sharemarket efficiency and the experts: some australian … · sufficiently large, so that a...

http://aum.sagepub.com/Australian Journal of Management

http://aum.sagepub.com/content/7/1/19The online version of this article can be found at:

DOI: 10.1177/031289628200700103

1982 7: 19Australian Journal of ManagementPhilip Brown and Terry Walter

Sharemarket Efficiency and the Experts: Some Australian Findings

Published by:

http://www.sagepublications.com

On behalf of:

Australian School of Business

can be found at:Australian Journal of ManagementAdditional services and information for

http://aum.sagepub.com/cgi/alertsEmail Alerts:

http://aum.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://aum.sagepub.com/content/7/1/19.refs.htmlCitations:

What is This?

- Jun 1, 1982Version of Record >>

at University of Technology Sydney on February 13, 2013aum.sagepub.comDownloaded from

http://aum.sagepub.com/

http://aum.sagepub.com/content/7/1/19

http://www.sagepublications.com

http://www.agsm.edu.au

http://aum.sagepub.com/cgi/alerts

http://aum.sagepub.com/subscriptions

http://www.sagepub.com/journalsReprints.nav

http://www.sagepub.com/journalsPermissions.nav

http://aum.sagepub.com/content/7/1/19.refs.html

http://aum.sagepub.com/content/7/1/19.full.pdf

http://online.sagepub.com/site/sphelp/vorhelp.xhtml


SHAREMARKET EFFICIENCY AND THE EXPERTS: SOME AUSTRALIAN FINDINGS

by Philip Brown Terry Walter*

Abstract:

This paper analyses the investment performance of 625 investment recommendations made by practitioners attending five Australian courses on portfolio management held between 1973 and 1979. It was found that course participants consistently out-performed the market, on a risk-adjusted basis. Their success was not due to picking winners to buy, but to nominating losers to sell.

Keywords:

EFFICIENT MARKET HYPOTHESIS; INVESTMENT PERFORMANCE; SHARE GAME; STRONG FORM EFFICIENCY

*Unive~sity of Western Australia. This study would not have been possible without the co-operation of the respective course organisers. Ratna Sengupta collected the rate of return data. Her skill and care are acknowledged gratefully.

19

Copyright © 2001 All Rights Reserved at University of Technology Sydney on February 13, 2013aum.sagepub.comDownloaded from


BROWN WALTER: SHAREMARKET EXPERTISE

INTRODUCTION

From 1973 to 1979, five one-week courses on modern portfolio theory were held at Macquarie University and the Australian Graduate School of Management. This paper shows that the participants in those courses consistently could out-perform the market. Their success was not due to picking winners to buy, but to nominating losers to sell.

Course participants included security analysts, investment advisers, portfolio managers, actuaries, share brokers and personal investors. They varied widely in experience and seniority. Some professed to be fundamental analysts and others admitted to more than just a passing interest in share charting. Prior knowledge that the resul ts would be published, a course organisers' guarantee of confidentiality, the general impression gained by the instructors and the results all point to the conclusion that the share game, as it was called,was taken seriously. I

Because the game was taken seriously and because the ~articipants were professionals and keen students of the Australian market place, we have a direct test of the ability of Fama's (1970) strong form of the Efficient Market Hypothesis3 to explain Australian share price behaviour. As foreshadowed in our opening remarks, our evidence is inconsistent with the EMH in its strong form. 4

THE SHARE GAME

Each participant was invited to nominate two shares to buy and two shares to sell, to be executed at the last sale up to the close of trading for the week in which the course was held. Altogether lS7 participants made 62S recommendations (314 buy and 311 sell) involving 415 separate securities chosen from the total of 6192 securities listed on the Australian Associated Stock Exchanges when the courses were held. Table 1 sets out some features of the game.

A degree of concordance underlies Table 1 in that more than one participant recommended the same share be bought or sold. Interestingly, in every course there was closer agreement on sell than buy recommendations, a pOint which we take up later. Table 2 explores this concordance by cross-tabulating a security according to the number of times it was recommended to be bought or sold. The null hypothesis, 0 f independence between buy and sell recommendations, is rejected at the a = .01 confidence level for all but one of the five courses. S

Closer analysis showed that participants in all courses had a significant preference (a = .01) for securities issued by companies in the top 100 by market capitalisation, and in all but Course 4, for securities in the top 25. There was no significant difference in the proportions of securities in the top 100 companies that were bought and sold (a = .05). These results are as one might

lOne participant recommended the same shares be bought and sold. His recommendation was included, since it was a valid strategy under the rules of the game.

2participants from outside Australia were not excluded from the game although they tended not to take part.

3The strong form of the EMH is "concerned with whether given investors or groups have monopolistic access to any information relevant for price formation" [Fama (1970), p.383; Cf Gonedes (1976)].

4See Finn (1981), for similar micro-evidence contradictory to the strong form.

SThe expected frequencies under the null hypothesis were generated by assuming a Po isson process.

20

COPYright © 2001 All Rights Reserved at University of Technology Sydney on February 13, 2013aum.sagepub.comDownloaded from



expect if the recommendations professionals. 6

game was taken from the shares

MEASURING INVESTMENT PERFORMANCE

seriously and the participants made their they would have studied the most closely as

Investment performance was measured in three ways:

(i) did each investment result in a profit or a loss?

(ii) how did its rate of return compare with the market average, before allowing for relative (beta) risk?

(iii) how did its rate of return compare on a risk-adjusted basis?

Measure (ii) and particularly measure <fii) are more in keeping with the spirit of the portfolio management courses. Consequently most of our discussion focusses on them.

Weekly prices were collected for every security nominated by the participants in a course, for the 105-week period beginning 53 weeks prior to the Friday of the event week, i.e., the execution date. There were some "problem" securities, as one might expect in a game of this type where single investment recommendations are analysed without penalty for catastrophies. Table 3 lists them and their stories. The final price was assumed to be zero for Stella options and the five that were liquidated. The final price for the five others, all taken over, was the last recorded sale.8 Adjustments were made to the prices for any dividends, bonus and rights issues etc., using the theoretical value of each change in the basis of quotation.

The Statex-Ac tuaries Accumulation Index (SAAI) was used as the market index, which we denote by Rm. An index computed by one of the authors and reported in Walter (1980), was used for the period prior to February 1973, when the SAAI became available on a daily basis. The 90-day Treasury Bill rate, supplied by the Reserve Bank of Australia, was used as the risk-free rate. This risk-free rate, Rf, was subtracted from the portfolio (RE) and market rates of return. Thus we worked with an ex post analogue of Sharpe s (1964) excess return mode1. 9

6Australian institutions often have policies that tend to restrict most of their investments to shares issued by relatively large companies. For example, they often prefer: companies with proven skills in "remote" management (Le., through formal planning and control systems); actively traded securities, which offer greater liquidity for larger parcels; and investments in companies that are sufficiently large, so that a "substantial" dollar holding is not a significant proportion (say, more than 10%) of issued capital. Advertiser Newspapers Ltd. was ranked the 100th largest A.A.S.E.-listed company, by market capitalisation, on January 8, 1982. Its capitalisation then was $101.6m (source: Australian Stock Exchange Journal, January 1982, p.28).

7For example, given the exactly equal numbers of requested buys and sells, there is little incentive for participants to forecast the market average.

8This price represents the last available market consensus of the worth of a share in the acquired firm, given the terms of the offer.

9The equation fitted was:

Rpt - Rft ap + Rp(Rmt - Rft ) + upt where the "hats" denote estimates and Upt is the disturbance term.

21




RESULTS

Initially we divided the 105-week period into two: weeks -52 to week 0 (i.e., the 53 weeks ended on the Friday of the week a course was held); and week +1 to week +52. For simplicity we refer to them as the pre- and post-event periods. Although the share game was stated to conclude within 14 to 29 weeks of the end of the course week (refer Table 1), we concentrat~d our tests on a full year, essentially for statistical reasons. We report later on a validity check which confirmed our initial belief that the conclusions would be unaffected by extending the game ending dates.

Results are presented for portfolios and for individual securities. Portfolio price relatives were formed by weighting the price relative of each security included in a portfolio, by the number of times it was recommended for inclusion. Thus 90 price relatives from 73 separate securities are averaged in the "buy" portfolio for Course 1; 60 securities appeared once, 10 securities appeared twice, two appeared three times, and one four times, for a total of 90 recommendations. Rates of return are continuously compounded for the portfolios and discrete for the individual securities, because the latter involved some securities with total losses.

Tables 4 and 5 contain the overall results for the Buy and Sell recommendations respectively. From column (2) it is evident there was a positive risk premium (i.e., Rm > Rf) in the post-event period for the last three games whereas it was negative for the first two. Column (3) gives the means of Sharpes excess return (Rp - Rf) measure for the portfolios. The buy and sell recommendations yielded identical excess return rankings across courses (Courses 5, 3, 4, 1 and 2 ranked from highest to lowest), which implies that they were diversified sufficiently for the market risk premiun to reveal itself-10 Before risk adjustments, buy recommendations outperformed the market for four of the five courses, whereas portfolios constructed on the basis of sell recommendations still returned more than the market for three of the five courses. However, based on a t-test, the average weekly portfolio excess returns were not significantly different from the market excess return (a = .05), apart from the sell recommendations for Course 5, for which the t-statistic for the difference in the means was 2.60. The sign of the difference was in the predicted direction implicit in the recommendation to sell.

Risk-adjusted results are contained in Columns (4) to (7). They are based on Scholes-Williams' (1977) estimation procedure, which is designed to overcome bias in alpha and beta when they are estimated by Ordinary Least Squares (OLS) and when the securities are either inactively or very actively traded. The alpha value is an estimate of the abnormal portfolio rate of return. We see from Table 4, for example, that the portfolio constructed according to the first course participants' buy recommendations, yielded an average weekly abnormal rate of return of 0.92 percent over the period week -53 to week O. N:lte that the buy portfolio (Table 4) recommended by each course always outperformed the sell portfolio (Table 5) in the pre-event period. The maximum average weekly margin was 1.10% (Course 4) and the minimum was 0.20% (Course 5). The differences for Courses 1, 2 and 4 were significant (a = .01), whereas the others were not.

The key to the share game, however, is the post-event period. On three of five courses the mean excess return on the buy portfolio did exceed that on the sell, though the difference between them was never significant (a = .05). Only six of the ten post-event alphas (3 buys, 3 sells) were in the correct direction, which is hardly a strong result in favour of rejecting the null hypothesis1l of no

l~ote that the ranking across courses for the market risk premium is identical to that of the buy/sell portfolios.

22

COPYright © 2001 All Rights Reserved at University of Technology Sydney on February 13, 2013aum.sagepub.comDownloaded from



revealed investment expertise. Thus it is difficult to gain a clear impression from the portfolio results. The results for the individual securities are much more revealing, as we shall see.

Scholes--Williams portfolio beta estimates are contained in columns (6) and (7). Eighteen of the 20 pre- and post-event betas were less than the market average beta of unity .l2 Columns (8) and (9) show that in every case the correlation between the portfolio and the market excess returns increased from the pre- to the post-event period. Intuitively, we would expect this if the participants' attention were attracted by large, abnormal individual security returns in the pre-event period. The absolute values of those large, abnormal security returns probably would have attenuated in the post-event period and thus have become a less important component of the total return (on a diversified portfolio).

Individual security results are contained in columns (10) and (11). For example, of the 73 separate securities recommended for purchase by participants in the first course, 34 had negative post-event alphas. OVer all courses, only 123 out of 248 securities, or very slightly less than half, had post-event alphas in the expected direction. The inescapable conclusion: no revealed expertise in picking winners.

However there is evidence in Table 5 that participants could identify securities which were more likely to be losers. There is less than a 1% probability that we would have observed a total of 124 negative post-event alphas were the null hypothesis true. 13 This result holds despite the anomalous result on Course 3. On an individual course basis, only Course 5's recommendations were significant at the ex = .05 level. An interesting feature of this result is that, since participants agreed more on the likely losers than winners, we expected the basis of those views to have been widely known and reflected in the pre-event prices. 14 Obviously we were wrong. 15 --- -----

Suppose we pooled the results in such a way that we excluded from the "buy" portfolio any security for which there was at least one sell recommendation; and vice versa for the portfolio of securities to be sold. Would the results change?

The answer, evidellt in Table 6, is that they would change very little. There is

lIThe probability of observing these results (under the hypothesis) is 0.38.

l2The sharply-increased post-event estimated beta for course 4's buy portfolio (i3 = 1.57) has no obvious explanation. Eighteen of the 30 individual security estimated betas increased from the pre- to the pose-event period. Possibly the apparent instability is due to sampling error: note the portfolio r2 values, particularly in the pre-event period.

l3rhe assuned null hypothesis is that positive and negative post-event alphas were equally likely.

14It is possible that some participants had decided not to act, in their professional capaci ties, on their sell recommendations (prio r to a ttending the courses), because a sale would establish an income tax liability, or because the marginal transactions costs (of realisation and re-investment) would have exceeded their avoidable losses.

l5A referee suggests the apparent asymmetry in the results (success at nominating "sells" but not "buys") could mean we are also wrong in assuning participants accepted the organisers guarantee of confidentiality. He reasons that maybe the participants did not trust the course organisers with their "buys", believing they could act on them; whereas they could be trusted with the "sells", in the belief that it was unlikely teachers (Le., non-practitioners) would own "sells" or engage in short-selling.

23




only one change in the rankings of Sharpe s post-event excess return measure: an exchange of rankings between the portfolios of buy recommendations of Courses 1 and 4. Based on mean portfolio excess returns, buy recommendations still outperformed sell-recommendations from only three of the five courses. EKactly half the 202 buy recommendations were winners and half were losers. In contrast, sell recommendations were significantly more accurate predictors of future abnormal losses, than we would expect by chance (a = .05).

VALIDITY CHECKS

Because studies such as this one can be affected by data errors ,16 we screened all data and verified questionable rates of return. We examined the effect of switching from continuous to discrete compounding in the portfolio rates of return and found it made negligible difference. It also made negligible difference whether OLS or the Scholes-Williams procedure was used to estimate the post-event alphas.

When we restricted our analysis to the post-event period stated in the game handouts, we reached conclusions similar to those discussed in detail above. For example, individual security post-event alphas were positive for 52% of the buy recommendations, which is insignificantly different from 50% (a = .05). For sell recommendations, 57% of the post-event alphas were negative, revealing significant expertise (a = .05) in picking securities to sell. Po st-event r iskadjusted portfolio results are also consistent with the extended period results.

CONCLUSIONS

There is little doubt that there were groups of professionals operating in the Australian sharemarket in the 1970's, who were able to outperform the sharemarket on a risk-adjusted basis. Their superior performance, which was measured without accounting for transactions costs,17 was due to their systematic ability to nominate future losers to sell.

These findings have interesting parallels with Finn's (1981) study of 361 recommendations made by security analysts working for a major Australian institutional investor. Finn concluded that the recommendations, if acted upon, would have yielded significant abnormal returns, particularly recommendations to sell. He also observed very few sell recommendations were acted upon.

(Date of receipt of final typescript: February 1982).

REFERENCES

Brown, S.J. and J.B. Warner, 1980, "}Easuring Security "Price Performance," Journal of Financial Economics, 8, 205-258.

Fama, E.F., 1970, "Efficient Capital Markets: A Review of Theory and Empirical Work'" Journal 0 f Finance, 25, 383-417.

Finn, F.J., 1981, "Internal Performance Evaluation of Managed Investment Funds," Unpublished Ph.D. thesis, University of Queensland.

16See also Brown and Warner (1980) and Shevlin (1981).

17It is often a moot point whether transactions costs are relevant, and this study is no exception.

24




Gonedes, N., 1976, "The Capital Market, The Market for Information, and External Accounting," Journal of Finance, 31, 611-30.

Scholes, M. and J. Williams, 1977, "Estimating Betas from Nonsynchronous Data," Journal of Financial Economics, 5, 309-27.

Sharpe, W.F., 1964, "Capital Asset Prices: A Theory of Market EquilibriUJ1 Under Conditions of Risk," Journal of Finance, 19,425-44.

Shevlin, T.J., 1981, "Measuring Abnormal Performance on the Australian Securities Market," Australian Journal of Management, 6,67-107.

Walter, T. S., 1980, "Australian Takeovers: Capital Market Efficiency and Shareholder Risk and Return," Unpublished Ph.D. thesis, University of Western Australia.

25



Execution date Game end date

Number of participants

Buy recommendations: total number

Table

Summary features of the share game

Course Number

1 2 3

23 Feb 73 17 May 74 29 Jul 77 31 May 73 30 Nov 74 31 Oct 77

45 45 27

90 90 54 N number securities 73 67 39 cr-.

Sell recommendations: total number number securities

Total number of securities: listed on A.A.S.E. recommended for buy/sell

90 59

1501 117

90 57

1335 111

Notes: (l)one participant nominated an unlisted share

(2)One participant did not nominate any securities to sell

53 (1)

35

1186 69

t,;

£ z

~ rl t'1 ~

IJ.l

[; ~ t'1

Total ~ 4 5 t'1

28 Jul 78 rl

27 Jul 79 * t'1 30 Nov 78 30 Nov 79 * :xl

'"d t'1

25 ~

15 157 rl H IJ.l t'1

30 50 314 26 43 248

28 (2) 50 311 22 40 213

1111 1059 6192 45 73 415




Buy/Sell

o

2

3

4

2:.5

Cross-tabulation of

0

5777

170

23

8

1

Table 2

buy/sell recommendations

2 3 4 2:.5

129 25 7 4 2

20 8 3 4

5 1

1 1

1 1

Note: Table 2 is a summary (aggregation) of five similar tables one for each course. Table 2 indicates that, in the five separate tables, there was a total of 20 entries where one person recommended a security be bought and one other person (only) recommended that same security be sold. Similarly there were 8 occasions on which one person recommended a security be bought and two others recommended it be sold.

27



t<l

~ Table 3 ~

t-l

Eleven "problem" securities and their stories t"j ::c

Ul

No. Recommendations Last Available ~ Course Number Buy Sell Story Quote Date t"j

~ Stellar Mining - options 1 1 Expired 8. 5.73 t"j t-l

Custom Credit Corp. 1 Taken over l3.7.73 t"j

~ 17.12.73

t"j Vanbro Corp. 1 1 Liquidated ~ I\..) H a Ul a Mainline Corp. 2 2 1 Liquidated 20. 8.74 t"j

~ N Cambridge Credit Corp. 2 2 Liquidated 27. 9.74

;U ex>

cC· Morris (Keith) 2 1 Liquidated 28.10.74 ::::r -en ;U TaIga 2 1 Liquidated l3. 9.74 CD en

IRH Industries 2 1 Taken over 3. 1. 7S CD :;: CD c. Consolidated Foods 3 1 Taken over 30. 9.77

Cleckheaton 3 1 Taken over 26. S.78

Tooheys 5 1 Taken over 21. 3.80



() o ~ ...., cC· ::::r -@ I\..) a a

~ ;u cC· ::::r en ;u CD en CD :;: CD C.

N

'"

Course (1)

1

2

3

4

5

Total

Post-event Mean Weekly Excess Return x 100

Market Portfolio (2) (3)

-.21 .00

-.52 -.47

.25 .34

.19 .21

.96 .69

* *

Table 4

Buy recomrnendations--overall results

Scholes-Williams Portfolio Estimates

Alpha x 100 Beta Pre Post Pre Post (4) (5) (6) (7)

.92 .20 .98 .87

.12 -.05 .85 .80

.26 .17 .81 .69

1.10 -.08 .96 1. 57

.27 .03 .66 .70

* * * *

0; :::0

~ ~ to< rl tTl :::0

Ul

~ Gl

2 SW Individual ~

OLS r Security Post- ~ event Alpha tTl

rl Pre Post N<O N>O

tTl (8) (9) (0) (11) :xl

'"d tTl

.26 .69 34 39 :::0 rl H

.61 .91 38 29 Ul tTl

.61 .72 14 25

.37 .66 16 10

.66 .81 23 20

* * 125 123



Table 5

Buy recommendations--overall results

Post-event Mean Weekly Scholes-Williams Portfolio Estimates Excess Return x 100

Alpha x 100 Beta Course Market Portfolio Pre Post Pre Post

(1) (2) (3) (4) (5) (6) (7)

-.21 -.30 .09 -.11 .98 .93 2 -.52 -.36 -.30 .20 .93 1.06 3 .25 .38 -.06 .16 .82 .89

w 0 4 .19 .02 .00 -.15 .75 .86

5 .96 .47 .07 -.13 .84 .62

Total * * * * * *

OLS r 2

Pre Post (8) (9)

.79 .90

.48 .58

.79 .84

.72 .73

.65 .77

* *

SW Individual Security Post-event Alpha N<O N>O (10) (11)

35 24

35 22

13 22

14 8

27 13

124 89

t:>::I :;0

~ ~ H I:%j :;0

Ul

I ~ I:%j H

I:%j

~ I:%j

~ H Ul I:%j



Table 6

Consensus recommendations--summary results

Buy Recommendations

() Market Post-event

0 Mean Weekly Excess "'0 Course Return x 100 '<

Portfolio Post- Individual Security event Mean Weekly SW Post-event a

Excess Return ...., cC· x 100 N<O N>O ::::r (1) (2) - (3) (4) (5) @

-.21 I\..)

.25 27 31 a 2 -.52 a -.45 31 23

~ w 3 .25 .30 12 22

;U 4 .19 .22 14 9 cC· ::::r 5 .96 - .70 17 16 en ;U CD Total 1, * 101 101 en CD :;: CD C.

Sell Recommendations Portfolio Post- Individual Security

event Mean Weekly SW Post-event a Excess Return

x 100 N<O N>O (6) (7) (8)

-.27 28 16

-.43 28 16

.39 11 19

.01 12 7

.44 19 11

1, 98 69

tJj

~ ~ r-< H too :;0

til

~ Gl

~ too H

too :x: 'tI too :;0 H H til too



sharemarket efficiency and the experts: some australian … · sufficiently large, so that a...

Documents