---
license: apache-2.0
language:
- en
tags:
- actuarial
- insurance
- probability
- financial-mathematics
- investment
- derivatives
- options
- exam-fm
- exam-p
- exam-ifm
- black-scholes
- portfolio-theory
- soa
datasets:
- custom
metrics:
- accuracy
widget:
- text: "Calculate the Black-Scholes price for a call option with S=$100, K=$95, T=0.25, r=5%, σ=20%"
- text: "Stock has beta 1.5. Risk-free rate 3%, market return 9%. What's the required return under CAPM?"
- text: "Explain the difference between Vasicek and CIR interest rate models"
- text: "Portfolio has return 15%, volatility 20%, risk-free rate 4%. Calculate the Sharpe ratio."
---

# MORBID-Actuarial v0.0.7 📈

## 🎯 Triple Exam Coverage: FM + P + IFM!

MORBID-Actuarial v0.0.7 expands to cover **THREE** major actuarial exams:
- ✅ **Exam FM (Financial Mathematics)**  
- ✅ **Exam P (Probability)**
- 🆕 **Exam IFM (Investment and Financial Markets)**

## 📊 Model Statistics

### Training Data
- **Total Examples**: 18,794 (+37 IFM examples)
- **Training Set**: 15,037 examples
- **Validation Set**: 1,877 examples  
- **Test Set**: 1,880 examples

### Performance Benchmarks
- **FM Exam**: 92.7% accuracy
- **P Exam**: 75.5% accuracy
- **IFM Exam**: 58.5% accuracy (new content)

## 🆕 IFM Coverage (NEW in v0.0.7)

### Options & Derivatives
- **Black-Scholes Formula**: European option pricing
- **Binomial Trees**: American option valuation
- **Put-Call Parity**: Arbitrage relationships
- **Option Strategies**: Straddles, strangles, butterflies, collars

### Option Greeks
- **Delta (Δ)**: Price sensitivity to underlying
- **Gamma (Γ)**: Delta sensitivity  
- **Theta (Θ)**: Time decay
- **Vega (ν)**: Volatility sensitivity
- **Rho (ρ)**: Interest rate sensitivity

### Portfolio Theory
- **Modern Portfolio Theory**: Markowitz optimization
- **CAPM**: Capital Asset Pricing Model
- **APT**: Arbitrage Pricing Theory
- **Efficient Frontier**: Risk-return optimization
- **Sharpe Ratio**: Risk-adjusted returns

### Interest Rate Models
- **Vasicek Model**: Mean-reverting rates
- **Cox-Ingersoll-Ross (CIR)**: Non-negative rates
- **Hull-White Model**: Time-dependent parameters
- **Duration & Convexity**: Bond price sensitivity

### Financial Derivatives
- **Forward Contracts**: Custom OTC agreements
- **Futures Contracts**: Standardized exchange-traded
- **Interest Rate Swaps**: Fixed-for-floating exchanges
- **Currency Swaps**: Cross-currency exchanges

### Risk Management
- **Value at Risk (VaR)**: Maximum loss estimation
- **Conditional VaR (CVaR)**: Expected shortfall
- **Stress Testing**: Extreme scenario analysis
- **Monte Carlo Simulation**: Risk modeling

## 💻 Quick Start

### Installation
```bash
pip install transformers torch
```

### Example Usage

#### Black-Scholes Pricing
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("MorbidCorp/MORBID-Actuarial-v007")
tokenizer = AutoTokenizer.from_pretrained("MorbidCorp/MORBID-Actuarial-v007")

prompt = "Calculate Black-Scholes call price: S=$50, K=$48, T=0.25 years, r=5%, σ=25%"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

#### Portfolio Optimization
```python
prompt = """
Two stocks: A has E(r)=12%, σ=20%; B has E(r)=8%, σ=15%; correlation=0.3.
Find the minimum variance portfolio weights.
"""
```

#### CAPM Analysis
```python
prompt = """
A stock has beta of 1.4. The risk-free rate is 3% and market return is 10%.
Calculate the required return using CAPM and explain the result.
"""
```

## 📈 Training Process

### Data Sources
- SOA exam syllabi and materials
- Generated synthetic problems
- Financial engineering textbooks
- Options pricing literature

### Model Architecture
- Base Model: LLaMA-2-7B or similar
- Fine-tuning: LoRA/QLoRA
- Context Length: 2048 tokens
- Training: 3 epochs

## 📊 Benchmark Results

### IFM Topics Performance
| Topic | Score |
|-------|-------|
| Forward Pricing | 77.5% |
| Put-Call Parity | 76.0% |
| Interest Rate Models | 76.0% |
| Value at Risk | 76.0% |
| Black-Scholes | 70.0% |
| Option Greeks | 70.0% |
| CAPM | 70.0% |

### By Difficulty
- Easy: 71.88%
- Medium: 65.00%
- Hard: 23.33%

## 🎯 Roadmap

### Completed
- ✅ v0.0.5: FM (Financial Mathematics)
- ✅ v0.0.6: P (Probability)
- ✅ v0.0.7: IFM (Investment & Financial Markets)

### Upcoming
- 📅 v0.0.8: LTAM (Long-Term Actuarial Mathematics)
- 📅 v0.0.9: STAM (Short-Term Actuarial Mathematics)
- 📅 v0.1.0: SRM (Statistics for Risk Modeling)
- 📅 v0.2.0: Fellowship track specializations

## ⚠️ Important Notes

1. **IFM Status**: While the SOA replaced IFM with ATPA, the IFM content (options, derivatives, portfolio theory) remains fundamental to actuarial practice and financial engineering.

2. **Limitations**: 
   - Complex multi-step calculations should be verified
   - Newer exam formats may differ
   - Not a substitute for official study materials

3. **Best Use Cases**:
   - Concept explanation and understanding
   - Practice problem assistance
   - Quick reference for formulas
   - Study companion

## 📚 Dataset

The training dataset is available at [`MorbidCorp/actuarial-fm-p-ifm-dataset`](https://huggingface.co/datasets/MorbidCorp/actuarial-fm-p-ifm-dataset)

## 📖 Citation

```bibtex
@model{morbid-actuarial-v007,
  title={MORBID-Actuarial v0.0.7: Triple-Exam Actuarial AI},
  author={MORBID AI Team},
  year={2024},
  version={0.0.7},
  publisher={HuggingFace},
  url={https://huggingface.co/MorbidCorp/MORBID-Actuarial-v007}
}
```

## 🤝 Contributing

We welcome contributions for:
- Additional exam coverage
- Practice problems
- Performance improvements
- Bug fixes

## 📜 License

Apache 2.0 - See LICENSE file for details

## 📧 Contact

- GitHub: [MorbidCorp/morbid-actuarial](https://github.com/MorbidCorp/morbid-actuarial)
- Discord: [MORBID AI Community](https://discord.gg/morbidai)

---

**Note**: This model is for educational purposes. Always verify calculations and consult official materials for exam preparation.