Assignment 2: Submit individually to me by Friday 22nd



Assignment 2: Submit individually to me by Friday 22nd

Part 1:

Dow Jones Data: Please try to repeat all the analysis I showed you in class. You can do more analysis if you can think of anything.

Report: I want the SPSS analysis reported as a short description with tables and figures. You can attach the SPSS output as Appendix. In tables report means with standard deviations / standard errors.

For hints on how to report analysis look up the SHORE online studies. Here is the URL for the results of one of the studies:

Part 2

Analyze the fundamentalism data. Do basic analysis for it. Report the relevant means, standard deviations etc.

Here’s some example questions you could pose to the dataset: Do people in the different groups differ in religious hope, optimism etc.

Are people who are high on optimism high on religious hope as well?

Report: Report the results to me as a short report with tables and figures. At the end draw some broad conclusions about what you found.

CEO’s Golf Handicap Dataset

Reproduced from CHANCE News 7.06 27 May 1998 to 26 June 1998)



Investing it; duffers need not apply.

The New York Times, 31 May 1998, Section 3, p. 1

Adam Bryant

An investment compensation expert, Graef Crystal, carried out a study purporting to show that the major companies, whose C.E.O's had low golf scores, had high performing stocks. Crystal obtained data for golf scores from the journal Golf Digest and used his own data on the stock market performance of the companies of 51 chief

executives. He created a Stock Rating which gave each company a stock rating based on how investors who held their stock did with 100 being highest and 0 lowest.

It is rare that an article in the New York Times includes the data set, but this article did. Here it is, as sent to us by Bruce King (we have saved it on the Chance Website in the data section of Teaching Aids):

CEO Company Handicap StockRate

Melvin R. Goodes Warner-Lambert 11 85

Jerry D. Choate Allstate 10.1 83

Charles K. Gifford BankBoston 20 82

Harvey Golub American Express 21.1 79

John F. Welch Jr. General Electric 3.8 77

Louis V. Gerstner Jr. IBM 13.1 75

Thomas H. O'Brien PNC Bank 7.1 74

Walter V. Shipley Chase Manhattan 17.2 73

John S. Reed Citicorp 13 72

Terrence Murray Fleet Financial 10.1 67

William T. Esrey Sprint 10.1 66

Hugh L. McColl Jr. Nationsbank 11 64

James E. Cayne Bear Stearns 12.6 64

John R. Stafford Amer. Home Products 10.9 58

John B. McCoy Banc One 7.6 58

Frank C. Herringer Transamerica 10.6 55

Ralph S. Larsen Johnson & Johnson 16.1 54

Paul Hazen Wells Fargo 10.9 54

Lawrence A. Bossidy Allied Signal 12.6 51

Charles R. Shoemate Bestfoods 17.6 49

James E. Perrella Ingersoll-Rand 12.8 49

William P. Stiritz Ralston Purina 13 48

Duane L. Burnham Abbott Laboratories 15.6 46

Richard C. Notebaert Ameritech 19.2 45

Raymond W. Smith Bell Atlantic 13.7 44

Warren E. Buffett Berkshire Hathaway 22 43

Donald V. Fites Caterpillar 18.6 41

Vernon R. Louckes Jr. Baxter International 11.9 40

Michael R. Bonsignore Honeywell 22 38

Edward E. Whitacre Jr. SBC Communications 10 37

Peter I. Bijur Texaco 27.1 35

Mike R. Bowlin Atlantic Richfield 16.6 35

H. Lawrence Fuller Amoco 8 33

Ray R. Irani Occidental Petroleum 15.5 31

Charles R. Lee GTE 14.8 29

John W. Snow CSX 12.8 29

Philip M. Condit Boeing 24.2 25

Joseph T. Gorman TRW 18.1 24

H. John Riley Jr. Cooper Industries 18 22

Richard B. Priory Duke Energy 10 22

Leland E. Tollett Tyson Foods 16 20

Bruce E. Ranck Browning-Ferris 23 15

William H. Joyce Union Carbide 19 13

Thomas E. Capps Dominion Resources 18 12

Scott G. McNealy Sun Microsystems 3.2 97

William H. Gates Microsoft 23.9 95

Sanford I. Weill Travelers Group 18 95

Frank V. Cahouet Mellon Bank 22 92

William C. Steere Jr. Pfizer 34 89

Donald B. Marron Paine Webber 25 89

Christopher B. Galvin Motorola 11.7 3

Crystal regarded the last seven as outliers and threw them out (described in the article as being scientifically sifted out).

Bruce also sent us a letter he wrote to The New York Times about the article. The Times (The New York Times, 14 June 1998, Money and Business/Financial Desk, Sect. 3, p 12.) published several letters omplaining about some of the points that Bruce made in his letter. However, we felt that Bruce's letter best described the many problems with the Times article, so we asked him to allow us to include it here.

To the Editor:

There are several reasons why Sunday's CEO golf/ performance study (Money & Business, pp.1,9) did not deserve an inch of column space, much less the 1+ pages you gave it. The study has at least four problems:

(1) The 74 CEOs who reported their golf handicaps probably are different in unknown ways from the CEOs who chose not to reveal their handicaps. You cannot safely generalize to the population of all CEOs the responses of those who volunteer information.

(2) Such an observational study cannot support an inference that A causes B. In particular, the suggestion that "executive wannabees ... spend more time on the links" is foolishness, and makes no more sense than assuming that moving closer to the Canadian border will improve your IQ (a puckish observation attributed to Senator Moynihan, I believe).

(3) One cannot be sure from the published article, but it seems likely that the observed correlation between golf handicap and executive prowess was the result of a fishing expedition. You quote Mr. Crystal as saying "For all the different factors I've tested ... this is certainly one of ... the strongest ...". Well, let's imagine that Mr. Crystal tested for 50 irrelevant factors for a link to executive prowess; it is likely that one or more of the 50 samples would nevertheless show a statistically significant correlation by chance alone. And if hat's the only correlation reported, it looks as if it might be important, rather than just a chance occurrence. It is reasonable to wonder whether Mr. Crystal just continued to fish until e finally found a "keeper".

(4) Mr. Crystal's treatment of seven "outliers" seems to be quite arbitrary. First of all, you may note that these seven constitute the six executives with the greatest performance ratings, and the one with the least. But they are NOT outliers in the usual sense: 1.5 times the interquartile range beyond the middle 50% of the ratings).

Secondly, outliers are not censored just because they "distort the trend lines". If that was the case, any scatterplot could be pruned to show a significant correlation. The conventional strategy is to seek to learn why an outlier is unusual, and to retain all the data that cannot be rejected for cause. (An outlier, for example, may merely be a data-recording error, and if the error cannot be corrected, there is sufficient cause to reject that observation.) Did you notice that the correlation between golf handicap and executive prowess was only -0.042 when the seven > outliers were included, and that deleting the seven changed it to -0.414?

As a long-time Times reader, I depend on it for accurate reporting of the sciences. It is extremely disturbing to see it purveying junk science as "rigorous," however cute it may be.

Bruce King

kingb@wcsub.ctstateu.edu

Darts vs. The Experts Dataset

The Wall Street Journal has a continuing contest beween the darts and the experts. As of this time, Nov. 23, 1998, they have had 101 overlapping six month contests. A new contest is started every month. This data gives the percent gain for the average of the

experts, the darts, and the Dow.

A discussion of the contest

In 1988 the Wall Street Journal began a contest that was inspired by Burton Malkiel’s book A Random Walk Down Wall Street. In the book, the Princeton Professor heorized that "a blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts."

The Journal set out to create an entertaining contest to test Malkiel's theory and give its readers some new investment ideas in the process. Wall Street Journal staff members typically play the role of the monkeys (the Journal listed liability insurance as one reason for not going all the way and actually using live monkeys).

The contest has become a popular feature for the Journal and has also drawn much interest and commentary from journalists, investors, and academics. Several academic papers have been written about the contest and its implications (summaries and links are included below).

The contest began on October 4, 1988 and since then more than 100 contests have been completed under the current rules. Initially the contest lasted one month, but recognizing that the publication of the contest was creating a publicity effect on the pro’s stock picks, the Journal began measuring the results over a six month period beginning in 1990.

The rules have changed at various times during the contest, but the current rules are as follows. Each month four "professionals" are given the opportunity to select one stock (long or short) for the following six months. The stocks must meet the following criteria.

1.Market capitalization must be at least $50 million.

2.Daily trading volume must be at least $100,000.

3.Price must be at least $2.

4.Stocks must be listed on the NYSE, AMEX, or NASDAQ and any foreign stocks must have an ADR.

The pro's stock picks compete against four stocks usually chosen by Journal staffers flinging darts at the Wall Street Journal stock tables, which are pasted to a board. At the end of six months, the price appreciation for the pro’s stocks and the dartboard stocks are compared (dividends are not included). The two best performing pros are invited back for the next contest and two new professionals are added. In the latest twist to the contest, the Journal has begun taking stock picks from Journal readers which will also be compared with the pro's and dart's picks (see 4/8/99 article $$).

On October 7, 1998 the Journal presented the results of the 100th dartboard contest. So who won the most contests and by how much? The pros won 61 of the 100 contests versus the darts. That’s better than the 50% that would be expected in an efficient market. On the other hand, the pros losing 39% of the time to a bunch of darts certainly could be viewed as somewhat of an embarrassment for the pros. Additionally, the performance of the pros versus the Dow Jones Industrial Average was less impressive. The pros barely edged the DJIA by a margin of 51 to 49 contests. In other words, simply investing passively in the Dow, an investor would have beaten the picks of the pros in roughly half the contests (that is, without even considering transactions costs or taxes for taxable investors).

The pro’s picks look more impressive when the actual returns of their stocks are compared with the dartboard and DJIA returns. The pros average gain was 10.8% versus 4.5% for the darts and 6.8% for the DJIA.

Some commentators have therefore concluded that the contest offers some proof that the pros have beaten the forces of chance and the Journal described the pros as "comfortably ahead of the darts" in the dartboard column published on 3/10/99 ($$). However, that conclusion is not shared by many others that have analyzed the contest.

Malkiel and other academics have responded to those that consider the contest to be a victory for the pros with what amounts to a collective response of "not so fast my friend" (as they like to say on ESPN).

Researchers that have come to the defense of the darts argue that the contest has some unique circumstances that deserve elaboration. It can easily be argued that the contest itself and the rules of the contest tilt the odds in the pro’s favor. In fact, the academics seem to argue that it's not the darts that are on the losing end. Rather, they argue that investors that buy the pro’s recommend stocks are "naïve" and that those investors are acting on nothing more than "noise."

Before the contest even began, Professor Malkiel had suggested that the results would be affected by an announcement effect. In other words, the very act of publishing the pro’s picks in the Journal could cause those stocks to rise as the hundreds of thousands of Journal readers (the Journal’s current circulation is listed at over1.7 million) open their morning paper and react to the recommendations of the pros. Professor Malkiel suggests to Investor Home that the pros advantage effectively disappears if you (1) account for the fact that the pros pick relatively riskier stocks and (2) measure returns from the day after the column appears (thereby eliminating the announcement effect).

There have actually been several very thorough studies that have analyzed the contest in great detail. In "The Dartboard Column: Second-Hand Information and Price Pressure," Brad Barber and Douglas Loeffler (Journal of Financial and Quantitative Analysis, June 1993) addressed the question of whether the pro's stock picks created temporary buying pressure by naïve investors (known as the "price pressure hypothesis") or reveal relevant information (otherwise known as the "information hypothesis"). The authors found evidence for both but also came to some interesting conclusions.

Two days following publication, the pro picks had average abnormal returns of 4%. However, those returns partially reversed within 25 days. Those returns were nearly twice the level of abnormal returns documented in previous research on analyst recommendations and the volume of pro’s stocks nearly doubled after the contest publication (which at the time was greater than the volume increase of the Journal's Heard on the Street" column). They also found that the pros picked stocks with (1) lower dividends, (2) higher historic and projected EPS growth, and (3) slightly higher PE ratios and betas.

Continued on

Fundamentalism Data Description

Introduction

It is often said that those with a strong religious faith have a more sanguine view of the world, perhaps because they see life in a broader context. Indeed, Marx is quoted as saying that "Religion is the opiate of the people." Those with more liberal views of religion may take to themselves a greater responsibility for the conduct of their own lives, whereas the more fundamentalists sects or religions encourage their followers to accept a hierarchical structure in which unquestioning faith is expected of all. Such unquestioning faith might be expected to lead to less concern with the cares of the world, which in turn might lead to a greater optimism (or less pessimism) about the world and one's place in it.

Sethi and Seligman (1993) reported a study in which they looked at the relationship between optimism and religious fundamentalism. They were concerned with two broad issues. The first was whether religious groups differing in degree of fundamentalism varied in their level of optimism. The second was whether optimism could be predicted on the basis of several variables measuring the role of religion is the person's life.

Sethi and Seligman collected data from over 600 adults from nine religious groups. These nine groups were sorted into three major categories (Fundamentalists, Moderates, and Liberals) and those three categories formed the basis of future analyses.

The data that Sethi and Seligman collected were a measure of Optimism and three measures of the role of religion in the person's life--hereafter termed Religiosity. The Optimism measure was calculated from the Attributional Style Questionnaire. This scale measures causal explanations for both positive and negative events along three dimensions: Stability-Instability, Internality- Externality, and Global-Specific. The

data involve a combined score across these three dimensions for both the positive and negative items, and the difference between the positive and negative scores was taken as the subject's optimism score.

Sethi and Seligman also used a questionnaire designed to measure Religiosity. They assessed the influence of religion in daily life (RInfluen) [e.g.,"To what extend do your religious views influence who you associate with?"], religious involvement (RInvolve) [e.g.,"How often do you attend religious services?"], and religious hope (RHope) [e.g.,"Do you believe there is a heaven?"] Each of these items was rated on

a 7 point Likert scale (where 7 = agree) and the mean across the items on each scale was used as the dependent variable.

Method

The data in this example were generated to match the data of Sethi and Seligman using a program written by Aguinis (1994). Many of the relevant statistics were given in the Sethi and Seligman paper, and

those that were not given were estimated based on what seemed to be reasonable values for the variables involved. The fact that the results largely agree with those given by Sethi and Seligman testifies both

to the fact that the estimates were reasonable and that with large sample sizes effects are sufficiently robust to overcome faulty estimates.

The data were generated by first using Aguinis's program to generate 600 cases sampled from a population with a specified pattern of correlations. Since these data were samples from a population, there was

no guarantee that the sample correlations would be exactly as specified, so repeated samples were taken until the pattern of correlations was satisfactory.

Once the data were generated, they were read by a SAS program which assigned them to three groups simply on the basis of desired sample sizeÑthe first 200 cases were assigned to the Fundamentalist category, the next 280 were assigned to the Moderate category, and the final 120 were assigned to the Liberal category. (The unequal sample sizes were a near approximation of the samples obtained by Sethi and Seligman.) The data were first standardized to a mean of 0 and a standard deviation of 1 for each group separately, and then transformed to have means and standard deviations matching those of Sethi and Seligman. This matching is easily accomplished because if you start with data with mean = 0 and std = 1, you can produce any mean and standard deviation you desire by

Newdata(i) = St. dev*Olddata(i) + Mean

The data were then rounded to integers to make them more realistic. The rounding affected the means and standard deviations only slightly, and had only a minor effect on the pattern of correlations. Again, the large sample sizes helped. It is important to keep in mind that a linear transformation of data changes the means and standard deviations, but does not alter the correlation between variables. Therefore once the data are produced to have a specific pattern of correlations, further transformations leave those unaffected.

The data that were generated can be found at Fundamentalism.dat in text form. The variables, in order, are

1.ID

2.Group (a string variable)

3.Optimism (-2 to +2)

4.Religious Influence

5.Religious Involvement

6.Religious Hope

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In order to avoid copyright disputes, this page is only a partial summary.

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