answer the queston
8. The Efficient Market Hypothesis
Instructor: Seongcheol Paeng
7/20/2020
1
Table
8.1 Random Walk and the Efficient Hypothesis
8.2 Implication of the EMH
8.3 Are Markets Efficient?
8.4 Mutual Fund and Analyst Performance
7/20/2020
2
8.1 Random Walk and the Efficient Hypothesis
This is the essence of the argument that stock prices should follow a random walk, that is, that price changes should be random and unpredictable.
Far from a proof of market irrationality, randomly evolving stock prices would be the necessary consequence of intelligent investors competing to discover relevant information on which to buy or sell stocks before the rest of the market becomes aware of that information.
If prices are determined rationally, then only new information will cause them to change. Therefore, a random walk would be the natural result of prices that always reflect all current knowledge.
Therefore, the notion that stocks already reflect all available information is referred to as the efficient market hypothesis (EMH).
7/20/2020
3
Figure 8.1 illustrates the response of stock prices to new information in an efficient market. The graph plots the price response of a sample of 194 firms that were targets of takeover attempts.
In most takeovers, the acquiring firm pays a substantial premium over current market prices. Therefore, announcement of a takeover attempt should cause the stock price to jump.
The figure shows that stock prices jump dramatically on the day the news becomes public.
However, there is no further drift in prices after the announcement date, suggesting that prices reflect the new information, including the likely magnitude of the takeover premium, by the end of the trading day.
7/20/2020
4
Figure 8.1 Cumulative abnormal returns before takeover attempts: Target companies
8.1 Random Walk and the Efficient Hypothesis
For example, Patel and Wolfson (1984) show that most of the stock price response to corporate dividend or earnings announcements occurs within 10 minutes of the announcement.
A nice illustration of such rapid adjustment is provided in a study by Busse and Green (2002), who track minute-by-minute stock prices of firms featured on CNBC’s “Morning” or “Midday Call” segments.
Minute 0 in Figure 8.2 is the time at which the stock is mentioned on the midday show. The top line is the average price movement of stocks that receive positive reports, while the bottom-line reports returns on stocks with negative reports.
7/20/2020
5
Figure 8.2 Stock price reaction to CNBC reports. The figure shows the reaction of stock prices to on-air stock reports during the “Midday Call” segment on CNBC. The chart plots cumulative returns beginning 15 minutes before the stock report.
8.1 Random Walk and the Efficient Hypothesis
Competition as the Source of Efficiency
This point has been stressed by Grossman and Stiglitz (1980). They argued that investors will have an incentive to spend time and resources to analyze and uncover new information only if such activity is likely to generate higher investment returns.
Example 8.1 Consider an investment management fund currently managing a $5 billion portfolio. Suppose that the fund manager can devise a research program that could increase the portfolio rate of return by one-tenth of 1% per year, a seemingly modest amount. This program would increase the dollar return to the portfolio by $5 billion × .001, or $5 million.
Therefore, the fund would be willing to spend up to $5 million per year on research to increase stock returns by a mere tenth of 1% per year. With such large rewards for such small increases in investment performance, it should not be surprising that professional portfolio managers are willing to spend large sums on industry analysts, computer support, and research effort, and therefore that price changes are, generally speaking, difficult to predict.
With so many well-backed analysts willing to spend considerable resources on research, easy pickings in the market will be rare. Moreover, the incremental rates of return on research activity may be so small that only managers of the largest portfolios will find them worth pursuing.
7/20/2020
6
8.1 Random Walk and the Efficient Hypothesis
Versions of the Efficient Market Hypothesis
It is common to distinguish among three versions of the EMH: the weak, semi-strong, and strong forms of the hypothesis.
The weak-form hypothesis asserts that stock prices already reflect all information that can be derived by examining market trading data such as the history of past prices, trading volume, or short interest.
The semi-strong-form hypothesis states that all publicly available information regarding the prospects of a firm already must be reflected in the stock price.
Finally, the strong-form version of the efficient market hypothesis states that stock prices reflect all information relevant to the firm, even including information available only to company insiders.
7/20/2020
7
8.1 Random Walk and the Efficient Hypothesis
Technical Analysis
Technical analysis is essentially the search for recurrent and predictable patterns in stock prices.
The key to successful technical analysis is a sluggish response of stock prices to fundamental supply-and-demand factors. This prerequisite, of course, is diametrically opposed to the notion of an efficient market.
An interesting question is whether a technical rule that seems to work will continue to work in the future once it becomes widely recognized. A clever analyst may occasionally uncover a profitable trading rule, but the real test of efficient markets is whether the rule itself becomes reflected in stock prices once its value is discovered.
Once a useful technical rule (or price pattern) is discovered, it ought to be invalidated when the mass of traders attempts to exploit it. In this sense, price patterns ought to be self-destructing.
7/20/2020
8
8.2 Implication of the EMH
Fundamental Analysis
Fundamental analysis uses earnings and dividend prospects of the firm, expectations of future interest rates, and risk evaluation of the firm to determine proper stock prices.
Fundamental analysts usually start with a study of past earnings and an examination of company financial statements. They supplement this analysis with further detailed economic analysis, ordinarily including an evaluation of the quality of the firm’s management, the firm’s standing within its industry, and the prospects for the industry as a whole.
Once again, the efficient market hypothesis predicts that most fundamental analysis also is doomed to failure. If the analyst relies on publicly available earnings and industry information, his or her evaluation of the firm’s prospects is not likely to be significantly more accurate than those of rival analysts.
This is why fundamental analysis is difficult. It is not enough to do a good analysis of a firm; you can make money only if your analysis is better than that of your competitors because the market price will already reflect all commonly recognized information.
7/20/2020
9
8.2 Implication of the EMH
Active versus Passive Portfolio Management
By now it is apparent that casual efforts to pick stocks are not likely to pay off. Competition among investors ensures that any easily implemented stock evaluation technique will be used widely enough so that any insights derived will be reflected in stock prices.
Only serious analysis and uncommon techniques are likely to generate the differential insight necessary to yield trading profits.
The small investor probably is better off investing in mutual funds or ETFs.
Proponents of the efficient market hypothesis believe that active management is largely wasted effort and unlikely to justify the expenses incurred. Therefore, they advocate a passive investment strategy that makes no attempt to outsmart the market. A passive strategy aims only at establishing a well-diversified portfolio of securities without attempting to find under or overvalued stocks.
Passive management is usually characterized by a buy-and-hold strategy. Because the efficient market theory indicates that stock prices are at fair levels, given all available information, it makes no sense to buy and sell securities frequently, which generates large brokerage fees without increasing expected performance.
One common strategy for passive management is to create an index fund, which is a fund designed to replicate the performance of a broad-based index of stocks.
7/20/2020
10
8.2 Implication of the EMH
The Role of Portfolio Management in an Efficient Market
You have learned that a basic principle in portfolio selection is diversification. Even if all stocks are priced fairly, each still poses firm-specific risk that can be eliminated through diversification. Therefore, rational security selection, even in an efficient market, calls for the selection of a well-diversified portfolio providing the systematic risk level that the investor wants.
Rational investment policy also requires that tax considerations be reflected in security choice. At an obvious level, high-bracket investors find it advantageous to buy tax-exempt municipal bonds despite their relatively low pretax yields, whereas those same bonds are unattractive to low-tax-bracket investors.
A third argument for rational portfolio management relates to the particular risk profile of the investor. For example, a Toyota executive whose annual bonus depends on Toyota’s profits generally should not invest additional amounts in auto stocks.
Investors of varying ages also might warrant different portfolio policies with regard to risk bearing. For example, older investors who are essentially living off savings might choose to avoid long-term bonds whose market values fluctuate dramatically with changes in interest rates (discussed in Part Four).
7/20/2020
11
8.2 Implication of the EMH
Resource Allocation
Deviations from informational efficiency would also result in a large cost that will be borne by all citizens, namely, inefficient resource allocation.
Recall that in a capitalist economy, investments in real assets such as plant, equipment, and know-how are guided in large part by the prices of financial assets.
If markets were inefficient and securities commonly mispriced, then resources would be systematically misallocated.
Corporations with overpriced securities will be able to obtain capital too cheaply, and corporations with undervalued securities might forgo investment opportunities because the cost of raising capital will be too high.
7/20/2020
12
8.2 Implication of the EMH
The Issues (The Magnitude Issue)
We noted that an investment manager overseeing a $5 billion portfolio who can improve performance by only .1% per year will increase investment earnings by .001 × $5 billion = $5 million annually. This manager clearly would be worth her salary!
Yet can we, as observers, statistically measure her contribution? Probably not.
1% contribution would be swamped by the yearly volatility of the market. Remember, the annual standard deviation of the well-diversified S&P 500 Index has been around 20%. Against these fluctuations a small increase in performance would be hard to detect.
7/20/2020
13
8.3 Are Markets Efficient?
The Issues (The Selection Bias Issue)
Suppose that you discover an investment scheme that could really make money. You have two choices: either publish your technique in The Wall Street Journal to win fleeting fame or keep your technique secret and use it to earn millions of dollars.
Most investors would choose the latter option, which presents us with a conundrum. Only investors who find that an investment scheme cannot generate abnormal returns will be willing to report their findings to the whole world.
The Issues (The Lucky Event Issue)
In virtually any month it seems we read an article about some investor or investment company with a fantastic investment performance over the recent past. Surely the superior records of such investors disprove the efficient market hypothesis. Yet this conclusion is far from obvious.
7/20/2020
14
8.3 Are Markets Efficient?
Weak-Form Tests: Pattern in Stock Returns (Returns Over Short Horizons)
While broad market indexes demonstrate only weak serial correlation, there appears to be stronger momentum in performance across market sectors exhibiting the best and worst recent returns.
Momentum Effect: The tendency of poorly performing stocks and well-performing stocks in one period to continue that abnormal performance in following periods.
In an investigation of intermediate-horizon stock price behavior (using 3- to 12-month holding periods), Jegadeesh and Titman (1993) found a momentum effect in which good or bad recent performance of particular stocks continues over time.
They conclude that while the performance of individual stocks is highly unpredictable, portfolios of the best-performing stocks in the recent past appear to outperform other stocks with enough reliability to offer profit opportunities.
7/20/2020
15
8.3 Are Markets Efficient?
Weak-Form Tests: Pattern in Stock Returns (Returns Over Long Horizons)
Although short- to intermediate-horizon returns suggest momentum in stock market prices, studies of long-horizon returns (i.e., returns over multiyear periods) by Fama and French (1988) and Poterba and Summers (1988) indicate pronounced negative long-term serial correlation in the performance of the aggregate market.
The latter result has given rise to a “fads hypothesis,” which asserts that the stock market might overreact to relevant news. Such overreaction leads to positive serial correlation (momentum) over short time horizons. Subsequent correction of the overreaction leads to poor performance following good performance and vice versa.
The corrections mean that a run of positive returns eventually will tend to be followed by negative returns, leading to negative serial correlation over longer horizons. These episodes of apparent overshooting followed by correction give the stock market the appearance of fluctuating around its fair value.
Reversal effect: The tendency of poorly performing stocks and well-performing stocks in one period to experience reversals in the following period.
7/20/2020
16
8.3 Are Markets Efficient?
Predictors of Broad Market Returns
Several studies have documented the ability of easily observed variables to predict market returns.
For example, Fama and French (1988) showed that the return on the aggregate stock market tends to be higher when the dividend/price ratio, the dividend yield, is high.
Campbell and Shiller (1988) found that the earnings yield can predict market returns.
Keim and Stambaugh (1986) showed that bond market data such as the spread between yields on high and low-grade corporate bonds also help predict broad market returns.
7/20/2020
17
8.3 Are Markets Efficient?
Predictors of Broad Market Returns
On the one hand, they may imply that abnormal stock returns can be predicted, in violation of the efficient market hypothesis.
More probably, however, these variables are proxying for variation in the market risk premium.
For example, given a level of dividends or earnings, stock prices will be lower and dividend and earnings yields will be higher when the risk premium (and therefore the expected market return) is higher.
Thus, a high dividend or earnings yield will be associated with higher market returns. This does not indicate a violation of market efficiency.
Fama and French (1989) showed that the yield spread between high- and low-grade bonds has greater predictive power for returns on low-grade bonds than for returns on high-grade bonds, and greater predictive power for stock returns than for bond returns, suggesting that the predictability in returns is in fact a risk premium rather than evidence of market inefficiency.
7/20/2020
18
8.3 Are Markets Efficient?
Semi-strong Test: Market Anomalies
Fundamental analysis uses a much wider range of information to create portfolios than does technical analysis. Investigations of the efficacy of fundamental analysis ask whether publicly available information beyond the trading history of a security can be used to improve investment performance, and therefore they are tests of semi-strong-form market efficiency.
Surprisingly, several easily accessible statistics, for example a stock’s price–earnings ratio or its market capitalization, seem to predict abnormal risk-adjusted returns. Findings such as these, which we will review in the following pages, are difficult to reconcile with the efficient market hypothesis and therefore are often referred to as efficient market anomalies.
anomalies: Patterns of returns that seem to contradict the efficient market hypothesis.
An example of this issue is the discovery by Basu (1977, 1983) that portfolios of low price–earnings (P/E) ratio stocks have higher returns than do high P/E portfolios.
7/20/2020
19
8.3 Are Markets Efficient?
Semi-strong Test: The Small-Firm Effect
The so-called size or small-firm effect, originally documented by Banz (1981), is illustrated in Figure 8.3.
It shows the historical performance of portfolios formed by dividing the NYSE stocks into 10 portfolios each year according to firm size (i.e., the total value of outstanding equity).
Average annual returns between 1926 and 2013 are consistently higher on the small-firm portfolios.
7/20/2020
20
8.3 Are Markets Efficient?
Figure 8.3 Average annual return for 10 size-based portfolios, 1926–2013
Semi-strong Test: The Neglected-Firm And Liquidity Effects
Merton (1987) provides a rationale for the neglected firm effect. He shows that neglected firms might be expected to earn higher equilibrium returns as compensation for the risk associated with limited information. In this sense the neglected-firm premium is not strictly a market inefficiency but is a type of risk premium.
Work by Amihud and Mendelson (1986, 1991) on the effect of liquidity on stock returns might be related to both the small-firm and neglected-firm effects. They argue that investors will demand a rate-of-return premium to invest in less liquid stocks that entail higher trading costs.
7/20/2020
21
8.3 Are Markets Efficient?
Semi-strong Test: Book-To-Market Ratio
Fama and French (1992) showed that a powerful predictor of returns across securities is the ratio of the book value of the firm’s equity to the market value of equity.
Fama and French stratified firms into 10 groups according to book-to-market ratios and examined the average rate of return of each of the 10 groups.
Figure 8.4 is an updated version of their results. The decile with the highest book-to-market ratio had an average annual return of 17.5%, while the lowest-ratio decile averaged only 11.0%.
7/20/2020
22
8.3 Are Markets Efficient?
Figure 8.4 Average annual return as a function of the book-to-market ratio, 1926–2013
Semi-strong Test: Book-To-Market Ratio
In fact, Fama and French found that after controlling for the size and book-to-market effects, beta seemed to have no power to explain average security returns.
This finding is an important challenge to the notion of rational markets because it seems to imply that a factor that should affect returns—systematic risk—seems not to matter, while a factor that should not matter—the book-to-market ratio—seems capable of predicting future returns.
Semi-strong Test: Post-Earnings-Announcement Price Drift
Rendleman, Jones, and Latané (1982) provide an influential study of sluggish price response to earnings announcements.
They calculate earnings surprises for a large sample of firms, rank the magnitude of the surprise, divide firms into 10 deciles based on the size of the surprise, and calculate abnormal returns for each decile.
The abnormal return of each portfolio is the return adjusting for both the market return in that period and the portfolio beta.
7/20/2020
23
8.3 Are Markets Efficient?
Semi-strong Test: Post-Earnings-Announcement Price Drift
Figure 8.5 plots cumulative abnormal returns by decile. Their results are dramatic. The correlation between ranking by earnings surprise and abnormal returns across deciles is as predicted.
There is a large abnormal return (a jump in cumulative abnormal return) on the earnings announcement day (time 0). The abnormal return is positive for positive-surprise firms and negative for negative-surprise firms.
The more remarkable, and interesting, result of the study concerns stock price movement after the announcement date.
7/20/2020
24
8.3 Are Markets Efficient?
Figure 8.4 Cumulative abnormal returns in response to earnings announcements
Semi-strong Test: Post-Earnings-Announcement Price Drift
The cumulative abnormal returns of positive-surprise stocks continue to rise—in other words, exhibit momentum—even after the earnings information becomes public, while the negative-surprise firms continue to suffer negative abnormal returns.
The market appears to adjust to the earnings information only gradually, resulting in a sustained period of abnormal returns.
Evidently, one could have earned abnormal profits simply by waiting for earnings announcements and purchasing a stock portfolio of positive-earnings-surprise companies.
These are precisely the types of predictable continuing trends that ought to be impossible in an efficient market.
7/20/2020
25
8.3 Are Markets Efficient?
Figure 8.4 Cumulative abnormal returns in response to earnings announcements
Semi-strong Test: Bubbles and Market Efficiency
It is hard to defend the position that security prices in these instances represented rational, unbiased assessments of intrinsic value.
And, in fact, some economists, most notably Hyman Minsky, have suggested that bubbles arise naturally.
During periods of stability and rising prices, investors extrapolate that stability into the future and become more willing to take on risk.
Risk premiums shrink, leading to further increases in asset prices, and expectations become even more optimistic in a self-fulfilling cycle.
But, in the end, pricing and risk taking become excessive and the bubble bursts.
7/20/2020
26
8.3 Are Markets Efficient?
Strong-Form Tests: Inside Information
The study by Seyhun (1986), which carefully tracked the public release dates of the Official Summary, found that following insider transactions would be to no avail.
Although there is some tendency for stock prices to increase even after the Official Summary reports insider buying, the abnormal returns are not of sufficient magnitude to overcome transaction costs.
7/20/2020
27
8.3 Are Markets Efficient?
Interpreting the Anomalies: Risk Premiums or Inefficiencies?
Figure 8.6 shows that returns on these portfolios tend to have positive returns in years prior to rapid growth in gross domestic product.
The opposite interpretation is offered by Lakonishok, Shleifer, and Vishny (1995), who argue that these phenomena are evidence of inefficient markets, more specifically, of systematic errors in the forecasts of stock analysts.
They believe that analysts extrapolate past performance too far into the future and therefore overprice firms with recent good performance and underprice firms with recent poor performance.
Ultimately, when market participants recognize their errors, prices reverse.
7/20/2020
28
8.3 Are Markets Efficient?
Interpreting the Anomalies: Risk Premiums or Inefficiencies?
When these too-extreme expectations are “corrected,” the low-expected-growth firms outperform high-expected-growth firms.
Interpreting the Anomalies: Anomalies or Data Mining?
It is noteworthy that some anomalies have not shown much staying power after being reported in the academic literature. For example, after the small-firm effect was published in the early 1980s, it promptly disappeared for much of the rest of the decade.
Interpreting the Anomalies: Anomalies Over Time
We pointed out previously that while no market can be perfectly efficient, in well-functioning markets, anomalies ought to be self-destructing. As market participants learn of profitable trading strategies, their attempts to exploit them should move prices to levels at which abnormal profits are no longer available.
7/20/2020
29
8.3 Are Markets Efficient?
Interpreting the Anomalies: Anomalies Over Time
Chordia, Subrahmanyam, and Tong (2012) look for this dynamic in the pattern of many of the anomalies discussed in this chapter.
They focus on abnormal returns associated with several characteristics including size, book-to-market ratio, momentum, and turnover (which may be inversely related to the neglected firm effect).
They break their sample at 1993 and show that the abnormal returns associated with these characteristics in the pre-1993 period largely disappear in the post-1993 period (with the notable exception of the book-to-market effect).
Their interpretation is that the market has become more efficient as knowledge about these anomalies percolated through the investment community.
Interestingly, they find that the attenuation of alphas is greatest in the most liquid stocks, where trading activity is least costly.
7/20/2020
30
8.3 Are Markets Efficient?
Interpreting the Anomalies: Anomalies Over Time
McLean and Pontiff (2015) provide further insight into this phenomenon. They identify more than 80 characteristics identified in the academic literature as associated with abnormal returns.
Rather than using a common break point for all characteristics, they carefully track both the publication date of each finding as well as the date the paper was first posted to the Social Science Research Network.
This allows them to break the sample for each finding at dates corresponding to when that particular finding became public. They conclude that the post-publication decay in abnormal return is about 35% (e.g., a 5% abnormal return prepublication falls on average to 3.25% after publication).
7/20/2020
31
8.3 Are Markets Efficient?
Stock Market Analysts
Womack (1996) focuses on changes in analysts’ recommendations and finds that positive changes are associated with increased stock prices of about 5% and negative changes result in average price decreases of 11%. One might wonder whether these price changes reflect the market’s recognition of analysts’ superior information or insight about firms or, instead, simply result from new buy or sell pressure brought on by the recommendations themselves.
Jegadeesh, Kim, Krische, and Lee (2004) also find that changes in consensus recommendations are associated with price changes, but that the level of consensus recommendations is an inconsistent predictor of future stock performance.
Barber, Lehavy, McNichols, and Trueman (2001) focus on the level of consensus recommendations and show that firms with the most favorable recommendations outperform those with the least favorable recommendations.
In sum, the literature suggests that some value is added by analysts, but ambiguity remains.
7/20/2020
32
8.4 Mutual Fund and Analyst Performance
Mutual Fund Managers
Figure 4.4 in that chapter demonstrated that between 1972 and 2013 the returns of a passive portfolio indexed to the Wilshire 5000 typically would have been better than those of the average equity fund.
On the other hand, there was some (admittedly inconsistent) evidence of persistence in performance, meaning that the better managers in one period tended to be better managers in following periods.
Such a pattern would suggest that the better managers can with some consistency outperform their competitors, and it would be inconsistent with the notion that market prices already reflect all relevant information.
7/20/2020
33
8.4 Mutual Fund and Analyst Performance
Mutual Fund Managers
Figure 8.7 shows a frequency distribution of four-factor alphas for U.S. domestic equity funds. The results show that the distribution of alpha is roughly bell-shaped, with a slightly negative mean. On average, it does not appear that these funds outperform their style-adjusted benchmarks.
Consistent with Figure 8.7, Fama and French (2010) use the four-factor model to assess the performance of equity mutual funds and show that while they may exhibit positive alphas before fees, after the fees charged to their customers, alphas were negative.
Likewise, Wermers (2000), who uses both style portfolios as well as the characteristics of the stocks held by mutual funds to control for performance, also finds positive gross alphas but negative net alphas after controlling for fees and risk.
7/20/2020
34
8.4 Mutual Fund and Analyst Performance
Figure 8.7 Mutual fund alphas computed using a four-factor model of expected return, 1993–2007. (The best and worst 2.5% of observations are excluded from this distribution.)
Mutual Fund Managers
Carhart (1997) reexamines the issue of consistency in mutual fund performance to see whether better performers in one period continue to outperform in later periods.
He uses the four-factor extension described above and finds that after controlling for these factors, there is only minor persistence in relative performance across managers.
Figure 8.8, from his study, documents performance persistence. Equity funds are ranked into 1 of 10 groups by performance in the formation year, and the performance of each group in the following years is plotted.
7/20/2020
35
8.4 Mutual Fund and Analyst Performance
Figure 8.8 Persistence of mutual fund performance. Performance over time of mutual fund groups ranked by initial-year performance
Mutual Fund Managers
It is clear that except for the best-performing top-decile group and the worst-performing 10th-decile group, performance in future periods is almost independent of earlier-year returns.
Carhart’s results suggest that there may be a small group of exceptional managers who can with some consistency outperform a passive strategy, but that for the majority of managers over- or underperformance in any period is largely a matter of chance.
7/20/2020
36
8.4 Mutual Fund and Analyst Performance
Figure 8.8 Persistence of mutual fund performance. Performance over time of mutual fund groups ranked by initial-year performance
Mutual Fund Managers
Bollen and Busse (2004) find more evidence of performance persistence, at least over short horizons.
They rank mutual fund performance using the four-factor model over a base quarter, assign funds into one of 10 deciles according to base-period alpha, and then look at performance in the following quarter.
Figure 8.9 illustrates their results. The solid line is the average alpha of funds within each of the deciles in the base period (expressed on a quarterly basis).
The steepness of that line reflects the considerable dispersion in performance in the ranking period.
7/20/2020
37
8.4 Mutual Fund and Analyst Performance
Figure 8.9 Risk-adjusted performance in ranking quarter and following quarter
Mutual Fund Managers
The dashed line is the average performance of the funds in each decile in the following quarter.
The shallowness of this line indicates that most of the original performance differential disappears.
Nevertheless, the plot is still clearly downward-sloping, so it appears that at least over a short horizon such as one quarter, there is some performance consistency.
However, that persistence is probably too small a fraction of the original performance differential to justify performance chasing by mutual fund customers.
7/20/2020
38
8.4 Mutual Fund and Analyst Performance
Figure 8.9 Risk-adjusted performance in ranking quarter and following quarter
So, Are Markets Efficient?
An overly doctrinaire belief in efficient markets can paralyze the investor and make it appear that no research effort can be justified. This extreme view is probably unwarranted. There are enough anomalies in the empirical evidence to justify the search for underpriced securities that clearly goes on.
7/20/2020
39
8.4 Mutual Fund and Analyst Performance
Assignments
Problem Sets (Paraphrase with your own words.)
Explain Competition as the Source of Efficiency.
Explain Versions of the Efficient Market Hypothesis.
Explain the Weak-Form with examples.
Explain the Semi-Strong Form with examples.
Explain the Strong Form with examples.
Deadline: 7/22 before the class
Submit it via email to seongcheol.paeng@csusb.edu
7/20/2020
40