Green Revenue

Alpha Vs Carbon

Alpha Vs Carbon

15Rock’s goal is to construct a model to measure the relationship between an Equity's excess return with its excess carbon to optimize portfolio returns against carbon targets and create a new dimension for sustainable decision making.


The Financial bottom line is the basis for many investment decisions. At 15Rock, we know the importance of Carbon data, but we have given equal importance to the prospective of our clients. Therefore, we have connected the outperformance of equity returns with its carbon performance. Through integrating alpha of a stock and the carbon footprint for the given time frame 15rock can create a new dimension for sustainable decision making.

The Alpha Vs Carbon model allows one to find the correlation between the equity returns and carbon, giving them insights on how changing carbon emissions can effect the equity returns of the company. Enabling our clients to identify carbon risk and take advantage of opportunities which can result in greater returns.

Results and Interpretation

Run a regression between alphas and excess carbon emissions or simply do a correlation to find the relationship between the two variables. If we do a regression we will get a coefficient for excess carbon. This way if we can predict next years carbon, we can get excess carbon and using the coefficient we can predict next years alpha.

Design and Modeling


  • Monthly prices of a single equity
  • Monthly observations for the 10 year risk free rate
  • Monthly observations of the S&P 500 (Market Pric)
  • Carbon emission for the same equity at monthly observations
  • Average industry Carbon Emissions of the respective industry
  • data frequency for returns from user(daily = 1, month = 20, year = 252, etc)
  • Market index ticker = GSPC.indx
  • Risk free index = EODprice from ticker = 'US10Y.INDX'
  • date range - how far back we go to calculate the rolling alpha/beta


  • Calculate equity returns (using EOD price range input calc returns of EODPrice for a given ticker )
  • LN (Equity price on day 15/equity price on day 14)
  • Calculate market returns (using the same range as the above EOD range).
  • Use the market exchange ticker
  • Get the risk free rates from EODprice from ticker = 'US10Y.INDX'
  • This is already in the % so no need to convert into returns %
  • Rate needs to be turned into a daily rate by taking the:
  • (date return range / 252 ) risk free rate*
  • Do a regression to get the return of equity:
  • Take the time series for equity returns(independent variable) and the time series market return(dependent variable), then the return of the regression is the alpha and beta.
  • The regression should be done on a rolling window based on the data range, if they say 100, then we will take their input(100) and double it. Then the rolling the range will start at at the data range they provided.
  • For example if the user requests 5 years with an interval of 1, then we will use 10 years of returns history, then we do the regression of the first 5 years days, for day 11th's regression results, then shifted to do day 12, etc.   If they select a interval of 2, then we will use 5 years to calculate the first day, then jump to t+2 then roll back 5 years to calculate the alpha and beta and continue at 2 interval jumps
  • Calculate excess Carbon returns for each observation
  • carbon emission of company - average carbon emission of the industry
  • both observed monthly
  • Run a regression on the excess carbon and the alpha
  • return the beta coefficient results to the endpoint.