive editor


  Tim O’Reilly



editor Jimmy Guterman


  Sara Winge By Cathleen M. Rittereiser art director Mark Paglietti


  copy editor

By Nathan Torkington

  Steven Sloan contributing writers

  Brady Forrest Marc Hedlund Jerry Michalski Sarah Milstein Peter Morville Nathan Torkington David Weinberger © 2008, O’Reilly Media, Inc.

  All rights reserved. No material in this publication may be reproduced without prior written permission; however, we gladly arrange for reprints, bulk orders, or site licenses. Individual subscriptions cost $495 per year. 80315 subscription information Release 2.0 PO Box 17046 North Hollywood, CA 91615-9588 customer service 1.800.889.8969 1.707.827.7019

  Jimmy Guterman is the editorial director of O’Reilly’s Radar group and editor of Release 2.0.

  Money 2.0 One year later, the connections between Wall Street and Web 2.0 are getting stronger and moving in surprising directions.

  One year ago, we published an issue of Release 2.0, entitled “When Markets Collide,” in which we considered what Wall Street and Web 2.0 might have to


  teach one another. Quite a bit, it turned out: the key parallels we uncovered

Release 2.0

  include latency (both have to do their jobs more or less instantly), connectivity Issue 2.0.2, April 2007 (that’s the liquidity of Web 2.0), sensors and actuators (and how to use them), and reputation (stockbrokers are no longer curators—they’re rated, in public).

  Since the publication of that issue, we’ve seen a tremendous amount of activity as Wall Street and Web 2.0 size up one another, culminating in our inaugural Money:Tech conference, intended to bring the two sides together in the same room, which we held in February. So it’s a ripe time to consider the status of the relationship. What’s new? What’s changed?

  On one level, there’s nothing new about Wall Street looking to new data

  “Web 2.0, like Wall Street, is a series of markets in which mere milliseconds can make an enormous difference.

  sources to discover alpha, an edge. In No Bull: My Life In and Out of Markets,

  The more you look, the more you see what the two sets

  hedge-fund legend Michael Steinhardt notes that long ago, in the early 1980s,

  of markets have to teach—and warn—one another.” Tim O’Reilly, from When Markets Collide, page 1

  he “constructed my own New York City taxi index relating the percentage with ‘available’ lights to those occupied, hoping for more to be ‘available,’ thereby

  Our original coverage of the collision

  signaling a slowdown in demand.” He discovered a proprietary economic between financial markets and web markets can be found at http://downloads.oreilly. indicator around the availability of taxis and traded on it. And Peter Lynch,

  com/radar/r2/issue2.0.2.1.pdf. You can find

  manager of the Magellan mutual fund for Fidelity Investments, famously

  out more about the inaugural Money:Tech conference at

  suggested that individuals buy stocks based, in part, by what they could see money2008/public/content/home. for themselves: in particular, how full a store’s parking lot was.

  The second one will take place in New York But Lynch didn’t limit himself to anecdotal data sources. As portfolio man- on February 5–6, 2009.

  ager Omid Malekan writes, “Lynch also looked at all sorts of fundamental and technical information on top of how full a store’s parking lot looks.” —>

  Release 2.0.8 May 2008 Money 2.0 Jimmy Guterman

  How do I know Omid Malekan? I don’t. But I do have a good sense of what his portfolio is, because he posts at least some of it online, via Stockpickr, a website that offers many professional portfolios and rates them. He has some interesting investing ideas, some of which I agree with and some of which I decide for myself what’s useful.

  The amount of financial data available publicly (that is, not including propri- etary systems like Bloomberg’s) is astonishing. That doesn’t mean it’s all useful, though. Here’s how the Federal Reserve Bank of Cleveland estimated the proba- bilities of various outcomes at the March Fed meeting:

  Source: Research/policy/fedfunds/index.cfm

  team to rip it apart, yet it does illuminate one of the key issues that come up when


  Wall Street and Web 2.0 executives talk to one another: There’s plenty of data out there, but it’s plenty confusing. You can’t extract alpha until you understand what you’re looking at. As Michael Simonsen, president and CEO of Altos Research, puts it, “free data on the internet is a mess.”

  If anyone doubts that financial markets and technology markets are deeply intertwined, consider this: the same day that JPMorgan Chase revealed its “pur- chase” of Bear Stearns, a Gartner Group analyst released a report showing that

  “the financial services industry continued to lead all vertical markets in server Two notable data points: revenue, as it accounted for 25.3 percent of worldwide server revenue in 2007.”

  1. Job listings are always a good place to As goes one set of markets, so will go the other. see where an industry is going. So we pass on that the venerable bank Wells Fargo is

  In this issue of Release 2.0, we consider the Wall Street/Web 2.0 mashup

  looking for a “social media developer” who will, among other things, “lead the develop- ment and implementation of complex web

  conference and an influential blogger on the topic (as well as others), about

  sites and web-based widgets and tools on

  why some on Wall Street hate Web 2.0—and what Web 2.0 can do to infiltrate

  our blogs and popular social networking

  Wall Street nonetheless. Entrepreneur Marc Hedlund, now chief product officer

  sites, like Facebook.”

  for personal finance startup Wesabe, examines what happens when hidden data

  2. Where a company decides to work says

  gets surfaced. Cathleen M. Rittereiser talks to hedge fund managers to discover

  something about what it’s doing to avoid latency. From an April 8 Reuters article, “New

  what they think they want from Web 2.0—and what they’re actually getting.

  York more fertile ground for tech startups”:

  Longtime Radar contributor Nathan Torkington digs deep into prediction markets

  “The pace of technology innovation is now

  and spells out both how to manage them and what companies might gain from far more rapid in New York, where a string of

  local start-ups are working on software prod- implementing them. ucts for the financial services and insurance industries.”

  In search of lost time

  The challenges facing those who want to extract alpha from new data sources or new ways of understanding older data sources are immense. And the most press- ing one may be time. Michael Stonebraker, chief technology officer of StreamBase Systems, says, “the minute you store data, you lose.” His company’s system, using a $1,000 generic PC, can, he says, run 300,000 messages per second.

  Some time, maybe not so far away, that may not seem so fast. David David Leinweber’s rarely-updated


  Leinweber, a Haas fellow at the University of California at Berkeley, says, “To get


  an edge, you have to get the news before the news people get there.” Leinweber estimates that in the 1980s, before the web, a market’s reaction time to certain market events was measured in weeks. By the late 1990s, as graphical web browsers achieved hegemony, the reaction time to earnings surprises could be measured in mere minutes. Now, as we’ve reported previously, firms are moving their data systems closer to Wall Street so they can shave a millisecond or two off their latency time.

  Time is a component of the three variables that measure the value of information, according to John Mahoney, cofounder and chief technology officer of Info(n)gen ( He says those three variables are quality, relevance, and scarcity. “Quality and relevance have needed to step up as a result of technology improvements,” he says, “but scarcity has been completely redefined.” —> Release 2.0.8 May 2008 Money 2.0 Jimmy Guterman

  Where is the useful information? It’s easy to say that financial markets are in

  There are plenty of ways

  trouble when, as Robert Passarella of Bear Stearns noted at Money:Tech, people working on trading floors are looking to Wikipedia to find what financial terms

  to track events without

  mean. But it might also mean that Wikipedia is a more important source of measuring them directly. ties for data miners (and data hackers). Information isn’t scare, but useful and tradeable information is. Renny Monaghan, senior director of product manage- ment at, says the question is “How do I avoid being a news junkie?” How do we know which of infinite possible inputs is worth our attention? And how do we do it quickly? Traders, after all, tend not to believe in delayed gratification.

  Any patient person can, like Michael Steinhardt, note the comings and goings of New York taxis. That’s not scarce information. Indeed, it’s so free that hardly anyone bothers to track it. It’s in paying attention to something so clear that it’s invisible that information becomes scarce—and valuable.

Beyond Bloomberg

  Paul Kedrosky expresses the problem with an ecological metaphor: all the obvious sources of information have long been overfished. So how to find new bodies of water? Ben Lorica of O’Reilly’s research team says there are plenty of ways to track events without measuring them directly. For example, someone can sit outside your house and find out what movie you’re watching on your Slingbox, just by following the packets. It’s easier to just ask, of course, but as Marc Hedlund notes (see page 12), sometimes the information we cast off without intention has the most value.

  Please write me at if There are plenty of companies scouring for the value in that information. you’re interested in learning about this

  SkyGrid ( gives hedge fund managers and research analysts research report as we develop it. tools to measure market sentiment in real-time. Collective Intellect (http://www. is an “alternate research” firm that provides insights from social media, also in real time. A future O’Reilly Radar research report will evalu- ate all these companies in detail.

  FirstRain ( is another one of the companies trying to sell new kinds of research to Wall Street. It is expensive—$10,000 per year per seat, but that’s a little over half what a Bloomberg Terminal costs. Indeed, Bloomberg must be FirstRain’s target, having signed a distribution deal with CapitalIQ, a Bloomberg competitor.

  “The disconnect continues,” says Martin Betz, FirstRain’s vice president of

  It’s a qualitative edge as

  technology. “I was surprised when I arrived here at how disconnected Wall Street is from technology. It’s amazing how much the web is untapped or simply

  much as a quantitative edge

  uninteresting to many finance people. A big part of what our sales force has to

  that Web 2.0 firms are selling

  on the web that they haven’t seen. Those few hedge funds who are using data to Wall Street. from the web usually say they’re getting analyst reports or Google alerts. Those are very primitive ways of thinking about data on the web. The perception that new and valuable information is available hasn’t taken hold in a widespread way. But it’s out there. Looking for new data sources is not what we do predomi- nantly. Most of the data we crawl and collect is publicly available. It’s just that much of it is obscure and unfiltered.”

  As you’d expect of an executive whose company is in the business of selling data to such a reluctant audience, Betz maintains that “data from the web, if collected and analyzed properly, will give you an advantage. The noise of the blog world has led the conservative people who run Wall Street to think that the whole system is noisy or junky and that’s simply not true. For companies like ours to succeed, we have to deliver the information in a format that’s understood by the program-trading tools these people are using.”

  Betz says speed isn’t necessarily the key part of the value proposition FirstRain is promoting. “We don’t promise you that you’ll see something first. What we promise is that you’ll see aggregated and filtered information you wouldn’t have seen otherwise.” So it’s a qualitative edge as much as a quantitative edge that Web 2.0 firms are selling to Wall Street. “Trading is still full of human beings collecting qualitative information and then testing it.”

  That qualitative information is what’s next for Wall Street. You can see the astonishing, ubiquitous success of the Bloomberg Terminal as the definitive— for now—solution to Wall Street’s quantitative data problem. And, despite its leading position, those who use it don’t find it bloated (in contrast to, say, recent iterations of Microsoft Windows). The proprietary Bloomberg Professional system is the standard for monitoring and analyzing financial market data in real time. It is speedy , in part because the two-monitor system is text-based rather than built around a graphical user interface. Those in “the business” say it’s a great vehicle for personal messages as well as quantitative information. Traders don’t send you a message; they “send you a Bloomberg.” —> Release 2.0.8 May 2008 Money 2.0 Jimmy Guterman

  But you can also see a double-screen Bloomberg Terminal, however imposing, as merely the finite solution to the first phase of Wall Street’s analytics problem. What’s emerging now is a second phase, one more aligned with Web 2.0 notions Visit Bill Tancer’s blog at http://weblogs. such as harnessing collective intelligence, that concerns itself with extracting For Tim O’Reilly’s

coverage of LinkedIn’s research platform data—and, ultimately, alpha—from unstructured text, sentiment, mentions, and


  buzz. It’s Bill Tancer of Hitwise predicting everything from unemployment statistics

  archiv to American Idol winners by tracking website visits. It’s MarkMail (http://markmail. researc.html.

  org/) analyzing mailing list archives in new ways that uncover unexpected patterns. It’s LinkedIn trying to turn its social network, nearly 20 million strong, into a research platform with Wall Street as its prime market. It’s companies like Weatherbill ( building risk-management services around the weather. It’s people like John Seo of Fermat Capital Management, constructing catastrophe bonds around such information. It’s Steve Skiena, formerly best- known for his jai-alai algorithms (, opening TextMap (http://, a specialized search engine that analyzes both the temporal and geographic distribution of news. It’s Rick Seaney of FareCompar extracting new meaning out of air travel data. It’ companies finding new applications for data that wasn’t considered worthy of mining—or wasn’t considered at all.

  “Don’t think in terms of new data sources,” says Joshua Schachter, formerly of Morgan Stanley and now at Yahoo! “New data sources are better described as new ways of viewing the data. In five years, I bet people will view the reading of finance articles on the web as a very dated activity.” He thinks some filter will have to come between the data and the consumer of the data. “It’ll grab all the interesting but obscure sources that you’d like to read, but even an RSS feed Joshua Schachter, inventor of,

  suggests that one project worth doing

  can’t keep up with them: local newspapers and industry publications, blogs

  would be a program that spiders the “who

  that you’d never want to read every day but one day every two years something

  we are” pages on a company’s website. That list of senior managers is often changed

  relevant to your business pops up.” prior to an explicit public announcement.

  Schachter says, “When someone is added

  APIs, web-page scrapers, and spiders can capture and organize massive amounts or ejected from the page and there’s no announcement, that’s an unusual thing. of information quickly, but they’re not as swift or effective at giving a bulletproof

  A spider would be a way to find out before

  indication of what sentiment is embedded in text. Computers don’t do irony or

  you’re supposed to find out, still using public data.”

  sarcasm all that well, at least not yet, and so much of the text on the web, partic- ularly in the blogosphere, is informal and full of all the complex sentiment that humans do so well and machine learning is slowly catching up to. Commerce- related sites are easy to track, because they’re formalized with standardized numbers like ISBNs and ASINs. Those listing products to sell on eBay, for example, have a strong incentive to identify those products precisely and quantitatively. But someone on her blog raving about the record she just bought isn’t so incented. The next Bloomberg will likely be the company that can do for the qualitative what Bloomberg did for the quantitative.

  Reuters is one of the companies competing with Bloomberg right now. At Money:Tech, Tim O’Reilly spoke with Devin Wenig, now CEO of the merged Reuters and Thomson Financial, and Wenig highlighted what he considers the two biggest trends hitting financial (and other professional) data: the increasing impact of consumer media on professional media (young traders are used to

  We covered the Semantic Web in the October

  more diverse data inputs) and—more provocatively—a decreasing importance

  2007 issue of Release 2.0, issue 2.0.5, entitled “Looking for The Web’s Edge,” available at

  in latency. Wenig suggested that, over time, Reuters’ business could move from

  news to insight derived from news, more about making connections than simply gathering information. It’s a move, in a sense, from descriptive data to predictive For Tim O’Reilly’s full report from his talk

  with Reuters CEO Devin Wenig, visit http://

  data. Wenig stated emphatically that semantic markup would be crucial to this


  As Tim wrote in his post-interview notes, “Ultimately, Reuters’ news is the raw material for analysis and application by investors and downstream news organizations. Adding metadata to make that job of analysis easier for those building additional value on top of your product is a really interesting way to view the publishing opportunity. If you don’t think of what you produce as the ‘final product’ but rather as a step in an information pipeline, what do you do differently to add value for downstream consumers? In Reuters’ case, Devin thinks you add hooks to make your information more programmable…That’s a really good case for the Semantic Web.” —> Release 2.0.8 May 2008 Money 2.0 Jimmy Guterman

  We are impressed by Wenig, but we’re not so sure that latency isn’t a problem

  Companies are learning that,

  anymore. As we quoted Haas fellow David Leinweber earlier, “To get an edge, you have to get the news before the news people get there.” Latency is becoming

  particularly when applied to

  less and less about the raw data and more and more about what people are

  consumers, buzz can have a

  of Silicon Alley Insider puts it, “The only research that’s valuable is the stuff nobody predictive quality. else has.” And what if everyone “has” it? Do something with it. In the words of Nouriel Roubini, who runs the premium news and analysis site RGE monitor (, “We filter what’s available from the web for free and people pay us for that.” There’s still an edge to be had, but it’s coming at a different, higher level. And that edge will come from a combination of what machines and humans (who Tim O’Reilly calls “the last mile of extracting meaning”) can recognize and act on.

From buzz to trade

  “You need both,” says Kate Niederhoffer, vice president of measurement science at Nielsen BuzzMetrics, a measurement firm. “You gather evidence and a panel of people corroborates the evidence. The panel tells you how people behave. The buzz data tells you why.”

  “Buzz” is elusive. It’s something companies and individuals want their offerings to have—and it’s something investors want to understand and predict. We’re still in the earliest stages of leveraging buzz and some of those early findings simply support time-worn notions (i.e., advertising spend is the best predictor of buzz), but companies are learning that, particularly when applied to consumers, buzz itself can have a predictive quality.

  Some hedge funds are looking for what Jerry Needel, senior vice president, product, at Nielsen BuzzMetrics, calls the “low-hanging fruit” of buzz: needles than can be extracted from haystacks, such as when an engineer at a large technology company reports to fellow developers that something will be delivered late. But the most interesting practice hedge funds have for this data so far is using buzz to aid in predictive modeling. Much buzz data right now parallels stock movement, but there have been cases in which buzz has offered early warning: Needel reports that the company saw buzz regarding the once-popular Atkins diet peak a full three quarters before sales topped out.

  Don’t expect Nielsen to start a buzz fund anytime soon, though. “Buzz is a piece of the equation when it comes to trading, but it’s certainly not the whole equation. And we’ve had our best performance with long strategic investors, not in-and-out daily investors.”

  “There are plenty of investors who might be interested in what teenagers are

  The growing use of Web 2.0

  saying about wireless or other brands,” says Niederhoffer. “What our data gives them is access to a leading indicator. Then the people who trade for a living, after

  means less data will stay

  they consult our index of overall tonality and buzz, can get a sense of how that

  hidden, and what’s hidden

  what people are saying on a particular topic.”

  will stay hidden for a shorter Best practices on both sides of the line period of time.

  As David Leinweber writes, “Whatever raw material you choose, fooling yourself remains an occupational hazard in quantitative trading.” With Leinweber’s warning in mind, here are four rules what those trying to bring financial markets and web markets should keep in mind.

  Web 2.0 companies looking to sell to Wall Street should 1. Sell solutions, not data.

  2. Be clear about the reach and the limitations of the offering.

  3. Make conservative, demonstrable arguments for your solution. Wall Street

  can be wary of Web 2.0. Overpromises will make you seem like a “twit” (see our interview with Paul Kedrosky, page 10).

  4. Understand that Wall Street is notoriously stingy with data. The notion of

  “collective intelligence” goes against the behind-closed-doors mentality of generations of Wall Street executives. You must be clear as to why more open can mean more profitable.

  Wall Street companies looking to take advantage of Web 2.0 should

  1. Remember that Web 2.0 is about harnessing collective intelligence, not snazzy-looking web pages. It’s not just MySpace out there.

  2. Be confident that the traders are still the traders. What Web 2.0 offers is new

  data and new ways of considering the data. They’re powerful inputs, but they’re part of a broader set of inputs.

  3. Accept that new data sources necessitate new ways of discovering and manipulating data.

  4. Accept that new data sources will be shared—just not immediately.

  It’s a truism that alpha lasts longest when it’s hidden. That may have been true in the past, but the growing use of Web 2.0 tools means that less data will stay hidden, and what’s hidden will stay hidden for a shorter period of time. We offer the last word to James Altucher of Stockpickr: “When it comes to data nowadays, n n closed source is a myth.”

  Release 2.0.8 May 2008 Looking for the New Pond Jimmy Guterman Looking for the New Pond Paul Kedrosky tells why Wall Street mistrusts Web 2.0— and offers some tools for getting around that.

  Paul Kedrosky, proprietor of the Infectious Greed onference chair of O’Reilly’s Money:Tech conference, is a leader in understanding what Wall Street and web markets have to teach one another. We asked him how Wall Street and Web 2.0 were getting along

  What doesn’t Wall Street understand about Web 2.0? nowadays.

  At a high level, Wall Street just thinks that Web 2.0 is frivolous. Wall Street, on the whole, equates Web 2.0 with Ajax wizziness or Facebook. The people on Wall Street think that’s all Web 2.0 is. And if that’s all Web 2.0 is, they don’t care.

  At a lower level, Wall Street goes wrong in understanding Web 2.0 when it assumes that people in the Web 2.0 world have the same motivation they assume

  “Wall Street doesn’t

  everyone involved in financial markets has: money. They don’t understand how

  understand how there

  there can be any value in community. If you know something, the thinking goes, you’re an idiot if you tell someone else. Collaborative behavior is fundamentally

  can be any value in antithetical to their being. They don’t just think it’s wrong. They think it’s stupid. community.”

  So how can these two groups play together nicely—and profitably?

  People talking about Web 2.0 or Wall Street 2.0 have to show that it’s about more than cosmetic changes, hosted apps, and snazzy apps. They have to show that these tools can help you make money, that it doesn’t matter anymore if you don’t know what, say, a synthetic option is. Different tribes speak different languages. So, to succeed on Wall Street, Web 2.0 people have to talk in the language that makes people money. Community, for example, can help you make more money. The logical chain around that has to be made obvious. That’s been missing so far.

  Several Wall Street old-timers I talked to for this issue of Release 2.0 said that they don’t trust any Web 2.0 people. One said, “If the idea is so good, why aren’t they trading on it themselves?”

  That’s so true. I’m surprised every person you spoke to didn’t say that. That’s how they think: If you have something that could make people make money, you’re either stupid or wrong or both. That’s the Number One objection any Web 2.0 person going to Wall Street has to be prepared to deal with. You can’t just say you have this fantastic piece of data that will give alpha. Do that and you’ve anointed yourself a twit. It’s more about how you come in and talk about your- self and what you have to offer. You’ve got to show sophistication and nuance.

  Can you get more specific about that? “New data requires

  There are two ways to show that you’re not a twit. First, at a high level, you have to say something like, “I know that constructing a trade is about finding

  more creativity.”

  an information edge—and then constructing a trade around that edge. I have a construct trades.” Be clear about what you do and what you don’t do. You almost have to pander. Say something like, “I’m a lonely technology guy with some unexploited data. You understand the market impact and risk control.” Promise less and you’ll show that you understand more.

  Second, make it clear that you understand that the information you’re selling them is just part of the mosaic. You’re not telling them that this piece of eBay data alone lets them trade eBay on a daily basis. You’re telling them that this piece of data—when it’s combined with other factors—might make it possible for them to trade eBay more effectively.

  If you were selling data to Wall Street, what would you want to sell?

  Alternative data sets. Unstructured data. They have fished to the point of extinction the existing pond of structured data. Quarterly and annual filings, institutional shareholder documents: that stuff is so widely available, so easily poured into your system in real time, that it’s almost impossible to get any edge from it. New data requires more creativity. Whether it’s weather data or commu- nity algorithms, you have to get outside the box of that overfished pond. You have to explore new ideas of what is data. If I was talking to hedge fund manag- ers, I’d talk about services like [freelance aggregation services] Elance and Guru, not because they’re going to help you build a system, but because they are flexible systems for finding new data. I know a hedge fund manager who’s using Elance to make 3,000 phone calls to find some specific information. He couldn’t have done that before, but now he has an online community he can exploit, an online community that can extract information that’s not available n n from Hoovers Direct or scraping the web. That’s brand new.

  Release 2.0.8 May 2008 The Wider Impact of Money 2.0 Marc Hedlund The Wider Impact of Money 2.0 Sometimes implicit measurements deliver more useful

  Marc Hedlund is the chief product officer of the personal finance startup Wesabe (http://www. information than explicit ones. and a contributor to the O’Reilly Radar ( Previously he was by Marc Hedlund entrepreneur in residence at O’Reilly Media. He blogs at “Wheaties for Your Wallet” (http://blog.

  In Berkeley, California, there are a number of auto shops that will service your BMW. The best-known and best-trafficked of these is Weatherford BMW, the local dealership, which has a prominent location just at the entrance of the free- way leading to San Francisco. A large “WEATHERFORD BMW” sign announces its People all over town recognize the building and use it as a landmark, regardless of whether they would ever drive a BMW or need one repaired. You can’t miss it.

  On average, people who get their cars serviced at Weatherford spend about $1,300.00, give or take, per visit. When asked to rate Weatherford’s service, on a scale of 1–100, 100 being best, Weatherford averages a score of 17.

  Across town, at the end of a one-way street in an industrial area, a nearly unmarked cinderblock building houses a small independent auto shop, Bavarian Professionals. You could park your BMW directly in front of it and go to the nearby brewpub — the only reason you’d be likely to wander by at all—and fail to notice that your car was in unusually similar company. You could search Google for “berkeley bmw auto shop” and entirely miss it—even Berkeley Parents’ Network, an extremely well-used local recommendations site, hardly mentions it.

  Yet on average, people who get their cars serviced at Bavarian Professionals spend about $600.00, give or take, per visit—less than half the cost at Weatherford. When asked to rate Bavarian Professionals’ service on the same 1–100 scale, Bavarian averages a 96—79 points higher than Weatherford.

  Quite a contrast! These two data points tell a clear story, one that would benefit any BMW driver in town. You’d certainly think that Weatherford would have no business at all, and Bavarian would be buying out the brewpub to make room for more customers. That isn’t the case, though; at least, not yet, since those data points aren’t easily available. Where would you go to find the average amount spent at one local auto shop versus another?

  This hidden economic data could make consumers into far better shoppers, if only it were not hidden.

Harnessing collective money intelligence

  One of the key tenets of Web 2.0 is that extremely powerful tools can be built by harnessing collective intelligence. When one person acts in a way that a web application can observe and record, the potential knowledge in that action can be released onto the web, and made available to everyone, usually permanently and for free. Tools such as aggregate information about web book- marks, revealing the reading and interests of its users., similarly, exposes the music listening patterns of its members, showing the new hot hits and the Systems built around collective money

  intelligence are, of course, susceptible to

  old favorites together in a way that a Billboard chart could never approach. Like

  fraud. What if the only people rating

  paths cut in the grass between buildings, the tracks of masses of users on these

  Baravarian high and Weatherford low were Bavaraian representatives? As collective

  sites point the way towards clarity.

  money intelligence becomes more popular, we’ll need systems that foster trust. —J.G.

  approach to collective intelligence and apply it to finances. Looking to the idea that greater transparency in markets leads to greater efficiency, these sites aim to give users financial power through information. Across a range of topics— cash management, investment, retirement, goals, loans, and more—people are starting to manage their finances on these sites, and in so doing, they are

  When one person acts in a

  contributing to, and benefiting from, the collective intelligence that emerges from their actions.

  way that a web application The impact of this movement goes far beyond cataloging tastes or interests.

  It has the potential to change markets, to shift the balance of power between

  can observe and record, the

  consumers and producers, to overwhelm brand association, convenience, and word-of-mouth, and replace those weak signs with more rigorous data inter- potential knowledge in that pretation and fundamental analysis.

  action can be released onto

  Take the example of the auto shops, above—a real set of data that emerged early in the development of Wesabe. Site members participate in the application

  the web, and made available

  by uploading their bank and credit card statements, editing their transactions to categorize and consolidate their spending records, and then giving feedback on

  to everyone, usually

  the merchants where they shop. Unlike five-star rating sites, which depend on permanently and for free. explicit review postings to get information on merchants, Wesabe learns from the shopping patterns of our users, boiling down a set of transactions into a report on a set of merchants. Each new user uploads several hundred transactions at a time, and each transaction speaks articulately about that user’s financial world.

  They tell us how much users spend, and in what circumstances—was this restaurant purchase tagged ‘lunch’ or ‘dinner’? What is the cost different between those tags?

  They tell us when users make switching decisions, such as going from Safeway to Whole Foods, and whether they stick with their choices or go back to their old vendors. And they tell us how a purchase fits into a consumer’s overall budget— where rent falls for this area, or the income of the people who buys clothes at a certain store. —> Release 2.0.8 May 2008 The Wider Impact of Money 2.0 Marc Hedlund Someone believes in Covestor. At press time, Likewise, Money 2.0 investment sites such as Cake Financial (http://www. it received $6.5 million in a Series A round of and Covestor ( allow members to

  funding led by Union Square Ventures and

  share investment decisions with peer groups and the world. Rather than looking

  Spark Capital. Cake Financial is funded by Alsop Louie and various angel investors.

  at market shifts as a dynamic of prices for holdings, these sites look at bundles of react to it.

  Looking at financial data differently; looking at the movement of an individual in a market; looking for patterns in spending that suggest sentiment: These common themes have started the Money 2.0 movement, and are leading us towards a new way of understanding finances altogether.

From personal finance to information that moves markets

  Our “hidden” fundamental metrics, then, are no longer hidden—or, at least, they are emerging from the fog. All of the Money 2.0 sites are limited by the data users have uploaded and—we hope—edited, tagged, or rated, so inevitably, the Weatherford BMW and Bavarian Professionals prices quoted above are biased by the sample of Wesabeans uploading—a self-selecting and technologically- biased group. Still, with more than half a million users of Money 2.0 personal finance sites, and hundreds of thousands more using Money 2.0 investment and loan sites, these populations are many times larger than those that make up the Nielsen rating panel or the Conference Board’s Consumer Confidence Index.

  How, then, should we look at this data—how do we understand the averages shown for Weatherford and Bavarian? Do they tell us anything about larger eco- nomic trends? Are they, essentially, emerging economic indicators?

  The Money 2.0 sites are pointing in the direction of far greater economic transparency for consumers and investors both. While it is too early to draw widespread economic conclusions from the current sites, it is easy to imagine that a site like Wesabe or one of its competitors reaching the scale of an earlier Web 2.0 success story such as Flickr would lead to a profound new data source with significant results for the economy as a whole. If the top Google result for “Weatherford BMW” shows a competitor charging half the price and returning five times the satisfaction, that is likely to change both Weatherford’s and Bavarian’s positions in the market—rewarding the most efficient supplier. Likewise, if consumers are setting and working towards financial goals,

  Uploading, editing, sharing,

  those goals and the progress towards them have the potential to tell us far more than a survey of consumer confidence. How many durable goods were

  reviewing: These simple acts

  ordered last quarter, and how far along are goals related to those goods in

  create a financial data stream

  It is not simply the availability of this data that is important—transaction

  richer and more evocative

  data is only part of the equation. Money 2.0 users affect this data with their edits, tags, reviews, comments, and recommendations, and all of these actions,

  than we have had before.

  undertaken in most cases for the user’s own record-keeping or enjoyment, lead to a richer set of data about the transaction: a view into the sentiment behind the purchase. A very low-rated mobile phone transaction, for instance, may suggest that a consumer will bolt as soon as a contract expires. Looking solely at the mobile phone company’s revenues or forecasts would miss the frustration users would be inclined to express towards a bad supplier in whose clutches they are (temporarily) captive. Uploading, editing, sharing, reviewing: These simple acts create a financial data stream richer and more evocative than we have had before. Money 2.0 tells its tales.

  The investment community has seen the broad impacts of greater information flow, from Bloomberg screens to automated trading networks and beyond. Until now, however, that benefit has been most broadly distributed for public markets and securities. With the advent of these Money 2.0 sites, tracking of financial fundamentals is moving “down-market,” into the hands of consumers, who are highly motivated to extract more value from each dollar they earn (especially now as the credit crunch takes hold). Show such a consumer a side- by-side comparison between two auto shops, or two plumbers, or two restau- rants at which they might eat dinner, and the understanding that data brings will change the way people shop. No one, it turns out, likes spending twice as n n฀ much to get a fraction of the value. Release 2.0.8 May 2008 What Do Hedge Fund Managers Want from Web 2.0? Cathleen Rittereiser What Do Hedge Fund Managers Want from Web 2.0? Many of them see Web 2.0 as being about consumer-facing

  Cathleen M. Rittereiser is a hedge fund marketing and investor relations expert focused on the new technologies. But what it offers them is less flashy and institutional investor market. She is the co-author of more likely to help their bottom line. Foundation and Endowment Investing: Philosophies and Strategies of Top Investors and Institutions.

  By Cathleen M. Rittereiser

  The inaugural Money:Tech conference, presented by O’Reilly Media in February, featured a lively panel on what hedge fund managers want from Web 2.0. Panelists included JP Rangaswami, managing director for BT Design and previously CIO for

  “Finbar Taggit,” a hedge fund manager who blogs pseudonymously. We asked Cathleen Rittereiser, moderator of the panel, to take the story further. —J.G.

  Hedge fund managers have put plenty of energy into dismissing Web 2.0, in large

  Banks unwittingly provide

  part because they equate it with its most public consumer implementations (see Paul Kedrosky interview, page 10). Indeed it’s hard to argue that “biting” a

  hedge fund competitors colleague on Facebook and turning him into a vampire will provide any alpha.

  But technologies in general—and Web 2.0 technologies in particular—are

  with an advantage by

  crucial to the business of hedge funds. Enlightened hedge fund managers do

  limiting employees’

  appreciate the impact of technology on what they do. Finbar Taggit attributed the growth and rapid expansion of the hedge fund industry to technology. “New

  Internet access to specific

  hedge funds can come up to speed now because of technology. For backtesting,

I have grabbed data off Yahoo, for free. There are day trader systems that are websites like Facebook

  comparable with those at the banks.” “It’s a battle between investment banks and hedge funds,” Taggit says. He believes banks unwittingly provide hedge fund competitors with an advantage by limiting employees’ Internet access to specific websites such as Facebook. “Hedge funds want to find market imperfections and trade on them.” With investment bank employees limited in what they can see, he feels he gains an advantage.