摘要

這份報告考慮多項因子,包含ESG評分, E向度分數,S向度分數, G向度分數, 碳排放曝險指數、碳排放管理指數、碳排放綜合分數、碳排放總量等。我們針對各項因子去做單因子分析,並取其中幾項因子與股價報酬做迴歸分析,並且去檢驗此模型,驗證該迴歸分析的有效性,觀察這些因子在迴歸分析下與股價的相關性。

使用套件與資源包概念解說

tidyverse

是一個匯總包,可以從數據探索到數據視覺化,集合了ggplot2、dplyr、stringr、tidyr等安裝包

ggplot2

數據可視化繪圖包。 ggplot2允許用戶在更抽象的層面上增加、刪除或轉換圖表中的元素。 但這種抽象化的代價是執行速度。ggplot2較lattice繪圖包而言更耗時。

dplyr

榮入很多概念與結構化查詢語言(Structured Query Language,SQL)相仿的函數,搭配 %>% 運算子一起使用

datatable

是表格套件的一種,DataTables套件是一種JQuery外掛套件,特別針對table此種資料的呈現方式,內部已實作了各種相關操作的功能,並提供具有彈性的客製化選項,開發人員只需要下載並引用相關函式庫,即可輕鬆實作出功能豐富的table介面。

training與testing

回歸模型當中訓練集與測試集,在完成模型訓練後,為了驗證模型訓練的好不好,需要用一組獨立的測試資料,來做模型的驗證

Regression analysis

迴歸分析主要用在了解兩個或多個變數間是否相關、相關方向與強度,並建立數學模型以便觀察特定變數來預測研究者感興趣的變數。

sapply

以矩陣行是來展現表格,是apply套件的一種


研究主題

碳排放管理對於企業股價之相關性分析


研究動機

近年來,各種以ESG為主題的金融產品如基金、ETF等如雨後春筍般湧出。從一開始作為投資熱潮,到現在偶爾為人所詬病,認為ESG對投資並無助益。

但若從基本面出發對ESG進行探討,為了達到2050年碳中和目標,歐盟已領先全球設立綠色政綱,旗下的三大法規CSRD、TR和SFDR環環相扣,它們將在近三年陸續被落實,互相補足各方的不足部分。CSRD規定企業與金融業必須以透明且統一的標準進行ESG資訊揭露,在SFDR下,金融業需要在進行放貸或設計金融商品時考慮標的物或客戶的永續程度,肩負「讓資金流向永續企業」之責任,這時CSRD所規範的資訊揭露就派上用場,讓企業作為資訊供給者,提供資訊給金融業。當各方都完全服從法規的情況下,整個市場就能達到永續金融目標。

近兩年開始,碳權價格日趨昂貴,在2050碳中和目標下,整體市場將不可避免地邁向零碳經濟,高碳排企業將在這過程中面臨轉型風險,即轉為低碳時可能造成的財務或經營面衝擊。為了評估相關風險,越來越多企業開始發佈TCFD報告書,利用其中所規定的氣候情景分析去衡量轉型風險,國際間也逐漸將TCFD列為法規,包含紐西蘭與英國規定在其國家上市的企業需分別在2023年與2024年發佈TCFD報告書。

除了以上所提到的法規制度,全球正在面臨嚴峻的氣候風險,過去五年美國發生的極端氣候災難事件,已帶來超過6,000億美元的損失,無論是公私部門都明顯感受到氣候風險的壓力、以及改善氣候變遷問題,因此達到2050碳中和目標刻不容緩。

經過以上研究,我們相信在企業所發佈ESG資訊將在近幾年逐漸標準化,比起現今將更能提供投資人有益的資訊。而歐洲作為領先全球制定法規的區域,其企業也將受到規範而必須進行資訊揭露,因此我們希望將樣本池鎖定歐盟企業,且著重於ESG中的碳排放管理指標,期許能夠針對這個課題得出一些新發現。


研究方法

利用MSCI ACWI Index 中的歐盟個股與其碳排管理指標,進行單因子迴歸分析,以探討「碳排管理是否能帶來超額報酬」之課題。由於產業間業務性質差異甚大,因此也會將個股按照產業區分,以達到產業中立之目的。

我們大致上會使用的方法與函式如下:

1.linear regression and random forest,比較這兩個模型的性能與差異,並將數據拆分為訓練/測試這兩個方式以進行建模

2.資料當中的carbon emission VS. total return, correlation chart (using y~x)

3.使用多變數 (use 2y as a try, can switch out Y variable, and/or take out “Industry”)

4.繪圖 map plot

5.使用library、filter、sapply、training、testing、ggplot、datatable等套件


Data 處理

# only using EU countries
esg_eu_df = esg_df %>% filter(ISSUER_CNTRY_DOMICILE %in% c("AT", "BE", "BG", "HR", "CY", "DK", "EE", "FI", "FR", "DE", "GR", "HU", "IE", "IT", "LV", "LT", "LU", "MT", "NL", "PL","PT","RO", "SK", "SI", "ES", "SE"))

# seperate into carb heavt/light and ENERGY data frames
carb_heavy_df = esg_df %>% filter(Industry %in% c("Materials", "Utilities", "Industrial"))
carb_light_df = esg_df %>% 
  filter(!Industry %in% c("Materials", "Utilities", "Energy", "Industrial")) 
energy_df = esg_df %>% filter(Industry %in% c("Energy"))

# chnange "return" variables from character to double
carb_heavy_df[, c(31:33)] <- sapply(carb_heavy_df[, c(31:33)], as.numeric)
carb_light_df[, c(31:33)] <- sapply(carb_light_df[, c(31:33)], as.numeric)
esg_eu_df[, c(31:33)] <- sapply(esg_eu_df[, c(31:33)], as.numeric)
energy_df[, c(31:33)] <- sapply(energy_df[, c(31:33)], as.numeric)

# split the data into train/test for modeling 
set.seed(100)
heavy_split = initial_split(carb_heavy_df, prop = 0.9)
heavy_train = training(heavy_split)
heavy_test = testing(heavy_split)

light_split = initial_split(carb_light_df, prop = 0.9)
light_train = training(light_split)
light_test = testing(light_split)

energy_split = initial_split(energy_df, prop = 0.9)
energy_train = training(energy_split)
energy_test = testing(energy_split)
# carb emission VS. total return, correlation chart (using y~x) 
heavy.graph <- ggplot(carb_heavy_df, aes(x=CARBON_EMISSIONS_SCORE, y=TotalReturn_2y))+
  geom_point() + geom_smooth(method="lm", col="red") + ylim(-10, 1000)+ 
  ggtitle("Correlation of Emission Score to Return - Carbon Heavy Industries") + 
  labs(y = "Total Return Rate - 2 years", x = "Carbon Emission Score",) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

light.graph <- ggplot(carb_light_df, aes(x=CARBON_EMISSIONS_SCORE, y=TotalReturn_2y))+
  geom_point()+ geom_smooth(method="lm", col="red") + ylim(-10, 1000)+ 
  ggtitle("Correlation of Emission Score to Return - Carbon Light Industries") + 
  labs(y = "Total Return Rate - 2 years", x = "Carbon Emission Score",) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

energy.graph <- ggplot(energy_df, aes(x=CARBON_EMISSIONS_SCORE, y=TotalReturn_2y))+
  geom_point()+ geom_smooth(method="lm", col="red") + ylim(-10, 1000)+ 
  ggtitle("Correlation of Emission Score to Return - ENergy") + 
  labs(y = "Total Return Rate - 2 years", x = "Carbon Emission Score",) +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

# map plot - below is just to build the map
# if need to change anything, visit the below link:
# https://egallic.fr/en/european-map-using-r/

map_eu = esg_eu_df %>%
  group_by(ISSUER_CNTRY_DOMICILE) %>%
  summarise(avg_score = mean(CARBON_EMISSIONS_SCORE))

map_eu$country = c("Austria","Belgium","Germany","Denmark","Spain","Finland","France","Greece","Hungry", "Ireland","Italy","Luxembourg","Netherlands","Poland","Portugal","Sweden")

world_map <- ne_countries(scale = 50, returnclass = 'sf')
european_union <- c("Austria","Belgium","Bulgaria","Croatia","Cyprus",
                    "Czech Rep.","Denmark","Estonia","Finland","France",
                    "Germany","Greece","Hungary","Ireland","Italy","Latvia",
                    "Lithuania","Luxembourg","Malta","Netherlands","Poland",
                    "Portugal","Romania","Slovakia","Slovenia","Spain",
                    "Sweden","United Kingdom")

european_union_map <- world_map %>% 
  filter(name %in% european_union)

bbox_europe <- st_bbox(c(xmin = -10, ymin = 20, xmax = 50, ymax = 80), crs = st_crs(european_union_map))
european_union_map_cropped <- st_crop(european_union_map, bbox_europe)

map <- european_union_map_cropped %>% 
  left_join(map_eu, by = c("name" = "country"))

map.graph <- ggplot(data = map) +
  geom_sf(mapping = aes(fill = avg_score)) +
  scale_fill_gradient(name = "Carbon Emission Score", high = "#FF0000FF", low = "#FFFF00FF", na.value = "grey50")+ 
  ggtitle("Carbon Emission Score Across EU") + 
  theme(plot.title = element_text(hjust = 0.5, face = "bold"))

#showtable_df = esg_eu_df %>%
#select(TotalReturn_2y, Industry,CARBON_EMISSIONS_SCORE,IVA_COMPANY_RATING, INDUSTRY_ADJUSTED_SCORE,GOVERNANCE_PILLAR_SCORE)

# showing the data frame - important variables 
#datatable(esg_eu_df, options = list(pageLength = 5), width = 500)

#, table.attr = "style = \"color: black;\""
# kable_classic

kable(esg_eu_df, "html", table.attr = "bgcolor = \"color: white;\"") %>%
  kable_styling("striped","condensed") %>%
  scroll_box(width = "100%", height = "300px")
ISSUERID ISSUER_NAME ISSUER_TICKER ISSUER_CUSIP ISSUER_SEDOL ISSUER_ISIN ISSUER_CNTRY_DOMICILE GICS_SUB_IND IVA_INDUSTRY Industry IVA_COMPANY_RATING WEIGHTED_AVERAGE_SCORE INDUSTRY_ADJUSTED_SCORE ENVIRONMENTAL_PILLAR_SCORE ENVIRONMENTAL_PILLAR_WEIGHT ENVIRONMENTAL_PILLAR_QUARTILE SOCIAL_PILLAR_WEIGHT SOCIAL_PILLAR_QUARTILE SOCIAL_PILLAR_SCORE GOVERNANCE_PILLAR_WEIGHT GOVERNANCE_PILLAR_QUARTILE GOVERNANCE_PILLAR_SCORE CARBON_EMISSIONS_SCORE CARBON_EMISSIONS_EXP_SCORE CARBON_EMISSIONS_MGMT_SCORE CARBON_EMISSIONS_QUARTILE CARBON_EMISSIONS_WEIGHT CARBON_EMISSIONS_SCOPE_12_INTEN CARBON_EMISSIONS_SCOPE_3 CARBON_EMISSIONS_GHG_MITIGATION_SCORE TotalReturn_2y TotalReturn_1y TotalReturn_6m
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IID000000002140445 Fortum Oyj FORTUM 34959F106 5579550 FI0009007132 FI Electric Utilities Utilities Utilities BBB 5.1 5.60 5.5 54 3 13 4 3.6 33 2 5.2 6.1 4.9 4.0 4 14 821.10 27836300 7.00 53.8078785 -18.8743767 -33.9619333
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IID000000002140464 GEA Group Aktiengesellschaft G1A NA 4557104 DE0006602006 DE Industrial Machinery Industrial Machinery Industrials AA 6.3 8.30 4.8 33 3 20 2 5.7 47 1 7.6 10.0 2.6 7.0 2 0 11.40 3602 7.00 115.0965901 8.7215395 -3.5418821
IID000000002142057 COLOPLAST A/S COLO B 19624Y200 B8FMRX8 DK0060448595 DK Health Care Supplies Health Care Equipment & Supplies Health Care AA 6.3 7.90 10.0 5 2 47 1 6.7 48 2 5.6 10.0 2.3 6.5 2 5 13.60 159700 7.00 4.9729210 9.4046199 3.9153772
IID000000002142079 COMPAGNIE DE SAINT-GOBAIN SA SGO 204280309 7380482 FR0000125007 FR Building Products Building Products Industrials A 6.1 6.80 6.9 40 2 25 3 5.0 35 1 5.9 10.0 3.3 7.2 2 15 222.90 17400000 7.00 157.3027988 9.7567582 -3.9639002
IID000000002142122 GENMAB A/S GMAB 372303206 4595739 DK0010272202 DK Biotechnology Biotechnology Health Care AA 5.6 7.20 4.7 10 4 55 2 4.6 35 1 7.3 7.3 3.0 3.3 2 0 22.20 NA 7.00 79.0773701 18.1121227 -10.5587808
IID000000002142414 Skandinaviska Enskilda Banken AB SEB A 83051GAM0 4813345 SE0000148884 SE Diversified Banks Banks Financials AA 5.5 7.90 7.9 13 1 54 3 4.4 33 1 6.3 10.0 0.9 6.3 3 0 0.50 8114 7.00 79.6558223 4.3454217 -10.5730066
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IID000000002142548 EIFFAGE SA FGR 282492107 B13X013 FR0000130452 FR Construction & Engineering Construction & Engineering Industrials AA 5.9 8.30 4.7 17 3 25 2 4.4 58 1 6.9 10.0 2.5 6.3 3 0 31.30 5957965 7.00 56.4872699 9.8444280 6.2685206
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IID000000002144233 Koninklijke DSM N.V. DSM 780249108 B0HZL93 NL0000009827 NL Specialty Chemicals Specialty Chemicals Materials AAA 6.7 9.60 7.2 53 1 14 1 5.8 33 1 6.4 10.0 3.7 7.8 1 14 125.00 12000000 7.00 65.9433571 14.1313151 -6.2049062
IID000000002144268 Aktiebolaget SKF SKF B 784375404 B1Q3J35 SE0000108227 SE Industrial Machinery Industrial Machinery Industrials AA 5.3 8.00 4.9 33 2 20 4 3.8 47 1 6.2 10.0 2.8 7.0 2 0 56.70 445443 7.00 28.9423991 -35.6299719 -21.3743657
IID000000002144506 VERBUND AG VER 92336Y107 4661607 AT0000746409 AT Electric Utilities Utilities Utilities AAA 7.6 9.30 9.2 52 1 15 2 6.0 33 2 5.8 10.0 0.5 7.3 2 7 335.10 358000 10.00 197.5214471 52.7151225 5.8758315
IID000000002144603 Koninklijke Philips N.V. PHIA 500472303 5986622 NL0000009538 NL Health Care Equipment Health Care Equipment & Supplies Health Care BB 5.3 4.20 10.0 5 2 56 4 4.7 39 2 5.7 10.0 2.0 7.2 2 5 1.50 500000 7.00 -19.5078674 -42.5643390 -26.5138466
IID000000002144644 Aktiebolaget Volvo VOLV B 928856301 B1QH830 SE0000115446 SE Construction Machinery & Heavy Trucks Construction & Farm Machinery & Heavy Trucks Industrials AA 5.7 7.70 5.8 24 2 35 3 5.3 41 1 6.1 10.0 2.5 7.0 3 NA 6.60 52000000 7.00 75.3986518 -12.1644648 -9.2030848
IID000000002144864 Randstad N.V. RAND 75279Q108 5228658 NL0000379121 NL Human Resource & Employment Services Professional Services Industrials AA 6.4 8.50 10.0 5 3 51 3 5.4 44 1 7.2 10.0 1.3 6.3 3 5 2.50 3277 7.00 102.9288579 -4.2276042 -1.7801590
IID000000002144871 SOFINA S.A. SOF NA 4820301 BE0003717312 BE Multi-Sector Holdings Diversified Financials Financials BB 4.4 4.10 4.3 5 4 44 4 2.8 51 2 5.8 4.3 4.7 2.0 4 5 34.30 NA 3.00 84.0218515 14.0178098 -4.4006948
IID000000002145132 TELECOM ITALIA SPA TIT 87927Y102 7634394 IT0003497168 IT Integrated Telecommunication Services Telecommunication Services Communication Services A 5.8 6.60 10.0 5 3 56 3 5.9 39 2 5.0 10.0 2.2 6.5 3 5 30.50 4424362 7.00 -5.8022011 -25.7399135 -1.2437074
IID000000002145155 TotalEnergies SE TTE 89151E109 B15C557 FR0000120271 FR Integrated Oil & Gas Integrated Oil & Gas Energy A 5.8 7.10 6.3 41 1 26 3 6.0 33 2 5.0 8.5 5.8 7.3 1 14 277.20 350000000 7.32 50.5365382 25.2855745 14.7805381
IID000000002145368 Red Electrica Corporacion, S.A. REE 756568101 BD6FXN3 ES0173093024 ES Electric Utilities Utilities Utilities AAA 7.7 9.50 9.0 50 1 15 1 8.5 35 2 5.4 10.0 1.2 7.0 2 5 257.80 381347 7.00 29.7685043 31.7846429 8.8717409
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IID000000002145665 SANOFI SA SAN 80105N105 5671735 FR0000120578 FR Pharmaceuticals Pharmaceuticals Health Care A 5.1 6.20 6.3 9 1 58 3 4.5 33 1 5.7 10.0 2.2 7.0 2 0 15.40 5555402 7.00 25.9564137 14.0256649 11.9840213
IID000000002147336 Symrise AG SY1 87155N109 B1JB4K8 DE000SYM9999 DE Specialty Chemicals Specialty Chemicals Materials A 5.7 7.10 6.2 53 2 14 3 2.7 33 2 6.1 10.0 4.2 8.3 1 14 69.80 1747178 7.00 30.1577861 4.8539101 -3.6725664
IID000000002148742 ALSTOM SA ALO 021244207 B0DJ8Q5 FR0010220475 FR Construction Machinery & Heavy Trucks Construction & Farm Machinery & Heavy Trucks Industrials AA 5.7 7.70 7.1 24 1 35 2 6.0 41 2 4.6 9.6 2.1 4.7 3 0 12.40 21782971 7.00 -37.9151414 -50.8348928 -32.9233680
IID000000002148771 Beiersdorf Aktiengesellschaft BEI 07724U103 5107401 DE0005200000 DE Personal Products Household & Personal Products Consumer Staples AA 5.7 7.60 6.7 35 1 30 4 3.7 35 1 6.3 10.0 3.5 6.5 2 NA 20.90 1096289 7.00 9.0366779 6.2540646 1.5358362
IID000000002148785 ACCOR SA AC 00435F309 5852842 FR0000120404 FR Hotels, Resorts & Cruise Lines Hotels & Travel Consumer Discretionary A 5.4 6.90 6.3 27 2 36 3 4.3 37 1 5.7 8.1 5.4 6.5 1 11 578.83 NA 7.00 21.9440968 -10.5842765 -8.2836523
IID000000002148926 Sampo Oyj SAMPO 79588J102 5226038 FI0009003305 FI Multi-line Insurance Multi-Line Insurance & Brokerage Financials AA 6.4 8.40 6.6 13 2 54 2 6.2 33 1 6.8 10.0 0.8 6.3 3 NA 0.40 6925 7.00 88.5408340 19.8556411 1.0933941
IID000000002148930 EDP - Energias de Portugal, S.A. EDP 268353109 4103596 PTEDP0AM0009 PT Electric Utilities Utilities Utilities AAA 8.4 10.00 9.5 54 1 13 1 8.4 33 1 6.7 10.0 1.8 8.0 2 14 650.30 11572000 10.00 39.3759284 -5.7369318 -2.5747327
IID000000002148933 Bayerische Motoren Werke Aktiengesellschaft BMW 072743305 5756029 DE0005190003 DE Automobile Manufacturers Automobiles Consumer Discretionary A 4.9 7.10 7.1 30 2 37 1 4.3 33 2 3.5 10.0 1.9 6.3 3 0 6.00 65100863 7.00 90.9080879 -9.2990221 -6.2388167
IID000000002148934 Continental Aktiengesellschaft CON 210771200 4598589 DE0005439004 DE Auto Parts & Equipment Auto Components Consumer Discretionary BBB 4.8 5.30 6.0 26 1 37 3 3.5 37 2 5.1 10.0 3.0 6.3 2 0 65.00 NA 7.00 25.0771921 -35.8800459 -29.7076526
IID000000002148935 HeidelbergCement AG HEI 42281P205 5120679 DE0006047004 DE Construction Materials Construction Materials Materials AA 5.6 8.40 6.5 35 1 32 3 3.4 33 1 6.7 5.6 8.6 6.9 1 13 3516.00 21260000 7.70 39.6179333 -30.8407214 -19.8075132
IID000000002148949 Fastighets AB Balder BALD B NA B0LC9F2 SE0000455057 SE Real Estate Operating Companies Real Estate Management & Services Real Estate BBB 5.2 5.00 5.3 31 3 25 3 5.6 44 3 4.9 6.7 4.4 4.0 4 NA 14.00 NA 7.00 86.6066066 40.1443392 13.5184509
IID000000002149686 PRYSMIAN S.P.A. PRY 74440L106 B1W4V69 IT0004176001 IT Electrical Components & Equipment Electrical Equipment Industrials AA 6.1 7.80 5.1 32 3 24 3 4.6 44 1 7.6 10.0 3.1 6.3 2 0 68.00 NA 7.00 114.8028223 12.4564674 2.6184952
IID000000002150860 DSV A/S DSV 26251A108 B1WT5G2 DK0060079531 DK Air Freight & Logistics Air Freight & Logistics Industrials A 5.4 6.80 10.0 15 1 31 4 0.4 54 1 6.9 10.0 3.5 7.8 1 15 9.10 12524000 7.00 115.7847656 4.9245950 -13.9230992
IID000000002150894 Naturgy Energy Group, S.A. NTGY 63903X103 5650422 ES0116870314 ES Gas Utilities Utilities Utilities AAA 8.4 10.00 10.0 21 1 29 1 9.0 50 1 7.4 10.0 3.5 6.8 2 21 823.20 123217903 7.00 98.5330425 37.5278832 29.3611579
IID000000002150936 Erste Group Bank AG EBS 296036304 5289837 AT0000652011 AT Diversified Banks Banks Financials AA 5.2 8.30 8.6 13 1 54 3 4.2 33 2 5.5 9.7 0.9 4.7 3 0 5.00 NA 7.00 116.7378809 18.4237906 -10.5913889
IID000000002150938 Securitas AB SECU B 81373F105 5554041 SE0000163594 SE Security & Alarm Services Commercial Services & Supplies Industrials BBB 5.6 5.70 9.3 5 1 62 4 4.8 33 2 6.5 9.3 1.9 4.3 1 5 11.50 57274 7.00 10.0473216 -25.9442485 -22.4690459
IID000000002151160 ENEL - SPA ENEL 29265W207 7144569 IT0003128367 IT Electric Utilities Utilities Utilities AAA 7.2 8.80 9.0 54 1 13 2 6.8 33 3 4.4 10.0 1.3 7.0 2 14 655.80 47701647 7.00 9.6425575 -24.2302495 -7.2524707
IID000000002151173 Investor Aktiebolag INVE B NA BMV7PQ4 SE0015811963 SE Multi-Sector Holdings Diversified Financials Financials A 5.4 7.00 8.5 5 3 44 1 5.9 51 3 4.7 8.5 4.8 6.3 3 5 0.00 284615 7.00 93.5209998 19.3809689 9.0187664
IID000000002151494 Siemens Gamesa Renewable Energy, S.A. SGRE 82621P101 B01CP21 ES0143416115 ES Heavy Electrical Equipment Electrical Equipment Industrials A 5.8 6.30 7.1 36 2 23 3 4.8 41 2 5.1 10.0 1.8 7.0 2 0 2.40 856082 7.00 19.0959306 -50.7704160 -23.9647787
IID000000002151502 Richter Gedeon Vegyeszeti Gyar Nyilvanosan Mukodo Rt. RICHTER 368467205 BC9ZH86 HU0000123096 HU Pharmaceuticals Pharmaceuticals Health Care BBB 4.5 4.60 4.2 9 3 58 2 4.9 33 3 3.8 9.0 2.0 4.0 3 0 51.00 NA 7.00 13.2683360 -21.0774040 -18.6512695
IID000000002151773 SOCIETE GENERALE SA GLE 83364L109 5966516 FR0000130809 FR Diversified Banks Banks Financials AAA 7.0 10.00 10.0 13 1 54 1 6.7 33 1 6.4 10.0 0.8 7.0 3 0 2.50 81854 7.00 79.8820397 10.1341272 -9.2729970
IID000000002151843 IPSEN SA IPN 462629205 B0R7JF1 FR0010259150 FR Pharmaceuticals Pharmaceuticals Health Care A 4.9 6.20 5.0 9 2 58 2 4.8 33 2 5.1 10.0 2.3 6.3 2 0 7.70 31366 7.00 130.9239445 55.3776261 36.0470701
IID000000002152142 AEGON N.V. AGN 007924103 5927375 NL0000303709 NL Life & Health Insurance Life & Health Insurance Financials AA 5.9 8.40 8.4 5 1 62 3 4.8 33 1 7.7 10.0 0.8 5.7 4 0 1.20 2139 7.00 148.7438015 21.8086535 9.5596623
IID000000002152716 Swedbank AB SWED A 870195104 4846523 SE0000242455 SE Diversified Banks Banks Financials AA 6.3 8.57 9.2 13 1 54 1 5.2 33 1 6.8 10.0 0.8 6.3 3 0 0.80 5683 7.00 52.3890366 3.3427191 -10.0729209
IID000000002152916 NOVOZYMES A/S NZYM B 670108109 B798FW0 DK0060336014 DK Specialty Chemicals Specialty Chemicals Materials AAA 6.4 8.60 6.8 53 1 14 1 5.7 33 2 6.0 8.7 6.5 8.2 2 14 110.70 35000 7.00 53.2011769 15.3744846 6.4410833
IID000000002153232 Porsche Automobil Holding SE PAH3 73328P106 B3KQQB1 US73328P1066 DE Automobile Manufacturers Automobiles Consumer Discretionary B 3.6 1.43 5.6 30 3 37 3 3.8 33 4 1.6 10.0 2.3 7.0 3 0 26.18 196642951 7.00 162.5140479 -11.2704057 -3.8152610
IID000000002153255 SOLVAY SA SOLB 834437402 4821100 BE0003470755 BE Diversified Chemicals Diversified Chemicals Materials AA 5.3 7.20 6.0 53 1 14 1 5.2 33 4 4.2 7.6 6.9 7.5 2 14 865.80 28800000 9.00 47.0378476 -13.1802878 -13.9256445
IID000000002153917 TENARIS S.A. TEN 88031M109 7538515 LU0156801721 LU Oil & Gas Equipment & Services Energy Equipment & Services Energy BB 5.4 3.80 6.1 50 4 15 2 6.3 35 3 3.9 5.1 7.6 5.7 4 20 309.70 1300000 5.50 159.3051856 45.8072997 53.3067589
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IID000000002165146 ELIA GROUP SA ELI NA B09M9F4 BE0003822393 BE Electric Utilities Utilities Utilities AA 7.0 8.20 7.9 50 1 15 1 7.2 35 2 5.6 9.3 1.2 4.3 2 5 343.10 NA 6.00 58.8971890 49.8365776 33.3976834
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IID000000002165914 EXPERIAN PLC EXPN 30215C101 B19NLV4 GB00B19NLV48 IE Research & Consulting Services Professional Services Industrials A 6.2 6.70 10.0 5 3 60 3 5.3 35 1 7.3 10.0 1.7 7.2 3 5 4.50 300 7.00 37.1398034 17.2762103 -4.7260919
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IID000000002168927 Hannover Rueck SE HNR1 410693105 4511809 DE0008402215 DE Reinsurance Multi-Line Insurance & Brokerage Financials A 5.8 7.00 4.5 17 4 50 1 6.7 33 3 5.2 10.0 0.7 6.3 3 NA 0.10 1772 7.00 31.8953551 1.6550570 2.0495868
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IID000000002172084 JUMBO S.A. BELA 48138V105 7243530 GRS282183003 GR Specialty Stores Retail - Consumer Discretionary Consumer Discretionary BB 4.0 3.90 1.2 12 4 47 3 4.6 41 3 4.1 10.0 2.3 5.7 2 NA 22.60 NA 7.00 27.3019173 -6.2776708 -0.5268282
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IID000000002172935 Koninklijke Ahold Delhaize N.V. AD 500467501 BD0Q398 NL0011794037 NL Food Retail Retail - Food & Staples Consumer Staples AA 5.6 8.40 5.9 15 1 52 2 5.0 33 1 6.4 10.0 3.0 7.0 3 NA 37.20 488000 7.00 44.7910220 26.5031302 1.7117904
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分析與結果

一、相關係數熱力圖
knitr::include_graphics("data/pic1.png")

對於歐洲企業來説,對E向度曝險程度最高的產業來説,ESG總分、E向度、碳排放這些ESG因子與股價呈現正相關。

解讀:為因為歐洲法規變得嚴格,各家企業的ESG資訊有了透明度和一致性以後,開始成為投資人評斷標的的因素之一。

knitr::include_graphics("data/pic2.png")

能源產業中,碳排曝險分數與報酬率呈現正相關,而碳排總分與報酬率呈現負相關。

解讀:近兩年油價上漲,加上對石油需求提升,能源產業透過提高產能(碳排增加),達到獲利成長。


二、迴歸分析

將各產業劃分為低碳排產業和高碳排產業

# showing plots
heavy.graph

# showing plots
light.graph

# showing plots
energy.graph


三、歐盟碳排放分數分佈圖
map.graph

#######
# linear regression 
#######
# use 2y as a try, can switch out Y variable, and/or take out "Industry"

lr_heavy = lm(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, data = heavy_train)

lr_light = lm(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, data = light_train, na.action=na.omit)

lr_energy = lm(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE, data = energy_train)

#######
# random forest
#######

forest_heavy = randomForest(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, data = heavy_train, control = rpart.control(cp = 0.002, minsplit=30), importance = TRUE)

forest_light = randomForest(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, data = light_train, control = rpart.control(cp = 0.002, minsplit=30), importance = TRUE, na.action=na.omit)

forest_energy = randomForest(TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE , data = energy_train, control = rpart.control(cp = 0.002, minsplit=30), importance = TRUE, na.action=na.omit)

# predict the yhat by using the "unused" test set 
p_heavy<- predict(lr_heavy, heavy_test)
p_light<- predict(lr_light, light_test)
p_energy<- predict(lr_energy, energy_test)

# Compute errors: difference between predicted and actual test set 
error_heavy <- p_heavy - heavy_test[["TotalReturn_2y"]]
error_light <- p_light - light_test[["TotalReturn_2y"]]
error_energy <- p_energy - light_test[["TotalReturn_2y"]]

# Calculate RMSE
rmse_lm_h =sqrt(mean(error_heavy^2))
rmse_lm_l =sqrt(mean(error_light^2))
rmse_lm_e =sqrt(mean(error_energy^2))

rmse_forest_h = rmse(forest_heavy, heavy_test) 
rmse_forest_l = rmse(forest_light, light_test) 
rmse_forest_e = rmse(forest_energy, energy_test) 


Model Results

1. Linear Model Result
# these are report of linear models
summary(lr_heavy)
## 
## Call:
## lm(formula = TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + 
##     GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, 
##     data = heavy_train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -200.24  -80.03  -40.72   23.05 2158.64 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              203.983    164.637   1.239    0.216
## IVA_COMPANY_RATINGAA     -38.246     48.960  -0.781    0.435
## IVA_COMPANY_RATINGAAA    -37.942     86.234  -0.440    0.660
## IVA_COMPANY_RATINGB      -10.864    116.596  -0.093    0.926
## IVA_COMPANY_RATINGBB     -26.480     82.212  -0.322    0.748
## IVA_COMPANY_RATINGBBB     40.578     51.434   0.789    0.431
## IVA_COMPANY_RATINGCCC    -93.234    156.541  -0.596    0.552
## INDUSTRY_ADJUSTED_SCORE  -12.174     26.694  -0.456    0.649
## GOVERNANCE_PILLAR_SCORE    1.949      9.090   0.214    0.830
## CARBON_EMISSIONS_SCORE     1.258      5.951   0.211    0.833
## IndustryUtilities        -38.547     25.098  -1.536    0.126
## 
## Residual standard error: 192.8 on 330 degrees of freedom
## Multiple R-squared:  0.05417,    Adjusted R-squared:  0.02551 
## F-statistic:  1.89 on 10 and 330 DF,  p-value: 0.04565
summary(lr_light)
## 
## Call:
## lm(formula = TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + 
##     GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE + Industry, 
##     data = light_train, na.action = na.omit)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -202.70  -58.84  -19.51   25.84 2897.19 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     53.1243    58.5521   0.907 0.364357    
## IVA_COMPANY_RATINGAA            -5.4488    15.5294  -0.351 0.725721    
## IVA_COMPANY_RATINGAAA          -13.0431    29.8172  -0.437 0.661843    
## IVA_COMPANY_RATINGB              8.2091    38.2268   0.215 0.829987    
## IVA_COMPANY_RATINGBB            -3.4726    26.6378  -0.130 0.896293    
## IVA_COMPANY_RATINGBBB            5.4837    15.8433   0.346 0.729290    
## IVA_COMPANY_RATINGCCC           43.1886    53.8123   0.803 0.422313    
## INDUSTRY_ADJUSTED_SCORE          3.2569     8.3788   0.389 0.697537    
## GOVERNANCE_PILLAR_SCORE          0.1116     2.9888   0.037 0.970212    
## CARBON_EMISSIONS_SCORE          -2.0703     2.3839  -0.868 0.385245    
## IndustryConsumer Discretionary  25.9206    15.5077   1.671 0.094785 .  
## IndustryConsumer Staples       -26.2199    16.1823  -1.620 0.105328    
## IndustryFinancials              27.9983    14.5376   1.926 0.054255 .  
## IndustryHealth Care              9.6746    15.8912   0.609 0.542722    
## IndustryIndustrials             50.6109    14.8053   3.418 0.000642 ***
## IndustryInformation Technology  38.1272    15.0654   2.531 0.011457 *  
## IndustryReal Estate             -7.9411    18.1915  -0.437 0.662500    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 151 on 2008 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.02727,    Adjusted R-squared:  0.01952 
## F-statistic: 3.519 on 16 and 2008 DF,  p-value: 2.707e-06
summary(lr_energy)
## 
## Call:
## lm(formula = TotalReturn_2y ~ IVA_COMPANY_RATING + INDUSTRY_ADJUSTED_SCORE + 
##     GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE, data = energy_train)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -229.65  -81.62  -17.03   50.98  580.56 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)   
## (Intercept)              791.393    276.150   2.866  0.00545 **
## IVA_COMPANY_RATINGAA      83.823     77.293   1.084  0.28177   
## IVA_COMPANY_RATINGAAA    148.773    152.625   0.975  0.33294   
## IVA_COMPANY_RATINGB     -412.300    181.904  -2.267  0.02642 * 
## IVA_COMPANY_RATINGBB    -337.876    138.621  -2.437  0.01726 * 
## IVA_COMPANY_RATINGBBB   -174.707     76.008  -2.299  0.02444 * 
## IVA_COMPANY_RATINGCCC   -605.997    279.870  -2.165  0.03368 * 
## INDUSTRY_ADJUSTED_SCORE  -85.820     45.210  -1.898  0.06168 . 
## GOVERNANCE_PILLAR_SCORE   24.529     13.930   1.761  0.08250 . 
## CARBON_EMISSIONS_SCORE   -26.018      9.242  -2.815  0.00629 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 138.5 on 72 degrees of freedom
## Multiple R-squared:  0.272,  Adjusted R-squared:  0.181 
## F-statistic: 2.989 on 9 and 72 DF,  p-value: 0.004419

從以上結果得知,碳排放分數只在能源行業顯著並係數為負,這不符合我們的初步預期。 但觀察市場現況,我們認為這是合理的。因近兩年以來油價高漲且需求上升,能源產業在期間不斷提高產能,取得大幅獲利的同時,碳排放總量也提升。

同樣的假設也可以應用在高碳排產業上,因為它們的碳排放得分係數也是負值。 碳排放分數的係數僅在低碳排工業中為正,符合我們的初步預期。

2. Random Forest Result
# report of random forest model
print(forest_heavy)
## 
## Call:
##  randomForest(formula = TotalReturn_2y ~ IVA_COMPANY_RATING +      INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE +      Industry, data = heavy_train, control = rpart.control(cp = 0.002,      minsplit = 30), importance = TRUE) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 1
## 
##           Mean of squared residuals: 39464.82
##                     % Var explained: -3.8
print(forest_light)
## 
## Call:
##  randomForest(formula = TotalReturn_2y ~ IVA_COMPANY_RATING +      INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE +      Industry, data = light_train, control = rpart.control(cp = 0.002,      minsplit = 30), importance = TRUE, na.action = na.omit) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 1
## 
##           Mean of squared residuals: 23621.1
##                     % Var explained: -1.62
print(forest_energy)
## 
## Call:
##  randomForest(formula = TotalReturn_2y ~ IVA_COMPANY_RATING +      INDUSTRY_ADJUSTED_SCORE + GOVERNANCE_PILLAR_SCORE + CARBON_EMISSIONS_SCORE,      data = energy_train, control = rpart.control(cp = 0.002,          minsplit = 30), importance = TRUE, na.action = na.omit) 
##                Type of random forest: regression
##                      Number of trees: 500
## No. of variables tried at each split: 1
## 
##           Mean of squared residuals: 18245.62
##                     % Var explained: 21.08
vi = varImpPlot(forest_energy, type=1, main = "Random Forest Variable Importance: Energy")

這個區塊使用隨機森林的模型,我們可以從這個變量重要性圖片,從中知道每個變量的重要性,像是碳排放得分是所有“得分”中第二重要的變量。

# lower RMSE means better moedle performance 
rmse_lm_h
## [1] 120.13
rmse_lm_l
## [1] 118.7333
rmse_lm_e
## [1] 204.0956
rmse_forest_h
## [1] 129.6973
rmse_forest_l
## [1] 116.8229
rmse_forest_e
## [1] 144.7815

這張圖片,我們可以根據它們的均方根誤差 (rmse) 來查看模型的性能,均方根誤差是一種常用的測量數值之間差異的量度 數字越小,模型的性能就越好。


Wrap up

我們可以發現使用隨機森林的模型,性能相較於其他模型還要好。 最後,每個模型都有自己的優點和缺點,都是為了幫助我們了解碳排放分數如何影響總回報,使用模型來查看哪些變量更重要,然後使用線性回歸中的變量,查看它對我們的 Y 變量的實際係數之影響。


結論

目前雖然國際間尚未有完整的ESG資訊揭露法規法案,但我們可以從以上結果分析得出,在法規逐步落實的地區(歐洲),ESG評等、碳排放分數與股價呈現正相關,可以間接説明ESG已經開始成為投資決策中重要的一環。

另外,我們發現碳排放指數與股價係數之間有較為明顯的相關性,與輕碳排產業呈現正向關係、與高碳排產業呈現負向關係。可解讀為近一兩年來,油價高漲且對處於製造業循環上升段,對能源、工業、原物料的需求提升,促使這些高碳排產業擴張業務,進而帶動營收成長,因此造成碳排放分數越低(碳排量越高及管理能力越低),股價表現越好的情況。

總結而言,我們可以推測,在法規逐步落實的地區,ESG評等與股價開始有正相關的趨勢。可持續觀察近年這些ESG因子是不是變得更有效,以及企業在面對價格變高的碳費用時是否會面臨成本衝擊,有無辦法把這些費用轉嫁出去,畢竟在成本上升的過程中,獲利將可能被侵蝕,進而影響股價表現。


附錄

我們在測試資料的過程中,偶然發現中國企業的股價與ESG相關因子呈現明顯的負相關。

knitr::include_graphics("data/pic1.png")

對中國企業來説,ESG分數、E向度分數、G向度分數、S向度分數、碳排放分數皆與近兩年、一年、半年的累積報酬率呈現明顯負相關。(即分數越糟糕,股價表現越好) 解讀:中國股票主要是政策導向概念股,股價波動會被政府政策牽動,且ESG資訊相對於歐洲、美國等國家不透明。


四個問題

(1) Where you get your project ideas?

ESG是近幾年的熱門主題,但「ESG選股」是否真的有效是還需要被檢驗的。我們觀察到近三年,歐盟將會陸續制定相關法規,强迫企業進行ESG資訊揭露,因此我們推測「ESG資訊揭露」這件事,將會在近幾年逐步變得透明化且標準化,並逐漸成為選股新趨勢。同時碳市場價格高漲,勢必為企業帶來轉型風險,例如營運成本提升,進而侵蝕獲利等。 因此,我們希望可以針對相關議題探討,期待可以找出一些新發現。

(2) What R techniques have been used?

graphical techniques、Quantitative techniques

(3) How many new packages and functions are used?

tidyverse、readxl、caret、rpart.plot、rsample、randomForest、modelr、corrplot、sf、rnaturalearth、rnaturalearthdata、DT、glmnet、ggplot2、dplyr 、datatable、training與testing、Regression analysis、apply

(4) What is the most difficult part of your analysis?

ESG議題雖然被討論了很多年,但實際上目前尚未完全受法規管制,國際之間也缺乏通用的揭露標準,步伐最快的歐洲也只會在2024年才會完整落實法規。在這樣的情況下,要想得到各家企業「透明且標準化的ESG資訊」仍有一段很長的距離。而我們處在這個變革的開端,就算是資料庫裏的raw data也是相當雜亂,無論是想要去收集資料,或是考量用何種因子才能取得有效的實證結果,都相當困難,我們能做的就只有透過現在的市場趨勢去預測未來。