This project's mission is to make climate investing actionable. It includes both open source software and a free book to help you identify relative value trades, optimize portfolios, and structure benchmarks for climate aligned investing.
The software is a multi-factor equity returns model which adds a climate factor, or Brown Minus Green, to the popular Fama French and Carhart models. See the short video
This additional Brown Minus Green (BMG) return factor could be used for a variety of climate investing applications, including:
- Calculate the market-implied carbon risk of a stock, investment portfolio, mutual fund, or bond based on historical returns
- Determine the market reaction to the climate policies of a company
- Optimize a portfolio to minimize carbon risk subject to other parameters, such as index tracking or growth-value-sector investment strategies.
Install the required python modules (use
pipaccording to your python installation):
pip install -r requirements.txt
Initialize the Database using:
python3 scripts/setup_db.py -R -d
Let's get the historical stock prices and returns of the MSCI World Index and its constituent sectors:
python scripts/get_stocks.py -f data/msci_etf_sector_mapping.csv
python scripts/get_stocks.py -f data/msci_constituent_details.csv
Now let's calculate the risk factor loadings for these stocks using 60 months of monthly data at a time:
python scripts/get_regressions.py -d -f data/msci_etf_sector_mapping.csv -s 2010-01-01 -e 2021-01-31 --frequency MONTHLY -n DEFAULT -i 60 -b
python scripts/get_regressions.py -d -f data/msci_constituent_details.csv -s 2010-01-01 -e 2021-01-31 --frequency MONTHLY -n DEFAULT -i 60 -b
Next, let's create a daily version of the BMG climate risk series based on the difference between the stocks XOP (brown) and SMOG (green):
python scripts/bmg_series.py -n XOP-SMOG -b XOP -g SMOG -s 2018-01-01 -e 2022-02-01 --frequency DAILY
Finally, let's calculate the risk factor loadings for stocks using 2 years of daily data. This will take a long time:
python3 scripts/get_regressions.py -d -f data/msci_etf_sector_mapping.csv -s 2018-01-01 -e 2021-01-31 --frequency DAILY -i 730 -n XOP-SMOG -b
python3 scripts/get_regressions.py -d -f data/msci_constituent_details.csv -s 2018-01-01 -e 2021-01-31 --frequency DAILY -i 703 -n XOP-SMOG -b
The included free book on climate investing explains both climate investing concepts and how to use this project. You can also read it online at gitbook.
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This content is published for informational purposes only and not investment advice or inducement or advertising to purchase or sell any security. See full disclaimer and license.