Data and Modeling Choices for Private Equity Cash Flow Forecasts
Summary
The discussion addresses forecasting private equity fund capital calls and distributions, including whether forecasts can be related to market and economic conditions. It points to a theoretical reference on modeling illiquid alternative asset funds and suggests that public pension disclosures may provide useful fund investment information. Researchers may be able to seek call and distribution dates and amounts through public records requests, though data access and coverage will vary.
The answers distinguish synthetic data for software development from evidence for prediction: Monte Carlo generated data can help test a model framework, but cannot establish predictive value when it is created without empirical grounding. One response also mentions a free forecasting service that requires registration. Overall, the material offers starting points rather than a tested forecasting method or a validated dataset. It does not explain how to specify market relationships, handle fund reporting differences, or evaluate out-of-sample accuracy.
Key ideas
- Private equity cash flow models can be informed by published work on illiquid alternative asset funds.
- Public pension disclosures may provide data on fund investments, calls, and distributions.
- Public records requests may help obtain dates and amounts, but disclosure varies by institution.
- Monte Carlo synthetic funds can test implementation, but do not provide empirical predictive evidence by themselves.
- The discussion identifies resources rather than presenting a validated forecasting approach.
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# References on cashflow modelling for private equity # References on cashflow modelling for private equity I would like to build a model to predict capital calls and distributions of a private equity fund. The first question is: does any of you can address me towards the state of art for it? also machine learning approaches are more than welcome (so far I have only found papers using ito's calculus, or other deterministic simple methods). In particular I would like to link/correlate the output to the market and economic situation. The second question is: in order to build the model I need data on some private equity funds (for training the model), however they are costy, so are you aware of any free database which I could be using? I was also thinking that if no database/data is available I could approach it but generating fake funds by using a Montecarlo simulation but I am not sure this would be reliable. Thanks ## Answer by cpage (score 1) https://quant.stackexchange.com/a/63178 As to the theory, I would recommend “Illiquid Alternative Asset Fund Modeling” by Dean Takahashi and Seth Alexander of the Yale Endowment. You’re right that private equity fund data often sits behind paywalls. I’d recommend checking out the public pensions, however, since they tend to provide a lot of disclosure about their investments since they are subject to FOIA requests (CalPERS Example). You may have luck submitting your own FOIA requests for the actual call and distribution dates and amounts. That should get you a good sample of data for major funds. Generating your own data via Monte Carlo might be good for testing your code & framework but it would have zero predictive value since it’s just random data. ## Answer by CrunchyMilk (score 0) https://quant.stackexchange.com/a/70009 Here is a free cashflow forecasting REST-api. Just need to sign up to use it. https://medium.com/@alexanderbea/a-free-public-api-providing-cash-flow-forecasting-for-funds-in-illiquid-alternative-asset-classes-74c439f08e22
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