Hochschule München

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, 1^st edition 2010

·         Weetman, P., Financial & Management Accounting, An introduction, Pearson, 7^th 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, 2^nd 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