Prof. G. William Schwert


E-Mail: schwert@schwert.ssb.rochester.edu
CS3-110L
Phone: 585-275-2470
Fax: 585-461-5475

Secretary: Kathleen Madsen, CS1-102 or CS3-110M, 585-275-8187
E-Mail: madsenka@simon.rochester.edu


The course's objective is to provide a systematic way to organize and make use of quantitative information in business decision-making. We will build on what you learned in GBA 412, extending that knowledge to include the situations frequently encountered in decision-making.

Why study this material?

In the short run -- factual evidence plays a key role in the Simon School curriculum. Ask any students that are further along in the program and have taken the more advanced classes in finance, marketing, operations, etc. In the longer run, for you to make effective decisions as a manager you must make sense of a variety of kinds of information. Some information will involve impressions, educated guesses, or gut feelings, which are not very quantitative. Other information will be more quantitative, such as financial statements, forecasts about the market for a new product, estimates of competitors' R&D expenditures, information on inventories, sales and orders, and so on. To make effective use of this kind of information, and managing the sheer volume of information of this kind, is a big issue. You must have an organized, logical way to think about it, which is what GBA 412 and APS 425 provide.

You have to compete with other managers, some of whom have a lot of experience, others are well trained, etc. Your advantage as a Simon School graduate is that you approach business decision-making from the standpoint of getting the analysis right. When you make a decision, you ask yourself: What is the logic of the situation? What does it tell me are the relevant facts to focus on? Do I have this information, or where can I get it? What is the reliability of the information I have or can acquire? The skills you learned in GBA 412, and that will be further developed in APS 425, are an integral part of the set of tools you will come to rely on to succeed in a competitive business environment.

Also, determining the right decision is only the beginning of a process. A sound factual basis for the decision is a major part of getting it implemented, but the effectiveness of this will be much enhanced if it is communicated well. Thus, another facet of the class is the effective communication of your argument in favor of a decision. Therefore, in class discussions, assignments, exams, and on the project, there will be a premium for avoidance of unnecessary terminology and effective managerial-style presentation.

One warning -- statistical analysis is no substitute for thinking. It can help to clarify, to sort out which of a number of plausible arguments best fits the facts, and so on. But it cannot tell you what the relevant things to think about are, or obviate the need for experience and good judgment.

Expectations of Student Performance

This is a valuable course, but it is also a difficult one in the sense that for the course material to be useful, as opposed to dangerous, in everyday decision making, you have to know quite a lot about it, be a little bit sophisticated. Thus the volume of material and depth of coverage is among the greatest in the program. Moreover, it is not the kind of material that one learns by listening and reading. It is like riding a bicycle -- everyone can do it, but there is no substitute for getting on the bike as a way to do so. This is why there are a lot of assignments, a serious project, etc.

APS 425 is a lot of work and very cumulative. This is the wrong course to get behind in. This is going to be a hard course, but the reason for this is that you are going to learn a lot. There is little point to coming to the School for years and not leaving a lot different than you arrived!

At the same time I can assure you that there is nothing really deep in this course. It is a course that responds well to effort.

It is a good idea to keep this in mind because about two thirds of the way through the class a lot of you will be feeling pretty anxious. We will have covered a lot of ground, and by that stage it may not have "come together." But I can tell you that if you keep at it, once you have cranked through the assignments and done your project, it will come together.

To reiterate, this is a hard course, but it is material that is worth learning, and you will learn it if you try.

All of the cases and the project are group assignments. I have a separate memo that describes my approach to grading group assignments, but I think it is worthwhile to share a few thoughts about the optimal way to participate in group work. If you have been involved in study groups that are diversified in terms of background and academic specializations in the past you may have noticed a tendency for the group to assign primary responsibility for a particular assignment to the person with the comparative advantage at completing the task. While this may result in the highest grade for the assignment, and it may involve the least aggregate amount of work for the members of the group, it is the WORST model for learning the material in the course. Usually, the members of the group glance through the memo, and perhaps help with editing the draft, but are not deeply involved in producing the analysis underlying the memo.

Instead, I recommend that all members of the group attempt to complete the analysis (including computer work) and meet to compare and contrast their proposed solutions to the problem. Only after a thorough discussion of the alternative approaches should the group reach a consensus on the final product. In this way, everyone learns to perform the analysis, and the process of reaching consensus teaches everyone how to do the analysis as well as how to communicate about it. Perhaps this maximizes the group effort, but it also maximizes the learning that occurs (and hence the benefit from participating in the course).

Since all exams are individual, not group, assignments, I strongly encourage every student to actively participate in the group work. In this way each of you will be prepared to respond to the challenges of the individual assignments. In the longer run, you will be able to produce valuable work on your own after you have left Simon and your class teammates (and me).

Academic Integrity

As an educational institution, the Simon Business School has a significant commitment to maintain its credibility in the marketplace. Because a graduate degree is an intangible asset, both faculty and students have strong incentives to assure potential employers and prospective students of the quality of the Simon degree. Further, honest behavior enhances the quality and fairness of the educational experience for all of those earning that degree. Therefore, it is an individual and a collective responsibility of the members of the Simon community to participate actively in maintaining the highest standards of honesty and integrity by promoting adherence to the Code of Academic Integrity.

Every Simon Business School student is expected to be completely honest in all academic matters. Simon students will not in any way misrepresent their academic work or attempt to advance their academic position through fraudulent or unauthorized means. No Simon student will be involved knowingly with another student's violation of this standard of honest behavior.

Please refer to the Student Handbook for any questions regarding the Code of Academic Integrity.

Since I have now been teaching this course for quite a few years, there may be some graded assignments that are similar in some ways to assignments that have been given in the past. It is a violation of the Simon Code of Academic Integrity to use material from any prior offering of APS 425 to aid in the completion of any graded assignment. A proven violation of the Code can lead to a failing grade on an assignment or project, course failure, suspension and/or dismissal from the program.

Grading

There will be a midterm (11/3/2015) and a final exam (12/8/2015), counting 20% and 30% of your course grade, respectively. You are required to make the necessary arrangements to attend each exam; i.e., attendance is mandatory (i.e., a grade of 0 will be given on any exam you do not take). Each will involve some analysis of real data using Eviews. You will have access to the data to be used for the exam at least a week before the test.

There will be several homework assignments that are group responsibilities. Homework grades will count 20% of your final grade. Also, there will be a major project due on 12/1/2015 that is also a group assignment worth 20% of your course grade. The project report should be less than 2,000 words (about 10 double-spaced pages) describing your analysis of an interesting dataset. You may include well-documented tables and figures as appendices as long as they are referred to in the report. The report should be written as if you were giving it to your boss (who took a regression course many years ago, but does not use it in his everyday work). Thus, you should not rely on statistical jargon to explain your analysis, but you should also not spend time explaining regression to him (i.e., me). You should start early in the quarter to plan and begin work on your project - last minute efforts are likely to produce poor results.

When you turn in your project (12/1/2015), each group will turn in their grade-allocation sheet containing:

  1. the percentage (summing to 100%) of the total group score that each member by name is to receive towards his/her final grade, and
  2. the signature of each group member.
If one group memberís signature is missing, the grade allocation sheet is valid and binding on all members. If two or more signatures are missing, the allocation sheet is invalid and the groupís score will be allocated equally among the members. I will not arbitrate disputes among group members. No grade allocation sheets will be accepted after December 1. If you do not turn in a grade allocation sheet, I will assume that you want an equal allocation of credit to all team members.

Finally, 10% of your grade will be based on Professionalism and Class Participation.

Course Information on the World Wide Web (WWW)

Most of the materials for this course will be posted on the home page for this course. For example, I plan to post copies of the slides used in the classroom presentations as Adobe Acrobat files (so they can be viewed and printed from a computer attached to the WWW). I want to encourage all students to use this resource throughout the course. Also, homework assignments, sample answers, datasets, grade distributions on assignments, and other course related communication will be communicated through the web page.

Books and Other Reference Material

The required text for this course is:

Wooldridge, Jeffrey M. Introductory Econometrics, 5th ed., 2013, South-Western, ISBN: 9781111531041 (henceforth JW) [Also available as an Ebook at a lower price]

The recommended text for this course is:

Francis X. Diebold, Forecasting in Economics, Business, Finance and Beyond, (available for free in PDF).

I will generally not lecture from these books. Rather, I think of them as references.

In addition, I will use Eviews for all of my lectures, answer sheets, etc. Moreover, the exams will require you to analyze data that are provided in Eviews datasets. Therefore, you need to buy and use Eviews to succeed in this course. Since the University of Rochester has a site license, you can purchase a full version of the software for $95 online

The Eviews Users Guide, Part 1, and Part II (henceforth EV) contain some useful discussion that relates the topics we will discuss in class to the options available in Eviews.

Topics and Readings

I. Review of Multiple Regression

JW, Chaps. 1-4

JW, Chap. 19 (read early - advice on how to write an empirical paper)

EV, Part V, Chap. 19, pp. 5-22; Chap. 24, pp. 163-176

II. Heteroskedasticity (non-constant variance of the errors)

JW, Chap. 8

EV, Part IV, Chap. 20, pp. 32-40; Chap. 24, p. 182

[sales data]

III. Analysis of Categorical Data

JW, Chap. 7.5-7.7 (pp. 249-257) , Chap. 8.5 (pp. 294-296), Chap. 17.1 (pp. 583-596)

EV, Part VI, Chap. 28, pp. 297-316 (some techniques that are beyond the scope of this course)

[epidural data] IV. Analysis of Time Series Data

EV, Part V, Chap. 22, pp. 87-134 (some techniques that are beyond the scope of this course)

V. Time-varying Volatility

EV, Part VI, Chap. 25

FD, Chaps. 10

*Hentschel, Ludger, "All in the Family: Nesting Symmetric and Asymmetric GARCH Models," Journal of Financial Economics, 39 (1995) 71-104.

Schwert, G. William, "Why Does Stock Market Volatility Change Over Time?" Journal of Finance, 44 (December 1989) 1115-1153.

French, Kenneth R., G. William Schwert, and Robert F. Stambaugh, "Expected Stock Returns and Volatility," Journal of Financial Economics, 19 (September 1987) 3-29.

[Xerox stock price and Forex data]


A full-text version of this course outline is available in Acrobat's portable data format (.pdf). The file is about 24K and can only be viewed (and printed) using a copy of Acrobat Reader.

If you want the current version of the Adobe Acrobat Reader for other platforms, visit Adobe's web page by clicking the image below.

Click here to download the full text of this course outline.


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© Copyright 2001-2015, G. William Schwert

Last Updated on 9/18/2015