Testing market efficiency with information on individual investor performance

TESTING MARKET EFFICIENCY WITH

INFORMATION ON INDIVIDUAL

INVESTOR PERFORMANCE

PAUL E. GABRIEL, JAMES R. MARSDEN, TIMOTHY J. STANTON

ABSTRACT

The efficient market hypothesis has been tested under a variety of settings. Several authors have utilized stock market data while others have used betting markets including National Football League games and horse racing. These betting markets are similarto stock markets in terms of their large amounts of information and ease of tracking results. This paper analyzes the efficient market hypothesis for a quite different, nonbetting, racetrack market known as claiming races. The nature of the market and the available data allow individual investor performance to be followed over time. While data limitations have restricted previous authors to ex-post determination of whether particular advantageous decision rules could have been utilized, our access to individual data enables us to analyze whether individuals were able to determine decision paths that resulted in superior performance. Our analysis indicates that the claiming race market is efficient in the sense that active participants do not systematically outperform the market average.

Direct all correspondence to: Paul E. Gabriel, Department of Economics, Loyola University of Chicago, 820 North Michigan Ave., Chicago, IL 60611 l James R. Marsden, Department of Decision Science and Information Systems, University of Kentucky l Timothy J. Stanton, Department of Business and Economics, Mount St. Mary’s College

International Review of Economics and Fiiance, 2(2) 149-162 Copyright Q 1993 by JAI F’ress, Inc. ISSN: 1059-0560 All rights of reproduction in any form reserved.

149

150 PAUL E. GABRIEL, JAMES R. MARSDEN, TIMOTHY J. STANTON

I. INTRODUCTION

The rather generic sounding “efficient market hypothesis” has been tested under a variety of settings. Numerous authors have utilized stock market data in analyzing market efficiency (see, for example, Shiller [ 198 11, Marsh and Merton [ 19861, De Bondt and Thaler [ 19871). Others have used point spread betting on National Football League games (see, for example, Vergin and Scriabin [ 19781, Gandar, Zuber, O’Brien and Russo [1988], and Sauer et al. [1988]). Still others have investigated market efficiency in racetrack betting (see, for example, Asch, Malkiel, and Quandt [1982], Hausch and Ziemba [1985], and Gabriel and Marsden [1990, 19911). Studies on stock market efficiency generally analyze whether an individual can earn a higher than average rate of return in the market. The focus of many studies on the efficiency of gambling markets has been to test whether inefficiencies exist that can be utilized in “beating the odds,” that is, take advantage of an existing market irrationality. The present research is directed at analyzing a quite different market in the horse racing industry, the market structure known as “the claiming race.” The nature of this market and the detailed information available make it possible to test market efficiency hypotheses in terms of actual actions taken by individual market participants.

Section II below describes the claiming race market as well as the special nature of available information. Section III outlines the data and the specific hypothesis to be tested. Section IV reports empirical results while section V presents the conclusion.

II. THE CLAIMING RACE MARKET AND ITS DATA AVAILABILITY

Several procedures are utilized in thoroughbred racing to equalize the competition likely to be entered in a given race. Specific conditions (e.g., age, sex, status as non-winner or non-winner of two races lifetime) are used by each track’s racing secretary to limit the horses eligible for a given race. Weight handicaps may be assigned in an attempt to better equalize the chances of winning the race. Discussions with racing secretaries have revealed several reasons for creating races that attract entrants of relatively homogeneous ability. First, homogeneous races benefit the track by increasing parimutuel betting returns because several horses in a race have a realistic chance of winning. Second, a larger number of horses can be drawn into a race if owners and trainers perceive that their likelihood of success is relatively equal to others. A third reason is that horses are able to gradually work their way up in class (competition) without repeated failures against superior entrants.

Though these various explanations are interesting, they are not directly relevant for our present purposes. What is relevant is that races are restricted. Further, the most common form of restriction places the horse’s owner in the position of having to price the asset (horse) and risk loss of the asset for that price. Such races are termed claiming

races, where each horse in the race may be claimed for the specified amount by a properly licensed owner who then directs a trainer to take possession of the horse. The

Testing Market Efficiency 151

licensing requirements are minimal and involve only small annual payments (e.g. $5 in some states). To make a claim, an owner completes a claim form and drops it (along with appropriate remuneration) in the racing secretary’s claim box prior to the race

(usually 15 minutes prior to the scheduled start of the race). If more than one claim is made on a horse, the new owner is determined by random drawing after the race. Change of ownership for a claimed horse occurs when the horse becomes an official starter (i.e., when the starting gate opens) with any purse money or awards reverting to the previous owner. A track representative takes possession of a claimed horse immediately following the running of the race and physically transfers the horse to the licensed trainer specified in the new owner’s claim form.

If the new owner chooses to run the horse in a claiming race within the first thirty days, the race must have a claiming price at least 25% greater than that of the race from which the horse was claimed. By restricting the options available to the new owner for earning a return on the asset, this rule acts to diminish claims somewhat and hence, diminish the number of ownership changes. But claims are not an uncommon event and the claiming market is fairly active. Across the United States, claiming prices range from $1,500 to a select few with tags of $1 ,OOO,OOO. Though the quality of such races varies widely, they have the unique ingredient that any entrant may be claimed (“bought”) out of the race. This puts the horse owner in the position of risking sale of his asset when he seeks to earn a return on that asset. Though no owner is required to enter his or her horse in a claiming race, such races may be the only realistic option available for them to earn a return on their asset.

As might be expected, the “claiming game” market is typically viewed as a risky and competitive market. Typical of such markets, stories of great success or significant failure add to the intrigue. In much the same way as stories of “future killings” or “pre-merger announcement snap ups” flow from the more commonly known futures markets and stock exchanges, stories of “sneaking one by” or of making an “epic claim” emanate from the claiming market.’ Thus, investors in the claiming race market must evaluate various forms of information before claiming a particular horse. As in deciding on what stocks to purchase or how to bet on an NFL game, claiming race investors have access to a substantial amount of publicly available information. Complete past per-

formances, workouts, bloodlines, expert opinions on conformation and movement can all be studied for help in choosing what horse, if any, to claim. There is also the assorted variety of misinformation common to risky markets+.g., backstretch rumors perhaps to encourage or to deter a claim, excessive bandaging to make a horse appear physically

troubled, etc.2 For every claim made during a given period, information is available on who made

the claim, the amount of the claim, the amount of subsequent returns (winnings), and whether the horse was claimed at a later date. This facilitates important extensions of previous efficient market hypothesis testing. In particular, the nature of available data allows us to consider individual pe~ormunce as well as overall market performance. Such tracking of individuals is a key element in setting our analysis apart. It provides


actual individual rates @return over the selected historical period. As detailed below, our focus is on analyzing whether any individual or group of individuals repetitively participating in this market was able to achieve rates of return significantly higher than average. We make no attempt to measure the overall risk of participating in the claiming market. Further, we make no attempt to compare this market to activities of commen- surate risk.3 Since we concentrate on individuals active in a particular market, our analysis concerns actual performance rather than general rates of return and risk achievable by overall market participants.

According to Fama’s [ 19701 seminal work, a market is efficient if current prices fully reflect all available relevant information (pp. 383-384). An additional implication of market efficiency is that no single group of investors is able to achieve higher than average returns with regularity. Efficiency in the stock and betting markets has pre- viously been tested with either statistical tests or direct economic tests (see Gandar et al., 1988). Statistical tests attempt to discern inefficiencies by analyzing data that present the results of many market participants competing for profit. Economic tests search for unexploited profit opportunities that could be achieved if market participants followed certain mechanical rules. While data limitations have restricted previous authors to ex-post determination of whether particular advantageous decision rules “could have” been utilized, our access to individual data enables us to directly analyze whether individuals were actually able to determine decision paths that resulted in superior

performance.

III. DATA AND HYPOTHESIS

Since a claim (purchase) of a horse may be made based on the residual or breeding value of the horse rather than on the racing value alone, we concentrated on a market subset for which this does not occur. By focusing on geldings (altered males) only, our data set includes horses with only a minimal residual value following their racing career. Thus, to avoid biases from future breeding potential, mares and complete horses

(stallions) were excluded from the present study.4

Among the most active claiming race markets (in terms of number of claims) is the major New York circuit including Belmont, Aqueduct and Saratoga. For our sample, information was gathered on all gelding claims for these three tracks in 1980. This year was chosen because of the format in which the data was stored and ease of access.5 For each claimed gelding we recorded the following: claim date, claiming price, total starts for the year, starts for new owner, total winnings for the year, winnings after claim and trainer listed on the claim.6 Since it is not uncommon for a horse to be claimed more than once, some horses appear as claims several times.

As in completing most data sets, trade-offs were involved. By restricting consideration to the major New York market, to a single year, and to only geldings, we improved our ability to focus on a well identified market with a fairly consistent set of participants. The trade-off, however, was that our data set was not as large as we would have liked.


Since expanding either the time frame, geographical market area, or type of horse (female or non-altered male) could introduce significant confounding effects, we made the choice described above for this initial analysis of the claiming market.

The payoff to an individual who claims a horse in this market comes from two sources: (1) a horse’s winnings; and (2) the gains (or losses) if the horse is reclaimed at a later date. The primary cash outlays, or investment costs, include initial claim price, upkeep expenses (veterinary bills, training, etc.) and racing fees.

With information on the necessary variables, we can construct and perform tests of market efficiency. Fama’s notion of efficiency (see above) is the basis of our hypothesis:

Hl: Individuals operating in the claiming industry do not consistently outperform

the average net market return.

IV. EMPIRICAL RESULTS

A. Geldings Claimed and Reclaimed During the Sample Period

Determining the percentage rate of return is not difficult if a claimed horse is reclaimed before year end. The subsequent resale value is recorded and the net return can be directly calculated. Of a total of 156 claims at the three New York tracks in 1980, eighty horses were subsequently reclaimed. We begin by focusing on this submarket where the complete return to ownership can be directly determined (i.e., revenue equals purse money won plus re-claim price received). Thirty-nine trainers participated in this reclaim market. The observations provided in our analysis are aggregated by trainer. This choice was made since the trainer is the primary market participant and since, in the New York market, the active participants in the claiming market are owner/trainers with a few consistent pairings of individual owners with individual trainers (e.g., V. Summers, owner, Frank Martin, trainer). In our sample, claim prices ranged from $7,500 to $50,000.

Typical operating expenses include training fees (feed, exercise riders, grooming, and stabling) plus blacksmith, veterinary and medicine fees, vanning and race related costs (jockey, lead pony). Discussions with owners and trainers provided us with cost estimates varying from $35 to $50 a day in 1980 and approximately $100 per start. Trainers indicated that daily fees paid by owners represent break-even amounts, i.e., the fees just cover expenses. Thus the costs largely represent out-of-pocket expenses.7

The available information on costs and revenues allow us to specify the following returns:

1. NR = Net Return = purse money won + re-claim price received - (claim price paid + expenses paid during ownership period)

2. RR1 = rate of return1 = (NR)/(claim price paid)


3. RR2 = rate of return2 = { (NR + (pro-rated safe asset rate)*(NR)/(claim price paid) } ifA& positive; = RR1 otherwise

4. RR3 = RR1 (annualized) = (RRl)*[365/(number of days asset held)]

RR1 is the gross rate of return that does not take into account either the length of time the asset is held or alternative uses of capital during the period the asset is not held. RR;! partially includes these two factors by including a safe asset (T-bill) rate of return during

the period remaining in the year after the gelding is re-claimed. This second rate of return assumes that the trainer holds the capital fully liquid until a claim is made. Following sale (re-claim) of the asset, the gross amount received (net winnings plus re-claim amount) is invested for the remainder of the year in a safe asset paying the T-bill rate (if no positive amount is received, then no funds are available for re-investment and RR1 = RR2). Both of these rates of return are conservative relative to the annualized value provided by RRs. This last rate adjusts for differing times assets are held by assuming that rates obtained could be extended for the entire period (365 days). In the results that follow, we report all three of the rates of return.

Table 1 presents summary statistics related to individual performance in the 1980 claiming race market for geldings.8 As explained earlier, this first set of results relates to all cases where rates of return could be calculated directly since the asset was dispersed (sold through a re-claim). Table Al, presented in Appendix A, provides information on each individual’s performance.

As shown in Table I, the values of RR1 range from -116.39% to +165.88% with a mean value of 36.15% and a standard deviation of 53.77%. Values for RR2 range from -116.39% to +169.43% with a mean of 42.94% and a standard deviation of 55.91%. Of

39 RR1 values, 29 (74%) fall within one standard deviation of the mean value. Only 3 of the 39 values fall more than two standard deviations from the mean. For RR2 values, 28 of 39 (72%) are within one standard deviation, while only 3 are greater than two

Table 1. Descriptive Statistics Geldings Claimed and Re-claimed Within Sample Period

RRi RR2 RR3

Range (in %) -116.39 to 165.88 -116.39 to 169.43 -262.24 to 2602.04

Mean (in %) 36.15 42.94 328.34

Std. dev (in %) 53.11 55.91 501.95

Std. Error of mean (in %) 8.61 8.95 80.38

# of observations 39.00 39.00 39.00

Outlier? (% of total) 3 (7.7%) 3 (7.7%) I (2.6%)

Outliersb (% of total) 10 (25.6%) 11 (28.2%) 8 (20.5%)

Notes: aNumber of observations greater than two standard deviations from the mean.

bNumber of observations greater than one standard deviation from the mean.

Testing Market Efficiency

standard deviations from the mean. We also note that a goodness of fit test (using a 5% level of significance) failed to reject the hypothesis that each rate of return was normally distributed across individual trainers. The normality of the distributions of RR1 and of RR2 is consistent with empirical findings from other market studies (see Fama and Miller [ 19721). As expected, the values for RRs, the annualized rate, are generally quite large, ranging from -262.24% to +2602&I%. For these figures to be useful, we would have to assume that the market participant could duplicate his or her performance repeatedly. For RR3, the hypothesis that the distribution is normal is rejected at the 5% level.

Examination of actual individual performance (see Table Al in Appendix A) yields further interesting results. Of the three observations outside the two standard deviation range, trainers 4 and 26 managed a return greater than the upper bound of the interval and trainer 34 received a return below the lower bound of the interval. However, none of these trainers were very active participants in the specified claiming market for 1980. Trainers 26 and 34 made only one claim during the year, and trainer 4 made two. Generally, those individuals who made three or more claims during the year earned a return near the mean. For example, trainer 21 with eight claims was closer than one-fourth of a standard deviation from the mean. Of the remaining trainers with three or more claims, only trainer 14 achieved a return greater than one deviation from the mean, but his was only very slightly beyond one deviation.

B. All Geldings Claimed During Sample Period

The results above apply to geldings for which we had a sales price, the subsequent reclaim amount paid. Approximately half of all the geldings claimed during the sample period were not reclaimed prior to the end of the period. Thus, for this group, we cannot directly calculate net return on investment values. Since a horse’s owner still has the potential for capital gain on his investment at year’s end, a method is needed to value this asset so that a rate of return can be estimated. Given that no market price exists for each horse until its sale, a proxy must be used to estimate the unrealized capital gain. The proxy we employ to impute a year-end value is the purchase price (claiming price paid) for the gelding.’ As noted earlier, as we expand the data set we encounter the possibility of confounding effects. Here, we take care to note that the empirical results cited rely upon the accuracy (at least average accuracy) of the chosen proxy.

When the definition of the claiming market is expanded to include also those horses claimed in the sample year but not subsequently reclaimed in the same year, 76 additional geldings are included with the original 80 to give a total of 156 claims. With these additional claims our data also include 26 more trainers for a total of 65 trainers who participated in this expanded market.

A slight difference occurs when we attempt to calculate RR1 and RR2 for the expanded sample. For horses that were not reclaimed in the period, there is no interest income from investment in the safe asset. Thus, the two rates of return are identical for such


Table 2. Descriptive Statistics All Geldings Claimed Within Sample Period

RR] RR:, RR3

Range (in %) -116.39 to 253.07 -116.39 to 253.07 -262.24 to 2602.04

Mean (in %) 16.35 19.48 160.82

Std. dev (in %) 65.67 66.95 412.69

Std. Error of mean (in %) 8.14 8.30 51.19

# of observations 65.00 65.00 65.00

Outlie& (% of total) 4 (6.2%) 4 (6.2%) 1(1.5%)

Outliersb (8 of total) 14 (21.5%) 16 (24.6%) 8 (12.5%)

Notes: aNumber of observations greater than hvo standard deviations from the mean

bNumber of observations greater than one standard deviation from the mean.

horses. Table A2 of Appendix A reports net return values (aggregated by trainer) for this expanded sample, and Table 2 summarizes these results.

As shown in Table 2, the average for RR1 was 16.35% with a standard deviation of 65.67%. The corresponding mean value for RR2 was 19.48% with a standard deviation

of 66.95%. The range of values for RR2 was the same as that for RR1. Of the 65 RR1 values, 5 1 or 78% were within one standard deviation from the mean value. Only four of the 65 values were more than two standard deviations from the mean value of 16.35%.

For RR2 values, 49 of the 65 were within a single standard deviation of the mean value. Again, only four were more than two standard deviations away from the mean. The lower average returns for the expanded sample suggest the possibility of self selection.

For example, some of the geldings that were not reclaimed during the year may have been judged by investors as less likely to produce returns through winnings; others may have moved up in class to nonclaiming stakes or allowance races.

Of the trainers involved in this claiming market, 12 made four or more claims in the

period. The mean RR1 for these trainers was 28.75% with a standard deviation of 19.29% compared to a mean of 16.35% with a standard deviation of 65.67% for all trainers

regardless of number of claims. The mean RR;? for trainers making four or more claims was 32.34% with a standard deviation of 19.64% compared to a mean of 19.48% and standard deviation of 66.95% for all trainers. Standard comparison of mean tests resulted in acceptance of the hypothesis that the means of the groups are equal, even at extremely high levels of significance. This again suggests that the most active participants were not able to significantly outperform the general market with respect to rates of return achieved. Active participants did, however, appear to be more likely to avoid negative returns than were less active market participants. Only one of the twelve active participants had a negative RR1 and none of the twelve had a negative RR2. This compares to 29 out of 53 negative RRls and 26 out of 53 negative RR2s for the less active market participants.


From the relatively high average returns reported here, one might be tempted to conclude that the claiming race market is a lucrative activity, and that these returns are somehow protected by entry barriers. For instance, the averages for RR1 and RR2 reported in Table 2 are similar to average annual returns for precious metals and U.S. equities found in Ibbotson et al. [ 19851. However, the coefficient of variation, a common measure of relative risk, is approximately twice as large for geldings as it is for metals and stocks [Ibbotson et al. 1985, p. 221. This suggests that the high average returns in the claiming market may be associated with a higher level of risk.‘O

V. CONCLUSION

While numerous authors have examined the efficient market hypothesis under a variety of settings, we offer an analysis that reaches beyond their work. Since the New York claiming race market is easily identified, and contains a fairly consistent set of participants, we are able to track individual performance. Our test of market efficiency examined if investors’ decision paths resulted in rates of returns that were systematically above (or below) the market average.

On the New York circuit in the sample year, 39 trainers purchased geldings and subsequently had them reclaimed by year end. Various assumptions about reinvestment possibilities yield three rates of return for each trainer. This market appears to be efficient in the sense that no single group of participants seem to have consistently earned above or below average returns. Only trainers who made two or fewer claims earned returns far (greater than two estimated standard deviations) from the mean. For an expanded market of 65 trainers that includes geldings held at year end, we also fail to find active trainers who consistently over perform or under perform the market average.

As noted earlier, we restricted our analysis to a single year and to only geldings in order to focus on a well identified market with a consistent group of participants. Though an expanded data set might appear a logical next step, the benefits of such an expanded information set are tempered by the potential confounding effects of time, geography, and nature of market item (type of horse). In the claiming race market, where the performance of individuals can be tracked over time, we find no evidence of market inefficiency.

APPENDIX A

Table A.I. Geldings Claimed and Reclaimed Within Samnle Period

Ohs. Average Claim ($looO) Number of Claims RR1

1 14.25 2 31.54 31.11 144.52

2 16.00 1 38.62 46.89 256.33

3 15.00 2 66.53 69.98 291.04

4 25.00 2 165.88 169.43 275.12

158 PAUL E. GABRIEL, JAMES R. MARSDEN, TIMOTHY j. STANTON

Obs.

Table A. 1. (Continued)

Average Claim ($1000) Number of Claims RR,

5 15.00

6 10.50

7 20.00

8 18.00

9 23.75

10 31.00

11 12.50

12 25.00

13 22.50

14 14.25

15 12.50

16 8.00

17 32.50

18 25.00

19 31.25

20 19.17

21 25.15

22 12.83

23 18.00

24 20.00

25 22.50

26 16.00

21 11.25

28 17.15

29 13.75

30 9.00

31 12.50

32 16.00

33 20.50

34 11.50

3.5 12.50

36 25.00

37 20.00

38 7.88

39 16.00

2

2

1

2

4

4

4

1

1

2

1

53.17

34.76

-1.00

16.61

14.34

-12.21

-18.88

46.08

9.62

89.35

22.40

-31.06

71.00

A.80

124.35

43.63

45.53

26.08

41.41

99.40

30.04

145.75

104.62

53.08

21.11

11.56

-0.06

17.62

48.32

-116.39

xHl4

64.16

104.90

11.07

49.12

- RR2

56.01

51.46

8.51

22.39

18.25

-7.79

-15.82

-66.08

14.67

102.17

38.18

-27.21

73.59

-3.18

138.09

50.21

49.81

31.94

50.06

101.85

38.51

159.48

111.75

56.75

36.19

16.97

0.37

27.15

60.88

-116.39

6.38

80.43

120.15

15.11

49.69

RR3

169.25

852.69

-8.30

119.13

171.09

43.15

-54.69

-110.64

-26.01

1059.46

545.07

-112.26

191.96

-18.25

671.41

886.71

55.68

510.28

836.99

394.36

162.45

554.15

833.55

69.68

93.80

124.05

XI.13

82.48

307.03

-262.24

-0.32

2602.04

933.87

25.16

134.82

Obs.

Table A.2. All Geldings Claimed During Sample Period

Average Claim ($1000) Number of Claims RR, RR2 RR3

1 16.13 4 14.90 17.65 65.85

2 13.00 2 22.15 21.24 84.04

3 30.00 I -13.97 -13.97 -49.49

4 15.00 2 66.53 69.98 291.04


Table A.2. (Continued)

Obs. Average Claim ($looO) Number of Claim RR2 RR3

5

6

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

25.00 2 165.88 169.43 275.12

12.50 1 -73.12 -73.12 -118.09

15.50 4 54.86 56.23 173.21

20.00 1 203.15 203.15 211.25

10.50 2 34.76 51.46 852.69

13.00 2 -75.46 -75.46 -107.40

26.25 2 -2.78 -2.78 -29.24

30.00 1 38.87 38.87 63.90

18.00 2 -21.85 -21.85 -27.88

18.00 1 -76.00 -76.00 -81.83

20.00 1 -1.00 -8.51 -8.30

14.00 4 13.71 17.43 61.40

23.75 2 14.34 18.25 171.09

28.10 10 1.37 4.06 38.78

13.25 2 -25.68 -24.24 -43.49

8.00 1 -8.00 -8.00 -182.50

25.00 1 -66.08 -66.08 -110.64

18.33 3 -7.14 -3.00 -37.16

15.40 5 50.67 60.15 765.75

12.50 1 22.40 38.18 545.07

8.00 1 -31.06 -27.21 -112.26

24.50 3 41.49 42.64 162.64

11.50 1 -94.65 -94.65 -108.30

16.00 1 49.13 -49.13 -86.20

11.25 2 -51.64 -51.64 -89.44

26.25 4 41.43 42.29 142.09

24.17 3 106.92 118.77 558.66

19.17 3 43.64 50.21 886.71

29.96 14 32.36 34.46 40.22

19.93 7 -1.23 0.39 119.87

18.00 2 41.47 50.06 836.99

12.50 1 253.07 253.07 710.55

12.50 1 A.16 -46.16 -68.21

20.00 1 99.40 101.85 394.36

19.00 1 -11.63 -11.63 -40.82

20.33 3 21.52 27.77 119.03

30.70 5 49.03 50.46 114.30

12.50 1 144.96 144.% 287.56

24.17 3 28.94 31.15 247.86

20.00 1 -30.55 -30.55 -59.31

20.13 8 41.11 42.73 113.04

30.00 1 70.27 70.27 71.14

160

Obs.

PAUL E. GABRIEL, JAMES R. MARSDEN, TIMOTHY J. STANTON

Table A. 2. (Continued)

Average Claim ($looO) Number of Claims RR! RR2 RR3

41 14.00 5 17.51 24.18 66.86

48 9.00 1 11.56 16.97 124.05

49 12.50 1 a.06 0.37 -0.13

50 16.00 1 -35.00 -35.00 -88.72

51 13.25 2 -0.72 5.03 35.98

52 10.00 1 48.94 48.94 50.46

53 19.50 6 29.23 38.04 197.02

54 12.50 1 -84.86 -84.86 -114.71

55 11.50 1 -116.39 -116.39 -262.24

56 12.50 I -0.04 6.38 a.32

51 30.00 I -16.80 -16.80 -22.54

58 25.00 1 64.16 80.43 2602.04

59 2o.cKl 1 104.90 120.15 933.87

60 7.88 2 11.07 15.11 25.12

61 16.00 1 49.12 49.69 134.82

62 15.00 2 -9.73 -9.73 -14.30

63 14.00 1 -0.86 4.86 -104.29

64 15.00 2 -19.80 -19.80 -110.03

65 25.00 1 -21.84 -21.84 -44.29

NOTES

1. “Sneaking one by”, refers to getting a horse in against lesser competition, winning the purse money, and not having the horse claimed. When one sees a horse show up in a $5,000 claimer following a sixty day layoff after a good showing against $25,000 claimers, suspicions are quickly raised. Is the horse injured or is the owner trying to “sneak one by”? If the latter, the horse may prove a valuable claim. If the former, claiming the horse may be a wasted $5,000. Active claiming stables search continually for “Epic claims”: horse ready to move up the ladder in class (and purse money) and provide exceptional financial reward. Some $5,000 claims have turned into graded stakes winners worth hundreds of thousands of dollars. More often, however, the movements up the class ladder (if any) are more modest, though still potentially financially rewarding. In addition, the downside of a bad claim (a money loser) is ever present.

2. Such information is similar to rumors about NFL player injuries or the health of a corporate CEO. If accurate, they may be crucial to optimal decision making. But how does one assess their validity?

3. For a given risk measure, such a comparison could be accomplished by constructing portfolios of assets yielding risk approximately equal to the equivalent measure for the claiming market.

4. A second, although remote, possibility is that a horse is claimed not for his long run racing potential, but because of particular characteristics that make the horse useful in structuring a betting coup. A betting coup occurs when an owner sets up a race in the future in such a way as


to reap financial return through gambling. There is no available means of checking for this problem, and we maintain that betting coups are an insignificant influence in our data.

5. Subsequent to 1980 the data was stored on different media and access involved a significant increase in cost.

6. Although the trainer does not always make the claim for himself and thus may not own the gelding, he is acting as the owner’s agent. The data was screened to eliminate geldings with questionable ownership (e.g., where different ownership was listed in subsequent races without a claim having been made).

7. Though most jockey contracts are on a race by race basis and pay a flat fee or 10% of the winning purse, there are differences across trainers and owners regarding paying jockeys percentages on purse money for placing (finishing second, third, or fourth). Our data set did not enable calculating precise individual jockey payments, so we used the per race cost together with fairly high per day expense estimates.

8. The returns are based on $40 per day costs. Returns calculated using $35 and $50 per day costs do not alter the main results presented below.

9. This proxy assumes no capital gain (or loss) from the previous claim. Other possible proxies include claim price in future years or claim price of the last race in which the gelding was entered, but our data do not include these values. We did apply the average capital gain from the reclaimed geldings (-3.1%) to this group and obtained results similar to those reported in Table 2. These calculations are available upon request.

10. The average annual returns for alternative assets reported in Ibbotson et al. [1985] are based on overall market portfolios for a 24-year interval. Since our returns are derived from individual investor data, they are not directly comparable.

REFERENCES

Asch, P., Malkiel, B.G., and Quandt, R. “Racetrack Betting and Informed Behavior.” Journal of Finuncial Economics 10 (July 1982): 187-194.

De Bondt, W. and Thaler, R. “Further Evidence on Investor Overreaction and Stock Market Seasonality.” Journal of Finance 42 (July 1987): 557-581.

Fama, E. F. “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance 25 (May 1970): 383417.

Fama, E. F., and Miller, M. H. The Theory of Finance. Hillsdale, IL: Dryden Press, 1972. Gabriel, P., and Marsden, J. R. “An Examination of Market Efficiency in British Racetrack

Betting.” Journal of Political Economy 98 (August 1990): 874-885. Gabriel, P., and Marsden, J. R. “An Examination of Market Efficiency in British Racetrack

Betting: Errata and Corrections.” Journal of Political Economy 99 (June 1991): 657-659. Gandar, J., Zuber, R., O’Brien, T., and Russo, B. ‘“Testing Rationality in the Point Spread Betting

Market.” Journul of Finance 63 (September 1988): 995-1008. Hausch, D. B., and Ziemba, W. T. “Transactions Costs, Extent of Inefficiencies, Entries, and

Multiple Wagers in a Racetrack Betting Model.” Management Science 31 (April 1985): 381-394.

Ibbotson, R. G., Siegle, L. B., and Love, K. S. “World Wealth: Market Values and Returns.” Journal of Portfolio Management 12 (Fall 1985): 4-23.

Marsh, T., and Merton, R. “Dividend Variability and Variance Bounds Tests for the Rationality of Stock Prices.” American Economic Review 76 (June 1986): 483-498.


Sauer, R. D., Brajer, V., Ferris, S. P., and Marr, M. W. “Hold Your Bets: Another Look at the Efficiency of the Gambling Market for National Football League Games.” Journal of Political Economy 96(l) (February 1988): 206-213.

Shiller, R. “Do Stock Prices Move Too Much to Be Justified By Subsequent Changes in Dividends?’ American Economic Review 71 (June 1981): 421436.

Vergin, R. C., and Scriabin, M. “Winning Strategies for Wagering on National Football League Games.” Management Science 30 (April 1978): 809-818.

Documents

Testing market efficiency with information on individual investor performance