This python package demonstrates possible applications of Stochastic Discount Factors (SDFs) estimated for private equity fund data. The estimation methodology is explained here.
How to apply an SDF ensemble to evaluate investment cash flows?
The general idea is to use an ensemble of many SDF models to obtain many reasonable net present value (NPV) estimates for a given payment stream. The empirical distribution of these NPVs allows you a probabilistic assessment of your investment success.
- random_stock_invest.py allows you to simulate a payment stream based on randomly timed investments and divestments in the US stock market.
- factor_model.py creates SDFs by combining the following factor model estimates with the corresponding Fama-French factor return data.
- evaluate_cash_flow.py evaluates the random cash flows (generated by random_stock_invest.py functions) by the SDFs (generated by factor_model.py functions).
- investment.py defines the investment object class, which relies on the functions defined in the aforementioned scripts.
Testing relies on the pytest framework.
Simply run pytest tests in your terminal to run all tests from the project_yes/tests folder.
- write test_evaluate_cash_flow and test_investment.py.