ECON 7200 Efficient Market Hypothesis in Behavioral Finance Paper

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ECON7200 – Semester 2, 2021 Individual Project Deadline: Monday 11 October 2021 16:00 Write an essay between 800 and 1200 words (excluding references), consisting of three tasks: 1. Read and summarize the following article: • Malkiel, B. G. (2012). The Efficient-Market Hypothesis and the Financial Crisis. In A. S. Blinder, A. W. Loh, and R. M. Solow (Eds.), Rethinking the Financial Crisis, Russell Sage Foundation, New York. 2. Explain the efficient market hypothesis (EMH), behavioral finance, and their relevance in explaining asset price bubbles. 3. In the end of the article, Malkiel concluded that“EMH and behavioral finance should not be considered as competitive models”. Discuss how he reached this view, and discuss your view on this statement. Criteria and Marking: The total mark for this assignment is 30, which consists of the following criteria: • • • Article summary (4 marks) EMH and behavioral finance (6 marks) Discuss the author’s view (10 marks) • • • Writing Quality (5 marks) Presentation (2 marks) Reference (3 marks) In the Writing Quality criterion, your writing style, essay flow and grammar/spelling will be examined. The Presentation criterion includes various details such as document format, identification and compliance of word limit. In addition, there are some general points to consider: • • • • • • • • Your essay must have a cover page detailing your ID and word count. Remember to give a total word count (excluding references) on the cover page. You cannot use any bullet points in explanation or summary. Any reference style is allowed, but it must be consistent (i.e. same style throughout the essay). You cannot cite Wikipedia or Investopedia. If graphs/figures are used, remember to quote the source. Use academic writing style and not creative writing style. Do not plagiarize! It will be dealt with very seriously in UQ and is simply not worth trying. Submission: Each student should submit a Word or PDF file through the Turnitin link on the Blackboard course website. Submission through email will not be accepted. All submissions will be run through the Turnitin anti-plagiarism software. ECON7200 – Economics of Financial Markets Individual Project An Essay on the Economic Behaviour Leading to Bubble Formation via the Lens of the Dot-Com Boom and Today. Name: Tyson Ruth ID: 40800756 Word Count: 1299 (Ex references/footnotes) ECON7200 – Individual Project Tyson Ruth: 40800756 Throughout the existence of markets and tradeable commodities, asset bubbles and speculative manias have formed and deflated ad-nauseum throughout recorded human history (Mackay, 1841). This essay aims to discuss several impactful factors with respect to asset price bubbles in the context of the dot-com boom, the global financial crisis, and today’s rapidly rising technology stock prices. This discussion will be framed in the lens of behavioural economics in contrast to the efficient market hypothesis (EMH). Three elements will be of focus – Goodnight and Green’s 2010 paper “Rhetoric, Risk and Markets: The Dot-Com Bubble”, behavioural economics’ explanation of said bubble, and whether today’s technology company share prices will result in another dot-com bubble crash. The work of Goodnight and Green (2010) is of particular interest in the sense that it approaches the breakdown of financial markets during the spectacular rise and subsequent fall of technology asset prices in 1999-2000 from the perspective of speech, mimetics, communicatory feedback loops, and an attention economy of worth. Their paper begins with a discussion of the circumstances of the Dutch tulip bubble, referring to Mackay’s (1841) book “Memoirs of Extraordinary Popular Delusions”, wherein speculation led tulip bulbs to substantially elevated prices before a significant collapse.1 Goodnight and Green find that bubbles spread in stark contrast to the efficient market hypothesis in circumstances where investors switch from “imitating standard, rational, probability based-models to copying arguably novel ventures with enticing uncertainties” (2010). The authors further show that behavioural and post-conventional economic methods attempt to explain such deviations in rationality. Goodnight and Green state that market participants exist within a survival game and their self-motivated constituent agents utilise mimetic isomorphism in order to imitate successful survival processes (DiMaggio & Powell, 1983). This feedback loop of (initially) rational imitation gradually converges with newer, degenerate narratives when unconventional speculative agents gain an initial outsize success – as communicative and mimetic elements disseminate their success to the greater market, imitators become copiers – resulting in a self-fulfilling prophecy. The authors conclude that economies of value eventually converge to economies of worth in bubble-environments. Upon signs of significant weakness (rapidly perpetuated by communication innovations), the initial rush to sell is further imitated by the copiers resulting in spectacular collapse. With a dramatic financial shock, participants (who survive) return to rational methods post-hoc (Goodnight & Green, 2010). The findings of Goodnight and Green (2010) can be developed further by discussing behavioural economics and its role in explaining the events of the dot-com bubble. Kahneman (2011) found that, while attempting to be rational, self-interested humans do not have the resources to pursue optimal rational decisions due to the time and mental cost of doing such. Instead of pure rational-optimal decision making (homo-economicus, who seemingly exist only in Economic theory), agents instead possess bounded-rationality – agents will make decisions at the individual’s optimal curve of effort/perceived-reward, resulting in an effort saving number of heuristics and biases. These heuristics and biases – while effective for surviving in the wild result in suboptimal decision making when applied to financial market dynamics. Kahneman discusses the reoccurrence of asset bubbles wherein particular agents mentioned “knowing” an asset collapse was inevitable – however, this post-hoc/hindsight approach to bubbles is another behavioural bias – humans believe that the past is understood, “but in fact we understand the This book was later reprinted as “Extraordinary Popular Delusions and the Madness of Crowds”. This author highly recommends a careful study as it also covers the Mississippi Scheme and the South-Sea Bubble from the worldview of the 1800s. Speculation is as old as time (Lefevre, 1923). 1 1 ECON7200 – Individual Project Tyson Ruth: 40800756 past less than we believe we do” (Kahneman, 2011, pp. 201-202). It is these same agents who go on to participate in future bubbles by nature of existing within the market.2 Thaler (2015, pp. 203-256) further develops Kahneman’s ideas by explicitly discussing financial markets. An optimal-rational agent should, in theory, select the best candidate equities based on fundamentals – a business most likely to generate excess future cashflows with strong growth prospects should perhaps be rewarded with stock price appreciation. However, Thaler finds that what occurs is Keynes’ (1936) “beauty-contest” of agent expectations – in order to select a winner (using a win-on-consensus constraint), one should select the candidate that they believe other competing agents will select – this is in stark contrast to the naïve selection of what the agent themselves believes will win. Therefore, it becomes rational for other agents to do the same – leading to the prior feedback loop discussed by Goodnight and Green (2010). It is these very behavioural aspects that led to the formation of the events of the dot-com bubble. In considering these findings with respect to the EMH, it would instead appear to be that, while seemingly efficient on the aggregate level, the EMH inherently breaks down due to the preponderance of the behavioural factor driving markets. Mandelbrot and Hudson dedicate significant time in dissecting the standard EMH model, taking note that “the biggest edge you can have is the private information on who’s buying what. We do not believe the market is efficient” (2004, pp. 79-107). This directly contradicts portions of the EMH wherein any profitable information should already be reflected within stock prices. Once again, this ties back to Keynes’ “beauty-contest”. With these behavioural components in mind, we can now discuss the question - “will this be another dot-com bubble and crash?”. There are significant similarities between the dotcom bubble and today’s tech stock valuations – companies are priced based on future growth potential instead of current business fundamentals (How does today’s tech, 2020). Market conditions where investors are frontrunning future potential “winners” by extrapolating significant growth or betting on outsize innovation/disruption by individual companies is leading, once-again, to high tech valuations. However, other market factors are different – labour and total-factor productivity were outsize in the 90s, enabling simultaneous wage and corporate profit growth. This is in stark contrast to the 2010s, which were low by comparison. However, it is noted that output per hour and total factor productivity have begun to accelerate from 2019 onwards, with some noting that this “might presage an economic transformation in the making” (How does today’s tech, 2020). Despite lower aggregate productivity (relative to the 90s), the current zero-to-low interest rate environment has significantly reduced the opportunity cost of borrowing money, leading to generalised asset (both equities and real estate) price inflation in many markets worldwide. In further comparing the current tech boom with the dot-com era, Mackenzie (2021) notes that investors are once-again willing to “put aside doubts on business models to bet on the promise of innovation”. He specifically mentions the Ark Innovation ETF which, as a significant holder of Tesla, has returned significantly outsize returns in the past year. With these in mind we can reach a number of conclusions - while it can be seen that behavioural aspects are similar to those of the dot-com bubble - crowded tech trades, indiscriminate purchasing of speculative stocks via ETF instruments, and significant outsize returns (swaying traders from the imitate to the copy side) – the underlying interest rate environment is significantly different. With almost no borrowing-cost of money, investors can Of note is Kahneman’s findings regarding the asymmetrical pain/gain function humans feel when trading – a loss is more painful to an agent in comparison to that of an equal monetary gain. This potentially explains why “markets take the stairs up and the elevator down”. 2 2 ECON7200 – Individual Project Tyson Ruth: 40800756 increase their market exposure at little cost. Keynes’ “beauty-contest” methodology of picking winners before other participants has led to investors treating speculative stocks as “long-dated bonds”, which hold significant interest rate risk (Mackenzie, 2021). It is also of note that, akin to the dot-com crash, the current market also has significant concentrations in its primary indexes (Apple and Microsoft comprise a significant portion of the S&P500). We can conclude that by the metrics discussed in this paper, the current tech market is almost certainly in a bubble, though by nature they are difficult to identify during (Shiller, 2005; Kahneman, 2011). This author (ironically speculating by doing so) concludes that at some indeterminate point in the future, a flight to quality will occur wherein strong tech companies (e.g. Google, Amazon) will once again assimilate the remaining capital plied into that of pure speculative plays (e.g. Pets.com, Peloton). However, this event will require potential catalysts which may include quantitative tightening, unseen shocks (pandemics), political turmoil and traditional inflation. 3 ECON7200 – Individual Project Tyson Ruth: 40800756 References DiMaggio, P., Powell, W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review. 48(2), 147-160. Goodnight, T., Green, E. (2010). Rhetoric, Risk, and Markets: The Dot-Com Bubble. Quarterly Journal of Speech, 96(2), 115-140. Kahneman, D. (2011). Thinking Fast & Slow. New York: Farrar, Straus & Giroux. Keynes, J. (1936). The General Theory of Employment, Interest and Money. United Kingdom: Palgrave Macmillan. Lefevre, E. (2005). Reminiscences of a Stock Operator. New Jersey: John Wiley & Sons. Mackay, C. (1841). Memoirs of Extraordinary Popular Delusions. Great Britain: Harriman House. Mackenzie, M. (2021, January 16). Tech stock booms: then and now. Financial Times. Mandelbrot, B., Hudson, R. (2006). The Misbehaviour of Markets: A Fractal View of Financial Turbulence. New York: Basic Books. Shiller, R. (2005). Irrational Exuberance. New Jersey: Princeton University Press. Thaler, R. (2015). Misbehaving: The Making of Behavioural Economics. Great Britain: Allen Lane. How does today's tech boom compare to the dotcom era? (2020, September 19). The Economist. 4 Rethinking the Financial Crisis Blinder, Alan S., Lo, Andrew W., Solow, Robert M. Published by Russell Sage Foundation Blinder, Alan S., et al. Rethinking the Financial Crisis. Russell Sage Foundation, 2012. Project MUSE. muse.jhu.edu/book/22174. For additional information about this book https://muse.jhu.edu/book/22174 [ Access provided at 9 Sep 2021 06:01 GMT from University of Queensland ] Chapter 4 The Efficient-Market Hypothesis and the Financial Crisis Burton G. Malkiel The worldwide financial crisis of 2008 to 2009 left in its wake severely damaged economies in the United States and Europe. The crisis also shook the foundations of modern-day financial theory, which rests on the proposition that our financial markets are basically efficient. Critics have even suggested that the efficient-market hypothesis (EMH) was in large part responsible for the crisis. This chapter argues that the critics of EMH are using a far too restrictive interpretation of what EMH means. EMH does not imply that asset prices are always “correct.” Prices are always wrong, but no one knows for sure if they are too high or too low. EMH does not imply that bubbles in asset prices are impossible, nor does it deny that environmental and behavioral factors cannot have profound influences on required rates of return and risk premiums. At its core, EMH implies that arbitrage opportunities for riskless gains do not exist in an efficiently functioning market and that, if they do appear from time to time, they do not persist. The evidence is clear that this version of EMH is strongly supported by the data. EMH can comfortably coexist with behavior finance, and the insights of Hyman Minsky are particularly relevant in eliminating the recent financial crisis. Bubbles, when they do exist, are particularly dangerous when they are financed with debt. The housing bubble and the derivative securities associated with it left both the consumer and financial sectors dangerously leveraged. Policymakers are unlikely to be able to identify bubbles in advance, but they must be better focused on asset-price increases that are financed with debt. T he worldwide financial crisis of 2008 to 2009 left in its wake severely damaged economies in the United States and Europe. Unemployment rates soared up to and in some cases above the double-digit level, and economies in Europe and the United States were still operating in 2012 well below economic capacity. Moreover, the high indebtedness of consumers, financial institutions, and governments made the severe recession unusually persistent and limited the fiscal policy responses of governments throughout the world. / 13075-04_CH04-4thPgs.indd 75 75 10/3/12 10:25 AM Rethinking the Financial Crisis The crisis also shook the very foundations of modern-day financial theory, which rests on the hypothesis that our financial markets are basically efficient. Financial writers and economists alike were ready to write obituaries for the efficient-market hypothesis, or EMH, as it is widely known. The financial writer Justin Fox published a best-selling book in 2009 entitled The Myth of the Rational Market. The economist Robert Shiller (1984, 459) described EMH as “one of the most remarkable errors in the history of economic thought.” Some professional investment managers went even further. Jeremy Grantham opined that EMH was “more or less directly responsible” for the financial crisis.1 Paul Krugman (2009) agreed, writing that “the belief in efficient financial markets blinded many if not most economists to the emergence of the biggest financial bubble in history. And efficient-market theory also played a role in inflating that bubble in the first place.” In this essay, I describe what the efficient-market hypothesis implies for the functioning of our financial markets. I suggest that a number of common misconceptions about EMH have led some analysts to reject the hypothesis prematurely. I then examine the abundant evidence that leads me to believe that our financial markets are remarkably efficient and that reports of the death of EMH are greatly exaggerated. Finally, I indicate what I believe are the important lessons that policymakers should learn from the financial crisis. What the Efficient-Market Hypothesis Means and What It Does Not Mean Two fundamental tenets make up the efficient-market hypothesis. EMH first asserts that public information is reflected in asset prices without delay. Information that should beneficially (adversely) affect the future price of any financial instrument is reflected in the asset’s price today. If a pharmaceutical company now selling at $20 per share receives approval for a new drug that will give the company a value of $40 tomorrow, the price will move to $40 right away, not slowly over time. Because any purchase of the stock at a price below $40 will yield an immediate profit, we can expect market participants to bid the price up to $40 without delay. It is, of course, possible that the full effect of the new information is not immediately obvious to market participants. It is also likely that the estimated sales and profits cannot be predicted with any precision and that the value of the discovery is amenable to a wide variety of estimates. Some market participants may vastly underestimate the significance of the newly approved drug, but others may greatly overestimate its value. In some cases, therefore, the market may underreact to a favorable piece of news. But in other cases, the market may overreact, and it is far from clear that systematic underreaction or overreaction to news presents an arbitrage opportunity promising traders easy, risk-adjusted, extraordinary gains. It is this aspect of EMH that implies its second, and more fundamental, tenet: in an efficient market, no arbitrage opportunities exist. This lack of opportunities for extraordinary profits is often explained by a joke popular with professors of finance. A professor who espouses EMH is walking 76 / 13075-04_CH04-4thPgs.indd 76 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis along the street with a graduate student. The student spots a $100 bill lying on the ground and stoops to pick it up. “Don’t bother to try to pick it up,” says the professor. “If it was really a $100 bill, it wouldn’t be there.” Perhaps a less extreme telling of the story would have the professor advising the student to pick the bill up right away because it will not be lying around very long. In an efficient market, competition ensures that opportunities for extraordinary risk-adjusted gain do not persist. EMH does not imply that prices are always “correct” or that all market participants are always rational. There is abundant evidence that many (perhaps even most) market participants are far from rational and suffer from systematic biases in their processing of information and their trading proclivities. But even if price-setting were always determined by rational, profit-maximizing investors, prices could never be “correct.” Suppose that stock prices are rationally determined as the discounted present value of all future cash flows. Future cash flows can only be estimated and are never known with certainty. There will always be errors in the forecasts of future sales and earnings. Moreover, equity risk premiums are unlikely to be stable over time. Prices are therefore likely to be “wrong” all the time. What EMH implies is that we never can be sure whether they are too high or too low at any given time. Some portfolio managers may correctly determine when some prices are too high and others too low. But other times such judgments are in error. And in any event, the profits that are attributable to correct judgments do not represent unexploited arbitrage possibilities. Complex financial investments are particularly susceptible to mispricing, especially when the loans that underlie the derivative are misrepresented. A full discussion of the causes of the financial crisis is beyond the scope of this essay, but there is no doubt that the mispricing of mortgage-backed securities played an important role in widening the crisis. Although the mispricing of the real estate securing the mortgages may correctly be described as a classic bubble, there was far from a lack of rationality throughout the market. Perverse incentives influenced both mortgage originators and investment bankers. And the financial institutions that held excessive amounts of the toxic instruments in highly leveraged portfolios were encouraged to do so by asymmetric compensation policies and by a breakdown of regulation that led to a failure to constrain excessive debt and inadequate liquidity. In any event, while some hedge funds profited from selling some of these instruments short, there were certainly no arbitrage opportunities that were obvious ex ante. EMH and the Adjustment of Market Prices to Different Types of New Information Since Eugene Fama’s (1970) influential survey article, it has been customary to distinguish between three versions of EMH depending on the type of information that is believed to be reflected in the current prices of financial assets (see also Fama 1991). In the “narrow” or “weak” form of the hypothesis, it is asserted that any information that might be contained in historical price series or trading / 13075-04_CH04-4thPgs.indd 77 77 10/3/12 10:25 AM Rethinking the Financial Crisis volume is already reflected in current prices. Since past trading data are widely available, any historical patterns that might have reliably predicted future price movements will already have been exploited. If, for example, there has been a reliable “Santa Claus Rally” (suggesting that stock prices will rise between Christmas and New Year’s Day), investors will act to anticipate the signal, and when they do, the historical pattern will self-destruct. According to this version of EMH, “technical analysis”—the interpretation of historical price charts—will be nugatory. Broader forms of the hypothesis have expanded on the types of information that are reflected in current prices. According to the “semi-strong” form of the ­hypo­thesis, any “fundamental” information about individual companies or about the stock market as a whole will be reflected in stock prices without delay. Thus, investors cannot profit from acting on some favorable piece of news concerning a company’s sales, earnings, dividends, and so on, because all of the available publicity on the subject will already be reflected in the company’s stock price. Profit-seeking traders and investors can be expected to exploit even the smallest informational advantage, and by so doing, they incorporate all information into market prices, thereby eliminating any profit opportunities. According to this version of the hypothesis, even “fundamental analysis”—in-depth analysis of the financial situation and the prospects for individual companies—will prove fruitless because all favorable information will already have been reflected in market prices. A third form of EMH suggests that not only anything that is known but also anything that is knowable has already been assimilated into market prices. This extreme version postulates that one cannot even benefit from “inside information.” It is unlikely that this “strong” form of the hypothesis is ever completely satisfied. But trading on inside information is illegal, and in the United States the Securities and Exchange Commission (SEC) has been increasingly diligent in going after company executives and hedge fund managers who are believed to have profited from trading on inside information. EMH and the Random Walk Hypothesis All forms of EMH imply that market prices cannot be forecast. Much of the empirical literature has focused on the “random walk” hypothesis, a statistical description of unforecastable price changes. The term was apparently first used in an exchange of correspondence that appeared in Nature in the early 1900s (Pearson 1905). The subject of the correspondence was the optimal search procedure for finding a drunk who had been left in the middle of a field. The answer was quite complex, but the place to start was simply the place where the drunkard had been left. Paul Samuelson (1965) made a seminal contribution to the EMH literature in his article entitled “Proof That Properly Anticipated Prices Fluctuate Randomly.” If market prices fully incorporate the information and expectations of all market participants, then price changes must be random. Prices will, of course, change as new information is revealed to the market, but true news is random—it cannot be forecast from past events. Thus, in an informationally efficient market, price 78 / 13075-04_CH04-4thPgs.indd 78 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis changes are unforecastable. Samuelson’s contribution has been extended to allow for risk-averse investors by Stephen LeRoy (1973) and Robert Lucas (1978) and in many other directions by other researchers, including directions that allow for heterogeneous expectations. Random price movements do not imply that the stock market is capricious. Randomness indicates a well-functioning and efficient market rather than an irrational one. The earliest empirical work on the random walk hypothesis was performed by Louis Bachelier (1900). He concluded that commodity prices follow a random walk, although he did not use that term. Corroborating evidence from other time series was provided by Holbrook Working (1960) and from U.S. stock prices by Alfred Cowles and Herbert Jones (1937) and Maurice Kendall (1953). These studies generally found that the serial correlations between successive changes are essentially zero. Harry Roberts (1959) found that a time series generated from a sequence of random numbers has the same appearance as a time series of U.S. stock prices. M. F. M. Osborne (1977) concluded that stock-price movements are similar to the random Brownian motion of physical particles and that the logarithms of price changes are independent of each other. Other empirical work, using alternative techniques and data sets, has searched for more complicated patterns in the sequence of prices in speculative markets. Clive Granger and Oskar Morgenstern (1963) used the technique of spectral analy­ sis but were unable to find any dependably repeatable patterns in stock-price movements. Eugene Fama (1965a, 1965b) not only looked at serial correlation coefficients (which were close to zero) but also corroborated his investigation by examining a series of lagged prices and performing a number of nonparametric “runs” tests. He also examined a variety of filter techniques—trading techniques where buy (sell) signals are generated by some upward (downward) price movements from recent troughs (peaks)—and found that they could not produce abnormal profits. Other investigations have done computer simulations of more complicated technical analysis of supposedly predictive stock chart patterns (such as “double tops,” “inverted head and shoulders,” and so on) and found that profitable trading strategies cannot be undertaken on the basis of these patterns. Bruno Solnik (1973) measured serial correlation coefficients for daily, weekly, and monthly price changes in nine countries and concluded that profitable investment strategies cannot be formulated on the basis of the extremely small dependencies found. Although most of the earliest studies of the stock market supported a general finding of randomness, more recent work has indicated that the random walk model does not strictly hold. Some patterns appear to exist in the development of stock prices. Over short holding periods, there is some evidence of momentum in the stock market, while mean reversion appears to be present over longer holding periods. Nevertheless, it is less clear that there are violations of the weak form of EMH, which states only that unexploited trading opportunities should not persist in any efficient market. Andrew Lo and Craig MacKinlay (1999), in a book entitled A Non-random Walk Down Wall Street, have found evidence inconsistent with the random walk model. Calculating weekly and monthly holding period returns for various stock indexes, / 13075-04_CH04-4thPgs.indd 79 79 10/3/12 10:25 AM Rethinking the Financial Crisis they find evidence of positive serial correlation, which implies some momentum in stock prices. Moreover, exploiting the fact that return variances scale linearly in a random walk market, they construct a variance ratio test that rejects the random walk hypothesis. This rejection of the random walk hypothesis for stock indexes may result, however, from the behavior of small company stocks that are infrequently traded. New information about the market as a whole is likely to be factored into the prices of large capitalization stocks first and into the prices of smaller stocks later. Interestingly, Lo and MacKinlay are unable to reject the random walk hypothesis when they perform tests on individual stocks. Narasimhan Jegadeesh and Sheridan Titman (1993) have also found some evidence of momentum in stock prices. Two possible explanations for the existence of momentum have been offered: the first is based on behavioral considerations, the second on sluggish responses to new information. Shiller (2000) emphasizes a psychological feedback mechanism that imparts a degree of momentum into stock prices, especially during periods of extreme enthusiasm. Individuals see stock prices rising and are drawn into the market in a kind of “bandwagon effect.” The second explanation is based on the argument that investors do not adjust their expectations immediately when news arises—especially news of company earnings that have exceeded (or fallen short of) expectations. Ray Ball and Phillip Brown (1968) and Richard Rendleman, Charles Jones, and Henry Latané (1982) have found that abnormally high returns follow positive earnings surprises as market prices appear to respond to earnings information only gradually. There is enough evidence in support of short-term momentum that researchers such as Mark Carhart (1997) have considered momentum to be a priced factor in explaining the cross-section of security and mutual fund returns. And Clifford Asness, Tobias Moskowitz, and Lasse Pederson (2010) have offered actual investment funds where stocks showing positive momentum are overweighted in the portfolio. In these two analyses, positive momentum is considered to be strong relative performance over the preceding twelve months (not including the most recent month to allow for any short-term return reversals). As is the case with many of the so-called predictable patterns in stock-price returns, investment strategies based on these are predictive during some periods but not in others. Although there is some evidence supporting the existence of short-term momentum in the stock market, many studies have shown evidence of negative serial ­correlation—that is, return reversals—over longer holding periods. For example, Eugene Fama and Kenneth French (1988) have found that 25 to 40 percent of the variation in long holding period returns can be predicted in terms of a negative correlation with past returns. Similarly, James Poterba and Lawrence Summers (1988) have found substantial mean reversion in stock market returns at longer horizons. Some studies have attributed this forecastability to the tendency of stock market prices to “overreact.” Werner De Bondt and Richard Thaler (1985), for example, argue that investors are subject to waves of optimism and pessimism that cause prices to deviate systematically from their fundamental values and later to exhibit mean reversion. They suggest that such overreaction to past events is consistent with the implication of the behavioral decision theory of Daniel Kahneman and 80 / 13075-04_CH04-4thPgs.indd 80 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis Amos Tversky (1974, 1979) that investors are systematically overconfident of their ability to forecast either future stock prices or future corporate earnings (see also Kahneman and Riepe 1998). These findings give some support to investment techniques that rest on a “contrarian” strategy—that is, buying the stocks, or groups of stocks, that have been out of favor for long periods of time. However, the finding of mean reversion is not uniform across studies and is quite a bit weaker in some periods than it is for others. Indeed, the strongest empirical results come from periods including the Great Depression, which may have been a time with patterns that do not generalize well. Moreover, such return reversals for the market as a whole may be quite consistent with the efficient functioning of the market, since they could result, in part, from the volatility of interest rates and the tendency of interest rates to be mean-reverting. Since stock returns must rise or fall to be competitive with bond returns, there is a tendency when interest rates go up for prices of both bonds and stocks to go down, and for prices of bonds and stocks to go up as interest rates go down. If interest rates revert to the mean over time, this pattern tends to generate return reversals, or mean reversion, in a way that is quite consistent with the efficient functioning of markets. Moreover, it may not be possible to profit from the tendency for individual stocks to exhibit return reversals. Zsuzsanna Fluck, Burton Malkiel, and Richard Quandt (1997) simulated a strategy of buying stocks over a thirteen-year period during the 1980s and early 1990s, and returns over smaller periods of three to five years within that time span were particularly poor. They found that stocks with very low returns over the past three to five years had higher returns in the next period, and that stocks with very high returns over the past three to five years had lower returns in the next period. Thus, they confirmed the very strong statistical evidence of return reversals. However, they also found that returns in the next period were similar for both groups, so they could not confirm that a contrarian approach would yield higher-than-average returns. There was a statistically strong pattern of return reversal, but not one that implied an inefficiency in the market that would enable investors to make excess returns. Moreover, many of the predictable patterns mentioned in the finance literature seemed to disappear after they were published. William Schwert (2003) suggests two possible explanations. First, researchers have a normal tendency to focus on results that challenge conventional wisdom. It is likely that in some particular sample a statistically significant result will emerge that appears to challenge EMH. Alternatively, practitioners may learn quickly about any “dependable” profit-making opportunities and exploit them until they are no longer profitable. In other words, if there are $100 bills available, they will be picked up as soon as they are discovered. My own view of the matter has been succinctly expressed by Richard Roll (1992, 28), an academic economist who also was a portfolio manager, investing billions of dollars of investment funds: I have personally tried to invest money, my client’s money and my own, in every single anomaly and predictive device that academics have dreamed up. . . . I have attempted to exploit a whole variety of strategies supposedly / 13075-04_CH04-4thPgs.indd 81 81 10/3/12 10:25 AM Rethinking the Financial Crisis documented by academic research. And I have yet to make a nickel on any of these supposed market inefficiencies. . . . But, I have to keep coming back to my point . . . that a true market inefficiency ought to be an exploitable opportunity. If there’s nothing investors can exploit in a systematic way, time in and time out, then it’s very hard to say that information is not being properly incorporated into stock prices. . . . Real money investment strategies don’t produce the results that academic papers say they should. The Semistrong Form of EMH The narrow or weak form of EMH suggests that any information contained in the history of stock prices will have already been reflected in current prices. Hence, “technical analysis,” the analysis of past price movements, cannot be employed to produce above-average returns. But most professional investment managers are “fundamental analysts” rather than technicians. Fundamental analysts study a wide range of information, including company sales, earnings, and asset values, in forming portfolios that they hope will earn excess returns. Studies attempting to determine whether publicly available information can be used to improve portfolio performances are tests of the semistrong form of EMH. Usually a finding that abnormal returns can be earned is referred to as an EMH anomaly. At the outset, it is important to note that any empirical test purporting to show that abnormal returns can be earned is based on some model of risk adjustment. For example, the capital asset pricing model (CAPM) is often used to adjust for risk. Thus, an anomalous finding that excess returns can be earned by exploiting publicly available “fundamental” information is actually a joint test of EMH and the risk adjustment procedures employed. If the CAPM beta is an inadequate measure of risk (or if beta is measured with error), it will be inappropriate to consider beta-adjusted excess returns to be inconsistent with EMH. Similarly, if market capitalization (size) and market-to-book factors are added to beta to account for risk, abnormal returns will be identified only if this three-factor model fully describes the cross-section of expected returns. Tests of the semistrong form of EMH have looked at how rapidly new information is reflected in market prices and whether the use of certain valuation metrics favored by security analysts can generate abnormal returns. Studies seeking to examine the rapidity of price responses to news announcements are called “event studies.” The “events” used in such studies have included dividend changes, earnings reports that have differed from estimates, and merger announcements. Various tests have been performed to ascertain the speed of adjustment of market prices to new information. Fama and his colleagues (1969) looked at the effect of stock splits on equity prices. Although splits themselves provide no economic benefit, splits are usually accompanied or followed by dividend increases that do convey information to the market concerning management’s confidence about the future progress of the enterprise. Although splits usually result in higher market 82 / 13075-04_CH04-4thPgs.indd 82 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis valuations, the market appears to adjust to such announcements fully and immediately. Substantial returns can be earned before the split announcement, but there is no evidence of abnormal returns after the public announcement. Similarly, merger announcements can raise market prices substantially, especially when premiums are being paid to the shareholders of the acquired firm, but it appears that the market adjusts fully to the public announcements. Arthur Keown and John Pinkerton (1981) found no evidence of abnormal price changes after the public release of merger information. James Patell and Mark Wolfson (1984) examined the intraday speed of adjustment to earnings and dividend announcements. They noted that the stock market assimilates publicly available information “very quickly.” The largest portion of the price response occurs in the first five to fifteen minutes after disclosure. Although most event studies have supported EMH, some have not. Ball and Brown (1968) found that stock-price reactions to earnings announcements are not complete. He found that abnormal returns can be earned in the period after the announcement date. Rendleman, Jones, and Latané (1982) also found that unexpected earnings announcements are not immediately reflected in stock prices and that abnormal returns can be earned by purchasing shares of companies with positive earnings surprises. These studies of sluggish adjustment (or under­reaction) support the momentum arguments referred to earlier. However, the pattern of underreaction to announcements is not consistent over time. Fama (1998) has argued that overreaction to news announcements appears about as often as underreaction (see also Bernard and Thomas 1990). In any event, such anomalies tend to be so small that only professional traders could have earned economic profits. There has been considerable work on the use of a variety of valuation metrics to isolate stocks that are expected to generate “excess” returns. An influential book by Benjamin Graham and David Dodd (1934) entitled Security Analysis spawned the development of a whole profession of security analysts who were trained to examine “fundamental” financial data for firms, such as earnings and asset values, and find stocks that represented “good value.” The approach remains popular today, especially with the growing appeal of behavioral finance. Behaviorists such as Daniel Kahneman and Amos Tversky (1974, 1979) have argued that investors tend to be overoptimistic and far more certain of their forecasts than is warranted. Thus, they tend to overestimate future growth and to pay more than they should for “growth” stocks—those stocks promising above-average future growth. Conversely, “value” stocks—those stocks that are less exciting and therefore sell at more modest valuation metrics, such as low multiples of earnings and of book value—are likely to generate excess returns. Of all the predictable patterns that have been discovered, this so-called value effect is among those most supported by the evidence. Basu (1977) found that portfolios of stocks with low price-to-earnings (P/E) multiples have tended to provide higher returns than portfolios of stocks with high P/E ratios. Using a somewhat different value criterion, Fama and French (1992, 1998) found that portfolios made up of stocks with low ratios of price-to-book-value (P/BV) provide relatively higher returns than the portfolios of high-P/BV firms. When the CAPM measure / 13075-04_CH04-4thPgs.indd 83 83 10/3/12 10:25 AM Rethinking the Financial Crisis of risk was used to adjust for risk, the higher return from value stocks appeared to represent an inefficiency. Another pattern that has found empirical support is the size or small-firm effect. Between 1926 and the present, an investor could have realized higher portfolio returns by concentrating on stocks with relatively small market capitalizations (see Banz 1981; Reinganum 1983; Ibbotson Associates). Fama and French (1998) have demonstrated that this effect can be documented in international as well as U.S. stock markets. In the United States, the excess returns from small-­capitalization stocks appear almost entirely in January—hence this size effect is often called “the January Effect.” Findings such as these have often been considered “anomalies” or “inefficiencies.” But again, we are driven back to the joint hypothesis problem. If CAPM is an insufficient model for the measurement of risk, then the result does not represent an inefficiency. Indeed, Fama and French (1993) have proposed that small company stocks and low-P/BV stocks are riskier. Small companies can be more vulnerable to economic shocks than larger firms, and low P/BV may be a reflection of some form of economic distress. For example, during the recent financial crisis, distressed bank stocks sold at unusually low prices relative to their book values. Hence, any excess returns that were earned were simply some compensation for risk. This interpretation has been vigorously disputed by Josef Lakonishok, Andrei Schleifer, and Robert Vishny (1994), who argue that these patterns are evidence of inefficiencies. Nevertheless, it has become standard to employ risk measurement techniques that augment the beta risk measure of CAPM with the addition of size and P/BV factors. In some models, a fourth factor, momentum, is added to the Fama-French three-factor risk model (see, for example, Carhart 1997). Predictable Time-Series Market Returns Based on Valuation Parameters Considerable empirical research has been conducted to determine whether future returns for the overall market can be predicted on the basis of initial valuation parameters. It is claimed that valuation ratios, such as the price-to-earnings multiple or the dividend yield of the stock market as a whole, have considerable time-series predictive power. Formal statistical tests of the ability of dividend yields (that is, the ratio of dividends to stock prices) to forecast future returns have been conducted by Eugene Fama and Kenneth French (1988) and by John Campbell and Robert Shiller (1998). Depending on the forecast horizon involved, as much as 40 percent of the variance of future returns for the stock market as a whole can be predicted on the basis of the initial dividend yield of the market index. This finding is not necessarily inconsistent with efficiency. Dividend yields of stocks tend to be high when interest rates are high, and they tend to be low when interest rates are low. Consequently, the ability of initial yields to predict returns may simply reflect the adjustment of the stock market to general economic 84 / 13075-04_CH04-4thPgs.indd 84 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis conditions. Moreover, the use of dividend yields to predict future returns has been much less effective since the mid-1980s. One possible explanation is that the dividend behavior of U.S. corporations has changed over time, as suggested by Laurie Bagwell and John Shoven (1989) and by Fama and French (2001). During more recent years, companies may have been more likely to institute a share repurchase program than to increase their dividends. Changing compensation practices—company executives are now more likely to be rewarded with stock options than with cash bonuses—have encouraged such a change in behavior. Buybacks tend to increase the value of executive stock options. The option holder does not receive any dividends that are paid. Finally, it is worth noting that this phenomenon does not work consistently with individual stocks. Investors who simply purchase a portfolio of individual stocks with the highest dividend yields in the market will not earn a particularly high rate of return (see, for example, Fluck et al. 1997). Time-series empirical studies have also found that price-to-earnings multiples for the market as a whole have considerable predictive power. Investors have tended to earn larger long-horizon returns when purchasing the market basket of stocks at relatively low price-to-earnings multiples. Campbell and Shiller (1998) have shown that initial P/E ratios explain as much as 40 percent of the variance of future returns. They conclude that equity returns have been predictable in the past to a considerable extent. Consider, however, the experience during the past fifteen years of investors who have attempted to undertake investment strategies based either on the level of the price-to-earnings multiple or on the size of the dividend yield to predict future long-horizon stock returns. Price-to-earnings multiples for the Standard & Poor’s 500 stock index were unusually high in mid-1987 (suggesting very low long-­horizon returns). Dividend yields fell below 3 percent. The average annual total return from the index over the next ten years was an extraordinarily generous 16.7 percent. Earnings multiples were also extremely high in the early 1990s, but returns remained extremely high until the end of the decade. We need to be very cautious in assessing the extent to which stock market returns are predictable on the basis of valuation metrics. Studies by Amit Goyal and Ivo Welch (2003) and by Kenneth Fisher and Meir Statman (2006) have found that neither dividend yields nor price-to-earnings multiples are useful in generating timing strategies to shift between stocks and bonds that would generate returns exceeding a simple buyand-hold strategy. Variance Bound Tests One kind of empirical test whose results have questioned market efficiency is called a “variance bound test.” In an efficient market, all assets should be priced at the discounted present value of all of their cash flows. In one well-known model of stock valuation popularized by Myron Gordon (1959), the price of a share was taken to be the discounted present value of the future stream of dividends. Stephen / 13075-04_CH04-4thPgs.indd 85 85 10/3/12 10:25 AM Rethinking the Financial Crisis LeRoy and Richard Porter (1981), as well as Robert Shiller (1981), then compared the realized variance of the dividend stream (the components of the ex post present value) with the variance of stock prices. They found that the variance of stock prices dramatically exceeds the variance of ex post present values. Stock prices are far too volatile to be explained by the variance of future dividends. Of course, it is far from clear how much deviation from “true value” is necessary to declare that stock prices are “too volatile.” In his influential article entitled “Noise,” Fischer Black (1986) argued that a market should still be considered efficient even if prices deviate in a range of plus-200 percent and minus-50 percent of fundamental value. Nevertheless, Shiller concluded that the excess volatility of stock prices implies that EMH must be false. Shiller’s conclusion has been extremely controversial. Allan Kleidon (1986) and Terry Marsh and Robert Merton (1986) showed that with the kinds of sample sizes used in the tests, sampling variation alone could have generated the Shiller results. But even if the LeRoy-Porter and Shiller findings survive the statistical critiques, there are several reasons to be cautious about interpreting the results as inconsistent with EMH. For one thing, it is well established that managers tend to smooth dividends; therefore, the ex post variance of dividends may understate the true variance in the fortunes of individual companies. In addition, it is highly unlikely that either real interest rates or required risk premiums are stable over time. Stock prices should adjust with changes in required rates of return, and such price volatility may be entirely consistent with EMH. There is no reason to believe that individual preferences and behavior are stable over time. Required risk premiums are likely to be influenced by environmental conditions, and when these conditions change, the behavior of investors can be expected to change as well. This perspective suggests a more nuanced view of the world of rational expectations. The approach has been championed by Andrew Lo and is called the “adaptive markets hypothesis.” This view suggests a quite complicated process to explain the determination of equilibrium risk premiums (see Farmer and Lo 1999; Lo 2004, 2005; Brennan and Lo 2009). Bubbles in Asset Prices Perhaps the most persuasive argument against market efficiency is that securities markets have often experienced spectacular bubbles. During the so-called Internet bubble that inflated in the late 1990s, any security associated with the “New Economy” soared in price. Companies that changed their names to include “dot. net” or a similar suffix would often double in price. When the bubble popped, Internet-related stocks lost 90 percent or more of their value. During the housing bubble in the first decade of the 2000s, the inflation-adjusted price of the median single family house doubled after being flat for the entire past century. The associated mispricing of mortgage-backed securities had far-reaching consequences for world financial institutions and for the entire world economy. Critics have considered these episodes to be obvious cases of market inefficiency. 86 / 13075-04_CH04-4thPgs.indd 86 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis Bubbles often start with some exogenous factor that can be interpreted rationally as presenting large future prospects for profit. In England in the early 1700s, it was the formation of the promising new corporation, the South Sea Company, and the rise of its stock price. The wave of new companies that followed was expected to provide profitable investment outlets for the savings of individuals. In the United States during the late 1990s, it was the promise of the Internet, which was expected to revolutionize the way consumers obtained information and purchased goods and services. The generation of sharply rising asset prices that followed, however, seemed to have more to do with the behavioral biases emphasized by scholars such as Kahneman and Shiller.2 Kahneman and Tversky (1974, 1979) argued that people forming subjective judgments tend to disregard base probabilities and to make judgments solely in terms of observed similarities to familiar patterns. Thus, investors may expect past price increases to continue even if they know from past experience that all skyrocketing stock markets eventually succumb to the laws of gravity. This phenomenon was certainly present during the great housing bubble of 2007 to 2008. Investors also tend to enjoy the self-esteem that comes from having invested early in some “new era” phenomenon, and they are overconfident of their ability to predict the future. Shiller (2000) emphasizes the role of “feedback loops” in the propagation of bubbles. Price increases for an asset lead to greater investor enthusiasm, which then leads to increased demand for the asset and therefore to further price increases. The very observation that prices have been rising alters the subjective judgment of investors and reinforces their belief that the price increases will continue. The news media play a prominent role in increasing the optimism of investors. The media are, in Shiller’s view, “generators of attention cascades” (60). One news story begets another, and the price increases themselves (whether of common stocks or single-family houses) appear to justify the superficially plausible story that started the rise in the price of the asset(s). According to Shiller, bubbles are inherently a social phenomenon. A feedback mechanism generates continuing rises in prices and an interaction back to the conventional wisdom that started the process. The bubble itself becomes the main topic of social conversation, and stories abound about certain individuals who have become wealthy from the price increases. As the economic historian Charles Kindleberger (1989) has stated, “There is nothing so disturbing to one’s well-being and judgment as to see a friend get rich.” The question naturally arises why the arbitrage mechanism of EMH does not prick the bubble as it continues to inflate. Enormous profit opportunities were certainly achievable during the Internet bubble for speculators who correctly judged that the prices of many technology stocks were “too high.” But the kind of arbitrage that would have been necessary was sometimes difficult to effect and in any event, it was very risky. There appear to be considerable “limits to arbitrage” (see, for example, Shleifer, Lakonishok, and Vishny 1992; DeLong et al. 1990). For example, in one celebrated case during the Internet bubble, the market price of Palm Pilot stock (which was 95 percent–owned by the company 3Com) implied a total capitalization considerably greater than that of its parent, suggesting that the rest of 3Com’s business had a negative value. But the arbitrage (sell Palm Pilot / 13075-04_CH04-4thPgs.indd 87 87 10/3/12 10:25 AM Rethinking the Financial Crisis stock short and buy 3Com stock) could not be achieved because it was impossible to borrow Palm Pilot stock to accomplish the short sale. Arbitrage is also risky; one never can be sure when the bubble will burst. The mantra of hedge fund managers (the natural arbitragers) in the United States was “markets can remain irrational much longer than we can remain solvent.” Moreover, some arbitragers may recognize that a bubble exists but are unable to synchronize their strategies to take advantage of it (see Abreu and Brunnermeier 2003). They might prefer to ride the bubble for as long as possible. Indeed, one empirical study by Markus Brunnermeier and Stefan Nagel (2004) has found that rather than shorting Internet stocks, hedge funds were actually buying them during the late 1990s. Hedge funds were embarking on a strategy of anticipating that the momentum of the price increases would continue and thus were contributing to the mispricing rather than trading against it. Critics consider the existence of spectacular bubbles in asset prices “damning” evidence against the EMH. But even when we know ex post that major errors were made, there were certainly no clear ex ante arbitrage opportunities available to rational investors. Equity valuations rest on uncertain future forecasts. Even if all market participants rationally price common stocks as the present value of all expected future cash flows, it is still possible for excesses to develop. We know, with the benefit of hindsight, that the outlandish claims regarding the growth of the Internet (and the related telecommunications structure needed to support it) were unsupportable. We know now that projections for the rates of growth and the stability and duration of those growth rates for “New Economy” companies were unsustainable. But neither sharp-penciled professional investors nor quantitative academics were able to accurately measure the dimensions of the bubble or the timing of its eventual collapse. As indicated earlier, there is evidence that initial dividend yields for the market as a whole have considerable predictive power to explain future long-horizon rates of return. But during the early 1990s, dividend yields in the United States fell well below 3 percent, implying very low rates of return for the next five to ten years. In fact, the U.S. stock market generated unusually large double-digit rates of return during the entire decade of the 1990s. In 1996, Campbell and Shiller presented a paper (later published as Campbell and Shiller 1998) to the board of governors of the U.S. Federal Reserve System showing that price-to-earnings multiples for the overall market possessed substantial ability to predict future rates of return. Since P/E multiples were extraordinarily high at that time, the work implied a likelihood of very low or even negative rates of return. This work influenced Alan Greenspan (1996), then chairman of the board of governors, to question whether the stock market was at bubble levels and to suggest that investors were exhibiting “irrational exuberance.” The stock market rallied strongly for more than four years thereafter. We know now (ex post) that market prices were at bubble levels in late 1999 and early 2000. No one was able accurately to identify the timing of the bubble in advance. And certainly no riskless arbitrage opportunities existed, even at the height of the bubble. 88 / 13075-04_CH04-4thPgs.indd 88 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis Hyman Minsky and the 2007 to 2008 Financial Crisis The financial crisis of 2007 to 2008 reinforces two important lessons that may sometimes be overlooked by policymakers. First, it is critical to distinguish between asset-price bubbles that are financed by debt and those that inflate without a major increase in indebtedness. The former are far more dangerous than the latter. The bursting of the Internet bubble in early 2000 did usher in a period of poor macro­ economic performance in the United States and in other world economies. But the recession that followed was moderate and relatively short-lived. The bursting of the real estate bubble in 2007 had far more serious consequences. Because individual balance sheets as well as those of financial institutions had become over­ extended in debt, there were serious adverse effects on consumer spending and on the ability and willingness of financial institutions to lend. The debt-to-income ratios of individuals, which have historically measured about one-third, rose to a level well above 100 percent during the boom as people bought houses with lower and lower down payments and tapped the equity in their houses by assuming second mortgages. The leverage ratios of financial institutions also increased dramatically. The debt-to-equity ratios of investment banks such as Bear Stearns and Lehman Brothers reportedly exceeded thirty-toone. Moreover, the debt was short-term rather than long-term. As investors in the short-term paper of those institutions began to worry about the quality of the mortgage-backed securities on the asset side of the investment banks’ balance sheets, they refused to roll over their loans and we experienced a classic run on the banks. Commercial banks also became dangerously overleveraged, and a collapse of the financial system was avoided only by extraordinary measures undertaken by government authorities. These events give us a renewed appreciation of the work of Hyman Minsky (1982, 2008), who stressed that stability itself breeds the seeds of instability in a capitalist system. Periods of economic expansion and relative stability lead individuals and institutions to reduce the premiums they demand to hold risky assets and to tolerate greater amounts of debt than they had previously accepted. The increased willingness of borrowers to borrow and lenders to lend leads to a growth in the availability and flow of credit, which in turn drives up asset prices to levels that may be inconsistent with their “fundamental valuations.” Precautionary lending practices are replaced with what Minsky has called “Ponzi finance.” Ponzi loans are characterized as loans to borrowers whose operating cash flow is insufficient to pay down principal so that the loans must continually be refinanced. The process ends with what has been called a “Minsky Moment.” Market participants begin to believe that asset prices are “unsustainably high,” and they attempt to cash in their profits before prices collapse. Lenders are reluctant to make new loans and refuse to renew the loans already outstanding. Investors demand higher-risk premiums and attempt to alter the composition of their portfolios to increase the liquidity of the instruments they hold. As a result of a rush to exit risky holdings, there are “fire sales” of all risk assets. Prices decline / 13075-04_CH04-4thPgs.indd 89 89 10/3/12 10:25 AM Rethinking the Financial Crisis dramatically, and markets become less liquid. In the extreme case, a full-fledged financial crisis ensues. There is little doubt that the Minsky model seems an especially good description of the recent financial crisis. Minsky’s “financial instability” hypothesis is also consistent with the insights of behavioral finance and with the tendency of market systems to experience periodic bubbles. But even when we know ex post that asset prices were “wrong,” the fundamental characteristic of efficient markets remains valid. Markets can make “mistakes,” sometimes egregious ones, and those mistakes can have extremely unfortunate macroeconomic consequences. But there were no obvious ex ante arbitrage opportunities. While some hedge funds did profit from selling short mortgage-backed securities, other investment funds and financial institutions went bankrupt because they held long positions in these same instruments and financed those positions exclusively with short-term debt.3 What Minsky’s work does make clear, however, is that policymakers need to be very alert to increases in asset prices that are financed with debt. Both the amount and the maturity of the debt on individual and institutional balance sheets are crucial variables. It is debt-financed asset-price bubbles that have the most serious macroeconomic effects. The “mistakes” that markets sometimes make can also have undesirable microeconomic effects. We count on financial markets to allocate the economy’s scarce capital resources to the most productive uses. We know that the overpricing of Internet stocks in 1999 and early 2000 led to the financing of many fanciful business ventures and to an overinvestment in long-distance fiber-optic cable that was sufficient to span the globe multiple times. We know that during the housing bubble of the first decade of the 2000s far too many houses were built and, again, investment capital was badly allocated. The more difficult question is evaluating the costs and benefits of a market-based allocation system and determining what it should be compared with. Certainly few would agree that a Soviet-type central planning system is likely to make better allocation decisions. The Performance of Professional Investors Perhaps the most convincing tests of market efficiency are direct tests of the ability of professional investment managers to outperform the market as a whole. If market prices are generally determined by irrational investors and it is easy to identify predictable patterns in security returns or exploitable anomalies in security prices, then professional investment managers should be able to beat the market. Direct tests of the actual returns earned by professionals, who are often compensated with strong incentives to outperform the market, should represent the most compelling evidence of market efficiency. A large body of evidence suggests that professional investment managers are not able to outperform index funds that buy and hold the broad stock market portfolio. One of the earliest studies of mutual fund performance was undertaken by Michael Jensen (1968). He found that active fund managers are unable 90 / 13075-04_CH04-4thPgs.indd 90 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis FIGURE 4.1 / Returns for Surviving Funds Compared with Returns for All Funds 800 Percent Change 700 600 All Funds Surviving Funds 500 400 300 200 100 0 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 Year Source: Author’s compilation of data from Lipper Analytic Services (various years). to add value. Using a risk-adjustment model motivated by the capital asset pricing model, he found that actively managed mutual funds tend to underperform the market by approximately the amount of their added expenses. I repeated Jensen’s study with data from a subsequent period and confirmed the earlier results (Malkiel 1995). Carhart (1997) used a different method of risk adjustment in appraising the performance of actively managed mutual funds. He measured risk in terms of a four-factor model. In addition to the CAPM beta, he used the two Fama-French risk factors of “value” (low price to book value) and “size” as well as a “momentum” factor. Carhart found that most mutual funds underperform the market on a risk-adjusted basis. Although the best funds are able to earn back their expenses with higher gross returns, net returns are no better than could be earned by a lowcost, broad-based index fund. Carhart’s study is consistent with previous work suggesting that professional investors are unable to beat the market. Studies of mutual fund returns must take account of certain biases in many data sets. The degree of “survivorship bias” in the data is often substantial. Poorly performing funds tend to be merged into other funds in the mutual fund’s family complex, thus burying the records of many of the underperformers. Figure 4.1 updates the study I performed during the mid-1990s through the first decade of the 2000s. The analysis shows that the returns for surviving funds are considerably better than the actual return for all funds, including funds liquidated or merged out of existence. Data available for mutual fund returns generally show only the returns for currently available funds—that is, for those funds that survived. / 13075-04_CH04-4thPgs.indd 91 91 10/3/12 10:25 AM Rethinking the Financial Crisis TABLE 4.1 / Percentage of U.S. Equity Funds Outperformed by Benchmarks, 2006 to 2010 Fund Category Benchmark Index All domestic equity All large-cap funds All mid-cap funds All small-cap funds All multi-cap funds Global funds International funds Emerging market funds S&P 1500 S&P 500 S&P Mid-Cap 400 S&P Small-Cap 600 S&P Small-Cap 1500 S&P Global 1200 S&P 700 S&P/IFCI Composite Percentage Outperformed 57 62 78 63 66 59 85 86 Source: Author’s compilation based on data from Standard & Poor’s (various years). Survivorship bias makes the interpretation of long-run mutual fund data sets very difficult. But even using data sets with some degree of survivorship bias, one cannot sustain the argument that professional investors can beat the market. Table 4.1 shows the percentage of actively managed mutual funds that have been outperformed by their relevant passive benchmarks. In general, two-thirds of actively managed funds are outperformed by their benchmark indexes. Similar results can be shown for earlier five-year periods, as well as for ten- and twentyyear periods. Moreover, the funds that do have superior records in one base period are not the same in the next. There is little persistence in mutual fund returns— with the possible exception of very high-expense, poorly performing funds in one period, which tend to do poorly in the next. Managed funds are regularly outperformed by broad index funds with equivalent risk. The median actively managed mutual fund underperforms its benchmark by about eighty to ninety basis points (eight- to nine-tenths of 1 percent), which is approximately the additional expenses charged by the fund’s management. Of course, for any period one can find a number of fund managers who have produced well-above-average returns. But there is no dependable persistence in performance. During the 1970s, the top twenty mutual funds enjoyed almost double the performance of the index. During the 1980s, those same funds underperformed the index. The best-performing funds of the 1980s failed to outperform in the 1990s. And the funds with the best records during the 1990s, which tended to be those with concentrations of “New Economy” stocks, had disastrous returns during the first decade of the 2000s. Figure 4.2 presents a forty-year record of actively managed mutual funds and the Standard & Poor 500 (S&P 500) stock index, a benchmark frequently used to measure overall market returns. It plots the performance of all mutual funds that have been available over the entire period. In 1970 there were 358 equity mutual funds in the United States. (Today there are thousands of funds.) Only 108 of those original funds survived through the end of 2010. All we can do is measure the relative performance versus the market for these surviving funds. We can be sure, 92 / 13075-04_CH04-4thPgs.indd 92 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis FIGURE 4.2 / Returns of Surviving Mutual Funds Compared with S&P 500 Returns, 1970 to 2010 Number of Equity Funds 30 28 25 23 19 20 15 18 Number of Equity Funds 1970 353 2010 108 Nonsurvivors 250 12 10 5 2 3 1 2 0 G pe 3 t rc o e 4 re nt at 4 er pe th rc an en t pe 2 t rc o en 3 t pe 1 t rc o en 2 t pe 0 t rc o en 1 t Le ss t pe han rc – en 4 t –3 pe to rc – en 4 t –2 pe to rc – en 3 t – pe 1 to rc – en 2 t 0 pe to rc – en 1 t 0 Worse than S&P Better than S&P Annualized Returns 1970 to 2010 Source: Author’s calculations based on data from Lipper Analytic Services (various years) and the Vanguard Group (various years). however, that the 250 funds that did not survive had even worse records. Yet even though these data are tainted by survivorship bias, we find that the vast majority of the mutual funds that have been in existence for forty years have under­performed an index that has served as the basis for the most popular indexed mutual funds and exchange-traded funds (ETFs). And one can count on the fingers of one hand the number of professionally managed mutual funds that have outperformed the S&P 500 index by two percentage points or more per year. Similar kinds of results have been observed for other professional investors, such as pension funds and insurance company portfolios (see, for example, Swensen 2005, 2009). A variety of biases—such as inclusion bias, backfill bias, and survivorship bias—make the interpretation of hedge fund returns problematic (see, for example, Malkiel and Saha 2005). But it does not appear that hedge funds, as a group, are able to produce abnormal returns for their clients.4 If markets were dominated by irrational investors who make systematic errors in valuing equities, we should expect that professional investors, who are well incentivized to beat the market, would realize relatively generous returns. If persistent anomalies were obvious and bubbles were easy to spot, a simple passively managed equity fund that buys and holds all the stocks in the market would not display the degree of superiority that it does. / 13075-04_CH04-4thPgs.indd 93 93 10/3/12 10:25 AM Rethinking the Financial Crisis Large arbitrage opportunities do not persist. And while markets can and do make mistakes—some of them horrendous—it is extraordinarily difficult to recognize such situations ex ante. Certainly such examples of mispricing that are recognized ex post do not provide opportunities for risk-adjusted extraordinary returns. The wisdom of the market appears to produce a tableau of prices that is certainly not always correct but is hard to second-guess. It is therefore difficult for me to resist the conclusion that our financial markets are remarkably efficient, and that EMH remains a most useful hypothesis approximating how our financial markets actually work. Conclusion In the final analysis, it is probably useful to think of the stock market in terms of “reasonable market efficiency” or “relative market efficiency” rather than absolute efficiency. Andrew Lo (2008) has suggested that few engineers would even contemplate performing a statistical test to determine whether a given engine is perfectly efficient. But they would attempt to measure the efficiency of that engine relative to a frictionless ideal. Similarly, it is unrealistic to require our financial markets to be perfectly efficient in order to accept the basic tenets of EMH. Indeed, as Sanford Grossman and Joseph Stiglitz (1980) argued, the perfect efficiency of our financial markets is an unrealizable ideal. Those traders who ensure that information is quickly reflected in market prices must be able at least to cover their costs. But it is reasonable to ask if our financial markets are relatively efficient, and I believe that the evidence is very powerful that our markets come very close to the EMH ideal. Information does get reflected rapidly in security prices. Thus, to return to our analogy of the $100 bill lying on the ground, it is highly unlikely that it will stay there—that is, that we will find those prices persisting for any length of time. There may well be some loose pennies around. They will be picked up only if justified by the cost involved in exploiting the opportunities available. Thus, some professional managers may even earn the fees they charge. Their profits, in effect, reflect economic rents. But what seems abundantly clear is that investors in actively managed funds do not reap any benefits over and above those they would earn from a low-cost, broad-based, passively managed index fund. I would draw one more conclusion from this discussion of the efficient-market hypothesis. EMH and behavioral finance should not be considered as competitive models. Behavioral finance provides important insights into the formation of expectations and the process by which valuations are determined. And as Hersh Shefrin and Meir Statman (this volume) make clear, behavioral finance does not argue that the behavioral biases of investors make the market “beatable.” Moreover, the insights of Minsky help explain how required risk premiums are influenced by environmental conditions. Policymakers will be well served by internalizing Minsky’s central theses that financial markets—even if efficient in the sense I have used the term—are able to inflict substantial damage on the real economy. 94 / 13075-04_CH04-4thPgs.indd 94 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis Notes 1. As quoted by Joe Nocera, “Poking Holes in a Theory of Markets,” New York Times, June 5, 2009, p. 81. 2. For excellent surveys of the behavioral finance literature, see Statman (2010). 3. Highly complex derivatives invite asymmetries of information and therefore present opportunities for large profits. Such opportunities are inconsistent with the strong form of EMH, which I have suggested is unlikely to hold in practice. Moreover, Robert Jarrow (this volume) has suggested that some arbitrage opportunities arose from improper ratings published by the rating agencies. 4. Not even the upwardly biased Hedge Fund Research, Inc. (HFRI) hedge fund index has outperformed the S&P 500 stock index, as shown by Jakub Jurek and Erik Stafford (2011). References Abreu, Dilip, and Markus K. Brunnermeier. 2003. “Bubbles and Crashes.” Econometrica 71(1): 173–204. Asness, Clifford S., Tobias J. Moskowitz, and Lasse H. Pederson. 2010. “Value and Momentum Everywhere.” Working paper. University of Chicago and AQR Capital Management. Bachelier, Louis. 1900. “Théorie de la speculation.” Annales Scientifiques de l’École Normale Supérieure 3: 17, 21–86. Bagwell, Laurie Simon, and John B. Shoven. 1989. “Cash Distributions to Shareholders.” Journal of Economic Perspectives 3(3): 129–40. Ball, Ray. 1978. “Anomalies in Relationships Between Securities’ Yields and Yield-Surrogates.” Journal of Financial Economics 6(2–3): 103–26. Ball, Ray, and Phillip Brown. 1968. “An Empirical Evaluation of Accounting Income Numbers.” Journal of Accounting Research 6(2): 159–78. Banz, Rolf W. 1981. “The Relationship Between Return and Market Value of Common Stock.” Journal of Financial Economics 9(1): 3–18. Basu, S. 1977. “The Investment Performance of Common Stocks in Relation to Their Price Earnings Ratios: A Test of the Efficient-Market Hypothesis.” Journal of Finance 32(3): 663–82. Bernard, Victor L., and Jacob K. Thomas. 1990. “Evidence That Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings.” Journal of Accounting and Economics 13(4): 305–40. Black, Fischer. 1986. “Noise.” The Journal of Finance 41(3): 529–43. Brennan, Thomas J., and Andrew W. Lo. 2009. “The Origin of Behavior.” Available at: http://ssrn.com/abstract=1506264 (accessed June 24, 2012). Brunnermeier, Markus, and Stefan Nagel. 2004. “Hedge Funds and the Technology B ­ ubble.” The Journal of Finance 59(5): 2013–40. Campbell, John Y., and Robert J. 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Fama, Eugene, Lawrence Fischer, Michael Jensen, and Richard Roll. 1969. “The Adjustment of Stock Prices to New Information.” International Economic Review 10(1): 1–21. Farmer, J. Doyne, and Andrew W. Lo. 1999. “Frontiers of Finance: Evolution and Efficient Markets.” Proceedings of the National Academy of Sciences 96(August): 9991–92. Fisher, L. Kenneth, and Meir Statman. 2006. “Market Timing in Regressions and Reality.” Journal of Financial Research 29(3): 293–304. Fluck, Zsuzsanna, Burton G. Malkiel, and Richard E. Quandt. 1997. “The Predictability of Stock Returns: A Cross-sectional Simulation.” Review of Economics and Statistics 79(2): 176–83. Fox, Justin. 2009. The Myth of the Rational Market. New York: HarperBusiness. Gordon, Myron J. 1959. “Dividends, Earnings, and Stock Prices.” Review of Economics and Statistics 41(2): 89–105. Goyal, Amit, and Ivo Welch. 2003. “Predicting the Equity Premium with Dividend Ratios.” Management Science 49(5): 639–54. Graham, Benjamin, and David Dodd. 1934. Security Analysis. New York: McGraw-Hill. Granger, Clive W. J., and Oskar Morgenstern. 1963. “Spectral Analysis of New York Stock Exchange Prices.” Kyklos 16(1): 1–27. Greenspan, Alan. 1996. Remarks at the annual dinner and Francis Boyer Lecture of the American Enterprise Institute for Public Policy Research. Washington, D.C. (December 5). Grossman, Sanford J., and Joseph E. Stiglitz. 1980. “On the Impossibility of Informationally Efficient Markets.” American Economic Review 70(3): 393–408. Ibbotson Associates. Various years. Stocks, Bonds, Bills, and Inflation (annual yearbooks). Chicago: Ibbotson Associates. Jegadeesh, Narasimhan, and Sheridan Titman. 1993. “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Journal of Finance 48(1): 65–91. Jensen, Michael. 1968. “The Performance of Mutual Funds in the Period 1945–1964.” Journal of Finance 23(2): 389–416. Jurek, Jakub W., and Erik Stafford. 2011. “The Cost of Capital for Alternative Investments.” Working paper. Cambridge, Mass.: Harvard Business School. Kahneman, Daniel, and Mark W. Riepe. 1998. “Aspects of Investor Psychology.” Journal of Portfolio Management 24(4): 52–65. 96 / 13075-04_CH04-4thPgs.indd 96 10/3/12 10:25 AM Efficient-Market Hypothesis and the Financial Crisis Kahneman, Daniel., and Amos Tversky. 1974. “Judgment Under Uncertainty: Heuristics and Biases.” Science 185(27 Sept.): 1124–31. ———. 1979. “Prospect Theory: An Analysis of Decision Under Risk.” Econometrica 47(2): 263–91. Kendall, Maurice. 1953. “The Analysis of Economic Time Series, Part I: Prices.” Journal of the Royal Statistical Society 96(1): 11–34. Keown, Arthur J., and John M. Pinkerton. 1981. “Merger Announcements and Insider Trading Activity: An Empirical Investigation.” Journal of Finance 36(4): 855–69. Kindleberger, Charles. 1989. Manias, Panics, and Crashes: A History of Financial Crises (revised and enlarged). New York: Basic Books. Kleidon, Allan W. 1986. “Variance Bounds Tests and Stock Price Valuation Models.” Journal of Political Economy 94(5): 953–1001. Krugman, Paul. 2009. “How Did Economists Get It So Wrong?” New York Times Magazine, September 2. Lakonishok, Josef, Andrei Schleifer, and Robert W. Vishny. 1994. “Contrarian Investment, Extrapolation, and Risk.” Journal of Finance 49(5): 1541–78. Leroy, Stephen F. 1973. “Risk Aversion and the Martingale Property of Stock Returns.” ­International Economic Review 14(2): 436–46. Leroy, Stephen F., and Richard D. Porter. 1981. “The Present Value Relation: Tests Based on Variance Bounds.” Econometrica 49(3): 555–74. Lipper Analytic Services. Various years. Proprietary data. New York: Lipper Analytic Services. 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Merton. 1986. “Dividend Variability and Variance Bounds Tests for the Rationality of Stock Market Prices.” American Economic Review 76(3): 483–98. Minsky, Hyman P. 1982. Can “It” Happen Again? Essays on Instability and Finance. Armonk, N.Y.: M. E. Sharpe. ———. 2008. Stabilizing an Unstable Economy. New York: McGraw-Hill. Osborne, M. F. M. 1977. The Stock Market and Finance from a Physicist’s Viewpoint. Washington, D.C.: Self-­published by author. Patell, James, and Mark Wolfson. 1984. “The Intraday Speed of Adjustment of Stock Prices to Earnings and Divident Announcements.” Journal of Financial Economics 13(2): 223–52. Pearson, Karl. 1905. “The Problem of the Random Walk.” Nature 72(294): 294–94. Poterba, James M., and Lawrence H. Summers. 1988. “Mean Reversion in Stock Prices.” Journal of Financial Economics 22(1): 27–59. / 13075-04_CH04-4thPgs.indd 97 97 10/3/12 10:25 AM Rethinking the Financial Crisis Reinganum, Marc R. 1983. “The Anomalous Stock Market Behavior of Small Firms in January: Empirical Tests for Tax-Loss Selling Effects.” Journal of Financial Economics 12(June): 89–104. Rendleman, Richard J., Charles Jones, and Henry Latané. 1982. “Empirical Anomalies Based on Unexpected Earnings and the Importance of Risk Adjustments.” Journal of Financial Economics 10(3): 269–87. Roberts, Harry V. 1959. “Stock Market ‘Patterns’ and Financial Analysis: Methodological Suggestions.” Journal of Finance 14(1): 1–10. Roll, Richard. 1992. “Volatility in U.S. and Japanese Stock Markets: A Symposium.” Journal of Applied Corporate Finance 5(1): 25–29. Samuelson, Paul. 1965. “Proof That Properly Anticipated Prices Fluctuate Randomly.” Industrial Management Review 6(2): 41–49. Schwert, G. William. 2003. “Anomalies and Market Efficiency.” In Handbook of the Economics of Finance, edited by George M. Constantinides, Milton Harris, and René M. Stulz. ­Amsterdam: North Holland. Shiller, Robert. 1981. “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” American Economic Review 71(June): 421–36. ———. 1984. Comment in Brookings Papers on Economic Activity: 459. ———. 2000. Irrational Exuberance. Princeton, N.J.: Princeton University Press. Shleifer, Andrei, Josef Lakonisok, and Robert Vishny. 1992. “The Structure and Performance of the Money Management Industry.” Brookings Papers on Economic Activity: Microeconomics 1992: 339–91. Solnik, Bruno H. 1973. “Note on the Validity of the Random Walk for European Stock Prices.” Journal of Finance 28(5): 1151–59. Standard & Poor’s. Various years. Proprietary data. New York: Standard & Poor’s. Statman, Meir. 2010. What Investors Really Want. New York: McGraw-Hill. Swensen, David F. 2005. Unconventional Success: A Fundamental Approach to Personal Investment. New York: Free Press. ———. 2009. Pioneering Portfolio Management. New York: Free Press. Vanguard Group of Investment Companies. Various years. Proprietary data. Malvern, Penn.: Vanguard Group. Working, Holbrook. 1960. “Note on the Correlation of First Differences of Averages in a Random Chain.” Econometrica 28(4): 916–18. 98 / 13075-04_CH04-4thPgs.indd 98 10/3/12 10:25 AM ECON7200 Individual Project– Semester 1, 2021 Student name: Student ID number: Total word count: 1242 (excluding references) 1 Introduction Most industries and firms have been affected seriously by the COVID-19 epidemic. However, the Nasdaq index of a tech-heavy stock has risen sharply again after the event of the dot-com bubble from 1992 to 2002. This paper will first summarize the article about the previous dotcom bubble event and then discuss it based on the contents of behavioral economics and the Efficient Market Hypothesis (EMH). Finally, this essay will compare the situation of the two dot-com booms and give my opinion on the recent dot-com boom. Article summary Goodnight and Green (2010) examined the process of the dot-com bubble event from 1992 to 2002 in this article. The efficient market hypothesis is difficult to explain the development of bubbles when new investments occur. This article first provides some ideas from three perspectives of post-conventional economic theories which are new institutional theory, behavioral economics and performative economics to explain bubbles by connecting imitation with the markets and participants. Then the article mentioned that from the perspective of rhetoric, mimesis is the strategic imitation and will have an important impact on the dot-com bubble event. Then the article shows the detailed situation of the dot-com bubble event. when the Federal Reserve decreased the interest rates and the country had abundant credit, economic bubbles appeared in the 1990s. From 1996, Internet companies and the country's overall economy developed rapidly and more and more investors invested in these companies which expanded the bubble to its peak in 2000. As the science of the human informational codes was soon faced to the public, the commercial opportunities of the internet were limited and stock prices fell sharply. However, there was a new enthusiasm for real estate after the economic effects of the dot-com bubble faded in 2000. With the development of new technology and times, it is easier to appear bubbles. Explain behavioral economics and discuss the dot-com bubble The following content will use behavioral economics and the EMH to discuss this dot-com bubble event. Behavioral economics combines psychology and economics to research the impact of individual biases and different factors on people's decision making (Camerer, 2014). Although traditional economics assumes that people are rational, this is not always happening in real life. Behavioural economics can explain why and how people sometimes make irrational 2 decisions. Besides, behavioral economics also explains how market decisions are made and the mechanisms that drive public choice. The decisions made by behavioral economics are different from those from traditional economics which can improve traditional economics. Behavioural economics provides methods for explaining how the dot-com bubble event happened. It can combine imitation with irrationality (Goodnight and Green, 2010). When the initial offerings of Netscape and similar Internet companies highly succeeded around 1996, the Internet industry was seen as a good investment opportunity that can not be missed. Under the context of abundant state credit, more and more people joined in the investment which led to the appearance of the bubbles. Then, more investors imitate others to support these popular Internet companies directly without rational judgment and analysis as they noticed and trusted the current high returns. And when most investors make more profit in Internet companies, they think they made the right decision and persist with it. This is known as confirmation bias in behavioral economics which people are more willing to support information or results that are similar to their own. As the investors' interest and hopes for Internet companies are growing, the bubble continued to expand. Besides, according to the status quo bias and the familiarity bias in behavioral economics, people prefer to keep the status quo and the investments they are familiar with rather than explore new alternatives, so the dot-com bubble lasted for a long time. Until internet business opportunities were restricted and stock prices decreased quickly, the dot-com bubble began to burst. The EMH is hard to explain the process of the dot-com bubble event. The EMH means that stock prices reflect all available information and people can use this information to make a good estimation of intrinsic value. (Ţiţan, 2015) In this hypothesis, the market's expectation of future prices is rational. However, new investments boom like the internet happened without people’s rational valuation and analysis. Therefore, behavioral economics is more suitable to explain the causes and persist situation of the dot-com bubble compared with the efficient market hypothesis. Compare the two booms Despite the COVID-19 has a serious impact on the global economy from 2019, the share prices of many technology companies are still largely increasing recently. But in my opinion, this boom may not be another dot-com bubble and crash. Due to the policy of isolation and restriction under the context of the COVID-19 epidemic in the world, many companies have 3 developed online businesses (Donthu and Gustafsson, 2020). Many digital and technology types of companies have developed services businesses and people get more stable profits and advantages from the digital services. This makes investors regard some Internet companies as high-quality investment choices, such as Amazon, Microsoft and other technology companies (The Economist, 2020). According to the status quo bias in behavioral economics, The situation is similar to the event of the dot-com bubble. More investors are willing to insist on current investments with high yields. And the country's subsidies and credit policies during the COVID-19 epidemic also contributed to this technology boom. There are some common features between these two booms as they are all related to the increase of new investment capital inflows and the abundant credit policies. These will directly lead to a significant increase in stock prices. Although the two booms had similar features, the current boom may not be the same uncontrollable as the dot-com bubble. In the 1990s, the growth rate of total factor productivity was very high which also represents the rapid pace of technological progress (Chou, Hao-Chun Chuang and Shao, 2014). And at that time, companies’ profits were growing fast and investors grasped the profit opportunities well so that the investment in computer equipment and software was rising quickly. While from the beginning of the 21st century, productivity growth has been slowing down. Even the companies' current profits are higher than 20 years ago, the investment in technology only accounts for a very small share of GDP around the 2010s (Mackenzie, 2021). Moreover, even large amount of companies’ economy was affected by the epidemic of COVID-19, there still exist some internet and technology companies performed well during the pandemic (Fang, Guo, Hu and Zhuang, 2020). This indicates that some of the companies have better resistance to risks when special events occur. From the perspective of rational investors, they should accept the risks during the investment. But, according to loss aversion in behavioral economics, some investors are more likely to avoid losses than making investment gains. So, they will choose to invest few companies with better resistance to risks and this will reduce the possibility of the appearance of bubbles. So in my opinion, the current boom may not be another dot-com bubble and crash. 4 Conclusion During the dot-com boom, the processes of people's decision making and preferences all had a huge impact on the happening of the dot-com bubble. This essay mainly analyzes the condition of the two dot-com booms using the content of behavioral economics. Although there are some similarities between these two booms, the recent boom will not be as serious and uncontrollable as the previous boom. So this may not be another dot-com bubble and crash. 5 References Camerer, C., 2014. Behavioral economics. Current Biology, 24(18), pp.R867-R871. Chou, Y., Hao-Chun Chuang, H. and Shao, B., 2014. The impacts of information technology on total factor productivity: A look at externalities and innovations. International Journal of Production Economics, 158, pp.290-299. Donthu, N. and Gustafsson, A., 2020. Effects of COVID-19 on business and research. Journal of Business Research, 117, pp.284-289. Fang, Y., Guo, G., Hu, Y. and Zhuang, L., 2020. Research on Opportunities of the Internet Industry Under the COVID-19 Epidemic. Proceedings of the 2020 International Confere...
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Efficient Market Hypothesis in Behavioral Finance
Outline Report
Introduction
Article Summary
Definition of EMH
EMH and the Bubble
EMH and Behavioral Finance
Conclusion
References


Efficient Market Hypothesis in Behavioral Finance
Student ID
ECON7200 - Individual Project
University Affiliation
Date of Submission

Efficient Market Hypothesis in Behavioral Finance
Market efficiency is a measure of association between the price of a stock and the
available information about that asset. In an efficient market, the current stock price is entirely
representative of all the information available about the stock. When all the information already
incorporates the stock price, the stock in the efficient market is traded at a fair value. It implies
that it is impossible to inflate the stock price during a trade or purchase it back at the significant
undervalue. Thus, the efficient market hypothesizes that, in the longer run, it is practically
impossible to overrun the market as all the public information about the asset is incorporated and
reflected in the stock price.
Article Summary
The economies of all the countries were significantly affected, especially the United
States and Europe, in 2008-2009. The article's author, Malkiel (2012), explained the severity of
the financial crisis that shook the foundation of all the modern-day financial theories, that the
financial market is efficient. To counter the critiques that make the efficient market hypothesis
the sole responsible for the severe recession, the author explains the founding definition of the
efficient market hypothesis. The information that benefits or affects the stock price in the future
is immediately included in the current asset price. The implication of this sudden inclusion might
not make sense at the time of the stocks price change to some investors as the significance of the
change is not reflected on the spot but in the longer run, and estimation of profit is not possible
with precisions. Thus, there is always a possibility of both underreaction and overreaction of the
market. Malkiel thus, explains that through appears to be an opportunity of arbitrage; however,
in an efficient market hypothesis, no such a...


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