• 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

^* 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

1. 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.
2. 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).
3. 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.
4. 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.
5. 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] 
6. 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.
7. 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.
8. 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.
9. 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)
10. Dhar, V. and R. M. Stein (2019) “Complete and Incomplete FinTech Platforms.” Journal Of Investment Management, 16(2), pp. 2-16.
11. 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.
12. 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.
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
14. 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.
15. 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.)
16. 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.
17. Stein, R. M. (2016), “Real Decision Support for Health Insurance Policy Selection.” Big Data, 4, 1, pp. 14-24.
18. 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).
19. 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.
20. Stein, R. M. (2015), “How to keep your data team happy, productive, innovative“, The Boston Globe, Saturday, August 22. (reprinted from Sloan Management Review)
21. Stein, R. M. (2015), “How to Build (and Keep) a World-Class Data Science Team.” Sloan Management Review, (post).
22. Stein, R. M. (2015).  “Why managing data scientists is different.” Sloan Management Review, (post).
23. 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.)
24. Lo, A. W. and R. M. Stein (2014) “To Cure Cancer, Provide a Profit Motive“, Scientific American,  March (Forum).
25. Stein, R. M. (2014). “Review of The Half Life of Facts.” Quantitative Finance. 14, 10.
26. Stein, R. M. (2014).  “How bad data can lead to good decisions (sometimes).” (blog post) Computerworld, February.
27. 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]
28. 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.
29. Stein, R. M., 2013. “Aligning models and data for systemic risk analysis,” in The Handbook of Systemic Risk. Oxford University Press. pp. 37-65.
30. Fernandez, J-M, D. Fagnan, A. W. Lo &  R. M. Stein. 2013.  “Can Financial Engineering Cure Cancer?”American Economic Review. 103. 3.
31. 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.
32. Das, A. and R. M. Stein. 2013.  “Differences in tranching methods:  Some results and implications.” Credit Securitisations and Derivatives.Wiley.
33. Stein, R. M. 2013.  “The role of stress testing in credit risk management.” Journal of Investment Management. 10. 4.
34. Fernandez, JM, R. M. Stein and A. W. Lo. 2012. “Commercializing biomedical research through securitization techniques,” nature biotechnology. 30. 10. [DOWNLOAD SOFTWARE] [FAQs]
35. Stein, R. M. 2012. “Review of Red Blooded Risk.” Quantitative Finance. 12. 8.
36. Stein, R. M. May 2011.  “The case for multiple approaches to retail credit portfolio analysis.” Working Paper. Moody’s Research Labs. New York.
37. 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.
38. Chinhalkar, S. and R. M. Stein. 2010.  “Comparing Loan-level and Pool-level Mortgage Portfolio Analysis.” Working Paper. Moody’s Research Labs. New York.
39. Das, A. and R. M. Stein. 2009.  “The mathematics of tranching.” Working Paper. Moody’s Research Labs. New York.
40. Das, A. and R. M. Stein, 2009.  “Underwriting Versus Economy:  A New Approach Decomposing Mortgage Losses.” Journal of Credit Risk. 5. 2.
41. Stein, R. M.  2007.  “Benchmarking default prediction models: pitfalls and remedies in model validation.”  Journal of Risk Model Validation. 1.
42. 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.
43. 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.
44. Stein, R. M. 2006.  “Evidence on the incompleteness of Merton-type structural models for default prediction.”  Working paper.  New York:  MKMV.
45. 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.
46. Gupton, G. M., and R. M. Stein.  2005.  “LossCalc V2.0:  Dynamic prediction of LGD.”  New York: Moody’s KMV.
47. 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.
48. Stein, R. M., and F. Jordao.  2005.  “Better predictions of income volatility using a structural default model.”  Working paper.  Moody’s. New York.
49. Dwyer, D. W., and R. M. Stein. 2004.  “Moody’s KMV RiskCalc v. 3.1 technical document.”  New York.  Moody’s KMV.
50. 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.
51. 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.
52. 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.
53. 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.
54. Stein, R. M., and F. Jordao.  2003.  “What is a more powerful model worth?” New York. Moody’s KMV.
55. 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.
56. Gupton, G. M., and R. M. Stein. 2002.  “LossCalc™: Moody’s model for predicting loss given default (LGD).”  New York.  Moody’s Investors Service.
57. 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.
58. Stein, R. M.  2002.  “Benchmarking default prediction models: Pitfalls and remedies in model validation.”  Technical Report #030124.  New York.  Moody’s KMV.
59. Gupton, G. M., and R. M. Stein. 2001.  “A matter of perspective. “ Credit.
60. Sobehart, J.,  S. Keenan, and R. Stein.  2000.  “Validation methodologies for default risk models.”  Credit.
61. 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.
62. 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.
63. 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.
64. Stein, R. M.  1999.  “Pattern discovery and simulation methods for evaluation risk control strategies for futures trading systems.”  New York.  New York University.
65. Dhar, V. and R. Stein. 1998. “Finding Robust & Usable Models with Data Mining: Examples from Finance,” PCIA. 12. 5.
66. 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.
67. Dhar, V. and R. Stein. 1998.  “Neural Networks in Finance: The Importance of methodology Over Technology.” PCIA, 12, 3.
68. 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.
69. 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.
70. 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.
71. 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.
72. 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.
73. 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.
74. Stein, R. and V. Dhar, 1994. “Satisfying Customers: Intelligently Scheduling High Volume Service Requests.” AI Expert. December.
75. 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.
76. Stein, R. 1993. “The Dempster-Shafer Theory of Evidential Reasoning.” AI Expert. August.
77. Stein, R. 1993. Preprocessing Data for Neural Networks.” AI Expert. March (reprinted Neural Network Special Report).
78. Stein, R., 1993. “Selecting Data for Neural Networks.” AI Expert. February (reprinted July, Neural Network  Special Report).