BOOKS
- Active Credit Portfolio Management in Practice. 2009. Bohn, Jeffrey R. and Roger M. Stein. Hoboken, New Jersey: John Wiley & Sons, Inc.
- Seven Methods for Transforming Corporate Data into Business Intelligence. 1997. Dhar, Vasant and Roger Stein. Prentice-Hall.
OPEN SOURCE SOFTWARE
RiskStratification Workbench | online tool for developing risk stratifications, rankings and policies. originally developed for COVID-19 using methodology in “Drawing the line for risk stratifications: Designing return-to-work policies that consider diagnostic error, costs, benefits and COVID-19“ |
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RBOToolbox 112713 ^{*} | academic version of RBO simulation software originally used for “Financing drug discovery for orphan diseases.” |
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RBOToolbox ^{*} | academic version of RBO simulation software written for first RBO publication “Commercializing biomedical research through securitization techniques.” |
^{*} This software was written for academic use and is not intended or suitable for real-world commercial applications without substantial extensions.
SELECTED ARTICLES AND PAPERS
- Khozin, Sean and Roger M. Stein (2022) “A Novel Approach to Reducing Disparities in Health Outcomes by Enhancing Interpretation of Cancer Clinical Trials for Underrepresented Patient Groups.” Biochimica et Biophysica Acta – Reviews on Cancer. Volume 1877, Issue 6, November.
- Stein, Roger M. and David L. (2022) “Interpreting global variations in the toll of COVID-19: The case for context and nuance in hypothesis generation and testing.” Frontiers in Public Health, 10 (1010011).
- Stein, Roger M. (2022) “General bounds on the area under the receiver operating characteristic curve and other performance measures when only a single sensitivity and specificity point is known” Journal of Risk Model Validation 16(2), 1–22.
- Stein, Roger M. (2022) “Using RBOs and megafunds to hedge longevity risk and specialty drug costs.” The Journal of Alternative Investments: 24 (3), pg. 99-123.
- Stein, Roger M. (2020),”Drawing the line for risk stratifications: Designing return-to-work policies that consider diagnostic error, costs, benefits and COVID-19.” Technical Paper. [LINK TO ONLINE SOFTWARE]
- Katz, David, L, Roger M. Stein, Wesley Pegdan, Maria Chikina, Dina Aronson (2020). “COVID-19 Risk Modeling Options, Conclusions & Concerns to Date.” THI White Paper Series/NYU Working Paper.
- Stein, Roger M, Daniel J. Arbess, Michael Kanef, David L. Katz, Timothy S. Walsh (2020) “A Total-Harm-Minimization Framework for Developing Expedient and Low-Risk Return-to-the-Workforce Policies During the COVID-19 Pandemic.” THI White Paper Series.
- Stein, Roger M, Daniel J. Arbess, Michael Kanef, David L. Katz, Timothy S. Walsh (2020) “An Example Outline for Applying the Total-Harm-Minimization Framework for Developing a Return-to-the-Workforce Policy.” THI White Paper Series.
- Hull, John C., Andrew W. Lo and Roger M. Stein (2020) “Funding Long Shots.” . (Earlier version available as a working paper at SSRN: https://ssrn.com/abstract=3058472). (Markowitz Award winning paper)
- Dhar, V. and R. M. Stein (2019) “Complete and Incomplete FinTech Platforms.” Journal Of Investment Management, 16(2), pp. 2-16.
- Dhar, V. and Stein R. M. (2017) “Economic and Business Dimensions on FinTech Platforms and Strategy.”, Communications of the ACM, 60, 10, October, pp. 32-35.
- Dhar, V. and R. M. Stein, (2017) “Your future financial adviser could be a robot.“(OpEd), MarketWatch: The Wall Street Journal Digital Network. March 13.
- Dhar, V. and R. M. Stein (2016) “FinTech Platforms and Strategy.” MIT Sloan Research Paper No. 5183-16. Available at SSRN: https://ssrn.com/abstract=2892098
- Stein, R. M. (2016). “This investment idea could help cure diseases and make you money.” (OpEd), MarketWatch: The Wall Street Journal Digital Network. June 6.
- Lo, A. W. and R. M. Stein (2016). “TRC networks and Systemic Risk.” Journal of Alternative Investments, 18, 4., Spring, pp. 52-67. (Draft version: MIT Sloan School Working Paper 5153-15.)
- Getmansky Sherman, M. and R. M. Stein (2016), “Systemic Risk and Alternative Investments: A Summary of Selections from the State of the Art.” Journal of Alternative Investments, 18, 1, Spring, pp. 6-12.
- Stein, R. M. (2016), “Real Decision Support for Health Insurance Policy Selection.” Big Data, 4, 1, pp. 14-24.
- Stein, R. M. (2016), “A simple hedge for longevity risk and reimbursement risk using research-backed obligations.” MIT Sloan School Working Paper 5165-16. (SSRN: http://ssrn.com/abstract=2736993).
- Stein, R. M. (2015), “Evaluating discrete choice prediction models when the evaluation data is corrupted: analytic results and bias corrections for the area under the ROC” Data Mining and Knowledge Discovery. pp 1-15.
- Stein, R. M. (2015), “How to keep your data team happy, productive, innovative“, The Boston Globe, Saturday, August 22. (reprinted from Sloan Management Review)
- Stein, R. M. (2015), “How to Build (and Keep) a World-Class Data Science Team.” Sloan Management Review, (post).
- Stein, R. M. (2015). “Why managing data scientists is different.” Sloan Management Review, (post).
- Reyngold, A., K. Shnyra. and R. M. Stein. Reyngold, (2015) “Aggregate and firm-level measures of systemic risk from a structural model of default.” Journal of Alternative Investments, Spring, pp. 58-78. (Working paper version: [2013] Aggregate and firm-level measures of systemic risk from a structural model of default”. MIT LFE Working Paper LFE-0501-13.)
- Lo, A. W. and R. M. Stein (2014) “To Cure Cancer, Provide a Profit Motive“, Scientific American, March (Forum).
- Stein, R. M. (2014). “Review of The Half Life of Facts.” Quantitative Finance. 14, 10.
- Stein, R. M. (2014). “How bad data can lead to good decisions (sometimes).” (blog post) Computerworld, February.
- Fagnan, D. E., Gromatzky, A. A., Stein, R. M., Fernandez, J-M and Lo, A. W., (2013) “Financing drug discovery for orphan diseases“, Drug Discovery Today (December). [DOWNLOAD SOFTWARE]
- Stein, R. M. 2013. “Validating risk models when the validation data is corrupted: Analytic results and bias corrections.” MIT LFE Working Paper LFE-0701-13.
- Stein, R. M., 2013. “Aligning models and data for systemic risk analysis,” in The Handbook of Systemic Risk. Oxford University Press. pp. 37-65.
- Fernandez, J-M, D. Fagnan, A. W. Lo & R. M. Stein. 2013. “Can Financial Engineering Cure Cancer?”American Economic Review. 103. 3.
- Bohn, J. R. and R. M. Stein. 2013. “Approaches to improving a bank’s share value using credit-portfolio management and credit-transfer pricing.“ Journal of Investment Management. 11. 2.
- Das, A. and R. M. Stein. 2013. “Differences in tranching methods: Some results and implications.” Credit Securitisations and Derivatives.Wiley.
- Stein, R. M. 2013. “The role of stress testing in credit risk management.” Journal of Investment Management. 10. 4.
- Fernandez, JM, R. M. Stein and A. W. Lo. 2012. “Commercializing biomedical research through securitization techniques,” nature biotechnology. 30. 10. [DOWNLOAD SOFTWARE] [FAQs]
- Stein, R. M. 2012. “Review of Red Blooded Risk.” Quantitative Finance. 12. 8.
- Stein, R. M. May 2011. “The case for multiple approaches to retail credit portfolio analysis.” Working Paper. Moody’s Research Labs. New York.
- Stein, R. M., A. Das, Y. Ding and S. Chinhalkar. 2011. “Mortgage Portfolio Analyzer: A Quasi-Structural Model of Mortgage Portfolio Losses” Working Paper. Moody’s Research Labs. New York.
- Chinhalkar, S. and R. M. Stein. 2010. “Comparing Loan-level and Pool-level Mortgage Portfolio Analysis.” Working Paper. Moody’s Research Labs. New York.
- Das, A. and R. M. Stein. 2009. “The mathematics of tranching.” Working Paper. Moody’s Research Labs. New York.
- Das, A. and R. M. Stein, 2009. “Underwriting Versus Economy: A New Approach Decomposing Mortgage Losses.” Journal of Credit Risk. 5. 2.
- Stein, R. M. 2007. “Benchmarking default prediction models: pitfalls and remedies in model validation.” Journal of Risk Model Validation. 1.
- Kumara, R., R. M. Stein, and I. Assersohn. 2006. “Assessing a knowledge-based approach to commercial loan underwriting.” Expert Systems with Applications. 30, 3, pp. 507-518.
- Stein, R. M. 2006. “Are the probabilities right? Dependent defaults and the number of observations required to test for default rate accuracy.” Journal of Investment Management. 4. pp. 61-71.
- Stein, R. M. 2006. “Evidence on the incompleteness of Merton-type structural models for default prediction.” Working paper. New York: MKMV.
- Dwyer, D. W. and R. M. Stein. 2005. “Inferring the Default Rate in a Population by Comparing Two Incomplete Default Databases.” Journal of Banking and Finance. 30. pp. 797-810.
- Gupton, G. M., and R. M. Stein. 2005. “LossCalc V2.0: Dynamic prediction of LGD.” New York: Moody’s KMV.
- Stein, R. M. 2005. “The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing.” Journal of Banking and Finance 29. pp. 1213-1236.
- Stein, R. M., and F. Jordao. 2005. “Better predictions of income volatility using a structural default model.” Working paper. Moody’s. New York.
- Dwyer, D. W., and R. M. Stein. 2004. “Moody’s KMV RiskCalc v. 3.1 technical document.” New York. Moody’s KMV.
- Dwyer, D. W., A. E. Kocagil and R. M. Stein. April 2004. “The Moody’s KMV EDF RiskCalc v.3.1 Model Next-Generation Technology for Predicting Private Firm Credit Risk.” New York. Moody’s KMV.
- Sobehart, J. R., S. C. Keenan and R. M. Stein. 2003. “Complexities and Validation of Default Risk Models.” in Frontiers in Credit Risk. G. Gaeta, ed. Wiley.
- Stein, R. M. 2003.”Power, profitability and prices: Why powerful models increase profits and how to set a lending cutoff if you must.” Technical Report. New York. MKMV.
- Stein, Roger M. 2003. “Are the probabilities right? A first approximation to the lower bound on the number of observations required to test for default rate accuracy.” Technical Report. New York. MKMV.
- Stein, R. M., and F. Jordao. 2003. “What is a more powerful model worth?” New York. Moody’s KMV.
- Stein, R. M., A. E. Kocagil, J. Bohn and J. Akhavain. 2003. “Systematic and Idiosyncratic Risk in Middle-Market Default Prediction: A Study of the Performance of the RiskCalc and PFM Models.” Moody’s KMV.
- Gupton, G. M., and R. M. Stein. 2002. “LossCalc™: Moody’s model for predicting loss given default (LGD).” New York. Moody’s Investors Service.
- Kocagil, A. E.; A. Reyngold, R. M. Stein and E. Ibarra. 2002. “Moody’s RiskCalc Model for Privately-Held US Banks.” New York. Moody’s KMV.
- Stein, R. M. 2002. “Benchmarking default prediction models: Pitfalls and remedies in model validation.” Technical Report #030124. New York. Moody’s KMV.
- Gupton, G. M., and R. M. Stein. 2001. “A matter of perspective. “ Credit.
- Sobehart, J., S. Keenan, and R. Stein. 2000. “Validation methodologies for default risk models.” Credit.
- Sobehart, J. R., S. C. Keenan, and R. M. Stein. 2000. “Benchmarking quantitative default Risk Models: A validation methodology.” New York. Moody’s Risk Management Services.
- Sobehart, J. R., R. Stein, Li L. and Mikityanskaya. 2000. “Moody’s public firm risk model: A hybrid approach to modeling default risk.” Moody’s Investors Special Comment. New York. Moody’s Investors Service, New York.
- Stein, R. M. 1999. “An Almost Assumption Free Methodology for Evaluating Financial Trading Models Using Large Scale Simulation with Applications to Risk Control.” Working Paper #IS-99-015. Working Paper Series. Stern School of Business.
- Stein, R. M. 1999. “Pattern discovery and simulation methods for evaluation risk control strategies for futures trading systems.” New York. New York University.
- Dhar, V. and R. Stein. 1998. “Finding Robust & Usable Models with Data Mining: Examples from Finance,” PCIA. 12. 5.
- Padmanabhan, B, B., S. Sen, A. Tuzhilin, A., N. White, R. Stein 1998. “The Identification and Satisfaction of Consumer Analysis-Driven Information Needs of Marketers on the WWW.” European Journal of Marketing. 32. 7/8.
- Dhar, V. and R. Stein. 1998. “Neural Networks in Finance: The Importance of methodology Over Technology.” PCIA, 12, 3.
- Stein, R. and R. Bernard, 1998. “Data Mining the Future: Genetic Discovery of Good Trading Rules in Agent Based Financial Market Simulations” in Proceedings of the IEEE / IAFE / INFORMS 1998 Conference on Computational Intelligence for Financial Engineering. March 29-31. New York.
- Stein, R., S. Schocken, and V. Dhar. 1998. “A Practical Methodology for Applying Neural Networks to Business Decision Problems.” Encyclopedia of Computer Science and Technology. 38. Marcel Dekker Publishing.
- Stein, R., 1997. “A Data Driven Approach to Determining the Rules of Price Movement in an Order Market Simulation.” Working Paper #IS-97-17. Stern School of Business.
- Stein R., and R. Bernard. 1997. “An Adaptive Simulation Approach for Investigating Information Processing Structures in Organizations.” INFORMS’97: Computational and Mathematical Organizational Theory Workshop. May 3-4. San Diego, CA.
- Padmanabhan, B., S. Sen, A. Tuzhilin, A., N. White, R. Stein. 1996. “Analysis of Web Site Usage Data: How Much Can We Learn About the Consumer From Web Logfiles?” Working Paper #IS-96-18. Working Paper Series. Stern School of Business.
- Stein, R. 1996. “Does Organizational Structure Matter? An Adaptive Simulation Approach for Investigating Information Processing Structures in Organizations” Working Paper #IS-96-4. Working Paper Series. Stern School of Business.
- Stein, R. and V. Dhar, 1994. “Satisfying Customers: Intelligently Scheduling High Volume Service Requests.” AI Expert. December.
- Stein, R. and V. Dhar. 1993. “Maximization of Organizational Uptime Using an Interactive Genetic-Fuzzy Scheduling and Support System.” Working Paper #IS-93-27, Working Paper Series, Stern School of Business.
- Stein, R. 1993. “The Dempster-Shafer Theory of Evidential Reasoning.” AI Expert. August.
- Stein, R. 1993. Preprocessing Data for Neural Networks.” AI Expert. March (reprinted Neural Network Special Report).
- Stein, R., 1993. “Selecting Data for Neural Networks.” AI Expert. February (reprinted July, Neural Network Special Report).