SELECTED PUBLICATIONS BOOKS:

  1. Bohn, Jeffrey R. and Roger M. Stein.  2009.  Active Credit Portfolio Management in Practice.  Hoboken, New Jersey:  John Wiley & Sons, Inc.
  2. Dhar, Vasant and Roger Stein. 1997.  Seven Methods for Transforming Corporate Data into Business Intelligence, Prentice-Hall.

ARTICLES, BOOK CHAPTERS AND WORKING PAPERS:

  1. Reyngold, A., K. Shnyra. and R. M. Stein. Reyngold, “Aggregate and firm-level measures of systemic risk from a structural model of default.”  Forthcoming in Journal of Alternative Investments. (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.)
  2. Lo, A. W. and R. M. Stein (2014) “To Cure Cancer, Provide a Profit Motive“, Scientific American,  March (Forum).
  3. Stein, R. M. (2014). “Review of The Half Life of Facts.” Quantitative Finance. 14, 10.
  4. Stein, R. M. (2014).  “How bad data can lead to good decisions (sometimes).” (blog post) Computerworld, February.
  5. 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).
  6. 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.
  7. Lo, A. W. and R. M. Stein. 2013. “TRC networks and visualizations: Combining credit risk measures and network analysis to characterize systemic risk”. (draft)
  8. Stein, R. M., 2013. “Aligning models and data for systemic risk analysis,” in The Handbook of Systemic Risk. Oxford University Press. pp. 37-65.
  9. Fernandez, J-M, D. Fagnan, A. W. Lo &  R. M. Stein. 2013.  “Can Financial Engineering Cure Cancer?American Economic Review. 103. 3.
  10. 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 Management11. 2.
  11. Das, A. and R. M. Stein. 2013.  “Differences in tranching methods:  Some results and implications.” Credit Securitisations and Derivatives.Wiley.
  12. Stein, R. M. 2013.  “The role of stress testing in credit risk management.” Journal of Investment Management. 10. 4.
  13. Fernandez, JM, R. M. Stein and A. W. Lo. 2012. “Commercializing biomedical research through securitization techniques,” nature biotechnology. 30. 10. [DOWNLOAD SOFTWARE] [FAQs]
  14. Stein, R. M. 2012. “Review of Red Blooded Risk.” Quantitative Finance. 12. 8.
  15. Stein, R. M. May 2011.  “The case for multiple approaches to retail credit portfolio analysis.” Working Paper. Moody’s Research Labs. New York.
  16. Stein, R. M., A. Das, Y. Ding and S. ****halkar. 2011.  “Mortgage Portfolio Analyzer: A Quasi-Structural Model of Mortgage Portfolio Losses” Working Paper. Moody’s Research Labs. New York.
  17. ****halkar, S. and R. M. Stein. 2010.  “Comparing Loan-level and Pool-level Mortgage Portfolio Analysis.” Working Paper. Moody’s Research Labs. New York.
  18. Das, A. and R. M. Stein. 2009.  “The mathematics of tranching.” Working Paper. Moody’s Research Labs. New York.
  19. Das, A. and R. M. Stein, 2009.  “Underwriting Versus Economy:  A New Approach Decomposing Mortgage Losses.” Journal of Credit Risk. 5. 2.
  20. Stein, R. M.  2007.  “Benchmarking default prediction models: pitfalls and remedies in model validation.”  Journal of Risk Model Validation. 1.
  21. 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.
  22. 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 Management4. pp. 61-71.
  23. Stein, R. M. 2006.  “Evidence on the incompleteness of Merton-type structural models for default prediction.”  Working paper.  New York:  MKMV.
  24. 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.
  25. Gupton, G. M., and R. M. Stein.  2005.  “LossCalc V2.0:  Dynamic prediction of LGD.”  New York: Moody’s KMV.
  26. 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.
  27. Stein, R. M., and F. Jordao.  2005.  “Better predictions of income volatility using a structural default model.”  Working paper.  Moody’s. New York.
  28. Dwyer, D. W., and R. M. Stein. 2004.  “Moody’s KMV RiskCalc v. 3.1 technical document.”  New York.  Moody’s KMV.
  29. Dwyer, D. W. 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.
  30. 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.
  31. 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.
  32. 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.
  33. Stein, R. M., and F. Jordao.  2003.  “What is a more powerful model worth?” New York. Moody’s KMV.
  34. 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.
  35. Gupton, G. M., and R. M. Stein. 2002.  “LossCalc™: Moody’s model for predicting loss given default (LGD).”  New York.  Moody’s Investors Service.
  36. 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.
  37. Stein, R. M.  2002.  “Benchmarking default prediction models: Pitfalls and remedies in model validation.”  Technical Report #030124.  New York.  Moody’s KMV.
  38. Gupton, G. M., and R. M. Stein. 2001.  “A matter of perspective. “ Credit.
  39. Sobehart, J.,  S. Keenan, and R. Stein.  2000.  “Validation methodologies for default risk models.”  Credit.
  40. Sobehart, J. R., S. C. Keenan, and R. M. Stein. 2000.  “Benchmarking quantitative default Ris Models:  A validation methodology.” New York.  Moody’s Risk Management Services.
  41. 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.
  42. 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.
  43. Stein, R. M.  1999.  “Pattern discovery and simulation methods for evaluation risk control strategies for futures trading systems.”  New York.  New York University.
  44. Dhar, V. and R. Stein. 1998. “Finding Robust & Usable Models with Data Mining: Examples from Finance,” PCIA. 12. 5.
  45. 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.
  46. Dhar, V. and R. Stein. 1998.  “Neural Networks in Finance: The Importance of methodology Over Technology.” PCIA, 12, 3.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. Stein, R. and V. Dhar, 1994. “Satisfying Customers: Intelligently Scheduling High Volume Service Requests.” AI Expert. December.
  54. 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.
  55. Stein, R. 1993. “The Dempster-Shafer Theory of Evidential Reasoning.” AI Expert. August.
  56. Stein, R. 1993. Preprocessing Data for Neural Networks.” AI Expert. March (reprinted Neural Network Special Report).
  57. Stein, R., 1993. “Selecting Data for Neural Networks.” AI Expert. February (reprinted July, Neural Network  Special Report).