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Building Predictive Models with the DecisionTools Suite

This webinar will present how to use the DecisionTool Suite to build predictive models based on historical data.
Both public and private databases can provide important insights for forecasting and planning, for example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.
We will present 3 tools from the DecisionTools Suite: @RISK, StatTools, and NeuralTools. We'll then apply them to the tasks of understanding historical data and forecasting their future value - including the uncertainty of the underlying variables and the error level of the models.
Recorded May 6 2021 72 mins
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Presented by
Rafael Hartke, CEO of Imagine Risks and Analytics
Presentation preview: Building Predictive Models with the DecisionTools Suite
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  • Análisis De Riesgo Estadístico De Plagas Jul 23 2021 3:00 pm UTC 75 mins
    Martha Elva Ramírez Guzmán, Ph.D Statistics, Colegio de Postgraduados
    El proceso de análisis de riesgo de plagas (ARP), es un metodología de prevención que se utiliza en los procesos de importación y exportación de productos agrícolas, para minimizar la probabilidad del riesgo de introducción de plagas como hongos, bacterias, malezas, nemátodos y virus, asociados a semillas, granos, frutas y hortalizas, material vegetal propagativo, flor cortada y follaje fresco. Por lo tanto, como sistema de prevención es fundamental, que Ministerios de Agricultura, académicos e investigadores, conozcan y manejen @RISK como una herramienta estadística amigable que les permita a estimar la probabilidad de la introducción, dispersión y establecimiento de plagas en su país, así como estimar el impacto económico y social de introducirse la plaga, utilizando miles de simulaciones Monte Carlo, las cuales simulan el conocimiento de expertos, en la introducción de plagas fitosanitarias. Por lo anterior, en esta plática se desarrollará un ejemplo en donde por medio de @RISK, se estima el riesgo fitosanitario de introducción de plagas que podrían causar daños muy importantes en la economía de un país.
  • Extreme Weather Event Implications for Energy Risk Management Jul 15 2021 3:00 pm UTC 75 mins
    Glen Justis, Experience On Demand
    This presentation shares concepts and leading practices for incorporating extreme weather and market events in energy risk management analysis for electric utilities. Examples for applying advanced features of @Risk and the Palisade DecisionTools Suite are provided. Energy risk management processes addressed in the presentation include, but are not limited to:
    • Market price modeling and risk exposure calculations
    • Importance of fuel hedging versus power hedging
    • Credit risk management
    • Regulatory/legislative response considerations
  • Quantifying the Impacts of Strategic and Extreme/Rare Event Risks Jul 7 2021 2:00 pm UTC 75 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    Risk matrices are widely used in risk analysis to account for the effects of external factors or rare/extreme events that may occur within a project. Typical industry cases include models for CAPEX, budgeting, production curves/targets, and schedules.
    We'll present how to use @RISK to move forward from a qualitative risk matrix (usually a matrix listing risks, probabilities, and impacts) towards a quantitative risk valuation, including the simulation of future scenarios, the estimation of proper contingencies for the risks (time, budget, etc.), the effects of mitigation strategies and their costs
  • Risk Analysis in Oil & Gas: Forecasting Production Curves Jun 30 2021 2:00 pm UTC 75 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will present a hands-on example of how to forecast production curves, including the effect of uncertainty and noise in the data and in the model.
    We will use the tools provided by the DecisionTools Suite to:
    (i) Understand historical production data
    (ii) Propose model and estimate parameters for the historical production data
    (iii) Build a forecasting model (including risk)
    (iv) Simulate the production curve using Monte Carlo Simulation
    (v) Analyze the simulation results and discuss how to best present them
  • Estimating the Required Returns of Entrepreneurs Jun 24 2021 3:00 pm UTC 75 mins
    Samuel Mongrut, Ph.D.
    In this talk, we will conduct an introduction to the prospective and risk analysis methodology proposed by Mongrut and Juarez (2018) to assess a new startup. In the process, we will explain how to estimate different possible entrepreneurs' required returns that will change across years depending on the business risk within each scenario. We will also explain the implication of the typical overconfidence bias in the estimation of forward required returns and the importance of designing contingent strategies per scenario.

    Presented by Samuel Mongrut, Ph.D.
  • Modeling Financial Options with Monte Carlo Simulation Jun 23 2021 2:00 pm UTC 75 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    Options are financial derivatives widely used for hedging or leveraging investment positions. They are contracts that give the buyer the right to buy or sell a specific financial asset at a specified price and date, and are typically connected to financial instruments such as stocks and commodity futures.
    Besides the standardized options contracts traded on stock exchanges (calls and puts), options can also be traded over-the-counter (OTC) or embedded into regular contracts between companies - usually including custom clauses, that give rise to exotic options who need to be priced correctly.
    This webinar will present a hands-on example of how to use @RISK to model financial options such as calls, puts, and exotic options, including the simulation of the underlying asset and the effects of uncertainty.
  • Predictive Models with the DTS - Distributions, Time Series, Regression Recorded: Jun 16 2021 70 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will present how to use the DecisionTools Suite to build predictive models based on historical data.
    Both public and private databases can provide important insights for forecasting and planning, for example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.
    We will present 3 tools from the DecisionTools Suite: @RISK, StatTools, and NeuralTools. We'll then apply them to the tasks of understanding historical data and forecasting their future value - including the uncertainty of the underlying variables and the error level of the models.
  • Practical Data Analytics for Project Risk & Contingency Management Recorded: Jun 11 2021 76 mins
    Pedram Danesh-Mand - Director Project Risk Consulting Audit, Assurance & Risk Consulting - KPMG
    The Engineering & Construction sector has definitely learned from its past recessions (and recently COVID-19) and is now well-positioned to drive changes that can help to address its long-standing biggest challenges, productivity and on-time budget delivery. In addition, as infrastructure projects get bigger and more complex with multi-facet interfaces, they are becoming inherently riskier. For the E&C sector, this trend means smarter approaches, connected technologies, and an increase in associated investments are now critical to help firms realize new operational and project delivery efficiencies. New business models and collaborative types of contracts as well as an increase in M&A activities are further accelerating the shift towards data-driven risk management approaches.

    By sharing key results of KPMG’s recent Future-Ready Index survey globally and in Australia – highlighting the scale of challenges ahead of the sector as well as the technologies with the highest adoption rates amongst the top 20% E&C firms, i.e. BIM (86%) and basic data analytics (83%) – in this presentation and through a couple of recent case studies, Pedram will illustrate how E&C companies can begin to embrace data solutions for better project risk management including integrated project controls, connected assurance from project to portfolio level, appropriate governance, predictive analytics, and machine learning while establishing a foundation which is essential for their efficient and effective success in winning and delivering capital projects. While many are failing to fully adopt these and other vital capabilities, placing their future productivity, profitability, and project delivery at risk, the key question is, “Are you, as a firm or as an individual, ready for the future?”
  • Intro to Risk Analysis with Monte Carlo Simulation Recorded: Jun 10 2021 68 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar presents an introduction on using Monte Carlo Simulation to perform Quantitative Risk Analysis.
    We'll present some practical models, including NPV and Cost Estimation models, to show how fast you can get started with probabilistic modeling in Excel with @RISK.
  • Simulation with Historical Data in Monte Carlo Simulation Recorded: Jun 8 2021 66 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    Both public and private databases can provide important insights for forecasting and planning. For example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.
    This webinar will present how to use the @RISK to interpret and use historical data in predictive and risk analysis models. We'll cover Data Visualizer, Distributions Fitting, Special Functions, Time Series Fitting, and Correlations with @RISK.
  • Intro to Forecasting Models using Regression with StatTools Recorded: May 26 2021 71 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will present how to use StatTools (part of the DecisionTools Suite) to build predictive models based on historical data using Multiple Linear Regression.
    Both public and private databases can provide important insights for forecasting and planning, for example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.
    We'll present how to use StatTools to estimate models, evaluate their quality, and improve them - all in a quick and automated way. We'll also discuss how we can interpret the regression results to gain insight about patterns within the historical data and to forecast future values, including the uncertainty of the underlying variables and the error level of the models.
  • Building an Asset Allocation Model with Monte Carlo Simulation Recorded: May 20 2021 64 mins
    Joe DiNunno, Chartered Alternative Investment Analyst
    This webinar will take a step back from our previous webinar that gave viewers a top-level view of the Asset Allocation model built by Insightful Ideas, Inc. and used at FiduciaryVest, LLC. This time around we're going to take a step back and show you how one might build a simpler asset allocation model by going through the steps of how you could use @RISK to build your model. We will cover assumptions, correlations, distributions, combining functions to add an asset class to the model, building a portfolio model, possible output, and if there is time talk a bit about incorporating 'fat tails' in equity distributions. The session will include both slides and some time interacting with @RISK in Excel.
  • Modelling Natural Catastrophe Risks from First Principles Recorded: May 18 2021 64 mins
    Derek Thrumble Managing Partner, Analytics – Gallagher Specialty London, UK
    The example will be based upon major US Hurricanes 1950-2020 to illustrate a model developed to analyze the risk to a portfolio of offshore oil and gas assets and to review and compare the effectiveness of traditional insurance products and alternative products (parametric solutions). The case study will consider an appropriate model for the frequency of events (Poisson or other distributions). A review of damage or vulnerability models (Beta or other distributions) to convert wind speed/intensity into a percentage of value at risk. An illustration of approaches to analyze the impact of insurance structure on the resulting financial losses (Monte Carlo Simulation combining frequency and severity; RISKCOMPOUND). Deriving key metrics to compare options (cost of capital compared to volatility reduction, SOLVER).

    Presented by Derek Thrumble Managing Partner, Analytics – Gallagher Specialty
  • Risk Analysis in Oil & Gas: Forecasting Production Curves Recorded: May 14 2021 69 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will present a hands-on example of how to forecast production curves, including the effect of uncertainty and noise in the data and in the model.
    We will use the tools provided by the DecisionTools Suite to:
    (i) Understand historical production data
    (ii) Propose model and estimate parameters for the historical production data
    (iii) Build a forecasting model (including risk)
    (iv) Simulate the production curve using Monte Carlo Simulation
    (v) Analyze the simulation results and discuss how to best present them
  • Intro to Risk Analysis with Monte Carlo Simulation Recorded: May 12 2021 61 mins
    Rishi Prabhakar, Senior Consultant, Palisade
    @RISK is used to glean insights from your historical data in several ways. In this webinar, we explore the tools built into @RISK for this purpose, including the Data Window for quick descriptive statistics, charts, and correlation estimates, as well as both the Distribution and Time Series fitting features. The webinar is interactive, so you will get a chance to ask questions about these features as we go to maximize the value for your time.
  • Building Predictive Models with the DecisionTools Suite Recorded: May 6 2021 72 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will present how to use the DecisionTool Suite to build predictive models based on historical data.
    Both public and private databases can provide important insights for forecasting and planning, for example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.
    We will present 3 tools from the DecisionTools Suite: @RISK, StatTools, and NeuralTools. We'll then apply them to the tasks of understanding historical data and forecasting their future value - including the uncertainty of the underlying variables and the error level of the models.
  • Simulation with Historical Data Using Monte Carlo Simulation Recorded: Apr 28 2021 67 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    Both public and private databases can provide important insights for forecasting and planning. For example, historical prices and volumes, production times, commodity and energy prices, demand curves, and macroeconomic variables.

    This webinar will present how to use the @RISK to interpret and use historical data in predictive and risk analysis models. We'll cover Data Visualizer, Distributions Fitting, Special Functions, Time Series Fitting, and Correlations with @RISK.
  • Measuring, Interpreting, and Modeling Correlations Recorded: Apr 22 2021 72 mins
    Rafael Hartke, CEO of Imagine Risks and Analytics
    This webinar will show the different ways we can visualize, interpret, model, and simulate historical data using @RISK and the DecisionTools Suite (DTS). We will use hands-on examples and provide an intuitive approach to modeling according to the business challenges at hand.
    We will use the tools provided by the DecisionTools Suite to:
    (i) Understand and interpret historical data
    (ii) Overview of available resources to model data in DTS: distribution fitting, time series, regression, neural networks, correlation
    (iii) Build a forecast model from data
    (iv) Simulate new data using Monte Carlo simulation
    (v) Analyze the simulation results and discuss how to best present them
  • Developing a Neural Network Model to Assign an Implied S&P Credit Rating Recorded: Apr 20 2021 59 mins
    Ronald J. Statt - Risk Advisor in Enterprise Risk Management, Arizona Public Service Company
    Prior to entering into a contract with a company where one may be financially exposed, it is important to access the company’s financial health. Specifically, it is imperative to determine the company’s capacity to meet its financial obligations to pay off debt. Gratefully, the credit risk industry has developed hierarchical credit rating systems to inform the public of the risk of default, relative to other public companies, based on their financial metrics. The three largest credit rating agencies, Standard & Poor’s, Moody's and Fitch Group, rate a large portion of companies who publicly trade their stock. However, there are tens of thousands of private companies who are not rated by these agencies. Many of these companies are willing to provide their audited financial metrics (confidentially) to a potential creditor or customer so they can to determine their credit worthiness.
    This webinar demonstrates how to build a neural network model using NeuralTools from the DecisionTools Suite of @RISK, that will assign an implied S&P credit rating to a private company who provides the necessary financial metrics. The webinar will discuss where to acquire the metrics for thousands of companies needed to build the model, how to scrub the financial data, how to select the final set of metrics to be used in the model, how to determine the size of the test group, how to identify and correct for possible bias, and how to apply a validation process to select the final neural network model.
  • Optimum Financial and Project Investment Portfolio Recorded: Apr 15 2021 41 mins
    Ing. Kazimir Kmet, Ph.D, MBA, TC Contact
    To analyze the risks of financial investments
    To build and optimize a portfolio of financial investments, investment projects
    To indicate possible developments in the creation and optimization of the financial investment portfolio
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  • Title: Building Predictive Models with the DecisionTools Suite
  • Live at: May 6 2021 2:00 pm
  • Presented by: Rafael Hartke, CEO of Imagine Risks and Analytics
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