Dr Furkan Mustafa Cetin

Dr Oscar Timmermans

This course is available on the MSc in Accounting and Data Analytics and MSc in Accounting and Finance. This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

The course cannot be taken concurrently with AC411 Accounting, Strategy, and Control.

Students will be admitted on a first-come-first-served basis, with priority given to MSc Accounting and Finance students who apply during the Autumn Term.

Other students who meet the eligibility criteria are likely to be offered a place, but this cannot be guaranteed.

To maximise your chances of securing a place, we strongly recommend selecting this course during initial course selection in the Autumn Term.

For queries contact: Dot Richards, PG Programme Manager. Department of Accounting d.richards@lse.ac.uk

Pre-requisites:

Students must have completed AC415 before taking this course.

Assumed prior knowledge:

The course is designed for students with a good working knowledge of elementary statistics and regression analysis.

• The course is focused on the study of the quintessential role of management control in decentralized organizations. Our focus will be on the measurement and evaluation of the performances of organizational entities and their managers. Management accounting at this level of analysis is an integral part of firms’ management control systems.

• Specifically, we discuss what it means for an organization to be in control, what options managers have for ensuring good control, and how managers should choose from among various control system alternatives. Then we will focus on each of the elements of financial control systems, which provide the dominant form of control in most decentralized organizations. These elements include financial target setting, performance measurement and evaluation, and the assignment of various forms of incentives.

• Short outline of the content of the course:

1. Introduction to management control systems (MCSs) and the role of data analytics in designing and evaluating MCSs
2. Analyzing and evaluating action control systems (“internal controls”)
3. Analyzing and evaluating result control systems
4. Choosing between management control alternatives
5. Analyzing and evaluating incentive systems
6. Selecting performance measures and setting performance targets
7. Constructing peer groups for relative performance evaluation systems
8. Capital budgeting: business models and resource allocations
9. The planning role of budgets: business stress testing

30 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

The course consists of three-hour sessions delivered in the Winter Term.

Case analysis / study weekly.

Weekly cases to be discussed in class. Students are expected to read the cases in advance and prepare for in-class discussion.

This course draws on the key textbook in this subject matter: Merchant, Kenneth A., and Wim A. Van der Stede [2023], Management Control Systems: Performance Measurement, Evaluation, and Incentives (Pearson, UK), fifth edition.

Additional materials (including datasets used throughout the course) and readings will be available on Moodle. The course commits to keeping additional or optional readings to a minimum. This is deliberate in a course that focuses on data analytics as a key theme because there is an infinite amount of sources that may be useful. Deciding what is and what is not reliable is a key skill in today’s world of “information overload”, Evaluating the type and quality of the relevant source of information brought to bear in the class presentations and discussions is a secondary learning outcome.

Exam (40%), duration: 120 Minutes in the Spring exam period.

Course participation (20%) weekly.

Report (40%).

This component of assessment includes an element of group work.

The reports include an element of group work.

Students are required to hand in several group reports, each counting for between 10-20% of the grade for the course, 40% in total, where the remaining 60% will be based on an exam (40%) and in-class participation (20%).

Group reports cannot be longer than 3 pages of core text that stands on its own and contains the key analysis of the problem, how it was addressed, the recommendations, and conclusions. Exhibits, appendices, and tables (which do not count towards the 3-page limit) can, and must, contain further detail, properly presented, about the data analyses and analytics the students have performed to support their report, giving the reader the confidence that the inferences, recommendations, and conclusions are based on robust analyses, though it should not necessarily be expected that the reader will study these materials in great depth (or at all).

The groups who must deliver a report will also have to be ready to present their work in class. Everyone else in class should be ready to discuss and constructively challenge the findings, assumptions, or propose alternative analyses that could have been considered. This will be the basis for each student’s individual class participation grade (20%).

Grading class participation motivates class participation, and having highly interactive sessions helps the learning process. Active class participation encourages students to be well prepared, and thus, to become active, rather than passive, learners. Participation provides students with the opportunity to gain from the experiences and talents of everyone in the course. Moreover, class participation helps students improve their oral communication skills. This is particularly important in a quant-based course because data analytics should not be seen as an alternative to problem solving but instead complementary to decision-making processes in real-world business settings. Class participation evaluations will be based primarily on the quality of the participation in classroom discussions.