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STOCK MARKET ANOMALIES

Purpose of this page is to critically review some of the reported stock market anomalies. Theentire literature is difficult to summarize briefly, because many contradictory results are reported

and frequently the origin of differences in empirical results is not very clear. Note that there is adefinite bias in the review presented below. I am interested in how the various results fit thegeneral efficient market model, and where results show the limitations of some of the auxiliary(simplifying) assumptions of the textbook model.

(Work in progress)

THE SIZE EFFECTThe size effect refers to the negative relation between stock returns and the equity value of a firm.Banz (1981) was the first to document this phenomenon for U.S. stocks (see also Reinganum,1981). Banz found that the coefficient on size has more explanatory power than the coefficient on

the CAPM beta in describing the cross section of returns. The size effect has been reproduced fornumerous sample periods and for most major securities markets around the world (Hawawini andKeim, 2000).Booth and Keim (2000, Table 1 NYSE-AMEX-NASDAQ stocks) estimate the small firm(Decile10)-large firm (Decile1) effect to be statistically significant at 0.81% per month for allmonths 1966-1981. Horowitz, Loughran and Savin (2000, Table 1 NYSE-AMEX-NASDAQ stocks)estimate 1.09% per month for all months 1963-1981.

* The size effect is only a January effect; no size effect exists in the average returns of othermonths of the year (Hawawini and Keim, 2000, Figure 1 Table 5 NYSE-AMEX stocks 1962-1994;Booth and Keim, 2000 Table 1 and 2 NYSE-AMEX-NASDAQ 1926-1981, 1982-1995).

* The size effect is exaggerated due to delisting bias in the CRSP database. Correction fornegative delisting returns eliminates the size effect in NASDAQ stocks (Shumway and Warther,

1999) although not in NYSE-AMEX stocks (Shumway, 1997).The size effect is exaggerated due to the bid-ask bounce and rebalancing effect in calculated

short-run (daily, monthly) returns (Conrad and Kaul, 1993).Boynton and Oppenheimer (2006) calculate that more than 40% of the commonly reported

size premium in monthly returns can be attributed to delisting and bid-ask spread bias: reducing itfrom 1.36% to 0.73% per month for the full-sample 1926-2002 period. Using geometric returns forlong-run returns reduces the size premium to 0.33% (no longer significant).

* The small price effect is driven by very small price stocks (penny stocks) that sometimesshow extremely large returns (for example, 500% to 2000%) (Horowitz, Loughran and Savin,2000). Eliminating small price or small cap stocks (less than $5mln) eliminates the size effect(Horowitz, Loughran and Savin, 2000 Table 2). Some of these returns may be related to CRSPdatabase errors, for example failures to adjust for (reverse) stock splits and stock conversionsfollowing bankruptcy reorganizations (Bhardwaj and Brooks, 1992 footnote 8).

* The size effect is unreliable. In the US the average size effect over all months hasdisappeared after 1981 (Booth and Keim, 2000 Table 2; Horowitz, Loughran and Savin, 2000Table 1 and Figure A) and was never consistent to begin with (Booth and Keim, 2000 Table 1).The disappearance of the size effect is mirrored in the decline of the January effect in smallstocks. The size effect is driven by brief periods of extraordinary high returns, such as the 1975-1983 period in the US (Siegel, 1998 Figure 6-1 1925-1997) or extraordinary low returns, such asthe 1969-1973 period (Brown, Kleidon and Marsh, 1983). The average size effect alsodisappeared in the UK after 1989 and in fact turned into a negative effect (Dimson and Marsh,2000 Table 2).

* The size effect is actually a small or low price effect; other indicators of size such as book

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value, sales, number of employees, etc. have no explanatory power (Berk 1995, 2000)* A relationship between low price and higher returns is not in any way anomalous. Higher

expected/required returns will result in a low price because there needs to be room future futureprice appreciation that creates the higher return (Berk 1995, 2000)

Failure to find strong effects of the traditional CAPM beta may be due to errors-in-variablesbias (Kim, 1995, 1997; see also Chan and Chen, 1988). The CAPM-beta effect also depends onthe return measurement interval (Hand, Kothari and Wasley, 1993; Kothari, Shanken and Sloan,1995)

Size portfolios (as well as B/M portfolios) are highly correlated with the KMV distance-to-default risk measure (Vassalou/Xing, 2004)

Liquidity risk proxied by the bid-ask spread variable is an additional risk-factor (Amihud andMendelson, 1989)

* The size effect is irrelevant with respect to the efficient market hypothesisBid-ask bounce effect: if on average prices in December reflect sell transactions at bid prices

and prices in January reflect buy transactions at ask prices (see the January effect), the averagehigh return over January simply reflects the high bid-ask spread for small or low price stocks.There is no trading opportunity in such observed data.

Including appropriate transaction costs for the relevant stocks eliminates any profits fromfrequent trading strategies based on the usual daily or monthly return studies (for example Stolland Whaley, 1983; Schultz, 1983; Bhardwaj and Brooks, 1992; Al-Rajoub and Hassan, 2004).

Long horizon buy-hold returns suffer less from trading costs (Stoll and Whaley, 1983; Schultz,1983), but one needs to be very careful in calculating the appropriate holding period returns(avoiding the portfolio rebalancing problem and other problems of aggregating monthly returns(for example, Conrad and Kaul, 1993; Roll, 1983; Blume and Stambaugh, 1983). Statisticalinference is difficult due to overlapping observations and/or small sample problems.

* Investment funds such as the DFA9-10 Small Company Portfolio find it impossible to capturethe size effect, as a result of practical constraints on investing in the smallest capitalization (lessthan $10mln) and smallest price stocks (less than $2) (Booth and Keim, 2000 Table 2).

CASE CLOSED?

THE JANUARY EFFECT

The January effect refers to the tendency for stock market returns to be higher in January than inany of the other months (Rozeff and Kinney, 1976). The January effect is particularly strong insmall size stocks (Keim, 1983), but is also present in large stocks, and also present in valuestocks based on CF/P, E/P, P/B (Hawawini and Keim, 2000 Figure 1). The January effect ismainly located in the first 2 weeks of January.Booth and Keim (2000, Table 1 NYSE-AMEX-NASDAQ stocks) estimate the small firm(Decile10)-large firm (Decile1) January effect to be statistically significant at 9.72% during 1966-1981 and 4.48% for 1982-1995. Note that the estimates of the small-firm January effect arevariable over time, with high returns for 1926-1945, lower returns for 1946-1965, higher returnsfor 1966-1981 and again lower returns for 1982-1995. Nevertheless, all estimates of the Januaryreturns are statistically significant.The results of Hawawini and Keim (2000, NYSE-AMEX 1962-1994) show that the January effect(relative to the other months) is appr. 11% for small stocks (Decile 10) and 1% for large stocks(Decile 1).

* The usual explanations suggested for the stock market January effect are tax-loss selling(selling loser stocks in December to realize tax-offsetting capital losses in the current year) andinstitutional window-dressing (selling assets in December not to be included in year-end publicdisclosures). The empirical evidence is mixed. The January effect also exists in countries withoutcapital gains tax (e.g. Japan before 1989, Canada before 1972). The UK and Australia have

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January effects, even though their tax years begin on April 1 and July 1. Large (and small) stocksthat have risen during the previous year also show January effects. On the other hand, the turn ofthe year effect in the US seems to have started after introduction of personal taxes in 1917(Schultz, 1985; Jones, Lee and Apenbrink, 1991). Following the Tax Reform Act 1986 change infiscal year-end for mutual funds to end-October, empirical evidence suggests a November effect(Bhabra, Dhillon and Ramirez, 1999). Window-dressing would suggest that the January effect isstrongest for firms with institutional shareholders, but the evidence suggests larger Januaryeffects for firms with individual shareholders (Sias and Starks, 1997).

Overall problem with these explanations is that there is no substantial negative Decembereffect that reflects downward selling pressure on prices. On the other hand, December tradingvolumes for loser stocks are high and for winner stocks low (Dyl, 1977), more transactions seemto occur at the (lower) bid price than the (higher) ask price (Keim,1989), more individual investorodd-lot sales than purchases (Dyl and Maberly, 1992).

* The January effect is not an isolated stock market phenomenon. A January effect has alsobeen found in returns and yields of US low rated corporate bonds (see Chang and Pinegar, 1986;Chang and Huang, 1990; Fama and French, 1993; Barnhill, Joutz, and Maxwell, 1997) andmunicipal bond returns (see Kihn, 1996). Evidence for the government bond and bill market is notso clear. Interest rates on commercial paper that matures across the year-end show a significanthigher premium (low price) in the run-up to year end, relative to Treasury bills (Musto, 1997;Griffiths and Winters, 2005).

*

At the end of the year, many investors seem to be exiting the financial markets with apreference for liquidity, probably to meet year-end cash flow obligations (Griffiths and Winters,2005). In fact, money growth is high in December and the money stock falls in January.Moreover, the size of the money changes in December and January appear to be significantlyrelated to the returns on the stock market at that time (Chen and Fishe, 1994 Table 4).

*

THE VALUE vs. GROWTH EFFECT

Basu (1977, 1983) found that firms with high E/P ratios earned positive abnormal returns relativeto the CAPM model. Subsequent research established similar effects of other accounting basedratios such as cash flow (CF/P) and book value of equity (BM). Current consensus seems to bethat earnings and cash flow effects are subsumed by the book-to-market ratio and firm size(Fama and French, 1993).

* The value effect based on CF/P, E/P, BM is only a January effect. No value effect exists in theaverage returns of the other months of the year (Hawawini and Keim, 2000, Figure 1 Table 5NYSE-AMEX stocks 1962-1994)

* The BM effect is exaggerated due to delisting bias and rebalancing effects.Boynton and Oppenheimer (2006) calculate that 25% of the commonly reported BM premium

(BM10-BM1) in monthly returns can be attributed to delisting and rebalancing bias: reducing it

from 1.32% to 1.01% (still significant) per month for the full-sample 1966-2002 period.* The value effect is irrelevant with respect to the efficient market hypothesis.Including appropriate transaction costs for the relevant stocks eliminates any profits from

value trading strategies based on an annual buy-hold return study (for example Agarwal andWang, 2007).

* Returns of the DFA US 6-10 value portfolio, that focuses on small firms with high B/M ratiosshow that the value effect cannot be captured in real time. The abnormal return for 1994-2002 is-0.2% (not significant) (Schwert, 2003 Table1).

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THE MOMENTUM EFFECT

Jegadeesh and Titman (1993) found that recent past winners (stocks with high returns in the past12 months) outperform recent past losers. The momentum effect for the winner-loser portfolio isestimated to be around 1% per month, after correction for CAMP-beta or Fama-French 3-factor

model (Schwert, 2003 Table 4). Whereas Schwert (2003) finds the momentum effect relativelyrobust across subsamples in 1926-2001, Kothari, Shanken and Sloan (1995) find the momentumeffect unreliable with in the post-1962 period winners earning negative abnormal returns andlosers earning positive abnormal returns.

*

The momentum effect in itself is not an anomaly. In a cross-section of stocks where average,expected returns are dispersed - presumably due to differences in risk premiums - high returnsand low returns tend to persist. Momentum is found to be strong and highly significant, but this isfundamental (Conrad and Kaul, 1998; Lo and MacKinlay, 1990). One needs to separating anyanomalous momentum effect (unwarranted time series predictability) from the fundamentalmomentum effect (cross-section dispersion effect and time-varying risk premiums), which may be

very difficult.However, Bulkley and Nawosah (2008) report that simply correcting for individual stock's

sample average returns eliminates the momentum effect. This in contrast to Jegadeesh andTitman (2002) who find that demeaning returns in their sample does not eliminate momentum.Their residual momentum effect appears to be related to some special feature of NASDAQstocks.

*

Approx. 40% of the momentum effect in NYSE-AMEX-Nasdaq stocks reflects the delistingreturns of 'bankruptcy' firms (Eisdorfer, 2008 Table 3). In order to capture this return, momentuminvestors must keep a short position in these stocks after the delisting day, which is not likely.(Note: Shumway 1997 and Shumway and Warther 1999 show that delisting returns reported inthe CRSP database need to be evaluated carefully.)

*

Momentum profitability is large and significant among firms with low credit ratings, butnonexistent among high-grade firms. Momentum is attributable to continuation among low-gradeloser firms that experience negative returns following rating downgrades. Loser stocks exhibitlower analyst following, negative analyst forecast revisions and negative earnings surprisesfollowing the portfolio formation date (Avramov, Chordia, Jostova and Philipov, 2006).

*

The cost of frequent trading in the momentum portfolio, together with the disproportionatelyhigh costs of precisely the stocks on which the anomaly relies, eliminates any perceived arbitrageprofits (Lesmond, Schill and Zhou, 2004).

THE LONG-HORIZON WINNER-LOSER EFFECT (a.k.a. return reversal, overreaction, contrarianstrategy)

* The Winner-Loser contrarian effect is exaggerated due to delisting bias and rebalancingeffects.

Boynton and Oppenheimer (2006) calculate that 25% of the reported winner-loser premium(C1-C20) in monthly returns on a 36 month horizon can be attributed to delisting and rebalancingbias: reducing it from 0.78% to 0.60% (still significant) per month (NYSE, 1930-2001). Shumway

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(1997) shows that the delisting effect is small, but using buy-hold returns instead of eliminates thewinner-loser effect in a replication of the De Bondt and Thaler results (NYSE, 1926-1989).

Ball, Kothari and Shanken (1995) show a 50% decline in the winner-loser premium afteradjusting for small price stock effects (increasing prices by one tick $1/8) and changing theportfolio formation from December to June.

Including data from the U.S. Great Depression also tends to bias the mean reversion effect(Kim/Nelson/Startz, 1991).

* Although still open to debate, a number of studies have found that the winner-loser contrarianeffect disappears with a correction for normal returns based on the Fama-French 3-factor model(for example, Fama and French, 1995, 1996; Clements, Drew, Reedman and Veeraraghavan,2007). The FF 3-factor model has been criticized for its lack of theoretical foundation in riskmodels. However, research has shown a strong link between measures of default risk and smallcompany size and company B/M-ratios (Vassalou/Xing, 2004). Others have also shown a strongrelationship between the big-small and high-low factor portfolios and the business cycle, but sofar fail to provide strong arguments for the type of risk that this would represent.

THE IPO (SHORT RUN ) UNDERPRICING AND (LONG RUN) UNDERPERFORMANCE

PUZZLE

Early studies by Reilly and Hatfield (1969) and Stoll and Curly (1970) showed a significantdifference between the offering price of IPOs (determined by the firm and underwriter) and thefirst-day/week closing market price. First-day returns on U.S. IPOs were approx. 25% during1990-2001 (Ritter and Welch, 2002). The puzzle is that firm and underwriter appear to 'leavemoney on the table' because it appears they could have sold the shares at a higher price. Ritter(1991) and Loughran and Ritter (1995) demonstrated that long-term investors in IPOs (as well asSEOs) who buy shares immediately after the offering at market prices, realize low returns. IPOsunderperform non-IPO firms by approx. 25% (50%) on a 3-year (5-year) horizon. This empiricalresult is interpreted as evidence in favor of "overvalued" IPOs during the first days of markettrading.

* The usual view seems to be that market prices are by definition correct, and therefore IPOoffer prices must be set too low (underpricing). In fact, on average, there is not much wrong withIPO offer prices when compared to (forward) P/E or RIM valuation using available informationand closely matched non-IPO firms. Key is to use 4-digit SIC matching rather than the the usual2-digit and to take into account the (long term) earnings growth premium of IPOs (Jagannathanand Gao, 2004, Table 5; contradicting the results of Purnanandam and Swaminathan, 2001). Theaverage offer price / fair value ratio in any given year fluctuates (+50%, -15%; below 1 most1980s and above or close to 1 most 1990s), which may reflect overall market circumstances.

Note that the mix of IPO firms is not constant over time. For example, during the 1990s IPOswere dominated by very young, high potential technology firms related to computers and theinternet (with a very high percentage of firms issuing shares while still having negative earnings intheir initial growth phase).

* There is no evidence of long-run underperformance of IPOs against comparable firms fromthe IPO offer price at horizons of 3 (taking into account skewness) or 5 years but there isunderperformance at 5 years from the initial market price (Jagannathan and Gao, 2004, Table 8).Brav, Geczy and Gompers (2000) found that there is no underperformance for IPOs when firmsare matched on size and B/M ratio; proxies for inadequately modeled differences in riskpremiums. (Brav, Geczy and Gompers (2000) and Brav and Gompers (1997) find thatunderperformance is limited to small growth IPOs.) Using the calendar-time approach, Ritter andWelch (2002) also find little evidence that IPOs over- or underperform in the long run.

* The high initial market price can be explained by the uniquely one-sided market of IPOshares. Buying is dominated by the most optimistic among generally heterogeneous investorswith different perspectives on future growth opportunities. Optimistic investors not having been

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allocated shares in the IPO are investors that have relatively high reservation prices (higher thanthe offer price). Room for arbitrage is limited because many/most IPO participants will not sell atshort notice and lock-up rules prevent sales by large shareholders (compare Malkiel, 2003 oninternet or dot-com firm valuation). Several studies show that there are negative abnormal returnsat the expiration of lock-up periods (e.g. Brav and Gompers, 2003)

20% of cross-section differences in first-day returns can be explained by differences in IBESconsensus earnings growth rate of IPO firms (Jagannathan and Gao, 2004, Table 13). Othervariables such as Lead Manager (reputation) and Age (of the IPO firm) seem to contributesomething, but much less to the explanation.

Why do issuing firms and their underwriters not increase offer prices to capture the apparentpremium in the market? One explanation is the uncertainty with respect to future marketdevelopments when issue prices have to be determined in advance. The share issue needs tosucceed even when the market moves (temporarily) against the new issuer. A second explanationis that first movers/buyers in the issue are rewarded by underwriters and firms with an extraexpected first-day return. A third explanation is to protect the underwriter and firm from (possiblylegal) claims of overselling the issue at higher prices than fair value.

* IPO firms have relatively high long-term earnings growth forecasts (Jagannathan and Gao,2004, Table 1) and many IPOs do outperform the control group firms.. However, on average,actual operating performance after the IPO declines relative to pre-IPO performance (Jain andKini, 1994). The same applies to firms in seasonal equity offerings (Loughland and Ritter, 1997).

Although frequently interpreted as market timing by firms, top executives of firms do not appear tobenefit from selling their supposedly overvalued shares (Lee, 1997). There is evidence that IPOand SEO firms exhibit unusually large gains in operating performance in the year prior to theequity offering (Jain and Kini, 1995; Cai and Loughran, 1998) possibly due to 'earningsmanagement'. Rangan (1998) and Teoh et al (1998) find that post-issue earningsunderperformance is related to discretionary (management controlled) current (rather than long-term) accruals from accounting adjustements, rather than actual operating income. Thesecomponents are particularly difficult to predict ex ante.

Whether earnings forecasts are irrationally based on obviously misleading past performanceis a separate issue, not especially related to a specific IPO puzzle.

SEASONAL EFFECTS, CALENDAR ANOMALIES, THE WEATHER, AND OTHER ODDITIES

Empirical studies have turned up a wide range of anomalies relating to seasonality in stockreturns. Among these are the Monday effect (Cross, 1973), the Holiday effect (Ariel, 1990), turn-of-the-month effect (Ariel, 1987) and some others. Other anomalies are based on such odditiesas the lunar cycle, geomagnetic storms (sunspots), weather (sunshine, rain, cloud cover,temperature), Super Bowl indicators. Many anomalies of the second category are interpreted assupporting a behavioral and psychological approach to stock market returns.

* Fama (1991), Keim (1988) have pointed out that calendar anomalies are anomalies in thesense that asset-pricing models do not predict them, but at the same time most anomalies areirrelevant due to the fact that the abnormal returns observed fall within the limits of relevant bid-ask spreads and therefore do not generate opportunities for arbitrage profits. Why at certain timesprices appear to have higher probabilities of being closer to bid or ask prices is subject of furtherstudy.

* Jacobsen and Marquering (2008) point out that various weather variables used in stockmarket anomalies have seasonal patterns closely related to their favorite "Sell in May" orHalloween seasonal dummy (Bouman and Jacobsen, 2002).

When weather variables are really fundamental driving forces in stock market returns, theeffects should be reasonably stable across countries. In fact, when considering countries in thenorthern hemisphere and southern hemisphere with opposite seasonal patterns in the weather,they find that southern hemisphere countries (of which there are few) exhibit weather effects

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(temperature and daylight) opposite to what is expected and suggested by the previous literature.Furthermore, countries close to the equator such as Thailand appear to have the largestresponse coefficients to weather effects, despite the fact that temperature and daylight in thesecountries have the smallest variability during the year. The conclusion is that weather variablesmust be proxies for some other global seasonal/calendar pattern in stock returns.

Goetzmann and Zhu (2002) cannot find evidence of a weather influence on behavior ofinvestors, examined at the same time but located in different regions. They suggest that theweather effect must be limited to traders and market makers working at the location of theexchange. (A cumbersome proposition in today's world of remote access and electronic tradingsystems.)

Kelly and Meschke (2007) revisit the widely cited Kamstra et al (2003) study on SeasonalAffective Disorder (SAD) and argue that the observed SAD effect is due to a fault in the originalempirical specification (overlapping-dummy Fall and Winter-Fall SAD).

In another multi-country study, Gregory-Allen, Jacobsen and Marquering (2008) reject theDaylight Saving Time effect reported in previous literature as insignificant, largely due tocorrection of test-statistics for non-normality in daily returns.

* Ciccone and Etebari (2007) find that in 6 major countries the Sell in May or Halloween effect(May-October) is in fact only a September effect in the large stock indices (However, in this studysample periods are short - post-1985/1991 - and the headline large stock indices do not includedividends). Maberly and Pierce (2004) show that in the U.S. the Sell in May effect disappears

from the 1970-1998 data when taking into account the January effect and two large outliers instock returns, October 1987 and August 1998.

* Psychological studies cited in support of behavioral/psychological influences on stock marketinvestors appear to be mistreated in the anomaly literature (Jacobsen and Marquering, 2008;Kelly and Meschke, 2007). The effect of weather/mood on risk taking is undetermined, the effectof temperature on mood/behavior is examined in studies of extreme temperature differences notrelevant for normal investors, weather variables do not necessarily influence behavior wheninvestors work indoor rather than face the weather outdoor. To suggest that psychological studiescontribute any evidence for systematic patterns in stock market behavior is a leap of faith.

(See Keller et al 2005 for a review of the psychological literature relating weather and mood,and the inconsistent relationships.)

* Gerlach (2007) finds that for the U.S. in the period 1980-2003, 5 of 6 statistically significantanomalies examined (turn-of-the-month effect, Fall effect or Halloween effect, lunar cycle effect,

rain effect, temperature effect) disappear when taking into account the days with major (macro)economic news announcements. The Holiday effect suffers the same fate, but is referred to onlyin a footnote due to the fact that the anomaly is not statistically significant from the start. TheJanuary effect is weakened by eliminating news days, but is the only anomaly to survive.

Thus, statistically significant calendar and weather anomalies are not caused by marketpsychology or institutions but reflect a process of data mining where certain variables happen tocoincide with days of news that is important for stock markets. Using days withoutannouncements, there is no evidence for calendar anomalies.

* Correlation does not imply causality - examples: http://pbil.univ-lyon1.fr/members/lobry/corcau

Datasnooping: http://data-snooping.martinsewell.com/"You need a plausible explanation for why the indicator should work. Without such an

explanation, then you run a big risk that the indicator is based on nothing more than a fluke of the

data. My favorite example of why this is so comes from David Leinweber, a visiting facultymember in CalTech's economics department. Several years ago, wanting to illustrate the perils ofmining the data for spurious correlations, he searched through all the data on a United NationsCD-ROM to find the indicator with the most statistically significant correlation with the S&P 500.His discovery: butter production in Bangladesh." [Hulbert, 2005; Is January special after all?MarketWatch.com]

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THE BIAS IN (FINANCIAL ANALYSTS') EARNINGS FORECASTS

Empirical studies have reported an optimistic bias in earnings forecasts by security analysts.Mean forecast errors are positive and analysts appear to underreact to negative news andoverreact to positive news.

* Some bias in mean errors is not necessarily irrational/inefficient. Forecasters interested inforecast accuracy (Mean Absolute Forecast Error, MAFE) need to forecast the median of theforecast distribution, not the mean. Many variables, particularly in finance, are not symmetricallydistributed. Therefore, evaluating forecast performance using mean forecast errors will simplyresult in the mean-median difference (Gu and Wu, 2003).

* Rationality of earnings forecasts is rejected as long as two complications are not taken intoaccount: (1) correlation in a given period of analysts' forecast errors due to 'macro' shocksaffecting all analysts' forecasts, (2) discretionary asset write-downs creating skewed earningsdistributions and which are not forecasted by analysts (Keane and Runkle, 1998).

* Aggregate long-run earnings forecasts appear to exceed the long-run economic growthpotential of the economy and exhibit long cyclical errors. However:

- Long-run earnings forecasts (5-year) may exceed long-run economic growth rates becausethe share of labor (wage income) and capital (corporate profi1t) is not constant over time. During

the 1970s and 1980s firms were catching up their profit share of total income, with higher growthrates of profits for many years.- Evaluation of long-run growth forecasts suffers from short samples (available data) and

overlapping observations (serially correlated errors).- Many other methodological issues and data issues exist in evaluating the rationality of

forecasts, similar to the issues in business cycle, inflation, interest rate, ea forecasts. Examplesare stale forecasts, revised historical accounting data, errors in adjusting for stock splits,identifying the precise earnings definition being forecasted, ... (For a review of many issues seeRamnath, Rock and Shane, 2008)

There are other puzzles and anomalies reported in the literature. At the same time, the literaturealso contains sufficient suggestions and empirical results that point to possible solutions to the

puzzles. There is little reason to declare default on the fundamental economic hypotheses ofrational behavior (on aggregate) and efficient markets (within real world constraints). But youneed to be willing to go beyond the simplistic, stylized textbook models and assumptionsmaintained in much of the mainstream literature.