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Welcome to the ROV QUANTITATIVE DATA MINER (QDM) Software, brought to you by Real Options Valuation, Inc. This software application is used for analytical data crunching and modeling. It runs in the Windows environment and can be used to link to databases to download and run large datasets at extreme high speeds. This software comes in three separate modules. The first module is the main ROV Quantitative Data Miner (QDM) with about 150 methods for running Data Modeling, Analytics, Forecasting, Simulation, Data Computation, and Charts. The second module is the ROV Optimizer for running static, dynamic, and stochastic optimization at high speeds on a large number of decision variables. The third module is the ROV Valuator, with over 600 closed-form, partial differential, lattice and analytical models. The following provides some highlights of our software application, and please contact us for a live or web demonstration of the power and functionality of our tool.

The software’s minimum requirements are:
Dual core processor or later
Windows XP, Vista or Windows 7 (MAC OS requires a Windows emulator such as Parallels or VM)
100MB free space and 1GB RAM minimum (2–4GB recommended)
Administrative rights to install software

A permanent or trial license is required to run the software for the first time. To obtain a trial or full corporate license, contact Real Options Valuation, Inc., at admin@realoptionsvaluation.com


The following lists the new enhancements and tools available in the latest version of ROV's QDM - Quantitative Data Miner software.


1. Autoeconometrics (Detailed)
2. Autoeconometrics (Quick)
3. Custom Econometric Model
4. Deseasonalize
5. Limited Dependent Variables (Logit)
6. Limited Dependent Variables (Probit)
7. Limited Dependent Variables (Tobit)
8. Linear Regression
9. Nonlinear Regression
10. Principal Component Analysis
11. Stepwise Regression (Correlation)
12. Stepwise Regression (Forward)
13. Stepwise Regression (Backward)
14. Stepwise Regression (Forward-Backward)
15. ROV Compiler EXE Model


16. ANOVA: Randomized Blocks Multiple Treatments
17. ANOVA: Single Factor Multiple Treatments
18. ANOVA: Two Way Analysis
19. Autocorrelation & Partial Autocorrelation
20. Correlation (Linear, Nonlinear)
21. Data Descriptive Statistics
22. Distributional Fitting
23. Heteroskedasticity
24. Nonparametric: Chi-Square Goodness of Fit
25. Nonparametric: Chi-Square Independence
26. Nonparametric: Chi-Square Population Variance
27. Nonparametric: Friedman’s Test
28. Nonparametric: Kruskal-Wallis Test
29. Nonparametric: Lilliefors Test
30. Nonparametric: Runs Test
31. Nonparametric: Wilcoxon Signed-Rank (One Var)
32. Nonparametric: Wilcoxon Signed-Rank (Two Var)
33. Parametric: One Variable (T) Mean
34. Parametric: One Variable (Z) Mean
35. Parametric: One Variable (Z) Proportion
36. Parametric: Two Variable (T) Dependent Means
37. Parametric: Two Variable (T) Independent Equal Variance
38. Parametric: Two Variable (T) Independent Unequal Variance
39. Parametric: Two Variable (Z) Independent Means
40. Parametric: Two Variable (Z) Independent Proportions
41. Parametric: Two Variable (F) Variances
42. Seasonality
43. Segmentation Clustering
44. Structural Break
45. ROV Compiler EXE Model


47. Auto ARIMA
48. Auto Econometrics (Quick)
49. Auto Econometrics (Detailed)
50. Basic Econometrics
51. Cubic Spline
52. Exponential J Curve
53. Linear Interpolation
54. Logistic S Curve
55. Markov Chain
56. Multiple Regression (Linear)
57. Multiple Regression (Nonlinear)
58. Stochastic Processes (Geometric Brownian Motion)
59. Stochastic Processes (Exponential Brownian Motion)
60. Stochastic Processes (Jump Diffusion)
61. Stochastic Processes (Mean Reversion)
62. Stochastic Processes (Mean Reversion with Jump Diffusion)
63. Time-Series Analysis (Auto)
64. Time-Series Analysis (Single Moving Average)
65. Time-Series Analysis (Double Moving Average)
66. Time-Series Analysis (Single Exponential Smoothing)
67. Time-Series Analysis (Double Exponential Smoothing)
68. Time-Series Analysis (Seasonal Additive)
69. Time-Series Analysis (Seasonal Multiplicative)
70. Time-Series Analysis (Holt-Winter’s Additive)
71. Time-Series Analysis (Holt-Winter’s Multiplicative)
72. Trend Line (Linear)
73. Trend Line (Exponential)
74. Trend Line (Logarithmic)
75. Trend Line (Moving Average)
76. Trend Line (Polynomial)
77. Trend Line (Power)
78. Trend Line (Linear Detrended)
79. Trend Line (Difference Detrended)
80. Trend Line (Exponential Detrended)
81. Trend Line (Logarithmic Detrended)
82. Trend Line (Moving Average Detrended)
83. Trend Line (Polynomial Detrended)
84. Trend Line (Power Detrended)
85. Trend Line (Rate Detrended)
86. Trend Line (Static Mean Detrended)
87. Trend Line (Static Median Detrended)
88. Volatility: Log Returns Approach
89. Volatility: GARCH
90. Volatility: GARCH-M
91. Volatility: EGARCH
92. Volatility: EGARCH-T
93. Volatility: GJR GARCH
94. Volatility: GJR TGARCH
95. Volatility: TGARCH
96. Volatility: TGARCH-M
97. Yield Curve (Bliss)
98. Yield Curve (Nelson-Siegel)


99. Standard 2D Line
100. Standard 3D Line
101. Standard 2D Bar
102. Standard 3D Bar
103. Standard 2D Area
104. Standard 3D Area
105. Standard 2D Point
106. Standard 3D Point
107. Standard 2D Scatter
108. Standard 3D Scatter
109. Control Chart: P
110. Control Chart: NP
111. Control Chart: U
112. Control Chart: C
113. Control Chart: X
114. Control Chart: R
115. Control Chart: XMR


116. Bernoulli Distribution
117. Beta Distribution
118. Binomial Distribution
119. Chi-Square Distribution
120. Discrete Uniform Distribution
121. Exponential Distribution
122. F Distribution
123. Gamma Distribution
124. Gumbel Min Distribution
125. Gumbel Max Distribution
126. Logistic Distribution
127. Lognormal Distribution
128. Normal Distribution
129. Pareto Distribution
130. Poisson Distribution
131. Rayleigh Distribution
132. Standard Normal Distribution
133. T Distribution
134. Triangular Distribution
135. Uniform Distribution
136. Weibull Distribution


137. Static Optimization
138. Dynamic Optimization
139. Stochastic Optimization


140. Absolute Values
141. Average
142. Correlation
143. Count
144. Covariance
145. Difference
146. GARCH
147. Lag
148. Lead
149. LN
150. Log
151. Max
152. Median
153. Min
154. Mode
155. Power
156. Rank Ascending
157. Rank Descending
158. Relative Returns
159. Relative LN Returns
160. Semi-Standard Deviation (Upper)
161. Semi-Standard Deviation (Lower)
162. Standard Deviation (Sample)
163. Standard Deviation (Population)
164. Sum
165. Variance (Sample)
166. Variance (Population)
167. Volatility


How do you make critical business decisions? Do you consider the risks of your projects and decisions, or are you more focused on returns? Do you have a hard time trying to understand what risk is, let alone quantifying risk? Well, our Risk Simulator software will help you identify, quantify, and value risk in your projects and decisions.


The QDM - Quantitative Data Miner can be downloaded immediately from our website with a default 10-day trial license for our existing clients. Please send us an e-mail requesting a secure link to download the trial software. Our philosophy is you get to try before you buy. Once you use it, we are convinced you will fall in love with the simplicity and the power of the tool, and it will become an indispensible part of your modeling toolbox. We also have academic licenses for full time professors teaching risk analysis (and their students) or other associated courses using QDM or our other software products. Contact admin@realoptionsvaluation.com for details.


Advanced analytical tools such as the QDM - Quantitative Data Miner software are built to be easy to use but may get the analyst in trouble if used inappropriately. Sufficient theoretical understanding coupled with pragmatic application experience is vital; therefore, training is critical.

Our Risk Analysis course is a two-day seminar focused on hands-on computer-based software training, with topics covering the basics of risk and uncertainty, using Monte Carlo simulation (pitfalls and due diligence), and all of the detailed methods in forecasting and optimization.

We also have a Real Options for Analysts course for the analysts who want to immediately begin applying strategic real options in their work, but lack the hands-on experience with real options analytics and modeling. This two-day course covers how to set up real options models, apply real options, and solve real options problems using simulation, closed-form mathematics, as well as binomial and multinomial lattices using the Real Options SLS software.

The Certified in Risk Management (CRM) seminar is a four-day hands-on class that covers the materials on our Risk Analysis and Real Options for Analysts courses and is geared towards the CRM certification provided by the International Institute of Professional Education and Research (AACSB member and eligible for 30 PDU credits with the PMI).

Our Risk Analysis for Senior Managers is a one-day course specially designed for senior executives, where we will review case studies in risk management from 3M, Airbus, Boeing, GE, and many others. It provides an executive overview of risk analysis, strategic real options, portfolio optimization, forecasting, and risk concepts without the technical details.

Also available are other customized decision, valuation, and risk analysis courses with an emphasis on on-site trainings customized to your firm’s exact needs based on your business cases and models. Consulting services are available, including the framing of risk analysis problems, simulation, forecasting, real options, risk analytics, model building, decision analysis, integrated OEM, and software customization.


Dr. Johnathan Mun is the software’s creator and teaches the Risk Analysis, Real Options for Analysts, Risk Analysis for Managers, CRM, and other courses. He has consulted for many Fortune 500 firms (from 3M, Airbus, and Boeing to GE and Motorola) and the government (Department of Defense, State and Federal Agencies) on risk analysis, valuation, and real options, and has written a number of books on the topic, including Real Options Analysis: Tools and Techniques, 1st and 2nd Edition (Wiley Finance, 2002, 2005); Real Options Analysis Course: Business Cases (Wiley Finance, 2003); Applied Risk Analysis: Moving Beyond Uncertainty in Business (Wiley, 2003); Valuing Employee Stock Options Under 2004 FAS 123 (Wiley Finance, 2004); Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting and Optimization (Wiley, 2006); Advanced Analytical Models: 800 Functions and 300 Models from Basel II to Wall Street and Beyond (Wiley 2008); The Banker’s Handbook on Credit Risk: Implementing Basel II (Elsevier Academic Press 2008); and others. He is the founder and CEO of Real Options Valuation, Inc., and is responsible for the development of analytical software products, consulting, and training services. He was formerly Vice President of Analytics at Decisioneering, Inc. (Oracle), and was a Consulting Manager in KPMG’s Global Financial Strategies practice. Before KPMG, he was head of financial forecasting for Viking, Inc. (an FDX/FedEx Company). Dr. Mun is also a full professor at the U.S. Naval Postgraduate School, a professor at the University of Applied Sciences and Swiss School of Management (Zurich and Frankfurt), and has held other adjunct professorships at various universities. He has a Ph.D. in finance and economics, an MBA in business administration, an M.S. in the area of management science, and a BS in applied sciences. He is certified in Financial Risk Management (FRM), Certified in Financial Consulting (CFC), and Certified in Risk Management (CRM).