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PE.Forecast

PE.Forecast
Portfolio forecasting for professional grade investors
The only multi-factor risk model for private equity

PE.Forecast is a professional grade forecasting and risk system that delivers advanced portfolio analysis methodologies to private market investors. Previously institutional investors were limited to guess work or simplistic and inaccurate linear models to predict cash flows and portfolio valuation for private market investments, but with PE.Forecast you can model macro-economic impacts using our multi-factor econometric system and combine with your assets for a complete multi-asset risk model.

  • VaR
  • Cash Forecasting
  • Portfolio Valuation
  • Systematic
  • Idiosyncratic

Built on the largest transaction data feed in the world
  • Proprietary models based on 300,000+ global private transactions over last 40 years
  • Portfolio optimization through scenario and back-testing
  • 15+ systematic and idiosyncratic risk factors
  • Monte-Carlo generation of VaR, cash and valuation projections

Empirical results driven by your own risk team

CEPRES will work with your in-house experts to input the appropriate factors and model assumptions and then based on your existing portfolio and planned investment strategy for the next 10-25 years, PE.Forecast will deliver:

  • Probability distribution of expected investment returns
  • Value at Risk (VaR), probability of default, loss given default, etc.
  • Cash flow forecast including investment and divestment expectations
  • Asset valuation with percentile distribution

Customer Insights
„With CEPRES I can complete due diligence in half the time and feel more confident in my decisions.“

Customer Insights
„We were consistently missing return targets until CEPRES optimized our allocation strategy.“

Customer Insights
„CEPRES benchmarked our infrastructure deals and helped us find investors that fit our unique strategy.“

Customer Insights
„We needed an ALM plan to mitigate risk for the next macro-cycle and only CEPRES has the predictive models we need.“