The purpose of this repository is to enable a fast analysis and visualization of cashflows.
Following steps are conducted:
-
Assumptions to be taken, such as initial and terminal growth characteristics
-
Compounding of future cash flows:
-
Discounting of cash flows
-
Terminal value is predicted, considering future cash flows till infinity with terminal growth rate:
- The present values
$PV(t)$ represent all discounted cash flows:
- Relevant information is provided in a DataFrame:
| t | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Years | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 | 2032 |
| Free Cash Flow in $ | 20000 | 21800 | 23762 | 25900.6 | 28231.6 | 30772.5 | 33542 | 36560.8 | 39851.3 | 43437.9 | 47347.3 |
| Terminal Value in $ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 620371 |
|
|
20000 | 19818.2 | 19638 | 19459.5 | 19282.6 | 19107.3 | 18933.6 | 18761.5 | 18590.9 | 18421.9 | 257434 |
- The enterprise value is derived from adding the present values
$PV(t_i)$ of all cash flows:
Enterprise value =
More information and inspiration can be found here:
Thanks and reference to: (Name, Version, License)
- matplotlib 3.4.2 Python Software Foundation License, Copyright (c) 2002 - 2012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team; 2012 - 2021 The Matplotlib development team: matplotlib license
- numpy 1.19.5 BSD, Copyright (c) 2005-2020, NumPy Developers: numpy license
- pandas 1.2.4 BSD 3-Clause License Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team: pandas license
Are you interested in more time series modeling?
Feel free to check out:
There have been several attempts to predict enterprise values and stock prices using time series analysis. Many of them were not successful! Neither trading nor investing decisions should be influenced by this repository or the code, which is built only to introduce and demonstrate a methodology for time series modeling. No responsibility is taken for correctness or completeness of historic, current or future data, models, information and / or predictions!
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