The topic "Group Accounting, Transfer Pricing and Data Analytics" will be taught by Prof. Ruhnke and Prof. Häcker. Group Accounting and Transfer Pricing is being conducted by Prof. Ruhnke. Data Analytics is being conducted by Prof. Häcker.
Due to Harald Ruhnke's research semester, Joachim Häcker will only teach this subject once in SS 2024 (4 SWS). From WS 2024/2025, Harald Ruhnke will then teach 2 SWS and Joachim Häcker 2 SWS.
1) Group Accounting and Transfer Pricing (conducted by Prof. Ruhnke)
Learning outcomes / skills
Most major companies operate
using a group structure: „Within a group there is a parent company which
controls subsidiary companies undertaking various different aspects of the
operations of the business. […].
For accounting purposes the
group as a whole is the economic entity for which financial statements are
prepared. […]. The process of combining all the financial statements of the
companies within the group is called consolidation.” (Weetman (Accounting 2016)
p. 191)
In the seminar sufficient
aspects of the preparation of consolidated financial statements will be
explained and trained to allow an understanding of annual reports of groups of
companies. This will be based on the IFRS (International Financial Reporting
Standard) and HGB (German Commercial Code). Also the basic concepts of IFRS and
HGB shall be part of this course.
The second theme of the seminar
is about Transfer Pricing: “The price that is assumed to have been charged for
the exchange of goods and services between different segments within the same
organization, or between related firms, in order to calculate each segment’s
profit and loss separately. The choice of the transfer price will affect the
allocation of the total profit among the parts of the entity or group.” (Wulf,
Jermakowicz, Eiselt (Standards 2010) p. 214)
Transfer pricing is also a
major tax compliance issue for multinational companies.
In the seminar the correct
usage of the different international and national rules and regulations
concerning transfer pricing out of the perspective of a German business as part
of an international group of companies will be explained and trained.
After the participation of
this module the students are able to use the fundamental knowledge about Group
Accounting and Transfer Pricing in a multinational context, especially with
multinational enterprises (MNEs). The students are able to work on concrete
problems out of these areas and to apply with the help of structured quality
analyses and usable formal concepts. In little working groups the student can
exchange, understand and test their new knowledge about these areas of the
module. Through the participation of this module the students recognize the
fundamental meaning of Group Accounting and Transfer Pricing in a multinational
context, especially for MNEs.
Contents
-
IAS 27 - Separate Financial Statements
-
IAS 28 - Investments in Associates and Joint Ventures
-
IFRS 3 - Business Combinations
-
IFRS 10 - Consolidated Financial Statements
-
IFRS 11 - Joint Arrangements
-
Rules and Regulations concerning Transfer Pricing out
of a German perspective
Deployed methods of business
administration:
·
Models and Methods of analytics (research- and analytic models):
·
Process models (e. g. procedure of due diligence)
·
Component models (e. g. time series analyses for key figures
development)
·
Normative decision theory (e. g. assessment of
the impacts of individual forms of financing)
·
Qualitative optimization models
·
Quantitative optimization models
·
Forecasting models (budget figures)
·
Models of interaction (communication)
·
Quantitative-empirical methods (comparative –
statistic, mathematic methods, data analyses):
·
Key figures on the financial situation (such as
liquidity, inventory turnover period), the income situation (e. g. return on
investment, interest expense ratio), the financial position (e. g. cash flow
figures, days payables outstanding); in this context, working with primary data
and secondary data
·
Quantitative comparative analyses (e. g. statistical
references from Standard & Poor’s)
·
Statistical analyses (e. g. medians of key figures in
rating classes)
·
Qualitative-interpretative methods
(expert interview, surveys, standardized inquiry):
·
Qualitative company analyses (branch, organizational structure,
management, business relationships, payment behavior)
·
Descriptive decision theory
·
Prescriptive decision theory
·
It is possible to conduct expert interviews as part of the project work
Methods of Teaching and Learning:
- Lectures and discussions: Theory and Reality
- Case studies and group work
- Self-controlled learning
- Special guests lectures (N.N.)
- Seminar paper
Literature
·
International Financial Reporting Standards (IFRS)
2017, The official standards and interpretations approved by the EU, English -
German, Wiley
·
Handelsgesetzbuch (HGB)
·
Wulf, I., Jermakowicz, E. K., Eiselt, A.,
International Financial Reporting Standards (IFRS), Dictionary, Wiley, 1st
edition 2010
·
Weetman, P., Financial & Management Accounting, An
introduction, Pearson, 7th edition 2016
·
More literature at the beginning of the course
2. Data Analytics (conducted by Prof. Häcker)
Students
perform a data analytics analysis for the company they have chosen in the
Investment Banking course.
Qualifications
targeted:
With
regard to the qualification category of expertise, the course
participants are able to:
- provide an overview of core data analytics methods and compare these.
- Relate data analytics to other modules such as investment banking,
mathematics and statistics as well as derivatives.
- Relate data analytics to corporate planning and corporate valuation and to
describe the linkages in their own words
-
Apply their knowledge in the field of data analytics in a self-selected
project.
With
regard to the qualification category of methodological competence, the
course participants are able to:
-
use their knowledge about planning, group accounting, taxes and valuation
methods to develop a professional data analytics model for which incorporates
the principles of financial modeling
- obtain the data necessary for a holistic model which incorporates the
principles of financial modeling.
-
to collect larger amounts of data and to analyse them with suitable software.
-
Independently structure complex task in data analytics and to develop
independent modules to solve these tasks.
- Critically evaluate the results of the data analytics analysis and to clarify
any differences.
- Interpret the results of the data analytics analysis and to independently
draw conclusions for corporate finance actions
- Review the structure of the data analytics analysis and the results of the
model with the help of a model review
With
regard to the qualification category of self-competence, the course
participants are able to:
- transfer the results from data analytics to other modules such as investment
banking, mathematics and statistics as well as derivatives and to combine them
with these modules.
- manage a project in the field of data analyticsand to develop proprietary
solutions in a team of big data experts
- compile a transparent and comprehensive documentation of assumptions and
methods for a given data analytics project.
- structure the process of data analytics and to apply the standards of
professional financial modeling
- master theoretical and empirical challenges of data analytics.
- apply their knowledge to specific big data projects and to adjust it to
actual data situations
- critically challenge the assumptions, algorithms and results of every data
analytics approach.
- apply predictive analytics and prognostic analytics models to larger data
sets and visualize the results.
With
regard to the qualification category of social competence, the course
participants are able to interact in groups and present their results in front
of experts. The student are able to draw conclusions for the given question and
present them.
Methods
of teaching and study:
Literature
study, case studies on the implementation of data analytics as well as
Excel-based exercises. The cases studies help to implement the valuation
methods for a given task in a model-based and applied fashion. The self-study
of the participants is supported via e-learning and videos.
Content:
-
Using data analytics in M&A, IPO, Private Equity as well as Venture Capital
- Benford’s law
-
The second digit approach
-
The third digit approach
-
The fourth digit approach
-
The last digit approach
-
Coding in Python versus Excel modeling – Lessons learned in Corporate Finance
-
The search for the holy grail in the sea of financial figures: How phi and
other numbers connect the dots.
- Applying the findings to other fields of interest
Literature/
Study materials
Häcker, J., Ernst, D. (2017): Financial Modeling – An Introductory Guide to
Excel and VBA Applications in Finance, 1st edition, New York, MacMillan.
Ernst, D. Häcker, J. (2011): Applied International
Corporate Finance, 2nd edition, Munich.
Häcker, J., Frühholz, M. (2023): „Valuation
with Python – the disruption has started.“
Alekseev, M.A. (2016): Applicability of Benford’s law for determining the
reliability of financial statements, Bulletin of the NSUEU – 2016, No 4
Benford, F. (1938): The
Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78 (4), 551–572. American Philosophical Society.http://www.jstor.org/pss/984802.
Cleff, T. (2019): Applied
Statistics and Multivariate Data Analysis for Business and Economics: A Modern
Approach Using SPSS, Stata, and
Excel. Cham: Springer
International Publishing. doi:10.1007/978-3-030-17767-6
Dlugosz, S. & Müller-Funk,
U. (2009): The value of the last digit: statistical fraud detection with
digit analysis. Advances in Data Analysis and Classification, 3 (3), 281–290.
doi:10.1007/s11634-009-0048-5. For German version see: https://www.wi.uni-muenster.de/sites/wi/files/public/research/arbeitsberichte/ab133.pdf
Hill, T. P. (1995): A
Statistical Derivation of the Significant-Digit Law. Statistical Science.
doi:10.1214/ss/1177009869
Group Accounting and Transfer Pricing