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  • What Is A Bubble?

    Economics is known as the dismal science and, in my opinion, we are living in a dismal era of economics in this post-crisis world. Whether we are talking about efficient markets or Keynesian economics or secular stagnation, there seems to be so much disagreement in the field. The Nobel Prize was awarded to two people who have ideas that are opposed to each other.

    Background

    One of the Nobel winners mentioned above does not recognize the terminology of a "bubble" because markets are said to be efficient and are only priced relative to expected risk and reward. There is no such thing as any significant under- or over- mispricing. The other winner of the prize is famous for his work on bubbles, particularly in real estate and for testing volatility of market returns.

    There are two ideas in play when talking about stock market bubbles. The first is that market participants digest and incorporate information into market pricing rather quickly.

    When this idea is underpinned with the idea of Rational Expectations (RE), conclusions get a little dicey. RE says that people make forecasts about the future and any forecast errors are randomly made. As such, there is just as likely a chance that one market participant makes a positive forecast error and another one a negative forecast error. The two offset each other and the market, as a whole, is priced to be the market's best guess. As such, future movements are random and are subject only to new information that could not have been known ahead of time, a random walk as they say.

    Taken together, the argument goes that no investor can beat the market consistently because the market price represents the statistically "right" price.

    The idea of a bubble speaks to something different than such a market outcome. To me, it can be either an over- or an under- material mispricing.

    Forecast Errors

    Now there are probably nuances in my explanation above. However, the outcome is the same: investors that beat the market consistently are said to be mere statistical anomalies. This conclusion is based on the randomness of forecasting errors and that economic agents are, on average, right over time, making market pricing right. Where I find this argument not realistic is in the fact that man is a social animal. The reason why people walk in pairs down dark alleys is that 4 eyes are better than 2. Predicting the future is tough enough and a person would be foolish not to seek alternative opinions about the future from someone else.

    If you take into account that people forge their expectations based on the expectations of someone else, you can see how one person making one forecast error can morph into many people making the same forecasting error. In other words, there may be less randomness in forecast errors than what RE suggests. Because people talk to each other, there might exist forecast errors that are correlated in a given market.

    Consider the modeling of MBS before the financial crisis in this article about David Li, the creator of the common quantitative model used to price these securities. The article makes mention that most investment managers adopted this model as the industry standard, yet the model was inappropriate. As a result, consider the effect it had on the overall market pricing of MBS. Everyone was using the same model. There was a correlation of market participants making the same forecast error at the same time.

    My Definition

    So, in light of this background, this is how I myself define a bubble:

    A bubble is a situation where a large group of market participants is making the same forecast error, whose effect on the market price is a material divergence from what that market price would be in the case where that forecast error was not being made.

    In laymen speak:

    A lot of people believe in the same bad idea, enough to have a real effect on the market price.

    This is the framework by which I consider investments in practice. The extent of such a material divergence is net of the extent that arbitrageurs or smart money can bring market prices back into line. In other words, I believe there are limits to arbitrage in the real world. The following section discusses specific examples where this framework came into play to illustrate my point.

    Investing Examples

    I am a value investor and have been writing on SA for the past one and a half years, writing commentary and making stock picks. My goal is to get an analyst job after graduation. At this point, my average pick at the one year mark subsequent to publication has beaten the market by +48.5%. My top three picks (out of 14 total) have outperformed the market by +160%, +143%, and +92%. What these picks have in common is that they are contrarian, relative to the crowd, a crowd that I figured was making a significant forecast error. Here is the thesis for each real-world example:

    hhgregg (HGG) - "hhgregg: Not Only Surviving but Will be Thriving"

    Return after 1 year: 187.9%

    Return after 1 year, relative to market: +160.4%

    Just like Best Buy (BBY) a little over a year ago, the market was talking about "showrooming" at consumer electronics stores and these stores were posting negative comp sales rates. Market participants figured that one was leading to the other. The market was pricing these companies to die. HGG had a P/E of less than 5.0 and short interest was over 30%; this is indicative of a market that believed in a dead model. Showrooming, they said, was going to put these retail stores out of business. The problem was that the negative comp rates were not a function of showrooming. There was a cyclical downtrend in consumer electronics that coincided with the housing downturn. When people bought fewer houses, there were fewer big screen TVs and large appliances being sold. BBY also saw declines in its music business which was largely a function of iTunes and other online content providers. This opportunity came about only because the showrooming forecast error was prevalently held by market participants and prices were driven down to such low levels.

    NuSkin (NUS) - "Citron, Thanks for a NuSkin Buying Opportunity"

    Return after 1 year: 161.4%

    Return after 1 year, relative to market: +142.9%

    The reason why NUS was undervalued, as the title of the article suggested, was because market participants were making a forecast error related to the unfavorable "research" published by Citron Research. Citron argued that regulators in China and the US were going to disallow the NuSkin business model, not unlike Bill Ackman's claims about Herbalife (HLF). I came out in favor of Herbalife with a little logic just a week after Ackman gave his lengthy presentation which turned out to be invalid as he was outmaneuvered by Carl Icahn. Investors just did not like the stink around these multi-level marketing stocks and overestimated the probability that regulators would actually take the step of outlawing such companies especially at the whim of activist investors who seemed not to appreciate the precedent of all the legal proceedings and history of this industry. This pick was made possible by the market overestimating regulatory action, again a large forecast error, resulting in bargain basement pricing.

    Pitney Bowes (PBI) - "Is Mail Dead? Analysis of Pitney Bowes" and "Is Mail Dead? Analysis of Pitney Bowes (Part 2)"

    Return after 1 year: 127.6%

    Return after 1 year, relative to market: +92.4%

    For some reason, many market participants thought that mail as we know it was going to end sometime in the near future. This, despite the fact that returns on investment in mail advertising (i.e. junk mail) is still competitive with other advertising mediums. Further, it will likely take an amendment to the Constitution (far more difficult than an act of Congress) to ever get rid of the Post Office. Therefore, there will be a need for mail stuffing and sorting machines. That, coupled with the fact that PBI sold a large percentage of its machines to financial institutions before the crisis (think of those 0% balance transfer days), made the negative comp rates more understandable. The valuation metrics were low and the short interest was high. The success of this pick was underpinned by the market thinking that mail was dead, a significant forecast error, resulting in a significant mispricing.

    Conclusion

    Now I will stop tooting my horn and admit that I will make many a forecast error myself. However, these examples illustrate that, if enough market participants believe in the same bad idea, that there could be an investing opportunity if you can justify an alternative thesis. This is the essence of value investing. I define a bubble to be a situation where there is a correlation of market participants that are making the same forecast error that is sizable enough to cause a market's price to diverge materially from the price that would exist in the case where that forecast error was not being made by those participants. Having the value investor perspective is the key to identifying these type of situations because you don't take for granted that markets are priced right at any given time. For the most part they are but sometimes you might find a great deal.

    Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

    Tags: BBY, HGG, HLF, NUS, PBI, economy
    Jan 07 12:37 PM | Link | 2 Comments
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