3. Empirical study of environmental events’ impact
3.3 Research design
As events for the research are chosen, author now wants to explain the reasoning behind such core things for event studies analysis as estimation windows, event windows, model of the research and criteria for the stocks.
Author proposes to use market model for the event study analysis, which is described in every aforementioned research papers on event studies.
Market model is a statistical model, which ties a return of a security to a return of a market portfolio. (MacKinlay, 1997) In contrast with Constant Mean Return model, market model is a more flexible tool and gives a more accurate results for estimates.
For any given security X, the market model equation for return is going to be the following:
𝑹𝒙𝒕 = 𝒂𝒙+ 𝜷𝒙 ∗ 𝑹𝒎𝒕+ 𝜺𝒙𝒕, where
𝑹𝒙𝒕 = Return on Security X in period T 𝑹𝒎𝒕 = Return on market portfolio in period T 𝜺𝒙𝒕 = Zero mean disturbance term
For Return on market portfolio researches usually take on market index, such as S&P 500 for U.S. equities or MOEX for Russian equities. In this research author is to take both S&P 500 and MOEX, as this research include in itself equities from both countries for comparison sake. Daily index returns are going to be used, as author
decides to construct model for the research on daily basis, therefore, both returns are going to be taken on a daily basis.
In order to capture the impact of the events, it is necessary to include such a measure as Abnormal Return. The abnormal return is an actual return of the security minus the estimated normal return of the security over the period of event window. The normal return is defined as the expected return without a possibility of prior information on the event taking place. (MacKinlay, 1997)
𝑨𝑹𝒙𝒕 = 𝑹𝒙𝒕 − 𝑹@ 𝒙𝒕
𝑨𝑹𝒙𝒕 = Abnormal Return of Security in period T;
𝑹𝒙𝒕 = Actual Return of Security in period T;
𝑹@𝒙𝒕 = Expected Return of Security in period T
Returns itself are to be calculated on a daily basis, since it is going to represent a more accurate picture, than returns accumulated on a monthly or yearly basis (MacKinlay, 1997). Formula for returns is the following:
𝑹𝒕 = 𝑷𝒕 𝑷𝒕B𝟏− 𝟏
𝑹𝒕 = Actual Return of Security in period T 𝑷𝒕 = Stock price in period T
𝑷𝒕B𝟏 = Stock price in period T
In order to actually derive the impact of abnormal returns amongst random walk, it is needed to cumulate abnormal returns:
𝑪𝑨𝑹𝒊(𝒕𝟏, 𝒕𝟐) = H 𝑨𝑹𝒙𝒕
𝒕𝟐
𝒕I𝒕𝟏
Event study analysis’ main idea lies in the assumption that expected value of CAR (E[CAR] = 0) is zero. If cumulated abnormal returns are zero, then the null hypothesis cannot be rejected. Therefore, if null is not rejected, it is stated that news does not affect the stock performance. (MacKinlay, 1997)
As it was previously briefly discussed, companies of this research should be classified in several groups. According to Engle’s research on hedging climate risk, as companies differ by their ESG scoring, their stocks’ performance also differs. For instance, some industries, such as Personal Services, Water Transportation and Motion
Pictures show the worst performance out of all industries. Therefore, in this research author also wants to include differentiated stocks, based on ESG scores and GICS industry classification, which are to be gathered through Yahoo Finance database and GICS identification code, which is gathered through public sources, such as stock description on stock exchanges. In order to accept or reject second research question of the thesis – namely, whether different industries react similarly to environmental events or not, it is needed to make a study not only on standalone stocks, but rather on industry as a whole. In addition, a comparison between U.S. stock market and Russian stock market is going to be done in order to answer third research question concerning market efficiency in regard to environmental news. Therefore, that can bring some fruitful insight in terms of not only ESG scoring, but also geographical stance of the companies.
Data sample of the study includes 130 stocks. All of these stocks are large-cap and most liquid in representative markets. The choice is done this way, in order to reach maximum effectiveness in the trade-off between data collection process and drawing relevant conclusions. U.S. stock market is represented as stocks, included into S&P 100 index. There are 100 stocks emitted and 99 companies overall. One additional stock is explained by the fact Google has two different shares, included into the index. These companies constitute the large share of S&P 500 at the point of almost 70%. Overall market capitalization of S&P 100 is around $17 trillion with S&P 500 market capitalization standing at around $25 trillion. This information is relevant as of April 30, 2020. Thus, it is fair to conclude such data sample of U.S. companies is representative in order to draw conclusion about the U.S. stock market as a whole.
Figure 1. Comparison between S&P500 market cap and S&P100 (Sample) market cap
25 460
16 884
100%
66%
Market capitalization S&P 500 Market capitalization S&P 100 (Sample)
As for the Russian stock market, 31 stocks are chosen. These stocks were chosen in accordance with MOEX Industry Indices and top stocks by their respective weighting in index structure are chosen. As a result, stocks chosen constitute to 78% of total market capitalization of stocks listed on MOEX. 31 stocks chosen are valued at around 31 billion roubles, while total market capitalization of stocks listed on MOEX valued at 39.6 billion roubles. Thus, it is also fair to conclude that such sample of companies from Moscow Exchange is representative to draw conclusions about the market tendencies.
Figure 2. Comparison between MOEX market cap and Russian Sample market cap
These companies are going to be categorized based on their respective sector classification by GICS and ESG Risk Rating. By categorizing it by ESG risk rating, we observe stocks from three main segments: high ESG risk profile, medium ESG risk profile and low ESG risk profile. These risk profiles are taken based on Sustainalytics’
ESG rating from Yahoo Finance. This rating is a measurement of risk of a company to ESG factors. Model for this risk is based on two dimensions: first, company’s exposure to industry material risks and second, ability of the company to manage these risks.
Three central parts of such rating are corporate governance, material ESG issues and idiosyncratic issues or “black swans”, unexpected events. Despite the fact this rating includes five risk profiles: negligible, low, medium, high and severe, our data sample are comprised of stocks which lie only between low and high-risk profiles. Choice of such proxy for ESG riskiness of companies is based on R. Engle’s paper “Hedging Climate Risk”. In his research paper, R. Engle used Sustainalytics ESG rating for the companies of his research, therefore, as it brought meaningful results, it is fair to conclude that this rating can be used in this research as well. Author believes there is a possibility for interesting insights about stocks with different ESG scores, because, as it was already mentioned, Engle’s research showed that O&G industry is not the worst in terms of performance under climate risk realization possibility. It is worth to mention
78%
39 644
30 969
100%
Market capitalization Whole Market capitalization Sample
that not all Russian stocks from the sample have this ESG Risk Rating by Sustainalytics, therefore it would be not insightful to make conclusions based on this rating alone, due to the fact not all companies within the sample have the rating.
GICS sector High Medium Low
Financials 0 1 0
Communication Services 1 1 0
Consumer Staples 1 0 0
Energy 5 0 0
Materials 4 1 0
Utilities 1 0 0
Table 2. Distribution of Russian sample by ESG Risk Rating and GICS sector
GICS sector High Medium Low
Information Technology 0 1 14
Health Care 6 8 1
Financials 2 12 1
Real Estate 0 0 2
Consumer Discretionary 2 3 5
Industrials 8 4 0
Communication Services 1 2 6
Consumer Staples 1 9 1
Energy 4 2 0
Materials 2 0 0
Utilities 3 1 0
Table 3. Distribution of U.S. sample by ESG Risk Rating and GICS sector
As for GICS classification, companies were divided into different sectors of economy. The Global Industry Classification Standard is a methodology for structuring companies into their respective sectors, industry group and industry in accordance with company’s primary business activity. GICS classification serves as a world-wide standard for investors and asset managers for understanding different sectors and industries. It helps financial community worldwide to assess trends within sectors or industries, with the main goal of providing relevant information for investors and their portfolios.
Figure 3. Sector structure of companies in U.S. sample
Figure 4. Sector structure of companies in Russian sample
As a result, the study will examine how these sectors are affected by environmental events and capture this impact by conducting an event study and testing whether abnormal returns are present and studying different sector’s reaction to this news in order to identify less influenced sectors, with the main goal of assisting whether institutional investors or retail investors how to properly allocate money in the light of uncertainty on the capital markets. In addition, different reaction by U.S. and Russian stock markets will be studied and compared with each other in order to understand which market is more efficient in regard to pricing environmental news into its prices.
In order to come up with a sound reasoning of the research, it is needed to identify both estimation window and event window. Estimation window allows the researcher to estimate empirical model, which is going to estimate future returns of the asset. In this case, estimation window should be substantial in order to prevent possible disruptive events for the research, such as dividends announcements, stock-splits or quarterly earnings. For all events in this study, an estimation window of 250 days before
15%
15%
15%
12%
11%
10%
9%
6%
4% 2% Information Technology
Consumer Discretionary
Utilities
Industrials
Health Care Energy
Financials Consumer Staples
Communication Services
Real Estate Materials 1%
100
30%
13%
17%
17%
10%
7%
7%
Energy
Materials Communication Services
Utilities
Consumer Staples Financials
Industrials
30
the event window is taken to estimate the econometric model and predict normal stock performance out of the sample and, as a result, calculate abnormal returns. In terms of estimation window, 51 day of estimation window is taken, in order to account for possible insider trading by taking 25 days before the event itself and possible late reaction of the markets by taking 25 days after the event. In support of a large event window, MacKinlay’s own research serves as an example, where he used 20 days before and 20 days after the event, an event window of 41 days took place and returned an insightful result.
Therefore, estimation window for latter is 3 months and for the former (Paris Agreement) is 6 months. 3 months interval is chosen according to Okulov’s research paper, where Mr. Okulov state 3 months prior interval is a norm for such an analysis. 6 months interval for the Paris Agreement event is chosen, because author assumes there was already a discussion about the Agreement, therefore, it is needed to include a longer period in order to catch possible information spillover across investors board. According to Okulov’s research paper, event window is to be determined due to economic nature of the event and aim of the research. In this study event windows is going to be determined as 25 days before and after. 25 days before are chosen in order to catch the possible insider trading taking place. It would be especially useful in the event of VW emission scandal, due to the fact that top management knew about the situation for sure, therefore, they could have been wanted to take on the opportunity of cashing-out before the event became public.
Figure 5. Event Study analysis timeline, based on MacKinlay, 1997