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Simulation-informed Revenue Extrapolation (SiRE) with confidence estimate for scaleup companies using scarce timeseries data.

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Overview • Usage • Example • ACM • Arxiv • Video • Slides • Poster • Citation

Overview

Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals’ experiences and insights. Furthermore, financial data on scaleups is typically proprietary, costly and scarce, ruling out the wide adoption of data-driven approaches.

We propose an algorithm SiRE (Simulation-informed Revenue Extrapolation) that generates fine-grained long-term revenue predictions on small datasets and short time-series. SiRE fulfills eight important practical requirements:

1. it performs well in multiple business sectors;
2. it works on small datasets with only a few hundred scaleups;
3. the extrapolation can commence from short revenue time-series;
4. it should produce a fine-grained time-series of at least three-year length;
5. each predicted revenue point should come with a confidence estimation;
6. it does not require any alternative data other than sector information;
7. the model can be timely and effortlessly adapted to any data change.
8. the prediction is explainable.

Usage

The source code and two datasets (transformed and anonymized) are already bundled in this repo. To run SiRE on both datasets, follow the steps below.

• To isolate the Python runtime for SiRE experimentation, install a virtual environment such as Anaconda. We hereby assume that you are using Anaconda. The following steps should be run in the project folder sire from a Terminal.

• Create a virtual Python environment by running:

$ conda create --name sire python=3.8
$ conda activate sire

• Install the dependent libraries:

$ pip install -r requirements.txt

• Start Python Notebook:

$ jupyter notebook

To experiment SiRE on SapiQ dataset, run through the notebook named sire_sapiq.ipynb.

To experiment SiRE on ARR129 dataset, run through the notebook named sire_arr129.ipynb.

Example Forecast

Following is the forecast (from 2018-05-01) visualization for company in ARR129 with ID 5cf74ff84639c79510ef8491d4725967336ad3627216a50553f030f9c51d93bc.

The corresponding predicted revenue points with 95% confidence interval (95CI) look like below.

DATE	MEAN	95CI_low	95CI_high	MIN	MAX
2018-05-01	52.6052	51.6823	53.5282	50.6054	54.8298
2018-06-01	55.3628	54.0489	56.6767	52.6138	58.5401
2018-07-01	57.8639	56.1423	59.5856	54.3638	62.0497
2018-08-01	60.2249	58.0926	62.3571	55.9818	65.4445
2018-09-01	62.5506	60.0036	65.0977	57.5778	68.819
2018-10-01	64.9284	61.9523	67.9044	59.2417	72.2662
...	...	...	...	...	...
2021-02-01	298.059	254.498	341.621	240.334	416.363
2021-03-01	314.914	268.262	361.567	253.592	442.045
2021-04-01	331.436	281.516	381.356	266.558	468.107

Cite This Work

@inproceedings{cao-etal-2022-sire,
    title = "Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time Series Data",
    author = "Cao, Lele  and
      Horn, Sonja  and
      von Ehrenheim, Vilhelm  and
      Stahl, Richard Anselmo  and
      Landgren, Henrik",
    booktitle = "Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM ’22), October 17–21, 2022, Atlanta, GA, USA",
    month = oct,
    year = "2022",
    address = "New York, NY, USA",
    location = "Atlanta, GA, USA",
    publisher = "Association for Computing Machinery (ACM)",
    pages = "2954–2963",
    numpages = "10",
    doi = "10.1145/3511808.3557110",
    isbn = "9781450392365",
    series = "CIKM '22"
}