Hybrid Factor Investing Model in MATLAB
Budget: $30 – $250 USD
I'm seeking an experienced data scientist with a strong background in finance to develop a Hybrid Factor Investing Model in MATLAB for me. This model should integrate both traditional characteristics-based factors (Value, Momentum, Size, Quality, Low Volatility) and ESG-based factors (Environmental, Social, Governance metrics). The goal is to optimize portfolio construction and enhance risk-adjusted returns.
Key Responsibilities:
- **Factor Construction**: The most crucial part of this project. You'll need to assess and incorporate the interaction of both traditional and ESG factors.
- **Portfolio Optimization**: Utilize various strategies including equal-weighted, risk-parity, and ESG-constrained mean-variance optimization (MVO).
- **Backtesting**: Implement rigorous backtesting over a period of 10-20 years using performance metrics like Sharpe Ratio, Information Ratio, and ESG impact.
Data Sourcing & Processing:
Data will be sourced from Bloomberg, Fama-French datasets, and MSCI ESG ratings. You'll need to apply preprocessing techniques such as normalization and Principal Component Analysis (PCA).
Ideal Skills:
- Proficiency in MATLAB
- Strong understanding of finance and investment strategies
- Experience with ESG metrics
- Familiarity with data preprocessing techniques
- Ability to write clear and simple code
The deliverables will include modules for data processing, factor construction, portfolio optimization, and backtesting. Please ensure that the code is as simple as possible for ease of understanding.
Key Responsibilities:
- **Factor Construction**: The most crucial part of this project. You'll need to assess and incorporate the interaction of both traditional and ESG factors.
- **Portfolio Optimization**: Utilize various strategies including equal-weighted, risk-parity, and ESG-constrained mean-variance optimization (MVO).
- **Backtesting**: Implement rigorous backtesting over a period of 10-20 years using performance metrics like Sharpe Ratio, Information Ratio, and ESG impact.
Data Sourcing & Processing:
Data will be sourced from Bloomberg, Fama-French datasets, and MSCI ESG ratings. You'll need to apply preprocessing techniques such as normalization and Principal Component Analysis (PCA).
Ideal Skills:
- Proficiency in MATLAB
- Strong understanding of finance and investment strategies
- Experience with ESG metrics
- Familiarity with data preprocessing techniques
- Ability to write clear and simple code
The deliverables will include modules for data processing, factor construction, portfolio optimization, and backtesting. Please ensure that the code is as simple as possible for ease of understanding.