A quantitative investment decision framework developed to evaluate and rank leading deep-tech companies for a hypothetical $1,000,000 venture capital investment.
The project combines multi-criteria decision analysis (MCDA) techniques with quantitative validation to identify the strongest long-term investment opportunity across 49 companies spanning Artificial Intelligence, Robotics, Defence Technology, Space Technology, and Quantum Computing.
Selecting investments in the deep-tech sector is challenging because companies differ significantly in:
- Business maturity
- Financial transparency
- Commercialization stage
- Technology readiness
- Risk profile
Traditional financial metrics alone are insufficient to compare private and public companies within the same investment universe.
To address this challenge, this project develops a Quantitative Deep-Tech Investment Screening Framework that integrates financial, technological, strategic, and qualitative indicators into a unified investment evaluation methodology.
- Evaluate 49 leading deep-tech companies
- Compare companies using 31 evaluation criteria
- Rank investments using multiple decision-making techniques
- Validate recommendation robustness using simulation
- Recommend the strongest candidate for a hypothetical $1 million investment
The investment framework follows the pipeline below:
49 Deep-Tech Companies
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Data Collection
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Feature Engineering
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31 Evaluation Criteria
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Data Normalization
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Weighted Scoring
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Analytic Hierarchy Process (AHP)
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TOPSIS Ranking
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Monte Carlo Simulation
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Sensitivity Analysis
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Investment Committee Review
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Final Investment Recommendation
The framework evaluates companies across multiple investment dimensions, including:
- Financial Strength
- Technology Leadership
- Commercialization
- Execution Capability
- Competitive Positioning
- Scalability
- Investment Risk
A total of 31 quantitative and qualitative features were used to generate the final rankings.
This project combines multiple quantitative methods:
- Weighted Scoring Model
- Analytic Hierarchy Process (AHP)
- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
- Monte Carlo Simulation
- Sensitivity Analysis
Using multiple approaches reduces dependence on a single ranking methodology and improves confidence in the final recommendation.
- 49 Deep-Tech Companies
- 5 Strategic Sectors
- 31 Evaluation Criteria
| Rank | Company | Weighted Score | TOPSIS |
|---|---|---|---|
| 1 | SpaceX | 85.85 | 0.703 |
| 2 | Anduril Industries | 84.91 | 0.691 |
| 3 | OpenAI | 84.36 | 0.684 |
| 4 | Anthropic | 83.92 | 0.678 |
| 5 | Figure AI | 83.51 | 0.671 |
The recommendation was validated using Monte Carlo simulation.
| Metric | Result |
|---|---|
| Probability of Rank #1 | 64.9% |
| Probability of Top 3 | 91.5% |
| Probability of Top 5 | 96.5% |
Sensitivity analysis further showed that SpaceX remained Rank 1 across all weighting strategies, indicating that the recommendation is robust to reasonable changes in evaluation assumptions.
DeepTech-Investment-Framework/
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├── deeptech_investment_framework.ipynb
├── Investment_Memorandum.pdf
├── README.md
├── requirements.txt
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└── outputs/
- Python
- Pandas
- NumPy
- Matplotlib
- Plotly
- SciPy
- Scikit-learn
- StatsModels
- MissingNo
- Yahoo Finance
Clone the repository:
git clone https://github.com/DebadattaLiku/DeepTech-Investment-Framework.gitInstall dependencies:
pip install -r requirements.txtLaunch Jupyter Notebook:
jupyter notebookOpen:
deeptech_investment_framework.ipynb
The complete investment memorandum is available in:
Investment_Memorandum.pdf
Potential extensions include:
- Automated real-time market data integration
- ESG scoring
- Reinforcement Learning for portfolio optimization
- Bayesian weight estimation
- Explainable AI for investment recommendations
- Interactive Streamlit dashboard
Debadatta Panda
M.Tech – Industrial Mathematics and Scientific Computing
Indian Institute of Technology Madras
GitHub: https://github.com/DebadattaLiku