Saturday, April 7, 2018

How Low Can Tesla Go?

        Given my writings on Tesla, a common question I am asked is how low can the stock price go.  To answer the question, let me first put aside the possibility of a takeover.  In that case, zero comes into play.  Tesla has over $10 billion in debt and it is not clear that the company is worth more than that as an operarting entity.  Yes, the company's cars have been a great innovation, but so was air travel.  And as Warren Buffett observes virtually every airline went bankrupt.  Tesla has not proven that it can profitably make, sell and service cars so bankruptcy is something to worry about.

         However, I do not think it will come to that because before the company collapses someone with deep pockets and a lot of cash, think Google or Apple, will buy it.  Given Tesla's brand name and technology, a company with sufficient resources and greater focus on the blocking and tackling of doing business should be willing to buy it - but not at anything like $300 per share.  In my view, $100 per share would be the most a buyer would be willing to pay and $50 is more likely.  So if you ask how low Tesla can go, my best answer is $50 per share.

Thursday, March 29, 2018

Corporate Stakeholders and Corporate Finance - The Case of Tesla

      Way back in 1988, Alan Shapiro and I published a paper called "Corporate Stakeholders and Corporate Finance."  The point of the paper was to highlight a manner in which corporate finance policy could have a major effect on corporate value - in contradiction to the famous Miller-Modigliani theorem.  The paper proposed that corporate finance affected value through its impact on corporate stakeholders, particularly customers.  Our point was that customers would shy away from a company in financial distress because they feared the products would be terminated, support and repairs would be withdrawn, and second hand prices would plummet, if the financial distress worsened.  This leads to a downward spiral.  If customers stay away, revenues drop and the financial crisis becomes more acute, leading to further loss of customers.  The implication is that companies that want to keep their customers need to be sure to have plenty of financial slack so that customers do not have to worry about the company's future.

      That leads to Tesla.  With cash dwindling and debt ratings falling Tesla is headed for possible financial distress.  For the first time, potential customers are lighting up the internet with concerns about buying cars from a company that might not be able to support them.  It is not yet a Cornell-Shapiro downward spiral yet, but it is something to worry about.

Wednesday, March 28, 2018

The Tesla Meltdown

      If you have followed this blog for the last couple of years, you have heard me harp on how difficult it was to reconcile Tesla's market price with fundamental value.  Tesla kept sailing along at a market price of abour $350 despite all the issues I stressed which included: the company's lack of experience with mass market production and service, no proprietary technology, numerous successful competitors with decades of experience, the need to grow rapidly while maintaining margins, the need to raise more capital, and so on. 

      For years, none of this seemed to matter and then it did.  In a space of about two weeks Tesla suddenly fell to $250 on no major fundamental news - no revelations about the Model 3, no sales data for the S and X, no admission that more capital would have to be raised, no major introductions of new cars by competitors.  Suddenly confidence just seemed to sag.  This is what I find so interesting and frustrating about companies whose values are based on optimistic projections without a reasonable tie to fundamentals.  They can sail along for years, causing many shorts to give up and cover, and then in a flash sentiment changes and the price drops.  It will be interesting to see how low Tesla can go.  Even a price of $250 is difficult to justify from a discounted cash flow perspective.  If people stop believing in the magic of Elon Musk $150 is a real possibility.

Sunday, March 11, 2018

The Bubble Wars

            A war continues to rage amongst finance professionals regarding the commonality of bubbles.  The problem is that present value relation can be interpreted as an identity.  There are always discounts rate and cash flow forecasts that can justify any price.  Therefore, a non-bubble story can always be constructed post hoc to explain any observed data.  The question, therefore, comes down to how reasonable are the required discount rates and cash flow forecasts.  That immediately suggests a laboratory environment in which information can be controlled.

              The need for a controlled environment was not lost on Vernon Smith who designed a series of classic experiments for which he was awarded the Nobel Prize.  It has been known at least since the work of Tirole (1982) that a critical source of bubbles are the beliefs of investors about the information and behavior of other investors.  To examine, the potential impact this source, Smith controlled the information carefully in his classic experiments.  Each of his  experiments had 15 trading periods during which investors could buy or sell a security.  The experiments were run in a double-auction setting in which the only source of cash flow from the security was a dividend paid at the end of each trading period.  Every participant was told that the security pays a random dividend with four equally probable outcomes in each of the 15 periods and becomes worthless at the end of the experiment. Hence, the fundamental value for a risk-neutral trader is each period’s expected dividend times the number of remaining periods. Even though there is no asymmetric information, and every trader knows that there is no asymmetric information, there is vigorous trading and prices evidence bubble like behavior.  More specifically, there is typically an initial boom phase that is followed by a period during which the price exceeds the fundamental value, before the price collapses towards the end of the experiment. A string of subsequent articles by Smith and others generalized the experimental environment and showed that bubbles still emerge after allowing for short sales, after introducing trading fees, and when using professional business people as subjects.

            Smith’s results are virtually impossible to explain without an appeal to bubbles.  The cash flow probability distribution is set and everyone knows what it is.  Time discounting is irrelevant over the course of a two hour experiment.  If there is risk discounting, it too should be constant over the life of the experiment.  The fact that prices frequently boomed and collapsed reveals that even in this tightly controlled environment, participants were speculating about and trading on the beliefs of counterparty investors and the associated behavior of prices.

            Despite Smith’s findings many financial economists dismiss his results as an artifact of the laboratory environment.  Such a conclusion seems odd, because Smith set up the experiments to reduce the likelihood that a bubble would arise.  For a real security, there is no common knowledge of the probability of distribution of future cash flows and there is no immediate horizon such as the end of the experiment.  In such a setting, it becomes much more likely that investors will speculate about and trade on estimates of the beliefs of other investor and the associated behavior of prices.  Or, to be more blunt, they will buy a security with the belief that they can sell it for more in the near future, independent of an assessment of the security’s fundamental value – that is they will attempt to ride the bubble.

            So how do you decide whether in any particular case whether a sharp run-up in price is the result of a bubble or is due falling discounts rates and rising cash flow expectations.  Quite frankly, there is no general solution.  Indeed there cannot be one because it all depends on whether the changes in discount rates and cash flow expectations required to rationalized a price run-up are reasonable.  That is why the war regarding the commonality of bubbles persists. 

            To examine a particular case, figure 1 plots paths of wealth for the CSRP market portfolio, Disney (arguably Netflix’s primary competitor) and Netflix for the period from January 1, 2015 to March 6, 2018.  The run-up in Netflix is clearly extraordinary.  The figure shows that during the period, the market advanced 36%, beating Disney that rose on 16%, but Netflix rocketed up 566%.  The run-up occurred despite the fact that the stock price on January 1, 2015 already impounded a lot of expected growth – the P/E ratio at that time was 102.

            So can discount rates and cash flow expectations explain the Netflix run-up without a bubble?  Discount rates do not work.  Changes in the risk-free rate or the equity risk premium would also have affected the market and Disney as well, but neither came close to matching the performance of Netflix.  It is possible that the beta of Netflix dove, but Figure 2, which plots the rolling six-month betas of Disney and Netflix, puts that hypothesis to bed.  The betas fluctuate quite a bit due to measurement error, but there is no observed tendency of the Netflix beta to fall, if anything the reverse is true.  In fact if any beta fell, it was that for Disney.  That leaves revision in cash flow expectations as the only possible non-bubble explanation.  Was the news during the period sufficiently positive that even from a starting point with a P/E of 102, it justified the price of Netflix rising by a factor of 6.66?  Our answer after reviewing all the news releases is no, but we recognize that our conclusion requires an interpretation of the news and interpreters can differ.  That is why the bubble wars continue.  However it is worth keeping in mind that a bubble does not have to be responsible for the entire increase.  Fundamental information likely explains part of the rise, but if it does not explain all the increase, what is the alternative to a bubble?  Saying it is “sentiment” is just another word for bubble – price increases that cannot be explained by fundamentals.

            Netflix is admittedly an extreme example.  Run-ups of that magnitude are rare.  The problem is that for smaller run-ups, it becomes more difficult to distinguish bubbles from rational valuation changes due to changing discount rate and cash flow expectations.  Prof. Smith’s experiments imply that bubbles are common, but he had the luxury of knowing the fundamental value of the securities.  Without that information, and with the possible exception of extraordinary examples like Netflix, the bubble wars are likely to continue.    

Thursday, March 1, 2018

The Market Can Stay Irrational Longer Than You Can Stay Solvent

     This famous old Wall Street warning is worth bearing in mind as we explore the possibility of bubbles. Because there remains no theory that explains how a bubble starts or when it will end, it is possible to be right in the assessment that current prices are irrationally high due to a bubble and still be wiped out as the bubble inflates further.

     A telling example is stock market performance during Zimbabwe’s hyperinflation in 2008 and 2009.  The hyperinflation is well-known.  Their stock market bubble less so.

     The figure below  shows the performance of the Zimbabwe overall stock market, in blue, while the currency was collapsing (in red, a rising exchange rate means that the currency is tumbling). The scale on this graph is quite astonishing.  During the three months from the beginning of August until the end of October, the currency fell from 10 to 1000 per US dollar, a 100-fold currency collapse in just three months.  Did this hurt the stock market?  Hardly!  The stock market rose 500-fold in just eight weeks, during which time the currency fell ten-fold.  So, In US dollars, the market rose an astounding 50-fold in eight weeks. In the next two weeks, the stock market plunged 85%, even as the currency tumbled another three-fold.  Again, adjusted for the tumbling currency, the stock market dropped 95% in two weeks.

     That’s when the currency and the stock market stepped up the volatility another order of magnitude.  When the hyperinflation went into overdrive, with purchasing power falling ten-fold in less than a week, the stock market fell 99% (99.9% adjusted for the tumbling currency) in that same week.  The stock market then ceased to exist. 

      Suppose an investor had the clairvoyance to know that the market was going to tumble 1000-fold (in US dollar terms, after adjusting for the tumbling currency), in the next three months.  And, suppose there was a way to short-sell that market.  This would seem to be a “can’t lose” proposition.  Even with prescience, knowing that the market was going to zero in three months, the investor would have first lost 50 times our money, and quite possibly bankrupted, before being proved correct!

     This is one of the most challenging attributes of bubbles.  They are hard to transform into profits, even for investors who correctly discern them, because the late stages can take valuations into the stratosphere. 

Tuesday, February 27, 2018

Yes, It's a Bubble, So What?

       A company is never so good or a situation so favorable that it cannot be overpriced.  In my view, that is our situation today.  The pricing of many companies and the market generally suggests a bubble.  The questions are why to I reach that conclusion and what should investors do if I am right?  This article starts a series of posts addressing the issue.

        The word “bubble” is thrown around carelessly and often, but it has no formal definition.  Let’s try.  I define a bubble as asset pricing that exceed the present value of any reasonable projection of expected cash future flows, thereby offering little chance of any positive risk premium relative to bonds or cash. I might add the provision that the pricing is sustained because investors believe that they can sell the asset to someone else for a higher price tomorrow, more or less regardless of the fundamentals.  In order to apply the term “bubble,” I need to strongly believe that this definition applies.  Borderline calls don’t qualify.

         Most academics, especially adherents of neoclassical finance, will dismiss our arguments.  Even practitioners will assuredly be split on the topic (after all, for every seller, there’s a buyer!).  That said, Vernon Smith won his Nobel Prize in part for his demonstrating irrationality in markets, including bubbles and crashes, even in laboratory situations where valuation uncertainty was carefully controlled.

        Are we in a bubble today?  Reasonable observers can disagree, but my answer is “yes, it’s a bubble.”  Start with bitcoin.  It is hard to argue that bitcoin has any fundamental value, but nonetheless its price rose by 1,600% in 2017.  Even if we assume that bitcoin has merit as a libertarian alternative to government-sourced fiat currency, it’s hard to justify 1500 different cryptocurrencies.  Many of these were launched with the singular goal of making the originator of the cryptocurrency wildly wealthy in a CPO (coin public offering), while creating something of no real value.  It’s even harder to justify the myriad exchanges, which offer a receipt indicating that you own cryptocurrencies on their platform, nearly half of which have been hacked by the time of this writing, costing customers billions.  As for the platforms that offer lofty interest rates for you to lend them your cryptocurrencies, there can be little doubt that these vendors view them as “kleptocurrencies,” ripe for the taking.  And now we have bitcoin futures, permitting leveraged investments in one of the most volatile “assets” ever created.    

       With regard to stocks, at the end of 2017, seven of the eight largest-cap stocks in the world were tech fliers.  When has any sector so dominated the list of largest market-cap companies?  Never.  At the peak of the tech boom, it was five of the top eight; at the peak of the oil bubble, it was four of the top eight.  Only the Japan bubble of 1989 matched today’s dominance of the top tier in global market capitalization, with nine the top ten market cap names in the world hailing from Japan, dominated by Japanese banks.  Not only do we have the FANGs, we have FANG+ futures, affording investors a chance to buy the worlds trendiest tech stocks with almost no collateral, with the list amended quarterly to make sure only the trendiest are on the list. 
        History shows that, on average, just two stocks from the global top ten list remain on the top ten list a decade later.  The survivors almost always are the number one stock, plus one other.  The number one stock has never been top dog a decade later; it has always underperformed, to a lower rank on the list.  The eight non-survivors have always underperformed, in order to drop off the list.  The second survivor sometimes is higher and sometimes lower on the list.  It has 50/50 odds of beating the market.  So, nine of the ten underperform, and one might or might not win.  

        Can Alphabet, Apple, Amazon, Microsoft, Facebook, Alibaba and Tencent collectively succeed  sufficient to justify their $4.3 trillion combined market capitalization at end-2017?  Nothing is impossible, but this outcome is implausible.  They’re at war for market share.  And they’re all competing for the same eyeballs.  If history is a useful guide, Apple will still be on the top ten list (but no longer number one) in 2028; perhaps one of the others will still be on the list.  Finally, history would suggest that, of the remaining eight, six or seven will have underperformed the market.  

Thursday, February 8, 2018

The VIX Collapse

     Last month I published an editorial in the Journal of Porfolio Management that turned out to be somewhat prescient.  It deals with the VIX index and the problem on non-stationarity that I have addressed here before.  The crux of the article follows.

            In my role as an academic, I play down the importance of stationarity to get on with research efforts.  When I have to make investment decisions, it is the elephant in the room.  In fact, the question of stationarity is so important that it often dominates my investment decision-making and as a result renders much academic research of little practical value.  The point of this commentary is to argue that finance research needs to take the question of stationarity more seriously to be more useful to investors.

            Formally a stationary stochastic process is a stochastic process whose joint probability distribution does not change when shifted in time.  Consequently, parameters such as mean and variance, if they are relevant, also do not change over time.  Non-stationarity should not be confused with unpredictability.  All random processes are unpredictable.  If the process is non-stationary, even the parameters of the random distribution cannot be estimated with confidence.  Putting aside formal definitions, I find the example of drawing colored balls from jugs with replacement to be a great way to explain how the problem of stationarity impacts investment decision making. 
            If there is one jug and the balls are drawn from it with replacement, the process describing the sequence of balls drawn is stationary even though the actual color of the ball to be drawn is random.  If suddenly a new jug is introduced with a different mix of balls and the next series of draws is from a mixture of the two jugs, the process is non-stationary.  However, this is what can be called a limited degree of non-stationarity.  By simply redefining the procedure for drawing balls, a new stationary process emerges that involves two steps.  At the first step, one of the two jugs is randomly selected.  At the second step, a ball is drawn from the chosen jug.  As long as this procedure is followed the new process, though more complicated, is stationary.  In fact, the new process can be interpreted as an example of a regime switching model in which first the regime is chosen and then a random ball draw occurs.
            The balls and jugs analogy is useful for conceptualizing differing degrees of non-stationarity.  The important questions include: How many jugs are there?  Can the number of jugs even be enumerated?  What is the distribution of balls within each of the jugs?  In the limit, think of the case where there are an immense number of jugs, the contents of which are unknown, and where the probabilities of selecting a given jug are also unknown and may be changing over time.  This limiting case I refer to as fundamental non-stationarity.  Although this may seem like an extreme case, I argue that it is a problem that investors face on a daily basis.  Fundamental non-stationarity is not a rarity, but the normal state of affairs.  To explore the issue further, I consider examples of four investment decisions.
The surprising behavior of the VIX index
            The VIX index, calculated by the Chicago Board Options Exchange measures the market's expectation of 30-day volatility. It is constructed from the implied volatilities of a wide range of S&P 500 index options with approximately 30 days to maturity.  As of October 2017, the VIX had been near record lows for more than a year.  The average was about 11% compared to a long-run historical average of 15% or more depending on the sample period.  The investment question is whether this abnormal behavior suggests taking a position in VIX derivatives.
            One way to approach the question is to turn to the academic literature on fitting stochastic models to the VIX index.  It turns out that the literature is both large and highly sophisticated mathematically.  A few recent examples among the many papers include Goard and Mazur (2013), “Stochastic volatility models and the pricing of fix options,” Zang, Ni, Huang, and Wu (2016), “Double-jump stochastic volatility model for VIX: Evidence from VVIX,”, and Kaeck and Alexander (2013) “Continuous time VIX dynamics: On the role of stochastic volatility of volatility.”  In their defense, these papers, and others like them, do allow for some non-stationarity along the lines of the two jug analogy.  They do so by incorporating the possibility of random jumps or stochastic volatility.  The problem I have as an investor is that I fear the process during the current quiescent period is not just a result of a random failure of jumps to materialize or a random drop in volatility in a stochastic volatility model, but a fundamentally different process. 
            Of course, if a model is fit with enough flexibility in its parameters, it will appear to account for the non-stationarity during the sample period but in doing so it will misstate the true nature of the process.  From an investment standpoint this is critical because if the true process is fundamentally non-stationary, at some point it will change in a manner unanticipated by investors.  If the change involves drawing from an entirely new jug among a vast number of jugs, a complex process fit to historical data will simply be misleading.  This is, in effect, the argument Taleb (2007) makes with regard to the financial crisis.  But the observation is not limited to the dramatic, “black swan” events that Taleb describes.  If the world is fundamentally non-stationary, it is a problem that investors face continually to varying degrees as the social, political and economic environments evolve. 
            In particular, the stochastic process for the VIX will change when the social, political and economic factors, which are yet to be delineated, that led to its historical low mean value, are transformed.  One such factor that could have altered market volatility was the election of Donald Trump.  However, the fact that such an hypothesis is speculative is precisely the problem.  As Ross (2005) observes, even after the fact it is difficult to identify events that may have altered the stochastic process of asset returns.
The cross section of expected returns
            Following the lead of Fama and French (2002), intense interest in factor models designed to explain the cross section of expected returns has led to extensive research in the area.  As Harvey, Liu and Zhu (2016) document, that research effort has produced a veritable zoo of allegedly significant factors.  Based on their review of the 313 articles, the authors report the identification of 316 priced factors.  This factor zoo led Harvey, Liu and Zhu to argue for the use higher cut-offs for statistical significance in order to overcome the impact of apparent data mining.
            Data mining and non-stationarity are different issues, but they can have a similar impact from a practical investment standpoint.  Data mining refers to the problems that arise when there is repeated sampling from the same historical data set.  The most common problem that results from data mining is the “discovery” of idiosyncratic quirks that are unique to the sample, but are not actual true relations.[1]  As a result of data mining, spurious relations uncovered in the sample period will fail to hold post sample.  When the data are non-stationary, a relation may be found that does, in fact, hold for the historical sample period but that is no longer true.  Once again, the relation fails to hold in the post sample period but for a different reason.
            The failure of factor models estimated in one period to hold in another may be due to either data mining, non-stationarity, or some combination.  Either way, given the vast zoo of factors that have been uncovered, we (the research profession) are almost assured of finding a factor model that explains the cross section of expected returns in any chosen historical sample period.  However, it remains unclear what practical value this has for investors who cannot be confident that the relations will hold going forward.
Individual Stocks
            With regard to individual stocks, language is an impediment to appreciating the full extent of potential non-stationarity.  Throughout its corporate life, Apple has always been called Apple but the company has reinvented itself numerous times.[2]  In the process, it transformed itself from a start-up maker of personal computers into a global consumer product and services powerhouse despite having several brushes with insolvency.  Of course, it is possible that the process for stock returns remained stationary while the company was continually transformed because stock returns depend on investor expectations.  But it would be foolhardy for an investor to assume that the dramatic evolution of the firm did not have a major impact on investor perceptions, including investor estimates of risk, and thereby on stock returns.
            It is worth noting that applied investment research, by that I mean the work of security analysts, appears to take the problem of stationarity for granted.  If the stochastic process generating key metrics of financial performance, such as revenue, earnings and free cash flow, were stationary then presumably the best way to project future financial performance would be to fit statistical models much like those used to analyze the VIX index.  This is not what analysts do.  Instead, they examine the details of the company’s business with the hope that the understanding they achieve will help them predict future financial performance.  This can be interpreted as an effort to overcome non-stationarity by attempting to predict how future business conditions will generate revenues, earnings and free cash flow given currently available information.  In the context of the balls and jugs analogy, security analysts are using fundamental analysis to select the jug.
Smart beta and factor premiums
            As a final example, there has been an active debate recently regarding so called “smart beta” and associated factor premiums.  As Asness (2016) notes, smart beta and factor-based strategies have become increasingly popular in recent years.  The goal of these strategies is to identify factors, of which Fama and French’s SML is an early example, and then to harvest the factor premium by investing in long-short portfolios.
            As Arnott, Beck, Kalesnik and West (ABKW, 2017) repeatedly state, though they do not couch their argument explicitly in terms of stationarity, this investment strategy is based on the assumption that the stochastic process governing factor returns is sufficiently stationary that past average premiums are reasonable estimates of future expected premiums.   ABKW argue that the assumption is false.  They claim that research identifying historical factor premiums has failed to adequately account for the extent to which rising valuations contributed to the lofty historical returns.  Based on their empirical research, ABKW conclude that valuation increases have been the primary driver of smart beta returns over the short term, and even long term, and as a result past excess returns are not likely to be sustainable in the future.  In fact, ABKW suggest that factor portfolios that have markedly appreciated could “go horribly wrong” and potentially crash.  The point here is not to evaluate whether ABKW are correct, and there are many authors including Asness (2016) who argue their conclusions are exaggerated, but to note that the entire debate is basically a dispute over stationarity.
            In the context of the jugs and balls analogy, valuation increases can be thought of as drawing from a jug without replacement.  Every time say a red ball is drawn, the probability of drawing another red ball declines.  For this reason, the distribution is non-stationary.  The probability of drawing a red ball can be interpreted as the probability that a factor portfolio will earn excess returns.  The more the valuation increases, the more red balls are drawn, and the less likely it will be that valuations will rise in the future.
            Perhaps the most controversial factor premium in this regard is momentum.  Early papers such as Jegadeesh and Titman (1993) found significant premiums associated with momentum.  Then later papers including Dolvin and Foltice (2017) argued that the anomaly had disappeared.  Simultaneously, Moskovitz and Daniel (2016) reported significant crash risk associated with momentum, but Barroso and Santa-Clara (2015) claimed that this risk could be ameliorated by varying leverage of the momentum portfolio.  And this is just a sliver of an immense and internally contradictory literature on momentum.  From the standpoint of a practical investor, the safe conclusion is that if there is a momentum effect, it is far from stationary.
            The four examples offered here are by no means unique.  Similar arguments apply to most every investment strategy based on estimates of statistical parameters derived from historical data.  All such strategies assume, explicitly or implicitly, that world is sufficiently stationary that such estimates are of practical value to investors.
Conclusions and implications
            The basic conclusion is straightforward.  Non-stationarity is not a minor statistical annoyance but a fundamental and unavoidable issue that investors face each time they make an investment decision.  I argue that there is generally insufficient evidence to support the assumption that the processes underlying social institutions (including financial markets), unlike those underlying many physical systems, are stationary.  Such non-stationarity includes not only the possibility of large, unexpected breaks from the past as occurred during the financial crisis, but daily changes in the stochastic processes governing asset returns.  It is not surprising, therefore, that fundamental security analysis, which takes non-stationarity for granted, remains the basis for most practitioner-based investment research.    

[1]  My favorite example of data mining involves Richard Feynman and the expansion of Pi.  Feynman would reel off the first 768 digits of the expansion, the last six of which are 9-9-9-9-9-9, and then say “and so on” before breaking into laughter.  The 763rd digit of Pi has now become known as the Feynman point, but the six 9s have no meaning.
[2]  To be fair, the original name of the company was Apple Computer which was shortened to Apple as other devices (which are actually computers) became the predominant source of the company’s revenue.  However, throughout its life the company has generally been referred to as Apple.

Monday, February 5, 2018

It was just a matter of time

      This is what makes investing so difficult.  On this blog, I have been wringing my hands for more than a year over what seemed like excessive stock prices.  As a result, my hedge fund missed most of the run-up in the past year.  But there is an upside.  By the end of January, our exposure to the market was minimal.  What goes round comes round.

Tuesday, December 19, 2017

Efficient Markets

      As a professor, I tend to be quite wedded to the idea of efficient markets that has played such a large role in academic research.  But every so often something comes along to remind that assets do get mispriced.  Below is one of the funniest examples.

U.S. regulators temporarily suspended trading in Crypto Co. over concerns that the stock is being manipulated after it surged more than 2,700 percent this month, making paper billionaires out of top executives. Crypto is the product of a reverse merger with a company that made water and radio-wave resistant sports bra pockets, according to a November filing. Last week it issued stock to accredited investors at $7, a 97 percent discount to the prior day’s closing price.