Artificial Intelligence in Finance and Accounting (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