Efficient-market hypothesis

In financial economics, the efficient-market hypothesis (EMH) states that asset prices fully reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information or changes in discount rates (the latter may be predictable or unpredictable).

The EMH was developed by Professor Eugene Fama who argued that stocks always trade at their fair value, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by chance or by purchasing riskier investments.[1] His 2012 study with Kenneth French confirmed this view, showing that the distribution of abnormal returns of US mutual funds is very similar to what would be expected if no fund managers had any skill—a necessary condition for the EMH to hold.[2]

There are three variants of the hypothesis: "weak", "semi-strong", and "strong" form. The weak form of the EMH claims that prices on traded assets (e.g., stocks, bonds, or property) already reflect all past publicly available information. The semi-strong form of the EMH claims both that prices reflect all publicly available information and that prices instantly change to reflect new public information. The strong form of the EMH additionally claims that prices instantly reflect even hidden "insider" information.

Critics have blamed the belief in rational markets for much of the late-2000s financial crisis.[3][4][5] In response, proponents of the hypothesis have stated that market efficiency does not mean having no uncertainty about the future, that market efficiency is a simplification of the world which may not always hold true, and that the market is practically efficient for investment purposes for most individuals.[6]

Historical background

Historically, the EMH is preceded by Hayek's (1945) argument that markets are the most effective way of aggregating the pieces of information dispersed amongst individuals within a society. Given the ability to profit from private information, self-interested traders are motivated to acquire and act on their private information. In doing so, traders contribute to more and more efficient market prices. In the competitive limit, market prices reflect all available information and prices can only move in response to news. Thus there is a very close link between EMH and the random walk hypothesis which was discussed in 1863 by Jules Regnault, a French broker. Later another French mathematician, Louis Bachelier, applied probability theory in his 1900 PhD thesis, "The Theory of Speculation".[7] His work was largely ignored until the 1950s when financial economists began making heavy use of probability theory and statistics to model asset prices (in particular, options prices).

Empirically, a number of studies indicated that US stock prices and related financial series followed a random walk model in the short-term.[8] Whilst there is some predictability over the long-term, the extent to which this is due to rational time-varying risk premia as opposed to behavioural reasons is a subject of debate. Research by Alfred Cowles in the 1930s and 1940s suggested that professional investors were in general unable to outperform the market.

EMH anomalies and rejection of the CAPM

While event studies of stock splits is consistent with the EMH (Fama, Fisher, Jensen, and Roll, 1969), other empirical analyses have found problems with the efficient-market hypothesis. Early examples include the observation that small neglected stocks and stocks with low book-market ratios (value stocks) tended to achieve abnormally high returns relative to what could be explained by the CAPM.[9][10] Further tests of portfolio efficiency by Gibbons, Ross and Shakens (1989) (GJR) led to rejections of the CAPM, although tests of efficiency inevitably run into the joint hypothesis problem (see, Roll's Critique).

Following GJR's results and mounting empirical evidence of EMH anomalies, academics began to move away from the CAPM towards risk factor models such as the Fama-French 3 factor model. It should be noted that these risk factor models are not properly founded on economic theory (whereas CAPM is founded on Modern Portfolio Theory), but rather, constructed with long-short portfolios in response to the observed empirical EMH anomalies. For instance, the "small-minus-big" (SMB) factor in the FF3 factor model is simply a portfolio that holds long positions on small stocks and short positions on large stocks to mimic the risks small stocks face. These risk factors are said to represent some aspect or dimension of undiversifiable systematic risk which should be compensated with higher expected returns. Additional popular risk factors include the "HML" value factor (Fama and French, 1993); "MOM" momentum factor (Carhart, 1997); "ILLIQ" liquidity factors (Amihud et al. 2002).

Joint hypothesis problem

When testing the EMH one inevitably finds that 1) either the market is inefficient, or 2) the asset pricing model used to test market efficiency is incorrect. Hence tests of market efficiency run into this joint hypothesis problem. For instance, early tests of market efficiency used the CAPM, which could be an incomplete model of asset prices; test results on market efficiency would thus be inconclusive.

Formally, the joint hypothesis problem says that it is never possible to test (sufficiently, to prove or disprove) market efficiency. A test of market efficiency must include some model for how prices may be set efficiently. Then actual prices can be examined to see whether this holds true. Usually this fails and then this supports the case that markets are not efficient. The joint hypothesis problem says that, when this happens, it shows that the model is not complete. There are some factors that are not accounted for.


The efficient-market hypothesis emerged as a prominent theory in the mid-1960s. Paul Samuelson had begun to circulate Bachelier's work among economists. In 1964 Bachelier's dissertation along with the empirical studies mentioned above were published in an anthology edited by Paul Cootner.[11] In 1965, Eugene Fama published his dissertation arguing for the random walk hypothesis.[12] Also, Samuelson published a proof showing that if the market is efficient prices will show random-walk behavior.[13] This is often cited in support of the efficient-market theory, by the method of affirming the consequent,[14][15] however in that same paper, Samuelson warns against such backward reasoning, saying "From a nonempirical base of axioms you never get empirical results."[16] In 1970, Fama published a review of both the theory and the evidence for the hypothesis. The paper extended and refined the theory, included the definitions for three forms of financial market efficiency: weak, semi-strong and strong (see below).[17]

It has been argued that the stock market is “micro efficient” but not “macro efficient”. The main proponent of this view was Samuelson, who asserted that the EMH is much better suited for individual stocks than it is for the aggregate stock market. Research based on regression and scatter diagrams has strongly supported Samuelson's dictum.[18] This result is also the theoretical justification for the forecasting of broad economic trends, which is provided by a variety of groups including non-profit groups as well as by for-profit private institutions (such as brokerage houses[19] and consulting companies[20]).

Further to this evidence that the UK stock market is weak-form efficient, other studies of capital markets have pointed toward their being semi-strong-form efficient. A study by Khan of the grain futures market indicated semi-strong form efficiency following the release of large trader position information (Khan, 1986). Studies by Firth (1976, 1979, and 1980) in the United Kingdom have compared the share prices existing after a takeover announcement with the bid offer. Firth found that the share prices were fully and instantaneously adjusted to their correct levels, thus concluding that the UK stock market was semi-strong-form efficient. However, the market's ability to efficiently respond to a short term, widely publicized event such as a takeover announcement does not necessarily prove market efficiency related to other more long term, amorphous factors. David Dreman has criticized the evidence provided by this instant "efficient" response, pointing out that an immediate response is not necessarily efficient, and that the long-term performance of the stock in response to certain movements are better indications.

Theoretical background

Beyond the normal utility maximizing agents, the efficient-market hypothesis requires that agents have rational expectations; that on average the population is correct (even if no one person is) and whenever new relevant information appears, the agents update their expectations appropriately. Note that it is not required that the agents be rational. EMH allows that when faced with new information, some investors may overreact and some may underreact. All that is required by the EMH is that investors' reactions be random and follow a normal distribution pattern so that the net effect on market prices cannot be reliably exploited to make an abnormal profit, especially when considering transaction costs (including commissions and spreads). Thus, any one person can be wrong about the market—indeed, everyone can be—but the market as a whole is always right. There are three common forms in which the efficient-market hypothesis is commonly stated—weak-form efficiency, semi-strong-form efficiency and strong-form efficiency, each of which has different implications for how markets work.

Weak-form efficiency

In weak-form efficiency, future prices cannot be predicted by analyzing prices from the past. Excess returns cannot be earned in the long run by using investment strategies based on historical share prices or other historical data. Technical analysis techniques will not be able to consistently produce excess returns, though some forms of fundamental analysis may still provide excess returns. Share prices exhibit no serial dependencies, meaning that there are no "patterns" to asset prices. This implies that future price movements are determined entirely by information not contained in the price series. Hence, prices must follow a random walk. This 'soft' EMH does not require that prices remain at or near equilibrium, but only that market participants not be able to systematically profit from market 'inefficiencies'. However, while EMH predicts that all price movement (in the absence of change in fundamental information) is random (i.e., non-trending), many studies have shown a marked tendency for the stock markets to trend over time periods of weeks or longer[21] and that, moreover, there is a positive correlation between degree of trending and length of time period studied (but note that over long time periods, the trending is sinusoidal in appearance).[22] Various explanations for such large and apparently non-random price movements have been promulgated.

There is a vast literature in academic finance dealing with the momentum effect identified by Jegadeesh and Titman.[23][24] Stocks that have performed relatively well (poorly) over the past 3 to 12 months continue to do well (poorly) over the next 3 to 12 months. The momentum strategy is long recent winners and shorts recent losers, and produces positive risk-adjusted average returns. Being simply based on past stock returns, the momentum effect produces strong evidence against weak-form market efficiency, and has been observed in the stock returns of most countries, in industry returns, and in national equity market indices. Moreover, Fama has accepted that momentum is the premier anomaly[25][26]

The problem of algorithmically constructing prices which reflect all available information has been studied extensively in the field of computer science.[27][28]

A novel approach for testing the weak form of the Efficient Market Hypothesis is using quantifers derived from Information Theory. In this line, Zunino et al.[29] found that informational efficiency is related to market size and the stage of development of the economy. Using a similar technique, Bariviera et al.[30] uncover the impact of important economic events on informational efficiency. The methodology proposed by econophysicists Zunino, Bariviera and coauthors is new and alternative to usual econometric techniques, and is able to detect changes in the stochastic and or chaotic underlying dynamics of prices time series.

Semi-strong-form efficiency

In semi-strong-form efficiency, it is implied that share prices adjust to publicly available new information very rapidly and in an unbiased fashion, such that no excess returns can be earned by trading on that information. Semi-strong-form efficiency implies that neither fundamental analysis nor technical analysis techniques will be able to reliably produce excess returns. To test for semi-strong-form efficiency, the adjustments to previously unknown news must be of a reasonable size and must be instantaneous. To test for this, consistent upward or downward adjustments after the initial change must be looked for. If there are any such adjustments it would suggest that investors had interpreted the information in a biased fashion and hence in an inefficient manner.

Strong-form efficiency

In strong-form efficiency, share prices reflect all information, public and private, and no one can earn excess returns. If there are legal barriers to private information becoming public, as with insider trading laws, strong-form efficiency is impossible, except in the case where the laws are universally ignored. To test for strong-form efficiency, a market needs to exist where investors cannot consistently earn excess returns over a long period of time. Even if some money managers are consistently observed to beat the market, no refutation even of strong-form efficiency follows: with hundreds of thousands of fund managers worldwide, even a normal distribution of returns (as efficiency predicts) should be expected to produce a few dozen "star" performers.

Criticism and behavioral finance

Price-Earnings ratios as a predictor of twenty-year returns based upon the plot by Robert Shiller (Figure 10.1,[31] source). The horizontal axis shows the real price-earnings ratio of the S&P Composite Stock Price Index as computed in Irrational Exuberance (inflation adjusted price divided by the prior ten-year mean of inflation-adjusted earnings). The vertical axis shows the geometric average real annual return on investing in the S&P Composite Stock Price Index, reinvesting dividends, and selling twenty years later. Data from different twenty-year periods is color-coded as shown in the key. See also ten-year returns. Shiller states that this plot "confirms that long-term investors—investors who commit their money to an investment for ten full years—did do well when prices were low relative to earnings at the beginning of the ten years. Long-term investors would be well advised, individually, to lower their exposure to the stock market when it is high, as it has been recently, and get into the market when it is low."[31] Burton Malkiel, a well-known proponent of the general validity of EMH, stated that this correlation may be consistent with an efficient market due to differences in interest rates.[32]

Investors, including the likes of Warren Buffett,[33] and researchers have disputed the efficient-market hypothesis both empirically and theoretically. Behavioral economists attribute the imperfections in financial markets to a combination of cognitive biases such as overconfidence, overreaction, representative bias, information bias, and various other predictable human errors in reasoning and information processing. These have been researched by psychologists such as Daniel Kahneman, Amos Tversky, Richard Thaler, and Paul Slovic. These errors in reasoning lead most investors to avoid value stocks and buy growth stocks at expensive prices, which allow those who reason correctly to profit from bargains in neglected value stocks and the overreacted selling of growth stocks. Investors prefer riskier funds in spring and safer funds in autumn.[34]

Empirical evidence has been mixed, but has generally not supported strong forms of the efficient-market hypothesis[9][10][35] According to Dreman and Berry, in a 1995 paper, low P/E stocks have greater returns.[36] In an earlier paper Dreman also refuted the assertion by Ray Ball that these higher returns could be attributed to higher beta,[37] whose research had been accepted by efficient market theorists as explaining the anomaly[38] in neat accordance with modern portfolio theory.

One can identify "losers" as stocks that have had poor returns over some number of past years. "Winners" would be those stocks that had high returns over a similar period. The main result of one such study is that losers have much higher average returns than winners over the following period of the same number of years.[39] A later study showed that beta (β) cannot account for this difference in average returns.[40] This tendency of returns to reverse over long horizons (i.e., losers become winners) is yet another contradiction of EMH. Losers would have to have much higher betas than winners in order to justify the return difference. The study showed that the beta difference required to save the EMH is just not there.

Economic bubbles and irrational exuberance

Speculative economic bubbles are an obvious anomaly, in that the market often appears to be driven by buyers operating on escalating market sentiment/ irrational exuberance, who take little notice of underlying value. These bubbles are typically followed by an overreaction of frantic selling, allowing shrewd investors to buy stocks at bargain prices. Rational investors have difficulty profiting by shorting irrational bubbles because, in the words of a famous saying attributed to John Maynard Keynes, "Markets can stay irrational longer than you can stay solvent."[41] Sudden market crashes as happened on Black Monday in 1987 are mysterious from the perspective of efficient markets, but allowed as a rare statistical event under the weak-form of EMH. Benoit Mandelbrot has argued that market bubbles are not anomalous but rather characteristic of price dynamics described by power laws such as Pareto, Zipf[42] or Tracy-Widom[43] combined with persistence in price change trends.[44]

Burton Malkiel has warned that certain emerging markets such as China are not empirically efficient; that the Shanghai and Shenzhen markets, unlike markets in United States, exhibit considerable serial correlation (price trends), non-random walk, and evidence of manipulation.[45]

Behavioral psychology

Behavioral psychology approaches to stock market trading are among some of the more promising alternatives to EMH (and some investment strategies seek to exploit exactly such inefficiencies). But Nobel Laureate co-founder of the programme Daniel Kahneman —announced his skepticism of investors beating the market: "They're [investors] just not going to do it [beat the market]. It's just not going to happen."[46] Indeed, defenders of EMH maintain that Behavioral Finance strengthens the case for EMH in that it highlights biases in individuals and committees and not competitive markets. For example, one prominent finding in Behaviorial Finance is that individuals employ hyperbolic discounting. It is demonstrably true that bonds, mortgages, annuities and other similar financial instruments subject to competitive market forces do not. Any manifestation of hyperbolic discounting in the pricing of these obligations would invite arbitrage thereby quickly eliminating any vestige of individual biases. Similarly, diversification, derivative securities and other hedging strategies assuage if not eliminate potential mispricings from the severe risk-intolerance (loss aversion) of individuals underscored by behavioral finance. On the other hand, economists, behaviorial psychologists and mutual fund managers are drawn from the human population and are therefore subject to the biases that behavioralists showcase. By contrast, the price signals in markets are far less subject to individual biases highlighted by the Behavioral Finance programme. Richard Thaler has started a fund based on his research on cognitive biases. In a 2008 report he identified complexity and herd behavior as central to the global financial crisis of 2008.[47]

Further empirical work has highlighted the impact transaction costs have on the concept of market efficiency, with much evidence suggesting that any anomalies pertaining to market inefficiencies are the result of a cost benefit analysis made by those willing to incur the cost of acquiring the valuable information in order to trade on it. Additionally the concept of liquidity is a critical component to capturing "inefficiencies" in tests for abnormal returns. Any test of this proposition faces the joint hypothesis problem, where it is impossible to ever test for market efficiency, since to do so requires the use of a measuring stick against which abnormal returns are compared —one cannot know if the market is efficient if one does not know if a model correctly stipulates the required rate of return. Consequently, a situation arises where either the asset pricing model is incorrect or the market is inefficient, but one has no way of knowing which is the case.

The performance of stock markets is correlated with the amount of sunshine in the city where the main exchange is located.[48]

A key work on random walk was done in the late 1980s by Profs. Andrew Lo and Craig MacKinlay; they effectively argue that a random walk does not exist, nor ever has.[49] Their paper took almost two years to be accepted by academia and in 1999 they published "A Non-random Walk Down Wall St." which collects their research papers on the topic up to that time.

View of some economists

Economists Matthew Bishop and Michael Green claim that full acceptance of the hypothesis goes against the thinking of Adam Smith and John Maynard Keynes, who both believed irrational behavior had a real impact on the markets.[50]

Economist John Quiggin has claimed that "Bitcoin is perhaps the finest example of a pure bubble", and that it provides a conclusive refutation of EMH.[51] While other assets used as currency (such as gold, tobacco and U.S. dollars) have value independent of people's willingness to accept them as payment, Quiggin argues that "in the case of Bitcoin there is no source of value whatsoever" and that:

Since Bitcoins do not generate any actual earnings, they must appreciate in value to ensure that people are willing to hold them. But an endless appreciation, with no flow of earnings or liquidation value, is precisely the kind of bubble the EMH says can’t happen.

In 2013, Kim Man Lui pointed out that there is difference of performance between experienced and novice traders in a controlled experiment. If the market really walks randomly, there should be no difference between these two kinds of traders. However, traders who are more knowledgeable on technical analysis significantly outperform those who are less knowledgeable.[52]

Tshilidzi Marwala surmised that artificial intelligence influences the applicability of the theory of the efficient market hypothesis in that the more artificial intelligence infused computer traders there are in the markets as traders the more efficient the markets become., ,

Warren Buffett has also argued against EMH, most notably in his 1984 presentation The Superinvestors of Graham-and-Doddsville, saying the preponderance of value investors among the world's best money managers rebuts the claim of EMH proponents that luck is the reason some investors appear more successful than others.[53] As Malkiel[54] has shown, over the 30 years (to 1996) more than two-thirds of professional portfolio managers have been outperformed by the S&P 500 Index (and, more to the point, there is little correlation between those who outperform in one year and those who outperform in the next.)

Late 2000s financial crisis

The financial crisis of 2007–08 led to renewed scrutiny and criticism of the hypothesis.[55] Market strategist Jeremy Grantham stated flatly that the EMH was responsible for the current financial crisis, claiming that belief in the hypothesis caused financial leaders to have a "chronic underestimation of the dangers of asset bubbles breaking".[4] Noted financial journalist Roger Lowenstein blasted the theory, declaring "The upside of the current Great Recession is that it could drive a stake through the heart of the academic nostrum known as the efficient-market hypothesis."[5] Former Federal Reserve chairman Paul Volcker chimed in, saying it's "clear that among the causes of the recent financial crisis was an unjustified faith in rational expectations [and] market efficiencies."[56] "By 2007–2009, you had to be a fanatic to believe in the literal truth of the EMH", noted one financial analyst.[57]

At the International Organization of Securities Commissions annual conference, held in June 2009, the hypothesis took center stage. Martin Wolf, the chief economics commentator for the Financial Times, dismissed the hypothesis as being a useless way to examine how markets function in reality. Paul McCulley, managing director of PIMCO, was less extreme in his criticism, saying that the hypothesis had not failed, but was "seriously flawed" in its neglect of human nature.[58][59]

The financial crisis led Richard Posner, a prominent judge, University of Chicago law professor, and innovator in the field of Law and Economics, to back away from the hypothesis and express some degree of belief in Keynesian economics. Posner accused some of his Chicago School colleagues of being "asleep at the switch", saying that "the movement to deregulate the financial industry went too far by exaggerating the resilience—the self healing powers—of laissez-faire capitalism."[60] Others, such as Fama, said that the hypothesis held up well during the crisis and that the markets were a casualty of the recession, not the cause of it. Despite this, Fama has conceded that "poorly informed investors could theoretically lead the market astray" and that stock prices could become "somewhat irrational" as a result.[61]

Critics have suggested that financial institutions and corporations have been able to reduce the efficiency of financial markets by creating private information and reducing the accuracy of conventional disclosures, and by developing new and complex products which are challenging for most market participants to evaluate and correctly price.[62][63]

Efficient markets applied in securities class action litigation

The theory of efficient markets has been practically applied in the field of Securities Class Action Litigation. Efficient market theory, in conjunction with "Fraud on the Market Theory," has been used in Securities Class Action Litigation to both justify and as mechanism for the calculation of damages.[64] In the Supreme Court Case, Halliburton v. Erica P. John Fund, U.S. Supreme Court, No. 13-317, the use of efficient market theory in supporting securities class action litigation was affirmed. Supreme Court Justice Roberts wrote that "the court’s ruling was consistent with the ruling in "Basic" because it allows“ direct evidence when such evidence is available” instead of relying exclusively on the efficient markets theory."[65]

See also


  1. http://www.investopedia.com/articles/basics/04/022004.asp
  2. Fama and French 2012
  3. Fox, Justin (2009). Myth of the Rational Market. Harper Business. ISBN 0-06-059899-9.
  4. 1 2 Nocera, Joe (5 June 2009). "Poking Holes in a Theory on Markets". New York Times. Retrieved 8 June 2009.
  5. 1 2 Lowenstein, Roger (7 June 2009). "Book Review: 'The Myth of the Rational Market' by Justin Fox". Washington Post. Retrieved 5 August 2011.
  6. Desai, Sameer (27 March 2011). "Efficient Market Hypothesis". Retrieved 2 June 2011.
  7. Kirman, Alan. "Economic theory and the crisis." Voxeu. 14 November 2009.
  8. See Working (1934), Cowles and Jones (1937), and Kendall (1953), and later Brealey, Dryden and Cunningham.
  9. 1 2 Empirical papers questioning EMH:
    • Francis Nicholson. Price-Earnings Ratios in Relation to Investment Results. Financial Analysts Journal. Jan/Feb 1968:105–109.
    • Basu, Sanjoy (1977). "Investment Performance of Common Stocks in Relation to Their Price-Earnings Ratios: A test of the Efficient Markets Hypothesis". Journal of Finance. 32: 663–682. doi:10.1111/j.1540-6261.1977.tb01979.x.
    • Rosenberg B, Reid K, Lanstein R. (1985). Persuasive Evidence of Market Inefficiency. Journal of Portfolio Management 13:9–17.
  10. 1 2 Fama, E; French, K (1992). "The Cross-Section of Expected Stock Returns". Journal of Finance. 47: 427–465. doi:10.1111/j.1540-6261.1992.tb04398.x.
  11. Cootner (ed.), Paul (1964). The Random Character of StockMarket Prices. MIT Press.
  12. Fama, Eugene (1965). "The Behavior of Stock Market Prices". Journal of Business. 38: 34–105. doi:10.1086/294743.
  13. Samuelson, Paul (1965). "Proof That Properly Anticipated Prices Fluctuate Randomly". Industrial Management Review. 6: 41–49.
  14. Market Sense and Nonsense: How the Markets Really Work (and How They Don't) - Jack D. Schwager
  15. Collin Read. The Efficient Market Hypothesists: Bachelier, Samuelson, Fama, Ross, Tobin, and Shiller.
  16. "The efficient market hypothesis: problems with interpretations of empirical tests".
  17. Fama, Eugene (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work". Journal of Finance. 25 (2): 383–417. doi:10.2307/2325486. JSTOR 2325486.
  18. Jung, Jeeman; Shiller, Robert (2005). "Samuelson's Dictum And The Stock Market". Economic Inquiry. 43 (2): 221–228. doi:10.1093/ei/cbi015.
  19. Fidelity. "2015 Stock Market Outlook", a sample outlook report by a brokerage house.
  20. McKinsey Insights & Publications. "Insights & Publications".
  21. Saad, Emad W., Student Member, IEEE; Prokhorov, Danil V. Member , IEEE; and Wunsch,II, Donald C. Senior Member, IEEE (November 1998). "Comparative Study of Stock Trend Prediction Using Time Delay, Recurrent and Probabilistic Neural Networks". IEEE Transactions on Neural Networks. 9 (6): 1456–1470. doi:10.1109/72.728395. PMID 18255823.
  22. Granger, Clive W. J.; Morgenstern, Oskar (5 May 2007). "Spectral Analysis Of New York Stock Market Prices". Kyklos. 16 (1): 1–27. doi:10.1111/j.1467-6435.1963.tb00270.x.
  23. Jegadeesh, N; Titman, S (1993). "Returns to Buying winners and selling losers: Implications for stock market efficiency". Journal of Finance. 48 (1): 65–91. doi:10.1111/j.1540-6261.1993.tb04702.x.
  24. Jegadeesh, N; Titman, S (2001). "Profitability of Momentum Strategies: An evaluation of alternative explanations". Journal of Finance. 56 (2): 699–720. doi:10.1111/0022-1082.00342.
  25. Fama, E; French, K (1996). "Multifactor explanation of asset pricing anomalies". Journal of Finance. 51 (1): 55–84. doi:10.1111/j.1540-6261.1996.tb05202.x.
  26. Fama, E; French, K (2008). "Dissecting Anomalies". Journal of Finance. 63 (4): 1653–78. doi:10.1111/j.1540-6261.2008.01371.x.
  27. Kleinberg, Jon; Tardos, Eva (2005). Algorithm Design. Addison Wesley. ISBN 0-321-29535-8.
  28. Vazirani, Vijay V.; Nisan, Noam; Roughgarden, Tim; Tardos, Éva (2007). Algorithmic Game Theory (PDF). Cambridge, UK: Cambridge University Press. ISBN 0-521-87282-0.
  29. Zunino, L.; Bariviera, A.F.; Guercio, M.B; Martinez, L.B.; Rosso, O.A. "On the efficiency of sovereign bond markets". Physica A. 391: 4342–4349. doi:10.1016/j.physa.2012.04.009.
  30. Bariviera, A.F.; Zunino, L; Guercio, M.B.; Martinez, L.B.; Rosso, O.A. (2014). "Revisiting the European sovereign bonds with a permutation-information- theory approach". Eur. Phys. J. B. 86: 509. doi:10.1140/epjb/e2013-40660-7.
  31. 1 2 Shiller, Robert (2005). Irrational Exuberance (2d ed.). Princeton University Press. ISBN 0-691-12335-7.
  32. Burton G. Malkiel (2006). A Random Walk Down Wall Street. ISBN 0-393-32535-0. p.254.
  33. http://www.businessinsider.com/warren-buffett-on-efficient-market-hypothesis-2010-12
  34. "Seasonal Asset Allocation: Evidence from Mutual Fund Flows by Mark J. Kamstra, Lisa A. Kramer, Maurice D. Levi, Russ Wermers :: SSRN". Social Science Research Network.
  35. Chan, Kam C.; Gup, Benton E.; Pan, Ming-Shiun (4 Mar 2003). "International Stock Market Efficiency and Integration: A Study of Eighteen Nations". Journal of Business Finance & Accounting. 24 (6): 803–813. doi:10.1111/1468-5957.00134.
  36. Dreman David N.; Berry Michael A. (1995). "Overreaction, Underreaction, and the Low-P/E Effect". Financial Analysts Journal. 51 (4): 21–30. doi:10.2469/faj.v51.n4.1917.
  37. Ball R. (1978). Anomalies in Relationships between Securities' Yields and Yield-Surrogates. Journal of Financial Economics 6:103–126
  38. Dreman D. (1998). Contrarian Investment Strategy: The Next Generation. Simon and Schuster.
  39. DeBondt, Werner F.M.; Thaler, Richard H. (1985). "Does the Stock Market Overreact". Journal of Finance. 40: 793–805. doi:10.2307/2327804.
  40. Chopra, Navin; Lakonishok, Josef; Ritter, Jay R. (1985). "Measuring Abnormal Performance: Do Stocks Overreact". Journal of Financial Economics. 31 (2): 235–268. doi:10.1016/0304-405X(92)90005-I.
  41. Harrod, R. F. (1951). The Life Of John Maynard Keynes.
  42. Mandelbrot, Benoit (2004). The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward. New York, NY: Basic Books. pp. 197–206. ISBN 0465043550.
  43. Wolchovor, Natalie (October 15, 2014). "At the Far Ends of a New Universal Law". Quanta Magazine.
  44. Peters, Edgar (1996). Chaos and Order in the Capital Markets 2nd: A New View of Cycles, Prices, and Market Volatility. New York, NY: John Wiley & Sons, Inc. pp. 86–110. ISBN 0471139386.
  45. Burton Malkiel. Investment Opportunities in China on YouTube. July 16, 2007. (34:15 mark)
  46. Hebner, Mark (12 August 2005). "Step 2: Nobel Laureates" (PDF). Index Funds: The 12-Step Program for Active Investors. Index Funds Advisors, Inc. Retrieved 12 August 2005.
  47. Thaler RH. (2008). 3Q2008. Fuller & Thaler Asset Management.
  48. "Good Day Sunshine: Stock Returns and the Weather by David A. Hirshleifer, Tyler Shumway :: SSRN". Social Science Research Network.
  49. "A Non-Random Walk Down Wall Street". Princeton University Press.
  50. Hurt III, Harry (19 March 2010). "The Case for Financial Reinvention". The New York Times. Retrieved 29 March 2010.
  51. Quiggin, John (16 April 2013). "The Bitcoin Bubble and a Bad Hypothesis". The National Interest.
  52. K.M. Lui and T.T.L Chong, "Do Technical Analysts Outperform Novice Traders: Experimental Evidence" Economics Bulletin. 33(4), 3080-3087, 2013.
  53. Hoffman, Greg (14 July 2010). "Paul the octopus proves Buffett was right". Sydney Morning Herald. Retrieved 4 August 2010.
  54. Malkiel, A Random Walk Down Wall Street, 1996
  55. "Sun finally sets on notion that markets are rational". The Globe and Mail. 7 July 2009. Retrieved 7 July 2009.
  56. Paul Volcker (October 27, 2011). "Financial Reform: Unfinished Business". New York Review of Books. Retrieved 22 November 2011.
  57. Siegel, Laurence B. (2010). "Black Swan or Black Turkey? The State of Economic Knowledge and the Crash of 2007–2009". Financial Analysts Journal. 66 (4): 6–10. doi:10.2469/faj.v66.n4.4. Quote on p. 7.
  58. "Has 'guiding model' for global markets gone haywire?". Jerusalem Post. 11 June 2009. Retrieved 17 June 2009.
  59. "Investors are finally seeing the nonsense in the efficient market theory". The Telegraph.
  60. "After the Blowup". The New Yorker. 11 January 2010. Retrieved 12 January 2010.
  61. Jon E. Hilsenrath, Stock Characters: As Two Economists Debate Markets, The Tide Shifts. Wall Street Journal 2004
  62. Michael Simkovic, "Secret Liens and the Financial Crisis of 2008", American Bankruptcy Law Journal 2009
  63. Michael Simkovic, "Competition and Crisis in Mortgage Securitization"
  64. http://www.nytimes.com/2014/06/29/your-money/are-markets-efficient-even-the-supreme-court-is-weighing-in.html?_r=0
  65. http://www.nytimes.com/2014/06/24/business/Justices-rule-on-class-actions-for-securities-fraud.html


This article is issued from Wikipedia - version of the 11/7/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.