Feedback
I’m open to any optional changes that you think would improve the analysis.
Hypotheses
H1: Investors pay a premium for firms scoring high on ESG and therefore the excess returns of
these firms are expected to be lower than that of a firm which scores low on ESG in prepandemic
times.
H2: During the pandemic the firms scoring high on ESG have generated higher excess returns
than firms which score low on ESG
H3: High ESG scoring firms have higher excess returns when there is positive public ESG
sentiment momentum surrounding them during pre-pandemic times.
H4: High ESG scoring firms have higher excess return when there is positive public sentiment
momentum surrounding them during the pandemic compared to pre-pandemic.
Dataset
Data preparation & transformation
I think I have prepared the data, but could you still look over it, I might have missed something.
I would like you to winsorize the data:
winsor2 ROA, cut(1, 99) replace
winsor2 OpMargin, cut(1, 99) replace
winsor2 ROE, cut(1, 99) replace
winsor2 Int, cut(5, 95) replace
winsor2 DE, cut(5, 95) replace
If I have missed any other variable that should be transformed, you can adjust it and could you
let me know. (I’m not sure whether I need to transform the returns variable)
Portfolio Construction
The portfolio’s will be constructed based on different levels of ESG scores and used to estimate
the risk-adjusted returns of the firms through risk factor models. Firstly, the companies are
ranked on their yearly ESG score. I follow the standards in the asset pricing literature by lagging
the ESG variable to make sure that the ESG information is available when investors are
constructing portfolio’s and before the stock market performance is measured (G. Gianfrate et
al., 2021). The ranking will be split into decile portfolio’s, P1 (with the lowest 10% of ESG
scores) to P10 (highest 10% of ESG scores). Every year the ESG scores are reevaluated and the
portfolio’s adjusted.
I would like the descriptive statistics of all 10 portfolios.
I would like the descriptive statistics of all 12 Industries. (SICFama = variable ranges from 1-12
indicating the industry)
I would like a figure which depicts the portfolio cumulative market-weighted returns of the
lowest and highest portfolio in one graph. (So, I can visualize whether they move roughly the
same or not)
Univariate Portfolio Analysis
Run regressions on each decile portfolio individually based on multifactor models to find alphas
relative to well-known risk factors. By adding the five risk factors, namely, the excess market
return, value factor, size factor, profitability factor and investment factor. Below the regression
formula is depicted for the univariate portfolio analysis.
Rt - rfti,t = α + β1 * MktRFt + β2 * SMBt +β3 * HMLt + β4 * CMAt +β5 * RMWt + εit
Rt – rfti,t = the excess return for firm i in month t.
α = the portfolio-level risk-adjusted return.
MktRFt = the return on the value-weighted market portfolio minus the risk-free return.
SMBt = the return on a diversified portfolio of small stocks minus the return on a
diversified portfolio of big stocks.
HMLt = the difference between the returns on diversified portfolios of high and low
book-to-market stocks.
CMAt = the difference between the returns on diversified portfolios of the stocks of low
and high investment firms, which we call conservative and aggressive.
RMWt = the difference between the returns on diversified portfolios of stocks with robust
and weak profitability.
εit = a zero-mean residual
The market beta’s, which measures systematic risk, will be constructed for each portfolio by
regressing the constituent firm average excess returns against the market excess returns. The
betas of the risk factors are the factor loadings estimated with a rolling regression window over
period of 60 months.
Is it possible to do this for equal and value-weighted portfolios? Or will value-weighted
portfolios be hard and take long to construct?
Bivariate Portfolio Analysis
Run a bivariate portfolio analysis because to control for common characteristics and risk factors.
They control for a company’s financial health in the year before the crisis and for other firm
characteristics that have been found to affect stock returns, to address this possibility the
financial health will be depicted through a set of variables and will display a firm’s ability to
survive a crisis. See below.
These drivers consist of a set of lagged accounting-based and market-based variables. The stockmarket based variables are observed immediately so they should be incorporated from month t-1.
The accounting variables are also assumed to be known by investors, so they will also be set at
month t-1.
I added the variables that I believe should be lagged. If you believe another variable should be
lagged, you can adjust and let me know.
I also construct a control variable to account for industry fixed effects. The industry fixed effect
variable will be a dummy variable ranging from 1 to 12.
By using a bivariate portfolio analysis more insight will be provided into the ESG effect because
it’s being controlled by stock characteristics or risk factors. The regression consists of sorting the
stocks twice. Firstly, two portfolios are created for a certain control variable, within these
portfolios the stocks are sorted again to five deciles based on their ESG scores. The average
portfolio returns across the two control variable deciles are taken. Decile 1 always contains the
lowest ESG numbers while decile 10 contains the highest. This procedure creates a set of ESG
decile portfolios controlling for stock characteristics and risk factors. The regression formula is
depicted below:
Rt - rfti,t = α + β1 * RMRFt + β2 * SMBt +β3 * HMLt + β4 * CMAt +β5 * RMWt + β6 *
FirmHealthi + Industryi + εit
Controlsi = set of variables that measure the health of firm i.
Industryi = a dummy variable representing the industry firm i operates in.
Dummy Variable for COVID19 Period
A dummy variable will be constructed to indicate whether a month is during the pandemic
(D_COVID = 1) or pre-pandemic (D_COVID = 0). Covid19 period is from 02/2020 – 12/2020.
In the regressions below I want to know whether there is a difference between the pandemic
period and pre-pandemic period.
I think the best way to do this by a stock-level regression and not a portfolio-level regression.
So I would like to run this through a panel regression. By regressing monthly stock returns on the
ESG rating, the dummy for the crisis months, and adding an interaction term between the
Covid19 dummy variable and the ESG variable.
The regression will also be run without the control variables for firm health and industry fixed
effects to view whether the results differ. To conclude, the implications regarding H1 and H2 can
be given. Don’t forget to lag the ESG variable and the appropriate control variables.
Rt - rft = α + β1 * RMRFt + β2 * SMBt +β3 * HMLt + β4 * CMAt +β5 * RMWt +
ESGi,t + D_COVID + (D_COVID * ESG) + ε
Rt - rft = α + β1 * RMRFt + β2 * SMBt +β3 * HMLt + β4 * CMAt +β5 * RMWt + β6 *
FirmHealthi + Industryi + ESGi,t + D_COVID + (D_COVID * ESG) + ε
ESGi,t = ESG performance measured by ESG score for firm in month t.
D_COVID = a dummy variable representing whether an observation is during the
pandemic or pre-pandemic period