Learning Risk and Behavioral Economics with the Santa Fe Institute

This week I had the privilege to attend the Sante Fe Institute’s conference in conjunction with Morgan Stanley entitled Risk: The Human Factor.  There was quite the lineup of speakers, on topics ranging from Federal Reserve policy to prospect theory to fMRI’s of the brain’s mechanics behind prediction.  The topics flowed together nicely and I believe helped cohesively construct an important lesson—rules-based systems are an outstanding, albeit imperfect way for people and institutions alike to increase the capacity for successful prediction and controlling risk.  In the past on this blog, I have spoken about the essence of financial markets as a means through which to raise capital.  However, in many key respects, financial markets have become a living being in their own right, and as presently orchestrated are vehicles where humans engage in continuous prediction and risk management, thus making the lessons learned from the SFI speakers amazingly important ones.

This notion of financial markets as living beings in SFI’s parlance can be described as a “complex adaptive system” and is precisely what SFI is geared towards learning about.  While financial markets (and human beings) are complex adaptive systems, SFI is a multi-disciplinary organization that seeks to understand such systems in many contexts, including financial markets, but also in biology, anthropology, social structures, genetics, chemistry, drug discovery and all else where the concepts can be applied. 

To highlight the multi-disciplinary nature of the event, John Rundle, one of the co-organizers of the event and a physics professor at the University of California Davis, with a special background in earthquake simulation and prediction introduced the theme for the day. Dr. Rundle presented results for his trading strategy founded upon his theories for earthquake prediction.   The strategy was built upon asking the following question: can models for market risk be constructed that implicitly or explicitly account for human risk?  Seems like things are off to a great start.

Some of the coolest, most interesting moments came during the Q&A sessions, where this year’s presenters, some past presenters, and many brilliant minds from finance including Michael Mauboussin, Bill Miller and Marty Whitman had the opportunity to engage each other on their theses, refining and expounding upon each other’s ideas.  Sitting in the room and absorbing conversations like John Rundle speaking with Ed Thorp during an intermission about their own risk management perspectives and how to maximize the Kelly Criterion in investments was a surreal experience that I sadly cannot impart in this blog post, but I hope to channel the spirit in sharing some of the important ideas I learned. Further, I'd like to invite any of you readers out there to add your own thoughts in the comments below. 

Let’s start with the first presentation and walk through the day together.  In each subsection, I will give the presenter and their lecture title, followed by some notes from the lecture that I felt were relevant to my practical needs (this is not meant to be a thorough overview of each and all presentations).  I will type up my notes from Ed Thorp’s presentation in its own blog post, for there seemed to be considerable interest from fellow Twitterers on that one lecture in particular.

David Laibson, Harvard University

  Can We Control Ourselves?

Does society have the capacity to prepare for demographic change?  Experiments consistently show that people want the right thing, particularly when the question is presented as one of future choice.  However, when faced with the very same choice in the present, we fail to make the right decision; the very same decision we would make for longer-term planning purposes.   There is a behavioral reason for this: we want the right thing, but right now gets the full brunt of the emotional psychological weight, while planning is not nearly as influenced by the emotional element.  As a result, humans have a knack for making terrific plans, with no follow-through.

There is a neural foundation for this, as we have 2 systems (this is derivative of the idea presented in Daniel Kahneman’s Thinking Fast, and Slow). 

• The planning and focused system
• The dopamine reward system based on immediate satisfaction

How can we help people follow-through on their goals in planning as it pertains to saving for retirement?

• We can change the system from opt-in to auto-enroll, also known as the Nudge. Nudge is based on an idea presented by behavioral economists, Richard Thaler and Cass Sunstein in the book Nudge: Improving Decisions about Health, Wealth, and Happiness.
• We can use what’s called “active choice” and punish inaction, such that people must call and make a decision about their savings, rather than delaying it.
• Make enrollment quicker by taking away the 30 minute paperwork barrier.

Which is most effective:

• 40% participate with opt-in
• 50% participate with an easier process
• 70% enroll with active choice
• 90% participate with a nudge

To that end, we were presented with information that showed people recognize self-control problems and opt for less liquid savings options if given the choice, EVEN IF the returns are exactly the same.  That is, people acknowledge their inability to control the itch to break their well-made plans.

Vincent Reinhart, Managing Director and Chief U.S. Economist at Morgan Stanley

FED Behavior and Its Implications

1. Our paradigm for monetary policy:
   1. We have an expectation for the path of the economy and the Fed sets policy to meet that expectation
   2. The difference in policy over 2 successive actions follows a random walk. You can only acquire so much new information about the economy over the course of six weeks, making decisions based primarily on prior knowledge.
   3. The puzzle of persistence:
      1. Despite the random walk on decision-making, a chart of the Fed Funds Rate doesn’t actually follow a random walk.  It is a persistent path, whereby if the interest rate went down the prior month, it is more likely to go down again in the present month.
      2. The source of persistence:
         1. If there is persistence, and policies are predictable, then there should be ways to generate returns off of it.  Prices then would be drive to a fundamental value by arbitrage.  However, in central banking there is no arbitrage opportunity, because the mechanisms are confined to just the Fed and commercial banks, with no open market participation.
         2. While many talk about recent actions being “unprecedented” this is unequivocally not true.  These actions are very consistent with central bank behavior—QE and its ilk are balance sheet actions. 
            1. Previously the Fed had a larger balance sheet as a % of GDP in the mid-1940s.
2. Policy decisions are made by committees:
   1. Larger committees lead to less variance
   2. The right model to think about this is the committee as a jury, not a sample of policy options. The committees deliberate and take the best argument.
   3. There is an hierarchy of status in the Fed, including titles and media-friendliness that lead to greater degrees of influence from some members, over others.  This leads to the perfect setting for herding outcomes.
   4. Thus the random walk fails.
   5. Why have we not had a strong bounce-back from this recession?
      1. Milton Friedman talks of “plucking on a string” whereby a big drop should lead to a big bounce.
         1. There are serious problems with this analogy:
            1. An equal percent decline, and rise will not get you back to your starting point. (1-x) * (1+x)=1-x²
            2. In a “pluck” in physics it never gets you back to your starting point, as there is a transfer of energy in the transition from down to up.
            3. The observation that recessions should work like a plucked string were misguided, since they focused on a small sample size ONLY covering 1946-1983, looking neither at the prior 100 years, or updating for the past 30 years.
3. After severe financial crisis, recoveries are consistently very poor.
4. What is the best paradigm for decision-making?
   1. Rules consistently do better than discretion.
   2. From June to December that conversation has started to change, and QE3 is far more analogous to a rules-based system. However, we don’t yet have enough information on when or how the rules will end.

I had the opportunity to ask a question, so I asked whether NGDP targeting would be such an optimal rules-based system, and if QE3 was something akin to NGDP.  Reinhart answered that while QE3 does get us closer to a rules-based system it is not like NGDP.  He further asserted that he wouldn’t necessarily be in favor of NGDP targeting, and that a system of NGDP targeting would be an implicit, under-the-radar way for the Fed to let the market know it will slacken on the inflation coefficient of its dual mandate.

Philip Tetlock, University of Pennsylvania

The IARPA Forecasting Tournament: How Good (Bad) Can Expert Political Judgment Become Under Favorable (Unfavorable) Conditions?

In the 1980s, the government funded a study looking into how well experts predict global events, called the IARPA Forecasting Tournament.  Today, this experiment is being recreated, with a focus on forecasting global events of interest to the US government.  The experiment uses the Brier Score, first developed for weather forecasters, in order to gauge accuracy.  The best Brier score is 0, a dart-throwing chimp registers a 0.5 and the worst possible score is 2.

Types of prediction ceilings:

• Perfectly predictable events (100% ability to predict)
• Partly predictable events
• Perfectly unpredictable

In the first year of the tournament, the average score in the baseline was 0.37, better than the chimp, but not quite perfect.  The best algorithms score 0.17 and sit 0.29 units away from the truth.

In the top performing groups of participants had the following traits in common (note: collaboration was welcomed and fostered by the moderators).  I’m injecting my opinion here, but I find these to be very important goals for any organization in attempting to participate in an arena where prediction is important (in this case, for investors the lessons can be particularly apt).

1. The best participants
2. Collaboration whereby people actually work together and deliberate about their predictions.
3. A training in probabilistic reasoning a la Kahneman’s ideas in Thinking, Fast and Slow
4. Combine the training and teamwork
5. Elitist aggregation methods whereby more weight is added to the best predictors/experts in certain areas when combining predictions to make one uniform “best” effort at prediction.

Two lessons/observations:

• Teams and algorithms consistently outperform individuals.
• Forecasters consistently tend to over-predict change.

Elke Weber, Columbia University

Individual and Cultural Differences in Perceptions of Risk

In finance we think of risk as volatility. Culturally however, risk is a parameter, not a model.  Risk is therefore subjective and intuitive on an individual level.  Further, when faced with extreme outcomes, emotion becomes an increasingly more powerful force on perceptions of risk.  It is the perceptions of risk that drive behavior, and these perceptions exist on a relative, not absolute scale. Humans are biologically wired to that end.

Weber’s Law (not Elke Weber, an earlier Weber): the differences in the magnitude required to perceive two stimuli is proportional to the starting point. i.e. all differences are measured by a relating the new position to the original.

Familiarity actually works to reduce perceptions of risk, but not risk itself. Experts in a certain field tend to underestimate risks due to familiarity.  Return expectations and perceived riskiness predict choice, NOT the expectation of volatility (i.e. risk is perceived on a relative scale, not through the formulaic calculation of volatility).

Cultural differences—Shanghai vs. US MBA students:

• The collectivist nature of Chinese culture mitigates the damaging effects of risk gone awry. This is called the “cushion hypothesis.”  As a result, Chinese MBA students tend to be more risk-seeking.
  • Families in China tend to help their members far more than in the US when it comes to transferables (people help mitigate the risk of a money-based decision gone wrong, but cannot do so on risky health decisions).
  • Risk was consistently based on relative perceptions of risk within the context of the safety net.

In the Animal Kingdom, the most base way to perceive risk is through experience.  Small probability events tend to be underweighted by experience, but overweighted by perception.

• When small probability events hit, the recency bias makes people overweight the chances it will happen again.
• Experience metrics tend to be more volatile in how they perceive risk.
• Studies show that crisis (like the Great Depression) do have an enduring impact on how risk is perceived.

I had the opportunity to ask Dr. Weber a question. I asked her about the point that familiarity tends to lead people to overlook risk, and how that can be reconciled with the value investing concept of sticking to a core competency? If through focusing on a core competency, rather than mitigating risk, investors were increasing it.  Dr. Weber rightly observed that focusing on a core competency does have some distinctions with familiarity in that the idea is to work in areas where one has the most skill, but that there could very well be such a connection. In fact, she thought my question to be “very interesting” and worth further observation.

Nicholas Barberis, Yale University

Prospect Theory Applications in Finance

Can we do better in financial markets replacing expected utility with prospect theory?

Some core elements of prospect theory in finance:

1. People care about gains and losses, not absolute levels of performance
2. People are more sensitive to losses than gains
3. People weight probabilities in a non-linear way (i.e. they overweight low probability, underweight high probability). 

There is little support for beta as a predictor of returns.  Prospect theory instead focuses on the idea that a security’s (or indices) own skewness will be priced based on the scale of the left or right tail. 

• In positively skewed stocks people tend to overweight small chances of big success, and thus get low returns as a result (and vice versa). 
• As a result, big right skewness should have a low average return and this is proven in IPOs, out of the money options, distress stocks and volatile stocks.
• Probability weighting in prospect theory is a better predictor of returns.
• If people are loss averse, as prospect theory holds, the equity premium will be higher.
• Overall, the market is negatively skewed, thus probability weighting produces a higher equity risk premium overall.

The Disposition Effect – people sell stocks that have gone up far quicker than stocks that have gone down.

• Do people get pleasure/pain from realizing gains/losses? i.e. realization utility. The model predicts that:
  • There is greater turnover in bull markets as a result.
  • There is a greater propensity for selling above historical level of highs.
  • There is a preference for volatile stocks.
  • Momentum is also preferred.

Gregory Berns, Emory University

When Brains are Better than People: Using fMRI to Predict Markets

Dr. Berns started with a history of using blood pressure in order to ascertain where/how/why certain stimuli impact the brain.  Today we can use fMRI in order to clearly see ventricular activity and this provides a nice window into how the brain works.  Blood flow to regions of the brain change based on which part of the brain is active/engaged at any given point in time.  Animals in the wild that are most adept at prediction can survive far better in changing environments than those who cannot.

Contrary to conventional wisdom, dopamine is not directly correlated to pleasure. Dopamine in fact is correlated to the anticipation (i.e. the delta) of pleasure.  It is the changes in dopamine levels which lead to decisions.  Dr. Berns showed a fascinating slide using the corking and drinking of a fine wine to illustrate this point.  It is in the moment of opening the bottle of wine that people experience the dopamine release, rather than during the pouring of the glass or taking the first sip.

Dr. Barberis had mentioned fMRI and its application to measuring the disposition effect and here Dr. Berns confirmed and illustrated.  There are three explanations for why the disposition effect happens:

1. People’s risk preference
2. The realization utility (i.e. people like realizing gains, loathe realizing losses)
3. Mean reversion

Using fMRI, we can see that there are different approaches to the disposition effect depending on how and where the brain reacts (note: boy do I wish I had these slides, because the images are amazing in highlighting the effects).  People tend to fall into 2 camps—those who are influenced by the disposition effect, and those who are not.  fMRI shows that in those who ARE influenced by the effect, the blood flow is most active in the stem of the brain, the area where dopamine is released.  In those who are NOT impacted by the disposition effect, there is brain activity in a much broader portion of the cerebrum (the bigger part of the brain).

This effect was studied using fMRI in 2 contexts involved in understanding prediction.

• Music: people were given fMRI while many songs were played, analyzing where in fact the brain was triggered. Only years later, when one of the obscure songs became a hit did Dr. Berns check his data and it showed that this hit song actually did in fact induce a higher degree of activity in the brain. Brain data correlated more with the likeability of success.
• Markets: MBA students were given fMRI while simulating the ownership of stocks into earnings. Their reactions were tested for beats or misses.  The tests were demonstrative of the fact that negative surprises hurt far more than positive ones feel good.  This could be a major explanatory force behind the disposition effect.  

Please note: I apologize for any formatting errors. This post was drafted in Word and did not transfer very cleanly at all into the Squarespace format. In the interest of sharing the ideas in a timely mannger, I will go ahead and publish before I have the chance to clean up all the spacing, tabbing, etc.  Please enjoy the content and try to look past the messy spacing.

My Media Consumption Habits with the iPad

I want to skip over reviewing the device, because its awesomeness has been acknowledged a million times over, with little unique to say.  Instead I’d like to jot down some notes on how the iPad 3 has impacted my web browsing and media consumption.  Over the course of the past month, there are some notable changes in my long-term habits that I have developed, which I think portend at least a little about the future of technology and media.  Please note, none of these points are supposed to be right or wrong, they are simply observations about how my personal habits have changed.

iPad and Video

By far the most impactful shift has been my embrace of web video.  I always watched the occasional video, and with my GoogleTV, I started watching a bit more online video content.  Now with the iPad, web-based video has chopped my TV viewing time in half, and has eroded a decent chunk of my web reading time allocation as well.  In fact, reading has been a far more significant loser since getting the iPad than anticipated.  Reading had always been the means through which I pursued my primary interests and my self-enrichment time, while video was primarily my escape time.  Even between cable, on demand and DVR, I didn’t have nearly enough video content that was “smart” and crafted for my desires.  Now with the iPad it is far simpler than ever before to seek out and consume interesting and informative content that meets my tastes. 

Now, I spend about 30 minutes a day watching TED Talks on different rewarding topics, and an additional 30 minutes of select YouTube content, ranging from old interviews, to lectures from some great thinkers, to cool videos of nature.  Altogether, I find that the video watching I do on the iPad is distinctly different from what I watch on TV, therefore it has only cut out of my mindless TV time, rather than my entertainment TV time

(Slight digression: to me, mindless and entertaining are distinct types of consumption. Mindless are those channels I put on because there is no other option, just to clear my head, while entertainment is the content that I am thoroughly addicted to seeking out.  Mindless TV for me is watching a Seinfeld rerun for the millionth time, while entertainment TV is watching the latest episode of Mad Men). 

To that end, the amount of mindless time I spend watching actual TV has shrunk substantially (i.e. watching something just for the hell of it, when there is little else to do, like the 45 minutes while in bed before sleep), and has been replaced predominantly with much smarter content.  I feel better for it at the end of the day too.  All this helps further confirm my belief that YouTube will be a big winner in the future of video.

iPad and my Computer

One of the biggest changes is on the bigger level, about how I use my computer.  My computer has become my exclusive domain for productivity functions, while the iPad, although not monopolizing consumption, has become the primary outlet through which I consume content.  It’s just so easy to read and watch on the iPad, while still relatively difficult to coherently build something.  I have used it in a complimentary role for my stock research, mostly as a 2^nd screen and an easy way to visually see something that I am manipulating in either Excel or Word. 

There are two important observations here.  First, while the iPad is great for consumption of information, it really isn’t all that good for productivity functions.  For this reason, I think all those who fear the imminent demise of the PC are overlooking the obvious—people use computers to both use stuff and to do stuff, and doing stuff isn’t going anywhere anytime soon.  For data analysis and writing, the iPad simply cannot compete with a computer, and there is no reason as of yet to make that transition.  Second, the iPad is much easier and more efficient for consumption, not because the screen is so shiny and pretty, but because the combination of finger flicks and taps is much simpler, smoother, easier and more fun than a keyboard and track pad, especially when making words out of buttons (aka typing) just is not necessary.  The simplicity and fun combined are a powerful force in driving said consumption to the iPad. 

The Future

The iPad alone brings cord cutting much closer to reality.  One of the real consequences is that quite a bit of my personal cable-watching time has shifted to the web, and that trend will definitely continue to accelerate.  With apps for each of the major networks, covering the majority of the shows I actually watch, the only real missing link continues to be live sports.  As soon as the day arrives that sports are available for streaming on the web (note to the cable companies: you can only fight the inevitable for so long) my cord will be cut and cable will be in my past.

YouTube: Bringing Disruption to a TV Near You

Want to invest in one of the best, most innovative startups in the world with taking none of the risk associated with a startup, and more potential upside than many of the most popularly watched private market web names like Facebook?  Look no further than the strongest brand hiding behind the Google name: YouTube.  YouTube is turning into a huge business, and were it a standalone start-up, the company would easily be one of the most valuable Internet companies and one of the most hyped.  Instead we hear little about YouTube’s business other than the obligatory question and non-answer answer on each Google quarterly conference call about when/if/how much money YouTube will make. 

Today, YouTube seems even more an afterthought in the narrative about Google the company than Android and Google + and I can’t help but find the irony and humor in it all.  While everyone is waiting for Google’s true “social” answer, and even Google itself is out there searching (pun intended), YouTube is in fact a social, technology and media behemoth in its own right.  After all, it is the place where “going viral” became the thing to do (speaking of which, here’s a cool video with Kevin Alloca, YouTube’s “trends manager” on what actually makes a video go viral).

Facebook is the startup darling of the world, Netflix at times has been the streaming superstar, and Apple is…well…the apple of everyone’s eye.  Meanwhile in between watching hours of YouTube videos a day, everyone forgets that a) YouTube has an amazing business model and b) Google is both cheap and sitting on a competitive Trojan Horse.  Let me explain.

The Cost of Content

I’m oversimplifying here, but in content, there are the producers, the distributors and the consumers.  When anyone talks about content distribution companies, and video in particular, the cost of content is important in determining the bottom-line profit.  The true advantage of streaming video is that on a relatively small fixed cost base, a distributor can reach every web-connected person on Earth.  In an ideal world, that sounds like a simple and great business, but the content producers have only been willing to engage the distributors with largely one-sided terms. 

The content producers know full well the value in distributing via the Internet, so they even created their own distribution service—Hulu.com.  Netflix has used its DVD business, the company’s cash flow machine, in order to fund content acquisition for its streaming service.  As streaming has gotten easier and more popular amongst the masses, many have fled DVDs for streaming, thus forcing Netflix to improve its content mix online.   As their early contracts expired, the company found itself having to negotiate in a position of need with the studios, and since then has paid a hefty price.

Meanwhile, YouTube just keeps doing its thing.  Worldwide, people view 3 billion YouTube videos per day, which for perspective is “the equivalent of nearly half the world’s population watching a YouTube video each day, or every U.S. resident watching at least nine videos a day.”  While Netflix and Amazon are out paying (more like begging for the right to pay) for content, YouTube users are willingly uploading 48 hours of content per minute for FREE.  Granted not all YouTube content is as desirable as the content others are paying for, but considering how many people watch videos on the site every day, it’s safe to conclude that at every minute YouTube is gaining more valuable content at no expense.   

With this recipe, YouTube has become the most watched online video site by a mile (h/t to TechCrunch for the chart):

In the process, YouTube has already become a heavily entrench business with a strong brand name and strong brand loyalty.  When people talk about the competition for online video success, particularly in financial circles, the competition pits Netflix against Amazon, with Apple sometimes entering the fray and Google a total afterthought.  That needs to stop. 

As of today, YouTube has already reached deals with CBS, BBC, Universal Music Group, Sony Music Group, Warner Music Group, the NBA and the Sundance channel, amongst others (contract list from the CrunchBase).  Some of these deals were reactionary to growing pressure from content producers at the copyright infringing uploads done by YouTube users, but at the end of the day, these content deals have done a whole lot to enhance the quality of the videos available on YouTube and entrench the site as THE go to platform for video. 

Notice a theme with these content deals?  Many of the early deals are music-centric.  There is no better place on the Web to watch high quality concert video than YouTube, nor is there a better place for an artist to debut their new song via a video (even VEVO uses YouTube), with MTV now a soap-opera-type channel lacking any coherent connection to music.  Witness Cee-lo Green’s catchy release of F*ck You with the words literally dancing across a blue projection screen.  This was both a powerful and catchy way for Cee-lo to get his song out to the masses, and was a catalyst behind the song’s ascent on the pop charts.

YouTube the Platform

The Cee-lo release provides the perfect segue to YouTube as a platform.  YouTube is a disruptive medium in itself, but more interestingly it has become a platform upon which other disruptions are launched.  At the same time, YouTube has also become a video platform that is ubiquitous across all viewing platforms.  Although a web-page in itself, YouTube is a “channel” (rather app, but what’s the difference these days?) on any web-enabled TV device from designated viewing devices like the Roku or AppleTV to gaming systems like the Xbox.  The point is that YouTube has both incredible reach to its audience, and is an innovative vehicle for some of the world’s foremost innovators.  Even Apple can’t deny this reality.

In addition to debuting singles on YouTube, there are successful artists who owe their entire careers to the medium.  Look no farther than Justin Bieber (geez I promised myself that name would never be typed into this blog…), someone who would never have been “found” had it not been for the site.  Bieber isn’t alone.  Daniel Tosh has built an incredibly popular show on Comedy Central based entirely off of YouTube videos, and the Young Turks have become serious news pundits from their YouTube show. 

Louis CK, a comedian with a strong cult following, used YouTube in an entirely new way by releasing his “Live at the Beacon” directly through the platform.  “Lucky” Louis went on to sell this show for $5, with customers able to pay and watch instantly.  This is far cheaper than a DVD retails for, and a much easier way to directly connect with an audience.  The move was both highly profitable for Louis, and rewarding for his fan base. 

When you have a business that works better for all the parties directly involved than the existing business model, you have a powerful force.  It’s cheaper for fans, more profitable for the artist, reaches a far wider audience than anything else, and has fewer intermediaries taking a cut.  Stuck in the middle is YouTube/Google easily (and happily) collecting its margin.  It’s only a matter of time before more artists follow Louis down this path. 

And YouTube’s appeal isn’t limited to music, comedy and entertainment.  The Khan Academy is using YouTube as platform to disrupt education.  While it is a non-profit that produces free, high quality educational content for view via the YouTube platform, let’s not dismiss the fact that the Khan Academy is proving the power of the platform as a way to not only reach those interested in learning, but to fundamentally change the way eager students learn.  TED Talks has also used YouTube in a similar manner as a platform to bring informative, educational videos to the masses.  We’re only just beginning.

YouTube and the Innovator’s Dilemma

YouTube is following the path of the Innovator’s Dilemma, and all of the dominant market leaders in the sectors impacted by the company are already on high alert (if you haven’t already done so, go read the book now, or for a short-cut read my blog post on it).  YouTube has firmly entrenched itself on the low-end of the video-watching marketplace, and I mean this both in terms of Internet video, and video in the broadest possible terms.  Further, YouTube has significantly deflated the cost of distribution and consumption, making both effectively free in many contexts for the content producer and the viewer respectively.  In doing so, YouTube generates a fairly high margin on its advertising dollars on.   

As YouTube has earned more and more money on the “low hanging fruit” (aka the free stuff) they have been able to step up their acquisition of premium, higher quality content.  Some of the aforementioned deals are evidence of this.  In other words, the company is using its entry level product, which has helped it gain market share, in order to fund its climb higher up the distribution tree.  As a result, the company is in the midst of a further pivot up its S-curve (H/T to Your Brand is Showing for the graph). 

The current technology is the combination of TV/Cable/Internet that we presently view video on, while the emerging technology is YouTube.  In my imprecise opinion, right now YouTube is somewhere around the big red dot that I drew on top of the chart.  The company is on its parabolic ascent, but has yet to reach and cross the current technology in its prowess, largely due to the defensive posturing of the existing infrastructure.  But that can’t last forever.  As the chart indicates, and as is typical with disruptive innovation, YouTube’s day is coming rather quickly.  Keep in mind we’re talking about a company and technology barely more than six years old.  It was within the past two years that we were introduced to high definition video for streaming, that we were able to watch YouTube video’s on our television, and even more recently, that content was divided into organized channels. 

Not long ago, YouTube had a major success in acquiring streaming rights to a premier Indian cricket tournament for live games.  Viewership surged far quicker than anticipated, and was more profitable than anyone expected. Soon many of the major sports leagues will have their television contracts expiring, and one can imagine that YouTube will be a player for the rights, especially considering their already strong relationship with leagues like the NBA and NHL.  If YouTube were in fact the first to bring mass-streaming of live sporting events to the American masses, they would be the first to truly liberate video viewing from the existing infrastructure and into the digital age.  After all, live sports viewership is probably the single largest impediment to would-be chord cutters today (myself included). 

YouTube gets this, as is evidenced by what CEO Salar Kamangar recently had to say: (h/t to New Markets Advisors, I strongly recommend reading their write-up on the disruptive power of YouTube as well):

"When you think about the impact cable had, we think we're in a position to have a similar impact for video delivery, like what cable has done with broadcast. In the early '80s, you had three or four networks. Now those three or four networks are responsible for 25 percent of viewership, and the cable networks are responsible for all the rest. Right now, the fraction of traffic that is Web video is small relative to broadcast and cable, but it's growing at a fast rate. What's amazing is that the Web enables you to build a kind of channel that wouldn't have made sense for cable, in the same way cable enabled you to build content that wouldn't have made sense for broadcast. You couldn't have done CNN with the broadcast networks; you couldn't have done MTV with the broadcast networks." 

And here’s a great TED talk with Chris Anderson, Editor in Chief of Wired Magazine, on “How YouTube is Driving Innovation”: (obviously an embedded YouTube video)

Putting the Story in Context with the Numbers

Now that I have established a benchmark for how awesome and disruptive YouTube is, the next step is to take a look at the number.  If you’ll remember, my initial premise was that one can buy one of the most disruptive, innovative companies today without taking any of the venture capital risk, for free.  This requires that I establish two premises: 1) that Google without YouTube is at most, fairly priced, if not downright cheap, and 2) that YouTube as a stand-alone entity would be worth a sum exceeding $10 billion in market cap, and in a range that reaches northward of $20 billion.  For that I will have to follow-up with a second post, but I will not leave just yet before beginning the next step of the argument.

Using consensus analyst estimates on revenue for 2012 (courtesy of Business Week), a 10% WACC and 4% perpetual growth, Google has an earnings power value plus growth of $680, that is a 7% premium to this price.  YouTube itself accounts for a mere 4% of Google’s in this estimate, and if you removed YouTube’s revenue contribution entirely from Google, the price drops to $653, a 3% premium to today’s price.  From this, we can deduce that YouTube represents about $27 per each share of Google, or a total intrinsic value of $8.7 billion. 

Before next week’s post on how to value YouTube, I just want to finish off by stating my belief that $8.7 billion is way too cheap for such a valuable web property, especially when companies with lower revenues like LinkedIn and Zynga are trading for near $10 billion, and Facebook is pricing at over $100 billion.  In a recent social brand value analysis by BV4, a brand value ratings agency, Facebook checked in with the highest brand value of any social web property at $29.1 billion, with YouTube in second place at $18.1 billion.  Assuming a $100 billion market cap for Facebook, and applying Facebook’s brand value to market cap ratio, that pegs YouTube at a $62.2 billion value.

UPDATE: The last paragraph has been edited to input the correct brand value and market cap numbers thanks to Matt in the comments below.  Again, more detailed numbers analysis to follow sometime next week.

Author Disclosure: Long GOOG

TED Talk with Barry Schuler on Genomics 101

Genomics today is one of those fields where we can witness the Innovator's Dilemma unfold in realtime.  I am particularly intrigued by genetic sequencing for a number of reasons.  The idea that we are simply complex programs with a coding system that has double the inputs of a binary computer system has major consequences in terms of religion, philosophy, and most visibly, health.  This is heavy stuff for a former philosophy major!  Further, the "anti-evolutionists" have a big problem with this reality, because it willingly confirms and implies we are of the same fiber as every living substance on Earth, have thus evolved from the Great Apes, and are already developing the capacity to manipulate the structure of our internal code.  Can we once again call this "debate" over?

In reality, this is a good, not bad thing!  With the understanding of how our most basic system works, we can officially launch into an entirely new era of medical diagnostics and treatment.  While medical costs have been soaring in the United States to the tune of a decade and a half of double-digit growth, genomics, once wildly expensive, holds the key to generating enormous cost and treatment efficiencies.  With knowledge of an individual's genome, we can came up with better treatments for each individual, skip many of the painfully unnecessary diagnostics, and develop a personalized course of action, all at a lower total cost.  

Plus, as an added bonus, we can refine our Pinot Noir grapes to taste as we want it, grow where we want it, how we want it.  I'll let Barry Schuler take over from here on what genomics is doing for us today, and what we can expect to see tomorrow (here's the link in case the embed doesn't work):