About BRAINKRUPTCY
BRAINKRUPTCY was co-founded by Prof. Dr. Christian Lohmann to give investors and market participants accurate, up-to-date information on the financial distress and bankruptcy risk of listed US companies.
Vision
Christian Lohmann believes that financial markets work most efficiently when market participants have easy access to vital market information — including a reliable measure of bankruptcy risk. BRAINKRUPTCY translates state-of-the-art academic research on bankruptcy prediction into clear, actionable risk information for professional investors.
Background
Christian Lohmann holds a PhD from the University of Munich and is a Professor at the University of Wuppertal (Schumpeter School of Business and Economics). He has deep expertise in data analytics and corporate bankruptcy prediction, actively conducts research in these fields, and serves as a risk management advisor to hedge funds.
His research covers, among other topics:
- Nonlinear effects and their impact on bankruptcy prediction
- Bankruptcy prediction of young firms
- The use of qualitative information in bankruptcy prediction
- The evaluation of misclassification in credit scoring
Selected publications by Prof. Dr. Christian Lohmann:
Lohmann, C., Möllenhoff, S., and Lehner, S. (2025). On the relationship between financial distress and ESG scores. Corporate Social Responsibility and Environmental Management 32 (5): 6377–6401.
Lohmann, C. and 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. and 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 accounting-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.
