About Us

Christian and Steffen share the view that financial markets work most efficiently when market participants have easy access to vital and key market information, such as the measure of bankruptcy risk. Christian and Steffen combined their expertise and skillsets to found BRAINKRUPTCY, to provide investors and other market participants with accurate and up-to-date information on the financial distress and the bankruptcy risk of listed US companies.

Both founders have a strong academic background in the area of bankruptcies. Christian holds a PhD from the University of Munich and currently serves as a Professor at the University of Wuppertal. Steffen holds a PhD in the field of bankruptcy prediction and investor attention, and is a Senior Researcher at the University of Wuppertal.

Both founders have deep expertise in data analytics and corporate bankruptcy prediction, actively conduct research in these fields, and serve as risk management advisors to hedge funds. Christian has published several academic papers on:

• Nonlinear effects and their effect on bankruptcy prediction
• Bankruptcy prediction of young firms
• The use of qualitative information in bankruptcy prediction
• The evaluation of misclassification in credit scoring

Steffen's research analyzes investor attention and behavior before a firm files for bankruptcy, and he is an advisor and lecturer in the fields of:

• Data analytics
• Machine learning
• Artificial intelligence

Lohmann, C./Ohliger, T. (2024). Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability. Research in International Business and Finance 70 B, Article 102395.

Lohmann, C./Möllenhoff, S. (2023). How do bankruptcy risk estimations change in time? Empirical evidence from listed US companies. Finance Research Letters 58 B, Article 104389.

Lohmann, C. and Möllenhoff, S. 2023. Dark premonitions: Pre-bankruptcy investor attention and behavior. Journal of Banking & Finance 151, Article 106853.

Lohmann, C., Möllenhoff, S., and Ohliger, T. 2023. Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models. Journal of Business Economics 93 (9): 1661–1690.

Lohmann, C. and Möllenhoff, S. 2023. The bankruptcy risk matrix as a tool for interpreting the outcome of bankruptcy prediction models. Finance Research Letters 50 A, Article 103851.

Lohmann, C. and Ohliger, T. 2021. Using Aaccounting-based and loan-related information to estimate the cure probability of a defaulted company. European Financial Management 27 (4): 620–640.

Lohmann, C. and Ohliger, T. 2020. Bankruptcy prediction and the discriminatory power of annual reports: Empirical evidence from financially distressed German companies. Journal of Business Economics 90 (1): 137–172.

Lohmann, C. and Ohliger, T. 2019. Using accounting-based information on young firms to predict bankruptcy. Journal of Forecasting 38 (8): 803–819.

Lohmann, C. and Ohliger, T. 2019. The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models based on empirical data. Journal of Forecasting 38 (5): 375–389.

Lohmann, C. and Ohliger, T. 2018. Nonlinear relationships in a logistic model of default for a high risk installment portfolio. Journal of Credit Risk 14 (1): 45–68.

Lohmann, C. and Ohliger, T. 2017. Nonlinear relationships and their effect on bankruptcy prediction. Schmalenbach Business Review 18 (3): 261–287.