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Glossary – Predictive Modeling

Predictive Modeling Terms:

  • Backtesting: The process of using historical data to test the performance of a predictive model.
  • Machine learning: A branch of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without explicit programming.
  • Predictive modeling: The process of using statistical techniques and algorithms to analyze historical data and make predictions about future events or outcomes.
  • Risk-adjusted return: The return on an investment after adjusting for the level of risk.
  • Valuation Modeling Approaches:
    1. Black-Scholes Model: This method is used to value a financial asset portfolio using option pricing theory, taking into account the volatility of the portfolio’s cash flows.
    2. Discounted Cash Flow (DCF) Model: This method calculates the present value of future cash flows generated by an asset portfolio using a discount rate that reflects the risk of the portfolio.
    3. Comparable Transactions: This method compares the subject financial asset portfolio to similar portfolios that have been recently sold in the market.
    4. Mark-to-Market: This technique compares the value of the financial asset portfolio to the current market value of similar assets.
    5. Mark-to-Model: This method uses a proprietary model to value the financial asset portfolio, taking into account factors such as credit risk and cash flow.
    6. Monte Carlo Simulation: This method uses computer simulations to model the potential performance of the financial asset portfolio under different economic scenarios.
    7. Risk-Adjusted Return on Capital (RAROC) Model: This technique calculates the return on capital of the financial asset portfolio adjusted for risk.
    8. Statistical Modeling: This technique uses historical data to predict future performance of the financial asset portfolio. It can include factors such as credit score, loan-to-value ratio, and geographic location.

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