MA Thesis Data Selection
Turgud Valiyev
2025-04-16
1. Brief Info
This is an R Markdown document for Master Thesis in the title of ANALYZING STOCK MARKET BEHAVIOR AND PRICE PREDICTIONS ACROSS GLOBAL MARKETS. The following analysises is conducted in this paper - data selection, data preparation, and explaratory data analysis. The next steps of the analysis such as modeling and results is not included in this file and is implemented in python environment.
2. Library Installation
# Install packages if you haven't already
# install.packages("quantmod")
# install.packages("dplyr")
# install.packages("ggplot2")
# install.packages("tidyquant")
# Load packages into R environment
library(quantmod)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(tidyquant)
library(readxl)3. Data Import (from stckdatanalysis websource retrieved January 2025)
stocks_details <- read_excel("C:/Users/User/Desktop/Master Thesis/All Stocks.xlsx")
#View(stocks_details)
colnames(stocks_details)## [1] "No" "Symbol" "Company Name"
## [4] "Sector" "Market Cap" "% Change"
## [7] "Volume" "Revenue" "Market Cap Numeric"View of the data
head(stocks_details)## # A tibble: 6 × 9
## No Symbol `Company Name` Sector `Market Cap` `% Change` Volume Revenue
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 AAPL Apple Inc. Techn… 3,698.01B -0.0014 10694… 391.04B
## 2 1 GE General Electric C… Indus… 187.38B 0.0039 24289… 69.95B
## 3 1 LLY Eli Lilly and Comp… Healt… 707.21B 0.0245 41386… 40.86B
## 4 1 BRK.B Berkshire Hathaway… Finan… 973.14B 0.0087 32551 369.89B
## 5 1 WMT Walmart Inc. Consu… 730.40B -0.0056 27341… 673.82B
## 6 1 XOM Exxon Mobil Corpor… Energ… 477.57B -0.0135 13925… 343.82B
## # ℹ 1 more variable: `Market Cap Numeric` <dbl>nrow(stocks_details)## [1] 3225ncol(stocks_details)## [1] 9Initially 3225 companies are taken from stockanalysis.com
the number of stocks per each sector
stocksbysector <- stocks_details %>%
group_by(Sector) %>%
summarize(count = n(), .groups = "drop")
stocksbysector## # A tibble: 6 × 2
## Sector count
## <chr> <int>
## 1 Consumer Staple Sector 245
## 2 Energy Sector 250
## 3 Financial Sector 603
## 4 Healthcare Sector 1164
## 5 Industrials Sector 453
## 6 Technology Sector 510The 8 companies are deleted because they are not available and facing the fetching problem from yahoo finance
# Remove rows with problematic symbols
# We need to remove them because they are not availbale in the Yahoo Finance
stocks_details <- stocks_details %>%
filter(!Symbol %in% c("BRK.B", "PBR.A", "AGM.A", "VG", "AKO.A", "MKC.V","BF.A","DRDB","HEI.A"))
# Print the updated dataframe to confirm the removal
head(stocks_details)## # A tibble: 6 × 9
## No Symbol `Company Name` Sector `Market Cap` `% Change` Volume Revenue
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 AAPL Apple Inc. Techn… 3,698.01B -0.0014 10694… 391.04B
## 2 1 GE General Electric C… Indus… 187.38B 0.0039 24289… 69.95B
## 3 1 LLY Eli Lilly and Comp… Healt… 707.21B 0.0245 41386… 40.86B
## 4 1 WMT Walmart Inc. Consu… 730.40B -0.0056 27341… 673.82B
## 5 1 XOM Exxon Mobil Corpor… Energ… 477.57B -0.0135 13925… 343.82B
## 6 2 NVDA NVIDIA Corporation Techn… 3,494.48B -0.0451 16666… 113.27B
## # ℹ 1 more variable: `Market Cap Numeric` <dbl># Optionally, confirm the removal by checking the absence of these symbols
cat("Checking for presence of probelmatic symbols in the dataset:\n")## Checking for presence of probelmatic symbols in the dataset:if(any(stocks_details$Symbol == "BRK.B") | any(stocks_details$Symbol == "PBR.A")) {
cat("Problematic Symbols are still present.\n")
} else {
cat("Problematic Symbols have been successfully removed.\n")
}## Problematic Symbols have been successfully removed.4. Defining Market Leaders
### ADDING MARKET LEADER VARIABLE TO THE DATASET BASED ON MEGA and LARGE CAP CRITERION
# Define a new column 'Market Leader' to indicate whether a stock is a Mega Cap
stocks_details <- stocks_details %>%
mutate(MarketLeader = `Market Cap Numeric` > 200e9) # 200 billion represented in scientific notation
# Print the updated dataframe to check the new column
head(stocks_details)## # A tibble: 6 × 10
## No Symbol `Company Name` Sector `Market Cap` `% Change` Volume Revenue
## <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 AAPL Apple Inc. Techn… 3,698.01B -0.0014 10694… 391.04B
## 2 1 GE General Electric C… Indus… 187.38B 0.0039 24289… 69.95B
## 3 1 LLY Eli Lilly and Comp… Healt… 707.21B 0.0245 41386… 40.86B
## 4 1 WMT Walmart Inc. Consu… 730.40B -0.0056 27341… 673.82B
## 5 1 XOM Exxon Mobil Corpor… Energ… 477.57B -0.0135 13925… 343.82B
## 6 2 NVDA NVIDIA Corporation Techn… 3,494.48B -0.0451 16666… 113.27B
## # ℹ 2 more variables: `Market Cap Numeric` <dbl>, MarketLeader <lgl>#view(stocks_details)
### COUNTING MARKET LEADERS IN EACH SECTOR
# Calculate the number of market leaders in each sector
market_leader_count <- stocks_details %>%
group_by(Sector) %>%
summarise(NumberOfMarketLeaders = sum(MarketLeader, na.rm = TRUE),
.groups = 'drop') # This line ensures that the grouping is dropped after summarise
# Print the table of market leaders count by sector
print(market_leader_count)## # A tibble: 6 × 2
## Sector NumberOfMarketLeaders
## <chr> <int>
## 1 Consumer Staple Sector 5
## 2 Energy Sector 3
## 3 Financial Sector 8
## 4 Healthcare Sector 12
## 5 Industrials Sector 0
## 6 Technology Sector 14Decreasing cutoff from 200B to 100B.
# Rearranging threshold from 200b to 100b
# Define a new column 'Market Leader' to indicate whether a stock is a Mega Cap
stocks_details <- stocks_details %>%
mutate(MarketLeader = `Market Cap Numeric` > 100e9) # 200 billion represented in scientific notation
### COUNTING MARKET LEADERS IN EACH SECTOR
# Calculate the number of market leaders in each sector
market_leader_count <- stocks_details %>%
group_by(Sector) %>%
summarise(NumberOfMarketLeaders = sum(MarketLeader, na.rm = TRUE),
.groups = 'drop') # This line ensures that the grouping is dropped after summarise
# Print the table of market leaders count by sector
print(market_leader_count)## # A tibble: 6 × 2
## Sector NumberOfMarketLeaders
## <chr> <int>
## 1 Consumer Staple Sector 7
## 2 Energy Sector 5
## 3 Financial Sector 23
## 4 Healthcare Sector 25
## 5 Industrials Sector 10
## 6 Technology Sector 33Decreasing threshold from 100B to 50B.
# Rearranging threshold from 100b to 50b
# Define a new column 'Market Leader' to indicate whether a stock is a Mega Cap
stocks_details <- stocks_details %>%
mutate(MarketLeader = `Market Cap Numeric` > 50e9) # 200 billion represented in scientific notation
### COUNTING MARKET LEADERS IN EACH SECTOR
# Calculate the number of market leaders in each sector
market_leader_count <- stocks_details %>%
group_by(Sector) %>%
summarise(NumberOfMarketLeaders = sum(MarketLeader, na.rm = TRUE),
.groups = 'drop') # This line ensures that the grouping is dropped after summarise
# Print the table of market leaders count by sector
print(market_leader_count)## # A tibble: 6 × 2
## Sector NumberOfMarketLeaders
## <chr> <int>
## 1 Consumer Staple Sector 15
## 2 Energy Sector 21
## 3 Financial Sector 52
## 4 Healthcare Sector 44
## 5 Industrials Sector 33
## 6 Technology Sector 53Decreasing the threshold from 50B to 10B
# Rearranging threshold from 50b to 10b
# Define a new column 'Market Leader' to indicate whether a stock is a Mega Cap
stocks_details <- stocks_details %>%
mutate(MarketLeader = `Market Cap Numeric` > 10e9) # 200 billion represented in scientific notation
### COUNTING MARKET LEADERS IN EACH SECTOR
# Calculate the number of market leaders in each sector
market_leader_count <- stocks_details %>%
group_by(Sector) %>%
summarise(NumberOfMarketLeaders = sum(MarketLeader, na.rm = TRUE),
.groups = 'drop') # This line ensures that the grouping is dropped after summarise
# Print the table of market leaders count by sector
print(market_leader_count)## # A tibble: 6 × 2
## Sector NumberOfMarketLeaders
## <chr> <int>
## 1 Consumer Staple Sector 53
## 2 Energy Sector 50
## 3 Financial Sector 134
## 4 Healthcare Sector 85
## 5 Industrials Sector 114
## 6 Technology Sector 150We see the $10 Billion cutoff is proper for this analysis and based on literature it is also seen that it is correct. 10 Billion and above includes companies inside the large cap and mega cap.
### COUNTING MARKET LEADERS AND NON-MARKET LEADERS IN EACH SECTOR
# Calculate the number of market leaders and non-market leaders in each sector
sector_leader_counts <- stocks_details %>%
group_by(Sector) %>%
summarise(NumberOfMarketLeaders = sum(MarketLeader, na.rm = TRUE),
NumberOfNonMarketLeaders = sum(!MarketLeader, na.rm = TRUE),
.groups = 'drop') # This line ensures that the grouping is dropped after summarise
# Print the table of market leaders and non-market leaders count by sector
print(sector_leader_counts)## # A tibble: 6 × 3
## Sector NumberOfMarketLeaders NumberOfNonMarketLeaders
## <chr> <int> <int>
## 1 Consumer Staple Sector 53 189
## 2 Energy Sector 50 198
## 3 Financial Sector 134 466
## 4 Healthcare Sector 85 1079
## 5 Industrials Sector 114 338
## 6 Technology Sector 150 360Market Leaders are already defined. As a next step the Volatility levels will be defined…
————————————————————————————-
5. Importing all defined stocks from YahooFinace
# Load necessary libraries
library(quantmod)
library(lubridate)
# Ensure all stock symbols from stock_details are unique and non-NA
symbols <- unique(na.omit(stocks_details$Symbol))
# Initialize a list to store the stock data
symbol_data_list <- list()
# Set the current date and start date to 25 years ago
#end_date <- Sys.Date()
#start_date <- end_date %m-% years(25)
# Set the date range explicitly
start_date <- as.Date("2000-01-01")
end_date <- Sys.Date()
# Loop through each symbol and fetch the data
for (symbol in symbols) {
# Print the symbol being fetched for tracking progress
cat("Fetching data for:", symbol, "\n")
# Try to fetch the data, continue even if some symbols fail
tryCatch({
# Fetch stock data from Yahoo Finance
stock_data <- getSymbols(symbol, src = 'yahoo', from = start_date, to = end_date, auto.assign = FALSE)
# Store the data in the list with the symbol as the key
symbol_data_list[[symbol]] <- stock_data
}, error = function(e) {
cat("Error fetching data for symbol:", symbol, "- Error message:", e$message, "\n")
})
}All defined stocks are fetched (some of them have not fetched due to of the problems)
# Check the contents of one of the symbols to verify (pick a symbol known to be in the list)
if(length(symbol_data_list) > 0) {
print(head(symbol_data_list[[names(symbol_data_list)[1]]]))
} else {
cat("No data fetched. Check symbol validity or connection settings.")
}## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2000-01-03 0.936384 1.004464 0.907924 0.999442 535796800 0.8421506
## 2000-01-04 0.966518 0.987723 0.903460 0.915179 512377600 0.7711489
## 2000-01-05 0.926339 0.987165 0.919643 0.928571 778321600 0.7824333
## 2000-01-06 0.947545 0.955357 0.848214 0.848214 767972800 0.7147226
## 2000-01-07 0.861607 0.901786 0.852679 0.888393 460734400 0.7485783
## 2000-01-10 0.910714 0.912946 0.845982 0.872768 505064000 0.7354123# Check and print the number of stocks fetched
cat("Number of stocks successfully fetched:", length(symbol_data_list), "\n")## Number of stocks successfully fetched: 3185# Optionally, you can also list the symbols for which data has been fetched
if(length(symbol_data_list) > 0) {
cat("Symbols fetched successfully include:\n")
print(names(symbol_data_list))
} else {
cat("No stocks have been successfully fetched.")
}## Symbols fetched successfully include:
## [1] "AAPL" "GE" "LLY" "WMT" "XOM" "NVDA" "CAT" "UNH" "JPM"
## [10] "COST" "CVX" "MSFT" "RTX" "NVO" "V" "PG" "SHEL" "AVGO"
## [19] "HON" "JNJ" "MA" "KO" "TTE" "TSM" "UNP" "ABBV" "BAC"
## [28] "PEP" "COP" "ORCL" "ETN" "MRK" "WFC" "PM" "ENB" "CRM"
## [37] "BA" "TMO" "AXP" "UL" "ASML" "DE" "ABT" "BX" "BUD"
## [46] "PBR" "SAP" "LMT" "AZN" "MS" "MO" "BP" "CSCO" "UPS"
## [55] "ISRG" "GS" "BTI" "EOG" "ACN" "TT" "NVS" "HSBC" "MDLZ"
## [64] "EPD" "NOW" "RELX" "DHR" "RY" "CL" "WMB" "IBM" "PH"
## [73] "SYK" "SPGI" "DEO" "ET" "AMD" "WM" "BSX" "BLK" "TGT"
## [82] "KMI" "ADBE" "CTAS" "AMGN" "HDB" "MNST" "CNQ" "QCOM" "ITW"
## [91] "PFE" "MUFG" "KR" "EQNR" "TXN" "TRI" "SNY" "PGR" "KMB"
## [100] "OKE" "INTU" "MMM" "BMY" "C" "KDP" "SLB" "PLTR" "GD"
## [109] "GILD" "SCHW" "STZ" "ARM" "CP" "MDT" "KKR" "KVUE" "MPLX"
## [118] "ANET" "TDG" "VRTX" "CB" "SYY" "LNG" "AMAT" "EMR" "ELV"
## [127] "IBN" "KHC" "FANG" "SHOP" "FDX" "CI" "MMC" "CCEP" "PSX"
## [136] "UBER" "NOC" "HCA" "UBS" "GIS" "TRP" "SONY" "CNI" "ZTS"
## [145] "SMFG" "HSY" "SU" "ADP" "RSG" "MCK" "TD" "ABEV" "MPC"
## [154] "MU" "CSX" "REGN" "APO" "FMX" "OXY" "FI" "CARR" "BDX"
## [163] "PYPL" "K" "TRGP" "PANW" "PCAR" "GSK" "BN" "EL" "BKR"
## [172] "ADI" "CPRT" "CVS" "MCO" "CHD" "HES" "APP" "NSC" "COR"
## [181] "ICE" "ADM" "VLO" "MRVL" "JCI" "ALC" "CME" "TSN" "E"
## [190] "LRCX" "GWW" "A" "PNC" "MKC" "IMO" "KLAC" "CMI" "HLN"
## [199] "USB" "EQT" "INFY" "VRT" "TAK" "AON" "CLX" "TPL" "CRWD"
## [208] "PWR" "EW" "SAN" "CQP" "INTC" "HWM" "GEHC" "AJG" "HRL"
## [217] "WDS" "MSTR" "URI" "ARGX" "BMO" "DG" "CVE" "DELL" "WCN"
## [226] "IQV" "COF" "DLTR" "EXE" "APH" "AXON" "RMD" "COIN" "KOF"
## [235] "HAL" "CDNS" "AME" "VEEV" "BNS" "USFD" "CCJ" "MSI" "FAST"
## [244] "ALNY" "MFG" "SFM" "DVN" "SNPS" "DAL" "IDXX" "CM" "PFGC"
## [253] "PBA" "FTNT" "LHX" "HUM" "TFC" "CAG" "CTRA" "WDAY" "VRSK"
## [262] "DXCM" "BBVA" "CPB" "TS" "TEAM" "ODFL" "CNC" "AFL" "PRMB"
## [271] "YPF" "ADSK" "OTIS" "CAH" "MET" "BJ" "EC" "TTD" "IR"
## [280] "BNTX" "BK" "SJM" "WES" "NXPI" "FERG" "MTD" "ARES" "TAP"
## [289] "PAA" "WAB" "PHG" "TRV" "FTI" "ROP" "UAL" "WST" "MFC"
## [298] "AR" "COKE" "SNOW" "HEI" "TEVA" "NU" "OVV" "ACI" "PAYX"
## [307] "ROK" "WAT" "AMP" "DTM" "BG" "DDOG" "FER" "ONC" "ING"
## [316] "VNOM" "PPC" "FICO" "EFX" "NTRA" "ALL" "KNTK" "EDU" "TEL"
## [325] "GPN" "ZBH" "BCS" "RRC" "BRBR" "FIS" "XYL" "ILMN" "ITUB"
## [334] "APA" "LW" "GLW" "STE" "MSCI" "HESM" "INGR" "GRMN" "DOV"
## [343] "BIIB" "AIG" "CHRD" "ELF" "NET" "VLTO" "LH" "DFS" "AM"
## [352] "FRPT" "IT" "HUBB" "PODD" "NDAQ" "MTDR" "CELH" "CTSH" "LII"
## [361] "COO" "PRU" "SUN" "OLLI" "HUBS" "EME" "RPRX" "LYG" "POST"
## [370] "WIT" "RYAAY" "SMMT" "NWG" "DINO" "BRFS" "HPQ" "LUV" "ALGN"
## [379] "HOOD" "NOV" "COTY" "MCHP" "JBHT" "MOH" "ACGL" "TGS" "HIMS"
## [388] "MPWR" "DGX" "DB" "LB" "TAL" "HPE" "AER" "BAX" "OWL"
## [397] "NFG" "DAR" "ANSS" "SNA" "UTHR" "SLF" "NE" "CALM" "KEYS"
## [406] "WSO" "ICLR" "RJF" "WFRD" "LANC" "ZS" "GFL" "HOLX" "MTB"
## [415] "CHX" "LOPE" "ON" "CSL" "MRNA" "HIG" "VIST" "LRN" "GDDY"
## [424] "RBA" "NBIX" "TW" "CRK" "FLO" "ERIC" "BLDR" "AVTR" "WTW"
## [433] "CRC" "NWL" "FTV" "BAH" "RVTY" "FCNCA" "WHD" "IPAR" "BR"
## [442] "J" "FMS" "FCNCO" "IEP" "FIZZ" "ZM" "PNR" "INSM" "BRO"
## [451] "SM" "GHC" "GIB" "FTAI" "INCY" "STT" "MGY" "SMPL" "CDW"
## [460] "FIX" "DVA" "FITB" "CIVI" "ATGE" "GFS" "XPO" "ITCI" "SYF"
## [469] "AROC" "TBBB" "IOT" "IEX" "GMAB" "TROW" "GLNG" "SAM" "TYL"
## [478] "EXPD" "VTRS" "LPLA" "MUR" "JJSF" "CPAY" "MAS" "SOLV" "IX"
## [487] "CNX" "RLX" "NTAP" "SYM" "THC" "HBAN" "PAGP" "PSMT" "NOK"
## [496] "ZTO" "GMED" "TPG" "NXE" "LAUR" "STM" "RKLB" "UHS" "BAM"
## [505] "CRGY" "NOMD" "ASX" "OC" "TECH" "CINF" "NOG" "TER" "ACM"
## [514] "RDY" "MKL" "KGS" "WDC" "CNH" "BMRN" "WRB" "RIG" "CENT"
## [523] "PTC" "TXT" "PCVX" "KB" "LBRT" "SPB" "ALAB" "GGG" "SRPT"
## [532] "RF" "FRO" "TR" "TDY" "UHAL" "SNN" "RKT" "ARLP" "STRA"
## [541] "UI" "CW" "MEDP" "ERIE" "PTEN" "CENTA" "TOST" "POOL" "DOCS"
## [550] "PUK" "VRN" "CCU" "FSLR" "ATR" "IBKR" "VAL" "UTZ" "SMCI"
## [559] "SWK" "PEN" "NTRS" "HP" "CHEF" "PSTG" "CLH" "EXAS" "CBOE"
## [568] "UEC" "COCO" "ZBRA" "CHRW" "WBA" "CFG" "GPOR" "WMK" "VRSN"
## [577] "RTO" "QGEN" "BBD" "PBF" "THS" "CHKP" "SAIA" "CG" "BSM"
## [586] "UNFI" "AFRM" "ESLT" "BIO" "PFG" "STR" "PRDO" "LDOS" "NDSN"
## [595] "EHC" "L" "DNUT" "GRAB" "ITT" "HSIC" "FDS" "EE" "VITL"
## [604] "STX" "AAL" "RGEN" "TRU" "TDW" "GO" "SSNC" "NVT" "HQY"
## [613] "CRBG" "UGP" "EPC" "MDB" "TFII" "EXEL" "NMR" "USAC" "KLG"
## [622] "KSPI" "MTZ" "MASI" "KEY" "OII" "FDP" "DOCU" "ALLE" "GKOS"
## [631] "SHG" "CSAN" "ANDE" "TRMB" "TTEK" "BRKR" "JEF" "SDRL" "UTI"
## [640] "JBL" "WWD" "CRL" "SOFI" "AESI" "COUR" "CYBR" "BWXT" "TFX"
## [649] "RYAN" "STNG" "HELE" "TWLO" "LECO" "CHE" "EG" "BTU" "AFYA"
## [658] "NTNX" "RRX" "ROIV" "FNF" "DKL" "UVV" "GEN" "APG" "TEM"
## [667] "EQH" "BTE" "FLEX" "CNM" "ENSG" "BAP" "SOC" "UDMY" "MANH"
## [676] "AOS" "ASND" "BSBR" "NEXT" "DOLE" "UMC" "ULS" "RVMD" "RGA"
## [685] "TRMD" "IMKTA" "AZPN" "AAON" "JAZZ" "ARCC" "CMBT" "JBSS" "DT"
## [694] "ARMK" "BPMC" "MORN" "BKV" "TPB" "ENTG" "GNRC" "MDGL" "UNM"
## [703] "CLMT" "AGRO" "COHR" "AIT" "BBIO" "EWBC" "DNN" "NGVC" "AUR"
## [712] "PAC" "HALO" "RNR" "CVI" "AVO" "FFIV" "AYI" "LNTH" "CNA"
## [721] "HPK" "STKL" "SWKS" "RBC" "LEGN" "GGAL" "INSW" "MGPI" "BSY"
## [730] "BLD" "WAY" "HLI" "SEI" "DAO" "OKTA" "MLI" "CORT" "BCH"
## [739] "KOS" "DDL" "GWRE" "CRS" "MMSI" "AFG" "TALO" "HLF" "AKAM"
## [748] "WCC" "BLCO" "EVR" "MNR" "USNA" "DUOL" "WMS" "CYTK" "SF"
## [757] "WTTR" "BRCC" "EPAM" "CR" "ELAN" "ALLY" "DMLP" "HNST" "LOGI"
## [766] "STN" "STVN" "FUTU" "WKC" "SPTN" "JNPR" "KNX" "NUVL" "FHN"
## [775] "DHT" "WEST" "JKHY" "FLR" "GH" "AIZ" "GLP" "BGS" "CRDO"
## [784] "DRS" "TLX" "SEIC" "VET" "SENEB" "WIX" "FBIN" "GRFS" "BEN"
## [793] "KRP" "GOTU" "RBRK" "GTLS" "INSP" "KNSL" "XPRO" "SENEA" "CIEN"
## [802] "ALK" "IONS" "AEG" "NRP" "SNDL" "CLS" "LTM" "OPCH" "BNT"
## [811] "TNK" "HAIN" "PAYC" "DCI" "ALKS" "WBS" "HLX" "FC" "DAY"
## [820] "ATI" "AXSM" "GL" "FLNG" "LINC" "MNDY" "TTC" "ITGR" "PRI"
## [829] "LEU" "LMNR" "PCOR" "ASR" "RDNT" "UWMC" "RES" "VLGEA" "KVYO"
## [838] "JOBY" "NARI" "WAL" "GEL" "CVGW" "PCTY" "FLS" "VRNA" "PNFP"
## [847] "EFXT" "OTLY" "IONQ" "SARO" "TGTX" "BSAC" "ACDC" "CDXC" "YMM"
## [856] "KBR" "KRYS" "CFR" "VTLE" "LND" "NICE" "CAE" "ICUI" "ORI"
## [865] "DK" "APEI" "ENPH" "LPX" "OGN" "BMA" "NVGS" "EWCZ" "SNX"
## [874] "HII" "OSCR" "WTFC" "UUUU" "NUS" "ESTC" "DLB" "ACHC" "MKTX"
## [883] "MRC" "LWAY" "GTLB" "MIDD" "SRRK" "CBSH" "LPG" "ZVIA" "MTSI"
## [892] "TREX" "RARE" "CMA" "INVX" "YSG" "DSGX" "ESAB" "BHVN" "HLNE"
## [901] "PUMP" "ISPR" "U" "RHI" "PRCT" "ZION" "VTOL" "NATR" "PSN"
## [910] "ERJ" "BTSG" "CIB" "BORR" "AFRI" "ONTO" "LOAR" "ALVO" "FRHC"
## [919] "SBR" "MAMA" "DOX" "AGCO" "XRAY" "WF" "PARR" "NAMI" "CFLT"
## [928] "FCN" "SHC" "IVZ" "PDS" "BYND" "FOUR" "WTS" "VKTX" "SNV"
## [937] "CLB" "GLOB" "SPXC" "ADMA" "AXS" "GRNT" "SKIL" "ALTR" "AZEK"
## [946] "PBH" "SSB" "DEC" "ALCO" "CACI" "CWST" "APLS" "RLI" "CAPL"
## [955] "WILC" "DBX" "R" "CRSP" "ONB" "NESR" "SKIN" "APPF" "NVST"
## [964] "PB" "VTS" "VSTA" "TTAN" "CRNX" "BOKF" "REPX" "BILL" "MSA"
## [973] "PTCT" "STEP" "TXO" "CHGG" "KD" "ADT" "HAE" "BPOP" "EU"
## [982] "FTLF" "FN" "WSC" "RYTM" "JHG" "CLNE" "PHH" "PEGA" "BECN"
## [991] "RNA" "VOYA" "NPKI" "HFFG" "VERX" "ZWS" "IRTC" "JXN" "NGL"
## [1000] "ACU" "INFA" "VMI" "ACLX" "OMF" "TK" "CLEU" "LSCC" "AWI"
## [1009] "VCYT" "CADE" "NBR" "DSY" "OSK" "IMVT" "PJT" "NOA" "QSG"
## [1018] "MKSI" "KEX" "PRGO" "MARA" "NAT" "BRLS" "G" "CSWI" "TWST"
## [1027] "FAF" "PBT" "COE" "OTEX" "LSTR" "DNLI" "XP" "TEN" "LGCY"
## [1036] "PATH" "SITE" "WRBY" "FSK" "GPRK" "STG" "OLED" "BE" "ACAD"
## [1045] "MTG" "TTI" "BRID" "S" "FSS" "LFST" "COOP" "HNRG" "PAVS"
## [1054] "WEX" "SMR" "XENE" "SLM" "GFR" "IH" "EXLS" "GXO" "AMED"
## [1063] "OBDC" "CLCO" "VFF" "CCCS" "GATX" "CON" "ESNT" "EGY" "DIT"
## [1072] "HCP" "AL" "QDEL" "COLB" "SD" "LSF" "QRVO" "AMTM" "RXRX"
## [1081] "CACC" "OBE" "MYND" "SMTC" "HRI" "MLTX" "GBCI" "URG" "BEDU"
## [1090] "SPSC" "GTES" "KYMR" "QFIN" "SMC" "BRFH" "CVLT" "HXL" "FOLD"
## [1099] "MC" "SGU" "GROV" "ASTS" "DY" "ALHC" "BBAR" "PNRG" "UG"
## [1108] "AMKR" "TKR" "ZLAB" "CRVL" "RNGR" "CLNN" "SOUN" "SNDR" "NVCR"
## [1117] "SIGI" "NGS" "GNS" "OS" "STRL" "BHC" "AMG" "BRY" "FARM"
## [1126] "CWAN" "ROAD" "VCEL" "HOMB" "OIS" "SHOT" "QXO" "ACHR" "MRUS"
## [1135] "UPST" "UROY" "ORIS" "SATS" "BBU" "PDCO" "THG" "TBN" "COOT"
## [1144] "CGNX" "AEIS" "PRVA" "VIRT" "REI" "SOWG" "NVMI" "ACA" "CPRX"
## [1153] "LNC" "FTK" "DTCK" "NXT" "MMS" "AMRX" "UMBF" "GTE" "AACG"
## [1162] "BMI" "AVAV" "SWTX" "DNB" "EP" "WVVI" "RMBS" "IESC" "EWTX"
## [1171] "FG" "AMPY" "NAII" "WK" "MATX" "LIVN" "FNB" "WTI" "MSS"
## [1180] "LFUS" "TNET" "ARWR" "MAIN" "NC" "MTEX" "LITE" "MSM" "SGRY"
## [1189] "PFSI" "KGEI" "RAY" "ARW" "EXPO" "JANX" "FFIN" "FET" "FEDU"
## [1198] "LYFT" "FELE" "SEM" "UBSI" "DLNG" "SANW" "QTWO" "KTOS" "NAMS"
## [1207] "VLY" "SJT" "JVA" "TSEM" "SKYW" "NEOG" "OZK" "EPM" "LOCL"
## [1216] "SAIC" "PRIM" "AZTA" "ACT" "PROP" "SDOT" "VNT" "KAI" "CLOV"
## [1225] "WTM" "BROG" "BOF" "CRUS" "RXO" "TNDM" "ESGR" "PHX" "EDTK"
## [1234] "SRAD" "CBZ" "CGON" "HWC" "AMTX" "YHC" "NOVT" "VRRM" "ADUS"
## [1243] "RDN" "EPSN" "RMCF" "SITM" "BCO" "HCM" "FCFS" "MMLP" "YQ"
## [1252] "PONY" "SPR" "IBRX" "PIPR" "PTLE" "NCRA" "ACIW" "GEO" "MESO"
## [1261] "AGO" "GEOS" "DDC" "GDS" "GVA" "APGE" "SFBS" "DTI" "EEIQ"
## [1270] "RGTI" "CPA" "MIRM" "CNS" "ABVE" "QLYS" "UNF" "PTGX" "BGC"
## [1279] "SLNG" "VINE" "VRNS" "ENS" "PACS" "LAZ" "SND" "SBEV" "NSIT"
## [1288] "MDU" "LMAT" "BWIN" "LSE" "CTCX" "IDCC" "MIR" "WGS" "ABCB"
## [1297] "KAVL" "ITRI" "NPO" "LGND" "KMPR" "IMPP" "WAFU" "FRSH" "ECG"
## [1306] "CNTA" "RIOT" "KLXE" "IMG" "INTA" "OMAB" "HRMY" "AB" "VOC"
## [1315] "RKDA" "INGM" "BRC" "PINC" "GBDC" "NCSM" "IBG" "ASAN" "CXT"
## [1324] "VERA" "AX" "PED" "TWG" "BRZE" "MWA" "CERT" "IBOC" "RCON"
## [1333] "AMBO" "TENB" "KFY" "SUPN" "VCTR" "CRT" "ATPC" "CLBT" "GFF"
## [1342] "CNMD" "FLG" "USEG" "GV" "ALGM" "REZI" "SLNO" "ASB" "NINE"
## [1351] "LXEH" "AI" "ATMU" "BEAM" "CNO" "PVL" "GSUN" "BDC" "CAAP"
## [1360] "UFPT" "GSHD" "DWSN" "EDBL" "AVT" "HAYW" "IDYA" "NNI" "PRT"
## [1369] "CASK" "RELY" "GMS" "TMDX" "BANF" "NRT" "BTTR" "BOX" "ABM"
## [1378] "ARDT" "UCB" "PXS" "EEFT" "FA" "OMCL" "AUB" "VIVK" "AGRI"
## [1387] "ZETA" "ATKR" "IART" "MCY" "INDO" "AQB" "ST" "TEX" "ATRC"
## [1396] "TCBI" "TOPS" "TCTM" "PLXS" "CAR" "AGIO" "WU" "CGBS" "SNAX"
## [1405] "PI" "LUNR" "NEO" "FULT" "BATL" "SISI" "SLAB" "TRN" "IOVA"
## [1414] "TFSL" "BANL" "XXII" "SANM" "POWL" "GDRX" "EBC" "CKX" "FAMI"
## [1423] "CAMT" "ATS" "PGNY" "FIBK" "MXC" "GNLN" "GBTG" "HAFN" "TARS"
## [1432] "IFS" "SKYQ" "TANH" "PYCR" "NSP" "BLTE" "CATY" "HUSA" "STKH"
## [1441] "CORZ" "ENVX" "WVE" "APAM" "BRN" "PAY" "PLUG" "AKRO" "SNEX"
## [1450] "MTR" "NCNO" "MGRC" "ASTH" "FHB" "BPT" "RUM" "HUBG" "GERN"
## [1459] "HGTY" "MARPS" "ASGN" "MAN" "SDGR" "CBU" "EONR" "YOU" "JBLU"
## [1468] "TXG" "HTGC" "TPET" "WRD" "AZZ" "TVTX" "CLSK" "BLKB" "MRCY"
## [1477] "AMPH" "WD" "DXC" "ENOV" "TDOC" "WSFS" "CLVT" "ZIM" "IRON"
## [1486] "FHI" "PAYO" "ENR" "DVAX" "BFH" "FORM" "MNKD" "FBP" "ALIT"
## [1495] "CXW" "IMCR" "GNW" "BL" "SEB" "PHR" "CVBF" "POWI" "MYRG"
## [1504] "ARQT" "BUR" "ALKT" "NPWR" "GPCR" "BHF" "ZI" "HNI" "FTRE"
## [1513] "BKU" "AGYS" "SXI" "CLDX" "NMIH" "FROG" "ARCB" "BCRX" "PLMR"
## [1522] "ESE" "WERN" "GLPG" "BOH" "DLO" "EPAC" "ATEC" "PRK" "LIF"
## [1531] "VSTS" "EVO" "SFNC" "DV" "ICFI" "AGL" "INDB" "IPGP" "HI"
## [1540] "INDV" "BANC" "AMBA" "AIR" "PRAX" "WAFD" "DOCN" "ALG" "AAPG"
## [1549] "LMND" "SYNA" "AGX" "NHC" "LPL" "XMTR" "NVAX" "ENVA" "RNG"
## [1558] "WOR" "VIR" "IREN" "AVPT" "CDLR" "VRDN" "FFBC" "DIOD" "HURN"
## [1567] "INMD" "TBBK" "GRND" "GBX" "KNSA" "TOWN" "CNXC" "VSEC" "MRVI"
## [1576] "AVAL" "TDC" "ULCC" "NRIX" "SPNT" "BTDR" "HLMN" "DYN" "PFS"
## [1585] "ALRM" "KMT" "XNCR" "HUT" "FIVN" "PCT" "SPRY" "PPBI" "PRGS"
## [1594] "CMPR" "GRDN" "WULF" "OSIS" "GOGL" "AHCO" "MRX" "KLIC" "ROCK"
## [1603] "USPH" "GCMG" "RUN" "PRG" "RXST" "FBK" "PAR" "ALGT" "ARDX"
## [1612] "BANR" "APPN" "BWLP" "MDXG" "FRME" "TTMI" "SBLK" "BLFS" "RNST"
## [1621] "ODD" "HEES" "AORT" "LION" "STNE" "EVEX" "SYRE" "SBCF" "KC"
## [1630] "REVG" "OCUL" "NBTB" "RPD" "CODI" "ARVN" "TRMK" "LSPD" "DSGR"
## [1639] "DAWN" "WSBC" "ACLS" "DAC" "SNDX" "LU" "NATL" "TNC" "LQDA"
## [1648] "AGM" "BB" "CMRE" "MD" "EFSC" "SWI" "VVX" "STAA" "SYBT"
## [1657] "VSH" "HLIO" "RCUS" "CALX" "KRNT" "CDNA" "TFIN" "FLYW" "AMRC"
## [1666] "HROW" "TRUP" "VICR" "ATSG" "ADPT" "PWP" "VIAV" "BV" "WEAV"
## [1675] "TSLX" "EXTR" "CMPO" "INVA" "INTR" "QUBT" "TGI" "MYGN" "OFG"
## [1684] "EVTC" "SFL" "EVH" "FIHL" "CXM" "NMM" "OMI" "HG" "QBTS"
## [1693] "WLFC" "AUPH" "PSEC" "AVDX" "DNOW" "ANIP" "CIFR" "APLD" "TILE"
## [1702] "PCRX" "HTH" "IBTA" "APOG" "NTLA" "SUPV" "VYX" "BLBD" "RCKT"
## [1711] "LC" "WNS" "LZ" "PLSE" "SKWD" "PAGS" "PBI" "EMBC" "CASH"
## [1720] "MQ" "CDRE" "TLRY" "STC" "PLUS" "LNN" "ELVN" "FBNC" "RAMP"
## [1729] "DXPE" "AMN" "PAX" "EVCM" "NSSC" "COLL" "LOB" "DAVA" "TPC"
## [1738] "REPL" "GB" "MRTN" "BGM" "FINV" "ROG" "PL" "ABCL" "LKFN"
## [1747] "SIMO" "HTZ" "PHVS" "CHCO" "JAMF" "NVEE" "ZYME" "BBUC" "CNXN"
## [1756] "CRAI" "OPK" "FCF" "KN" "EOSE" "BKDT" "NWBI" "SPT" "UP"
## [1765] "BKD" "MBIN" "GDYN" "NNE" "HSTM" "CLBK" "SONO" "AMSC" "FNA"
## [1774] "NBHC" "UCTT" "ARLO" "GRAL" "NTB" "NABL" "RDW" "GYRE" "TWFG"
## [1783] "MXL" "CECO" "PNTG" "NIC" "NN" "ULH" "OCS" "HMN" "SEMR"
## [1792] "CTOS" "AVAH" "CUBI" "DFIN" "ASPN" "COGT" "VRTS" "BHE" "NX"
## [1801] "ESTA" "PX" "VRNT" "BBSI" "AVBP" "STEL" "VECO" "CCEC" "EOLS"
## [1810] "SASR" "UPBD" "LMB" "BCYC" "HOPE" "PD" "JBI" "AVXL" "SEZL"
## [1819] "DBD" "CMCO" "PAHC" "NAVI" "PLAB" "KFRC" "CVAC" "GSBD" "CTS"
## [1828] "DLX" "NUVB" "STBA" "MTTR" "GRC" "PRTA" "VBTX" "INFN" "THR"
## [1837] "ETNB" "SRCE" "HLIT" "TRNS" "TYRA" "TCBK" "NTCT" "EH" "BFLY"
## [1846] "WT" "TH" "HCSG" "WABC" "ADEA" "CRESY" "CBLL" "BLX" "AAOI"
## [1855] "PRLB" "SEPN" "DCOM" "MLNK" "DCO" "RLAY" "QCRH" "JKS" "FWRD"
## [1864] "AXGN" "BUSE" "TASK" "GIC" "RBCAA" "KARO" "FBYD" "IRMD" "CET"
## [1873] "SPNS" "HSII" "QURE" "MFIC" "VNET" "VLRS" "OPT" "OCSL" "HIMX"
## [1882] "HY" "AVDL" "BY" "CSGS" "ERII" "SANA" "TIGR" "AMPL" "FIP"
## [1891] "CRMD" "EIG" "DQ" "HTLD" "DH" "NMFC" "FSLY" "SERV" "CSTL"
## [1900] "SAFT" "SABR" "SNCY" "CTKB" "HCI" "FORTY" "BXC" "ORIC" "BHLB"
## [1909] "DCBO" "PSIX" "CRON" "CCB" "ATEN" "SKYH" "OFIX" "GABC" "ENFN"
## [1918] "SPLP" "TECX" "PFBC" "VSAT" "EVTL" "AVNS" "ROOT" "COHU" "MATW"
## [1927] "SIBN" "BCSF" "OLO" "GSL" "DNA" "ECPG" "CRCT" "ADSE" "SLP"
## [1936] "FSUN" "NYAX" "GLDD" "LENZ" "FBMS" "SMWB" "SWIM" "CAPR" "PEBO"
## [1945] "RCAT" "ASTE" "SENS" "SII" "SCSC" "WNC" "VERV" "IGIC" "AOSL"
## [1954] "MEG" "IMTX" "CSWC" "DSP" "MVST" "PLRX" "BOW" "ICHR" "CVLG"
## [1963] "BVS" "OCFC" "INOD" "ECO" "KIDS" "OBK" "TIXT" "ZIP" "EBS"
## [1972] "BRKL" "PDFS" "NVRI" "BCAX" "AMAL" "COMM" "NPK" "AUTL" "BBDC"
## [1981] "VTEX" "JELD" "ABUS" "BFC" "DAVE" "MAGN" "DNTH" "AMSF" "XRX"
## [1990] "ZJK" "IMNM" "TMP" "BELFA" "PLPC" "KURA" "LX" "VMEO" "BYRN"
## [1999] "PHAR" "SBSI" "DGII" "GNK" "CRGX" "BRDG" "EXOD" "BLDP" "CGEM"
## [2008] "CTBI" "PENG" "RGR" "LAB" "PFLT" "ARRY" "RYI" "AUNA" "NCDL"
## [2017] "SEDG" "EVLV" "EYPT" "CGBD" "SHLS" "CASS" "IRWD" "AMTB" "PSFE"
## [2026] "ATRO" "HUMA" "PFC" "BELFB" "FLX" "CCRN" "BHRB" "PRO" "SBC"
## [2035] "VREX" "SLRC" "DMRC" "EBF" "SVRA" "FUFU" "BBAI" "ASC" "TMCI"
## [2044] "GHLD" "ACMR" "WLDN" "STOK" "MTAL" "WOLF" "KODK" "ACCD" "CNOB"
## [2053] "BLND" "KELYB" "UPB" "TRIN" "ML" "KELYA" "ERAS" "FMBH" "TUYA"
## [2062] "IIIN" "SRDX" "BITF" "INDI" "ACCO" "TALK" "UVSP" "RDWR" "GHM"
## [2071] "FLGT" "HFWA" "ETWO" "AIRJ" "AKBA" "ATLC" "CRNC" "NWPX" "TBPH"
## [2080] "PRAA" "CSIQ" "KE" "XERS" "OSBC" "PRTH" "SWBI" "SMLR" "PRA"
## [2089] "CINT" "BWMN" "ANAB" "MCBS" "BASE" "EAF" "MNMD" "NBN" "GCT"
## [2098] "BOC" "DCTH" "TYG" "HCKT" "TWI" "RAPP" "EGBN" "YEXT" "TRC"
## [2107] "INNV" "CFFN" "AIOT" "OFLX" "MXCT" "AC" "CRSR" "ACTG" "PROK"
## [2116] "NBBK" "RZLV" "CYRX" "DCGO" "TCPC" "IIIV" "FREY" "ALT" "CPF"
## [2125] "DJCO" "RR" "MLYS" "TIPT" "DAKT" "SHYF" "RNAC" "FCBC" "RSKD"
## [2134] "AMPX" "ANGO" "CFB" "CEVA" "NL" "CLPT" "BFST" "NTGR" "LNZA"
## [2143] "MGTX" "ABL" "MLAB" "SB" "OMER" "FDUS" "NVTS" "PAMT" "NNOX"
## [2152] "EQBK" "ADTN" "BLDE" "KROS" "IBCP" "BBCP" "QTRX" "SLQT" "PGY"
## [2161] "SPIR" "PHAT" "CCAP" "OUST" "MTRX" "ARCT" "MBWM" "SKYT" "LXFR"
## [2170] "PACB" "UFCS" "PUBM" "PKOH" "SIGA" "AACT" "RPAY" "QUAD" "ABSI"
## [2179] "ORRF" "KULR" "ASLE" "GHRS" "GSBC" "OSPN" "MEC" "TRDA" "HBNC"
## [2188] "IMOS" "TITN" "YI" "NOAH" "SSYS" "SATL" "MREO" "HAFC" "YALA"
## [2197] "CIX" "CYH" "HBT" "CTLP" "MTW" "TRML" "LPRO" "IMXI" "RLGT"
## [2206] "ORGO" "OPY" "ITRN" "ARQ" "OLMA" "EZPW" "CGNT" "RGP" "ANNX"
## [2215] "MCB" "CNDT" "FSTR" "NPCE" "AMRK" "CAN" "TATT" "PSNL" "GLAD"
## [2224] "MGIC" "PKE" "SAGE" "VINP" "NOVA" "ORN" "URGN" "SMBC" "HKD"
## [2233] "NVX" "CVRX" "FSBC" "CLMB" "FORR" "SNDA" "INV" "LASR" "MG"
## [2242] "ETON" "ESQ" "LYTS" "AMBI" "ZVRA" "HIPO" "AEHR" "FCEL" "TKNO"
## [2251] "OPFI" "RXT" "TG" "KALV" "VEL" "NNDM" "PANL" "CELC" "CAC"
## [2260] "BIGC" "ESEA" "NRC" "TRST" "FARO" "FLYX" "KOD" "WRLD" "PRCH"
## [2269] "AZUL" "PRTC" "CCBG" "CLFD" "GENC" "ATXS" "CION" "DDD" "QSI"
## [2278] "ALTI" "RDVT" "TCMD" "WASH" "MITK" "KMDA" "LDI" "ARQQ" "ESPR"
## [2287] "MOFG" "XPER" "EHAB" "ACIC" "ATOM" "PGEN" "AMBC" "CCSI" "ALLO"
## [2296] "HTBI" "MTLS" "ABVX" "UVE" "BAND" "PRME" "HTBK" "AIP" "CDXS"
## [2305] "HIFS" "CRNT" "ZIMV" "SPFI" "UIS" "NYXH" "MPB" "POET" "AURA"
## [2314] "BTBT" "MEI" "RIGL" "PGC" "ALNT" "RGNX" "NVEC" "VALN" "THFF"
## [2323] "API" "OABI" "TRAK" "TREE" "LSAK" "TERN" "QD" "ILLR" "AHG"
## [2332] "SMBK" "KLTR" "CERS" "FISI" "ARBE" "ENGN" "GDOT" "OOMA" "CMPX"
## [2341] "BCAL" "BKKT" "ALMS" "SHBI" "GILT" "MBX" "FFWM" "LGTY" "LXRX"
## [2350] "FMNB" "IBEX" "CMRX" "DGICA" "HCAT" "PFIS" "EGHT" "KRRO" "CCNE"
## [2359] "BKSY" "TRVI" "PSBD" "EB" "NMRA" "DGICB" "FRGT" "CRVS" "GBLI"
## [2368] "MVIS" "GLUE" "MSBI" "SMRT" "TBRG" "HONE" "BLZE" "SPOK" "NFBK"
## [2377] "VUZI" "VMD" "GAIN" "NRDY" "TNGX" "ALRS" "VLN" "SMTI" "GLRE"
## [2386] "TSSI" "ZBIO" "VALU" "VPG" "XOMA" "EBTC" "LAW" "TSHA" "PNNT"
## [2395] "EXFY" "VYGR" "VBNK" "AUDC" "ALDX" "BHB" "PERF" "IMMP" "CBNK"
## [2404] "ASUR" "ATYR" "GCBC" "AEVA" "OSUR" "ANSC" "IMMR" "AIRS" "AROW"
## [2413] "ONTF" "CADL" "HIVE" "DOMO" "CTNM" "EQV" "PDYN" "ELMD" "AAM"
## [2422] "ICG" "DRTS" "KRNY" "TSAT" "AQST" "TCBX" "ALLT" "DSGN" "WDH"
## [2431] "ATGL" "YMAB" "ITIC" "SVCO" "ITOS" "YRD" "MAPS" "SOPH" "DHIL"
## [2440] "RZLT" "FFIC" "HRTX" "NRIM" "BDMD" "UNTY" "INGN" "NETD" "VSTM"
## [2449] "RWAY" "CGC" "BSVN" "CATX" "ASA" "ACIU" "BSRR" "DMAC" "CARE"
## [2458] "SLN" "WALD" "ACRS" "XYF" "AVIR" "GNTY" "ZYXI" "CCIX" "CMPS"
## [2467] "MFH" "ATAI" "BMRC" "ELDN" "SCM" "MPLN" "FMAO" "AMRN" "FBIZ"
## [2476] "VNDA" "HRZN" "CCCC" "GPAT" "MNPR" "ALF" "BNTC" "MBAV" "RCEL"
## [2485] "BWB" "AMLX" "MBI" "THRD" "CWBC" "SGMO" "HBCP" "LUNG" "RBB"
## [2494] "NAUT" "WTBA" "HITI" "RRBI" "ANIK" "USCB" "TVGN" "PMTS" "LFCR"
## [2503] "FRBA" "NUTX" "HRTG" "ARAY" "ACNB" "ABEO" "RM" "LRMR" "GIG"
## [2512] "OCGN" "NEWT" "CRDF" "SAR" "DRUG" "GOCO" "FHTX" "VACH" "FULC"
## [2521] "HYAC" "NGNE" "FSBW" "PRQR" "CCIR" "LFMD" "CIVB" "SERA" "NODK"
## [2530] "CGEN" "SSBK" "FDMT" "CUB" "MDWD" "BEAG" "UTMD" "SIMA" "ACB"
## [2539] "COFS" "PROF" "EHTH" "GOSS" "TPVG" "IVA" "OBT" "CDTX" "MLAC"
## [2548] "BWAY" "SFST" "AVR" "ALDF" "MGNX" "POLE" "OBIO" "VCIC" "ZYBT"
## [2557] "PDLB" "MYO" "FNLC" "CYBN" "FOA" "BTMD" "GRAF" "OGI" "GSRT"
## [2566] "SLRN" "INBK" "ACTU" "HOND" "MOLN" "CZFS" "BIOA" "LIEN" "NVRO"
## [2575] "LPBB" "INBX" "BCML" "PHLT" "PBFS" "KRMD" "SNFCA" "IKT" "JMSB"
## [2584] "INMB" "LPAA" "PROC" "CZNC" "STXS" "BRBS" "LXEO" "NECB" "HURA"
## [2593] "FRST" "VOR" "HIT" "PLX" "PCB" "GNFT" "CBAN" "VXRT" "LNKB"
## [2602] "VERU" "TDAC" "LYEL" "PLBC" "NKTX" "ALEC" "FDBC" "INFU" "FLIC"
## [2611] "GLSI" "MVBF" "ADCT" "GHI" "FENC" "HLXB" "ACRV" "SBXD" "JYNT"
## [2620] "SAMG" "NBTX" "BACQ" "AMWL" "SBT" "STRO" "SCPH" "CLLS" "NKTR"
## [2629] "BMEA" "RAPT" "VTYX" "CSBR" "ZNTL" "TLSI" "KYTX" "TIL" "AKYA"
## [2638] "FATE" "ADAP" "IPHA" "BDSX" "TELO" "SGHT" "MCRB" "CKPT" "PBYI"
## [2647] "ARTV" "CHRS" "TCRX" "RENB" "SEER" "BDTX" "LCTX" "ZJYL" "NOTV"
## [2656] "IFRX" "CRBU" "MIST" "SGMT" "GNLX" "TSVT" "PDEX" "QIPT" "SLDB"
## [2665] "CRBP" "ANRO" "NVCT" "CABA" "CLYM" "ZOM" "SAVA" "CRDL" "EUDA"
## [2674] "ZTEK" "ENTA" "ATOS" "SRTS" "EDIT" "ALGS" "ZURA" "ECOR" "XBIT"
## [2683] "MGX" "LNSR" "ELUT" "EPRX" "ALLK" "TELA" "MLSS" "CAMP" "ACHV"
## [2692] "FONR" "PETS" "GUTS" "IGMS" "OPRX" "ABOS" "COYA" "MGRM" "FBRX"
## [2701] "TNYA" "PYXS" "HLVX" "SY" "NTRB" "MDXH" "CUE" "DBVT" "ASMB"
## [2710] "TARA" "ADVM" "MNOV" "IMUX" "ORMP" "MASS" "ANIX" "INZY" "ME"
## [2719] "VIGL" "CTOR" "RVPH" "ENTX" "KPTI" "IMAB" "DERM" "TLSA" "RANI"
## [2728] "JSPR" "ADAG" "RBOT" "SKYE" "AGEN" "ELTX" "LPTX" "VTGN" "CBUS"
## [2737] "XFOR" "RGLS" "XTNT" "SCLX" "EDAP" "ASRT" "ALXO" "MRSN" "THTX"
## [2746] "AVTX" "INCR" "INO" "EPIX" "ARMP" "AADI" "GALT" "IPSC" "ANL"
## [2755] "ACET" "GNTA" "HBIO" "BLUE" "OKUR" "BNR" "AVTE" "MODV" "APLT"
## [2764] "STIM" "NSPR" "VANI" "OWLT" "IKNA" "HYPR" "TNXP" "RMTI" "SLS"
## [2773] "PIII" "PMVP" "XGN" "CLSD" "WOK" "QNCX" "CTMX" "BYSI" "FORA"
## [2782] "ICCM" "ANVS" "CNTX" "MURA" "PRE" "PRLD" "OPTN" "VRCA" "ABP"
## [2791] "IMRX" "ICAD" "PEPG" "KRON" "UNCY" "SGN" "GBIO" "RGC" "IOBT"
## [2800] "HOWL" "CCEL" "NEUE" "ONCY" "NVNO" "FBLG" "GANX" "RPID" "LTRN"
## [2809] "PDSB" "VNRX" "TOI" "OSTX" "CTSO" "IVVD" "BEAT" "IMMX" "CCLD"
## [2818] "CELU" "APYX" "STTK" "CNTB" "BOLD" "MODD" "OVID" "RPTX" "FGEN"
## [2827] "PTHL" "OMIC" "ALVR" "FBIO" "LSB" "LUCD" "OTLK" "SHLT" "MAIA"
## [2836] "RNTX" "ACHL" "ATRA" "SPRO" "JUNS" "KZR" "ICCC" "OM" "VTVT"
## [2845] "SER" "DYAI" "QTTB" "CLGN" "ITRM" "TPST" "CASI" "ESLA" "VYNE"
## [2854] "BRNS" "SCYX" "ATNM" "UBX" "ANEB" "DXR" "GRCE" "CMMB" "ELEV"
## [2863] "DTIL" "NRXP" "SCNX" "SABS" "ONMD" "MDAI" "PASG" "KALA" "ANTX"
## [2872] "RADX" "INKT" "NMTC" "CALC" "SRZN" "INTS" "XCUR" "RLYB" "CPIX"
## [2881] "XLO" "IRD" "DHAI" "ZCMD" "OKYO" "NXL" "CVKD" "MBOT" "BIVI"
## [2890] "OCX" "ENZ" "RNXT" "COCH" "MOVE" "POCI" "SNYR" "XAIR" "CGTX"
## [2899] "IXHL" "CVM" "LEXX" "MLEC" "LGHL" "GDTC" "RAIN" "AXDX" "PMN"
## [2908] "ERNA" "BCAB" "CRIS" "AKTX" "NSYS" "CTXR" "NRXS" "OMGA" "IBIO"
## [2917] "DARE" "CARA" "IRIX" "ATHE" "DRIO" "GELS" "PULM" "FEMY" "HOOK"
## [2926] "DRRX" "ENLV" "IGC" "VSEE" "CODX" "NRSN" "RVP" "LPCN" "NXGL"
## [2935] "LVTX" "LGVN" "PLUR" "SLGL" "COEP" "EQ" "CYTH" "NXTC" "VVOS"
## [2944] "MRKR" "APRE" "BLRX" "ALUR" "LSTA" "PTN" "TENX" "PYPD" "SNTI"
## [2953] "COCP" "ATHA" "BFRG" "CCM" "AMS" "TRAW" "IBO" "INAB" "CARM"
## [2962] "GOVX" "BOLT" "IPA" "KAPA" "CRVO" "MEIP" "MIRA" "NKGN" "CDIO"
## [2971] "IINN" "AFMD" "BCTX" "GLYC" "STRM" "BSGM" "NEPH" "LFWD" "BTAI"
## [2980] "SYBX" "MTVA" "COSM" "CING" "BRTX" "FLGC" "NNVC" "CANF" "SPRB"
## [2989] "XTLB" "HCWB" "DWTX" "NERV" "PHGE" "ACXP" "CHRO" "ADGM" "OCEA"
## [2998] "CLDI" "LUCY" "EKSO" "IMNN" "AIM" "TRIB" "SNSE" "INDP" "TXMD"
## [3007] "PRPH" "BCLI" "APTO" "SSKN" "BNGO" "IMRN" "LYRA" "EVGN" "RLMD"
## [3016] "TLPH" "KPRX" "PMCB" "BIAF" "AIMD" "DOMH" "BMRA" "CJJD" "CLRB"
## [3025] "TSBX" "MNDR" "AYTU" "APLM" "MBIO" "JAGX" "CSCI" "BCDA" "ENSC"
## [3034] "POAI" "PRPO" "APDN" "ELAB" "HOTH" "CUTR" "VRAX" "PPBT" "EVOK"
## [3043] "KLTO" "MYNZ" "AEMD" "SILO" "SYRA" "NURO" "BFRI" "MHUA" "RDHL"
## [3052] "KDLY" "EDSA" "CHEK" "MGRX" "GLTO" "KZIA" "OTRK" "PSTV" "APM"
## [3061] "PAVM" "NEUP" "EVAX" "STRR" "ICU" "AWH" "ADXN" "VIRX" "CYCN"
## [3070] "XBIO" "IMCC" "SNGX" "ABVC" "EYEN" "QNTM" "INBS" "ALZN" "SSY"
## [3079] "ADIL" "XWEL" "PHIO" "CNSP" "PLRZ" "ALLR" "CMND" "GNPX" "SBFM"
## [3088] "SYRS" "AEON" "PBM" "TNON" "SONN" "GRI" "CDT" "LIXT" "GTBP"
## [3097] "TTOO" "ONVO" "MBRX" "NUWE" "FOXO" "CERO" "PRTG" "SNPX" "HCTI"
## [3106] "NCNA" "GLMD" "SINT" "CELZ" "TOVX" "ONCO" "CPHI" "ARTL" "SNOA"
## [3115] "OGEN" "SLRX" "RNAZ" "BPTH" "ATXI" "NIVF" "AMIX" "NBY" "VINC"
## [3124] "ZVSA" "LIPO" "NAYA" "STSS" "XYLO" "TTNP" "HSCS" "PTPI" "VTAK"
## [3133] "TNFA" "YCBD" "SHPH" "WORX" "RSLS" "ATNF" "XRTX" "QLGN" "ENVB"
## [3142] "MTNB" "KTTA" "THAR" "INM" "PTIX" "NDRA" "ICCT" "SCNI" "TCRT"
## [3151] "NLSP" "TCBP" "BDRX" "BBLG" "HSDT" "VERO" "LGMK" "DRMA" "BJDX"
## [3160] "ENTO" "APVO" "AKAN" "CYCC" "TIVC" "PCSA" "SPRC" "ISPC" "SXTP"
## [3169] "AZTR" "PALI" "PRFX" "QNRX" "AVGR" "ADTX" "WINT" "GCTK" "NAOV"
## [3178] "SXTC" "BACK" "VRPX" "SCPX" "HEPA" "SLXN" "REVB" "ACON"3 example companies to verify the fetched data.
# VIEW FOR 3 EXAMPLE STOCKS
# Print the first few rows of data for Apple, Microsoft, and Alphabet
if("AAPL" %in% names(symbol_data_list)) {
cat("Data for Apple (AAPL):\n")
print(head(symbol_data_list[["AAPL"]]))
} else {
cat("Data for AAPL not fetched or does not exist.\n")
}## Data for Apple (AAPL):
## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2000-01-03 0.936384 1.004464 0.907924 0.999442 535796800 0.8421506
## 2000-01-04 0.966518 0.987723 0.903460 0.915179 512377600 0.7711489
## 2000-01-05 0.926339 0.987165 0.919643 0.928571 778321600 0.7824333
## 2000-01-06 0.947545 0.955357 0.848214 0.848214 767972800 0.7147226
## 2000-01-07 0.861607 0.901786 0.852679 0.888393 460734400 0.7485783
## 2000-01-10 0.910714 0.912946 0.845982 0.872768 505064000 0.7354123if("MSFT" %in% names(symbol_data_list)) {
cat("Data for Microsoft (MSFT):\n")
print(head(symbol_data_list[["MSFT"]]))
} else {
cat("Data for MSFT not fetched or does not exist.\n")
}## Data for Microsoft (MSFT):
## MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2000-01-03 58.68750 59.31250 56.00000 58.28125 53228400 35.79232
## 2000-01-04 56.78125 58.56250 56.12500 56.31250 54119000 34.58323
## 2000-01-05 55.56250 58.18750 54.68750 56.90625 64059600 34.94787
## 2000-01-06 56.09375 56.93750 54.18750 55.00000 54976600 33.77720
## 2000-01-07 54.31250 56.12500 53.65625 55.71875 62013600 34.21859
## 2000-01-10 56.71875 56.84375 55.68750 56.12500 44963600 34.46809if("MEDP" %in% names(symbol_data_list)) {
cat("Data for Medpace (MEDP):\n")
print(head(symbol_data_list[["MEDP"]]))
} else {
cat("Data for MEDP not fetched or does not exist.\n")
}## Data for Medpace (MEDP):
## MEDP.Open MEDP.High MEDP.Low MEDP.Close MEDP.Volume MEDP.Adjusted
## 2016-08-11 28.15 28.740 27.100 27.79 5356300 27.79
## 2016-08-12 27.55 28.500 27.130 28.04 472700 28.04
## 2016-08-15 28.29 29.600 27.907 29.22 620600 29.22
## 2016-08-16 29.56 29.980 29.200 29.32 315200 29.32
## 2016-08-17 29.43 29.790 28.120 28.14 507900 28.14
## 2016-08-18 28.23 28.843 27.720 27.77 330700 27.77Finding failed stocks in fetching process and eliminate them
# Get the list of successfully fetched symbols
fetched_symbols <- names(symbol_data_list)
# Find symbols that failed to fetch
failed_symbols <- setdiff(symbols, fetched_symbols)
# Print them
cat("Failed to fetch data for the following symbols:\n")## Failed to fetch data for the following symbols:print(failed_symbols)## [1] "SQ" "TAP.A" "ENLC" "WSO.B" "AKO.B" "UHAL.B" "BIO.B" "CEIX"
## [9] "ASAI" "MOG.B" "MOG.A" "SMAR" "PFIE" "AE" "EAST" "B"
## [17] "HTLF" "OBDE" "ZUO" "CDMO" "SCWX" "CRD.A" "CRD.B" "RVNC"
## [25] "HEAR" "MRNS" "QTI" "RHE"All stocks are fetched and prepared successfully. # ———————————————————————————–
There are more than 3k stocks fetched but the majority of them do not include data from 2000s and they have missing values. Now, I am implementing the filter and only keep the companies which have available data since 2000.
# Filter stocks that start exactly on 2000-01-03
filtered_symbol_data_list <- list()
for (symbol in names(symbol_data_list)) {
stock_data <- symbol_data_list[[symbol]]
# Get the first available date
start_date <- index(stock_data)[1]
# Keep only those starting exactly on 2000-01-03
if (start_date == as.Date("2000-01-03")) {
filtered_symbol_data_list[[symbol]] <- stock_data
}
}
# Show how many passed the filter
cat("✅ Number of stocks starting exactly on 2000-01-03:", length(filtered_symbol_data_list), "\n")## ✅ Number of stocks starting exactly on 2000-01-03: 967# Show a few for verification
cat("📅 Start dates preview:\n")## 📅 Start dates preview:for (symbol in head(names(filtered_symbol_data_list), 10)) {
cat(symbol, "starts at:", as.character(index(filtered_symbol_data_list[[symbol]])[1]), "\n")
}## AAPL starts at: 2000-01-03
## GE starts at: 2000-01-03
## LLY starts at: 2000-01-03
## WMT starts at: 2000-01-03
## XOM starts at: 2000-01-03
## NVDA starts at: 2000-01-03
## CAT starts at: 2000-01-03
## UNH starts at: 2000-01-03
## JPM starts at: 2000-01-03
## COST starts at: 2000-01-03# Replace the original list if everything looks fine
symbol_data_list <- filtered_symbol_data_listThe number of fetched stocks are more than 3k+, and the number of stocks decreased to 967 after applying filter and take stocks which have start date from January 2000.
————————————————————————
6. Defining the Volatility
Calculating and Defining the volatility of stocks , using Annualized Volatility of recent 1 year.
# Load necessary libraries
library(quantmod)
library(dplyr)
library(lubridate)
# Initialize the volatility data frame
volatility_data <- data.frame(Symbol = character(),
Sector = character(),
MarketLeader = logical(),
Volatility = numeric(),
stringsAsFactors = FALSE)
# Loop through each symbol to calculate 1-year annualized volatility
for (symbol in names(symbol_data_list)) {
# Extract the stock data
stock_data <- symbol_data_list[[symbol]]
# Calculate daily returns using logarithmic return
daily_returns <- dailyReturn(Cl(stock_data), type = 'log')
# Filter the last 1 year of returns data
last_1_year <- daily_returns[index(daily_returns) >= (Sys.Date() %m-% years(1))]
# Compute annualized volatility
annualized_volatility <- sd(last_1_year, na.rm = TRUE) * sqrt(252)
# Get sector and market leader status from 'stocks_details'
stock_info <- stocks_details %>%
filter(Symbol == symbol) %>%
select(Sector, MarketLeader) %>%
slice(1)
# Append to the volatility data frame
volatility_data <- rbind(volatility_data, data.frame(Symbol = symbol,
Sector = stock_info$Sector,
MarketLeader = stock_info$MarketLeader,
Volatility = annualized_volatility))
}
# Calculate the mean annualized volatility
mean_volatility <- mean(volatility_data$Volatility, na.rm = TRUE)
print(paste("Mean Annualized Volatility (Last 1 Year):", round(mean_volatility, 4)))## [1] "Mean Annualized Volatility (Last 1 Year): 0.4167"# View the first few rows of the volatility data frame
head(volatility_data)## Symbol Sector MarketLeader Volatility
## 1 AAPL Technology Sector TRUE 0.3220947
## 2 GE Industrials Sector TRUE 0.3511154
## 3 LLY Healthcare Sector TRUE 0.3581632
## 4 WMT Consumer Staple Sector TRUE 0.2475146
## 5 XOM Energy Sector TRUE 0.2396942
## 6 NVDA Technology Sector TRUE 0.6019358nrow(volatility_data)## [1] 967ncol(volatility_data)## [1] 4Average volatility stocks of the 967 stocks is 0.4166 which is also proving that the stock market is one of the safest market to invest.
First division based on the thresholds of (<0.2, <0.5, and >0.5).
# Load the dplyr library if not already loaded
library(dplyr)
# Add a new column for volatility classification
volatility_data <- volatility_data %>%
mutate(VolatilityClass = case_when(
Volatility < 0.2 ~ "Stable",
Volatility >= 0.2 & Volatility < 0.5 ~ "Moderate",
Volatility >= 0.5 ~ "Volatile"
))
# View the first few rows of the updated volatility data frame
head(volatility_data)## Symbol Sector MarketLeader Volatility VolatilityClass
## 1 AAPL Technology Sector TRUE 0.3220947 Moderate
## 2 GE Industrials Sector TRUE 0.3511154 Moderate
## 3 LLY Healthcare Sector TRUE 0.3581632 Moderate
## 4 WMT Consumer Staple Sector TRUE 0.2475146 Moderate
## 5 XOM Energy Sector TRUE 0.2396942 Moderate
## 6 NVDA Technology Sector TRUE 0.6019358 Volatile# Load the dplyr library if not already loaded
library(dplyr)
# Group by Sector, MarketLeader, and VolatilityClass, then count the number of stocks
sector_leader_volatility_counts <- volatility_data %>%
group_by(Sector, MarketLeader, VolatilityClass) %>%
summarise(StockCount = n(), .groups = 'drop') # 'drop' ensures the grouping is dropped after summarise
# View the grouped and counted data
# Print all rows of the grouped and counted data
print(sector_leader_volatility_counts, n = Inf) # 'Inf' indicates to print all rows## # A tibble: 31 × 4
## Sector MarketLeader VolatilityClass StockCount
## <chr> <lgl> <chr> <int>
## 1 Consumer Staple Sector FALSE Moderate 36
## 2 Consumer Staple Sector FALSE Volatile 12
## 3 Consumer Staple Sector TRUE Moderate 27
## 4 Consumer Staple Sector TRUE Stable 10
## 5 Consumer Staple Sector TRUE Volatile 1
## 6 Energy Sector FALSE Moderate 30
## 7 Energy Sector FALSE Volatile 24
## 8 Energy Sector TRUE Moderate 27
## 9 Energy Sector TRUE Stable 2
## 10 Energy Sector TRUE Volatile 1
## 11 Financial Sector FALSE Moderate 162
## 12 Financial Sector FALSE Stable 2
## 13 Financial Sector FALSE Volatile 8
## 14 Financial Sector TRUE Moderate 66
## 15 Financial Sector TRUE Stable 3
## 16 Healthcare Sector FALSE Moderate 59
## 17 Healthcare Sector FALSE Stable 2
## 18 Healthcare Sector FALSE Volatile 78
## 19 Healthcare Sector TRUE Moderate 25
## 20 Healthcare Sector TRUE Stable 3
## 21 Healthcare Sector TRUE Volatile 2
## 22 Industrials Sector FALSE Moderate 108
## 23 Industrials Sector FALSE Volatile 37
## 24 Industrials Sector TRUE Moderate 73
## 25 Industrials Sector TRUE Stable 2
## 26 Industrials Sector TRUE Volatile 1
## 27 Technology Sector FALSE Moderate 69
## 28 Technology Sector FALSE Volatile 29
## 29 Technology Sector TRUE Moderate 53
## 30 Technology Sector TRUE Stable 2
## 31 Technology Sector TRUE Volatile 13Next and Proper division for the analysis 0.25 and below stable stocks and 0.25 above volatile stocks
# Reclassify volatility using updated thresholds
volatility_data <- volatility_data %>%
mutate(VolatilityClass = case_when(
Volatility <= 0.25 ~ "Stable",
Volatility > 0.25 ~ "Volatile"
))
# Recalculate stock counts based on the new VolatilityClass
sector_leader_volatility_counts <- volatility_data %>%
group_by(Sector, MarketLeader, VolatilityClass) %>%
summarise(StockCount = n(), .groups = 'drop')
# Print all rows of the grouped and counted data
print(sector_leader_volatility_counts, n = Inf)## # A tibble: 24 × 4
## Sector MarketLeader VolatilityClass StockCount
## <chr> <lgl> <chr> <int>
## 1 Consumer Staple Sector FALSE Stable 3
## 2 Consumer Staple Sector FALSE Volatile 45
## 3 Consumer Staple Sector TRUE Stable 24
## 4 Consumer Staple Sector TRUE Volatile 14
## 5 Energy Sector FALSE Stable 2
## 6 Energy Sector FALSE Volatile 52
## 7 Energy Sector TRUE Stable 7
## 8 Energy Sector TRUE Volatile 23
## 9 Financial Sector FALSE Stable 11
## 10 Financial Sector FALSE Volatile 161
## 11 Financial Sector TRUE Stable 22
## 12 Financial Sector TRUE Volatile 47
## 13 Healthcare Sector FALSE Stable 11
## 14 Healthcare Sector FALSE Volatile 128
## 15 Healthcare Sector TRUE Stable 10
## 16 Healthcare Sector TRUE Volatile 20
## 17 Industrials Sector FALSE Stable 6
## 18 Industrials Sector FALSE Volatile 139
## 19 Industrials Sector TRUE Stable 20
## 20 Industrials Sector TRUE Volatile 56
## 21 Technology Sector FALSE Stable 1
## 22 Technology Sector FALSE Volatile 97
## 23 Technology Sector TRUE Stable 13
## 24 Technology Sector TRUE Volatile 55—————————————————————————————–
7. EXPLORATORY DATA ANALYSIS
This table shows average volatility by each sector ##### Table 1
# Load necessary library
library(dplyr)
# Calculate average annualized volatility score by sector
average_volatility_by_sector <- volatility_data %>%
group_by(Sector) %>%
summarise(AverageVolatility = mean(Volatility, na.rm = TRUE))
# View the result
print(average_volatility_by_sector)## # A tibble: 6 × 2
## Sector AverageVolatility
## <chr> <dbl>
## 1 Consumer Staple Sector 0.342
## 2 Energy Sector 0.430
## 3 Financial Sector 0.330
## 4 Healthcare Sector 0.583
## 5 Industrials Sector 0.385
## 6 Technology Sector 0.447Plot 1
This plot is bar plot version of the table 1 and showing same thing with table 1 which is average volatility score by each sector of comapnies.
# plot also for this: .....
# Load necessary libraries
library(ggplot2)
library(dplyr)
# Create a bar plot with black background and professional style
ggplot(average_volatility_by_sector, aes(x = reorder(Sector, AverageVolatility), y = AverageVolatility, fill = Sector)) +
geom_bar(stat = "identity", color = "gray30", show.legend = FALSE) + # Bars with light gray borders
geom_text(aes(label = round(AverageVolatility, 2)), vjust = -0.5, color = "white", size = 4) + # White labels on top
theme_minimal(base_size = 14) + # Use minimal theme with a professional font size
theme(
panel.background = element_rect(fill = "#121212"), # Darker background for the plot panel
plot.background = element_rect(fill = "#121212"), # Dark background for the whole plot
axis.text.x = element_text(color = "white"), # White axis text
axis.text.y = element_text(color = "white"), # White axis text
axis.title.x = element_text(color = "white", size = 14), # White axis titles
axis.title.y = element_text(color = "white", size = 14), # White axis titles
plot.title = element_text(color = "white", size = 16, face = "bold", hjust = 0.5), # Title centered with bold style
panel.grid.major = element_line(color = "gray40", size = 0.5), # Light gray major grid lines
panel.grid.minor = element_line(color = "gray40", size = 0.25), # Light gray minor grid lines
axis.ticks = element_line(color = "white"), # White axis ticks
axis.line = element_line(color = "white") # White axis line
) +
labs(
title = "Average Volatility by Sector",
x = "Sector",
y = "Average Volatility"
) +
coord_flip() # Flip the coordinates for a more professional horizontal bar chart
Plot 2
In this code chunk we can observe again average volatility by each sector but this time, it is also groupped by market leaders and non market leaders.
# 2. Calculate average volatility score by sector and market leader status
average_volatility_by_sector_leader <- volatility_data %>%
group_by(Sector, MarketLeader) %>%
summarise(AverageVolatility = mean(Volatility, na.rm = TRUE), .groups = 'drop')
# View the result
print(average_volatility_by_sector_leader)## # A tibble: 12 × 3
## Sector MarketLeader AverageVolatility
## <chr> <lgl> <dbl>
## 1 Consumer Staple Sector FALSE 0.410
## 2 Consumer Staple Sector TRUE 0.256
## 3 Energy Sector FALSE 0.495
## 4 Energy Sector TRUE 0.315
## 5 Financial Sector FALSE 0.349
## 6 Financial Sector TRUE 0.280
## 7 Healthcare Sector FALSE 0.642
## 8 Healthcare Sector TRUE 0.313
## 9 Industrials Sector FALSE 0.427
## 10 Industrials Sector TRUE 0.304
## 11 Technology Sector FALSE 0.490
## 12 Technology Sector TRUE 0.387# plot for this: ......
# Load necessary libraries for plotting
library(ggplot2)
# Plot 2.1: Market Leaders' average volatility
market_leader_plot <- average_volatility_by_sector_leader %>%
filter(MarketLeader == TRUE) %>%
ggplot(aes(x = Sector, y = AverageVolatility, fill = Sector)) +
geom_bar(stat = "identity", show.legend = FALSE) +
geom_text(aes(label = round(AverageVolatility, 2)), vjust = -0.3, size = 5, fontface = "bold") +
theme_minimal(base_size = 15) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
plot.background = element_rect(fill = "black", color = "black"),
panel.background = element_rect(fill = "black"),
axis.text = element_text(color = "white"),
axis.title = element_text(color = "white"),
plot.title = element_text(color = "white", size = 16)) +
labs(title = "Market Leaders' Average Volatility",
x = "Sector",
y = "Average Volatility")
# Show the plot
print(market_leader_plot)
# Plot 2.2: Non-Market Leaders' average volatility with large labels
non_market_leader_plot <- average_volatility_by_sector_leader %>%
filter(MarketLeader == FALSE) %>%
ggplot(aes(x = Sector, y = AverageVolatility, fill = Sector)) +
geom_bar(stat = "identity", show.legend = FALSE) +
geom_text(aes(label = round(AverageVolatility, 2)), vjust = -0.3, size = 8, fontface = "bold") +
theme_minimal(base_size = 15) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 14),
plot.background = element_rect(fill = "black", color = "black"),
panel.background = element_rect(fill = "black"),
axis.text = element_text(color = "white"),
axis.title = element_text(color = "white"),
plot.title = element_text(color = "white", size = 16)) +
labs(title = "Non-Market Leaders' Average Volatility",
x = "Sector",
y = "Average Volatility")
# Show the plot
print(non_market_leader_plot)
Plot 3
This plot showing the number of market leaders and non market leaders in each sector
# Calculate the count of market leaders and non-leaders per sector
market_leader_distribution <- volatility_data %>%
group_by(Sector, MarketLeader) %>%
summarise(Count = n(), .groups = 'drop')
# View the result
print(market_leader_distribution)## # A tibble: 12 × 3
## Sector MarketLeader Count
## <chr> <lgl> <int>
## 1 Consumer Staple Sector FALSE 48
## 2 Consumer Staple Sector TRUE 38
## 3 Energy Sector FALSE 54
## 4 Energy Sector TRUE 30
## 5 Financial Sector FALSE 172
## 6 Financial Sector TRUE 69
## 7 Healthcare Sector FALSE 139
## 8 Healthcare Sector TRUE 30
## 9 Industrials Sector FALSE 145
## 10 Industrials Sector TRUE 76
## 11 Technology Sector FALSE 98
## 12 Technology Sector TRUE 68# Load the necessary library
library(ggplot2)
# Bar plot for market leader distribution
ggplot(market_leader_distribution, aes(x = Sector, y = Count, fill = as.factor(MarketLeader))) +
geom_bar(stat = "identity", position = "fill", color = "white") + # stacked to 100%
scale_fill_manual(values = c("red", "darkblue"), labels = c("Non-Leader", "Leader")) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
theme_minimal(base_size = 16) +
theme(
plot.background = element_rect(fill = "black"),
panel.background = element_rect(fill = "black", color = "black"),
text = element_text(color = "white"),
axis.title = element_text(color = "white"),
axis.text = element_text(color = "white"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_text(color = "white"),
legend.text = element_text(color = "white")
) +
labs(title = "Market Leadership Distribution by Sector",
x = "Sector",
y = "Percentage of Stocks",
fill = "Market Leader")
PLOT 4
Volatile Stocks
This plot showing the number of stable companies and risky (volatile) comapnies in each sector
# Calculate the count of volatile and stable stocks per sector
volatility_distribution <- volatility_data %>%
group_by(Sector, VolatilityClass) %>%
summarise(Count = n(), .groups = 'drop')
# View the result
print(volatility_distribution)## # A tibble: 12 × 3
## Sector VolatilityClass Count
## <chr> <chr> <int>
## 1 Consumer Staple Sector Stable 27
## 2 Consumer Staple Sector Volatile 59
## 3 Energy Sector Stable 9
## 4 Energy Sector Volatile 75
## 5 Financial Sector Stable 33
## 6 Financial Sector Volatile 208
## 7 Healthcare Sector Stable 21
## 8 Healthcare Sector Volatile 148
## 9 Industrials Sector Stable 26
## 10 Industrials Sector Volatile 195
## 11 Technology Sector Stable 14
## 12 Technology Sector Volatile 152# Bar plot for volatility distribution
ggplot(volatility_distribution, aes(x = Sector, y = Count, fill = as.factor(VolatilityClass))) +
geom_bar(stat = "identity", position = "fill", color = "white") +
scale_fill_manual(values = c("green", "darkblue"), labels = c("Stable", "Volatile")) +
scale_y_continuous(labels = scales::percent_format()) +
theme_minimal(base_size = 16) +
theme(
plot.background = element_rect(fill = "black"),
panel.background = element_rect(fill = "black", color = "black"),
text = element_text(color = "white"),
axis.title = element_text(color = "white"),
axis.text = element_text(color = "white"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.title = element_text(color = "white"),
legend.text = element_text(color = "white")
) +
labs(title = "Volatility Distribution by Sector",
x = "Sector",
y = "Percentage",
fill = "Volatility Class")
Table 5
It is important to see the market gigants which are in the top based on market shares and also important to be aware of the most stable companies in each industry. This table shows the top 3 and bottom 3 companies by each 6 sector.
library(dplyr)
# Merge the market capitalization data with the original volatility data
full_data <- left_join(volatility_data, stocks_details, by = "Symbol")
# Remove duplicate columns after the merge and keep only the necessary ones
full_data <- full_data %>%
select(-c(Sector.x, MarketLeader.x)) %>% # Remove Sector.x and MarketLeader.x
rename(Sector = Sector.y, MarketLeader = MarketLeader.y) # Rename Sector.y and MarketLeader.y
# Now, find the top 3 and bottom 3 Market Capitalizations per sector
max_min_market_cap <- full_data %>%
group_by(Sector) %>%
arrange(Sector, desc(`Market Cap Numeric`)) %>%
slice_head(n = 3) %>%
bind_rows(
full_data %>%
group_by(Sector) %>%
arrange(Sector, `Market Cap Numeric`) %>%
slice_head(n = 3)
) %>%
select(Sector, `Company Name`, Symbol, `Market Cap Numeric`) %>%
arrange(Sector, `Market Cap Numeric`)
# Print the results for market capitalization
print(max_min_market_cap, n = Inf)## # A tibble: 36 × 4
## # Groups: Sector [6]
## Sector `Company Name` Symbol `Market Cap Numeric`
## <chr> <chr> <chr> <dbl>
## 1 Consumer Staple Sector Rocky Mountain Chocolate … RMCF 15450000
## 2 Consumer Staple Sector Mannatech, Incorporated MTEX 23090000
## 3 Consumer Staple Sector Natural Alternatives Inte… NAII 25020000
## 4 Consumer Staple Sector The Procter & Gamble Comp… PG 380390000000
## 5 Consumer Staple Sector Costco Wholesale Corporat… COST 408390000000
## 6 Consumer Staple Sector Walmart Inc. WMT 730400000000
## 7 Energy Sector Marine Petroleum Trust MARPS 8180000
## 8 Energy Sector BP Prudhoe Bay Royalty Tr… BPT 11660000
## 9 Energy Sector Mesa Royalty Trust MTR 11720000
## 10 Energy Sector Shell plc SHEL 202800000000
## 11 Energy Sector Chevron Corporation CVX 279720000000
## 12 Energy Sector Exxon Mobil Corporation XOM 477570000000
## 13 Financial Sector Greystone Housing Impact … GHI 259460000
## 14 Financial Sector The First of Long Island … FLIC 260380000
## 15 Financial Sector Fidelity D & D Bancorp, I… FDBC 265120000
## 16 Financial Sector Wells Fargo & Company WFC 241850000000
## 17 Financial Sector Bank of America Corporati… BAC 355560000000
## 18 Financial Sector JPMorgan Chase & Co. JPM 687570000000
## 19 Healthcare Sector Windtree Therapeutics, In… WINT 1610000
## 20 Healthcare Sector Titan Pharmaceuticals, In… TTNP 3310000
## 21 Healthcare Sector Becton, Dickinson and Com… BDX 3920000
## 22 Healthcare Sector Novo Nordisk A/S NVO 389380000000
## 23 Healthcare Sector UnitedHealth Group Incorp… UNH 490060000000
## 24 Healthcare Sector Eli Lilly and Company LLY 707210000000
## 25 Industrials Sector Tredegar Corporation TG 254590000
## 26 Industrials Sector FuelCell Energy, Inc. FCEL 266660000
## 27 Industrials Sector Forrester Research, Inc. FORR 279240000
## 28 Industrials Sector RTX Corporation RTX 152670000000
## 29 Industrials Sector Caterpillar Inc. CAT 176130000000
## 30 Industrials Sector General Electric Company GE 187380000000
## 31 Technology Sector Immersion Corporation IMMR 278860000
## 32 Technology Sector Asure Software, Inc. ASUR 288770000
## 33 Technology Sector AudioCodes Ltd. AUDC 302490000
## 34 Technology Sector Microsoft Corporation MSFT 3175510000000
## 35 Technology Sector NVIDIA Corporation NVDA 3494480000000
## 36 Technology Sector Apple Inc. AAPL 3698010000000Table 6
This table shows the most stable 3 and the most volatile 3 comapnies based on each sector.
# Find the top 3 and bottom 3 Volatility scores per sector
max_min_volatility <- full_data %>%
group_by(Sector) %>%
arrange(Sector, desc(Volatility)) %>%
slice_head(n = 3) %>%
bind_rows(
full_data %>%
group_by(Sector) %>%
arrange(Sector, Volatility) %>%
slice_head(n = 3)
) %>%
select(Sector, `Company Name`, Symbol, Volatility) %>%
arrange(Sector, Volatility)
# Print the results for volatility
print(max_min_volatility, n = Inf)## # A tibble: 36 × 4
## # Groups: Sector [6]
## Sector `Company Name` Symbol Volatility
## <chr> <chr> <chr> <dbl>
## 1 Consumer Staple Sector The Coca-Cola Company KO 0.164
## 2 Consumer Staple Sector The Procter & Gamble Company PG 0.185
## 3 Consumer Staple Sector Altria Group, Inc. MO 0.190
## 4 Consumer Staple Sector Newell Brands Inc. NWL 0.698
## 5 Consumer Staple Sector Mannatech, Incorporated MTEX 0.719
## 6 Consumer Staple Sector Lifeway Foods, Inc. LWAY 0.749
## 7 Energy Sector Enbridge Inc. ENB 0.174
## 8 Energy Sector Enterprise Products Partners L.P. EPD 0.186
## 9 Energy Sector National Fuel Gas Company NFG 0.206
## 10 Energy Sector BP Prudhoe Bay Royalty Trust BPT 0.881
## 11 Energy Sector U.S. Energy Corp. USEG 0.894
## 12 Energy Sector Centrus Energy Corp. LEU 0.962
## 13 Financial Sector Enstar Group Limited ESGR 0.146
## 14 Financial Sector Central Securities Corporation CET 0.153
## 15 Financial Sector Marsh & McLennan Companies, Inc. MMC 0.168
## 16 Financial Sector MBIA Inc. MBI 0.620
## 17 Financial Sector Banco BBVA Argentina S.A. BBAR 0.679
## 18 Financial Sector TeraWulf Inc. WULF 1.19
## 19 Healthcare Sector Amedisys, Inc. AMED 0.137
## 20 Healthcare Sector Utah Medical Products, Inc. UTMD 0.191
## 21 Healthcare Sector Cencora, Inc. COR 0.191
## 22 Healthcare Sector Soligenix, Inc. SNGX 1.64
## 23 Healthcare Sector Windtree Therapeutics, Inc. WINT 2.01
## 24 Healthcare Sector Kazia Therapeutics Limited KZIA 2.06
## 25 Industrials Sector Waste Connections, Inc. WCN 0.170
## 26 Industrials Sector Republic Services, Inc. RSG 0.178
## 27 Industrials Sector Waste Management, Inc. WM 0.202
## 28 Industrials Sector American Superconductor Corporation AMSC 0.889
## 29 Industrials Sector Plug Power Inc. PLUG 0.918
## 30 Industrials Sector FuelCell Energy, Inc. FCEL 1.04
## 31 Technology Sector Juniper Networks, Inc. JNPR 0.146
## 32 Technology Sector Automatic Data Processing, Inc. ADP 0.189
## 33 Technology Sector Amdocs Limited DOX 0.202
## 34 Technology Sector MicroStrategy Incorporated MSTR 1.02
## 35 Technology Sector Innodata Inc. INOD 1.21
## 36 Technology Sector Wolfspeed, Inc. WOLF 1.46————————————————————————————–
8. DATA SELECTION
Simple View of the Full data
head(full_data)## Symbol Volatility VolatilityClass No Company Name
## 1 AAPL 0.3220947 Volatile 1 Apple Inc.
## 2 GE 0.3511154 Volatile 1 General Electric Company
## 3 LLY 0.3581632 Volatile 1 Eli Lilly and Company
## 4 WMT 0.2475146 Stable 1 Walmart Inc.
## 5 XOM 0.2396942 Stable 1 Exxon Mobil Corporation
## 6 NVDA 0.6019358 Volatile 2 NVIDIA Corporation
## Sector Market Cap % Change Volume Revenue
## 1 Technology Sector 3,698.01B -0.0014 10694412 391.04B
## 2 Industrials Sector 187.38B 0.0039 2428942 69.95B
## 3 Healthcare Sector 707.21B 0.0245 4138602 40.86B
## 4 Consumer Staple Sector 730.40B -0.0056 2734176 673.82B
## 5 Energy Sector 477.57B -0.0135 13925409 343.82B
## 6 Technology Sector 3,494.48B -0.0451 166664997 113.27B
## Market Cap Numeric MarketLeader
## 1 3.69801e+12 TRUE
## 2 1.87380e+11 TRUE
## 3 7.07210e+11 TRUE
## 4 7.30400e+11 TRUE
## 5 4.77570e+11 TRUE
## 6 3.49448e+12 TRUEThe columnames of the full data
colnames(full_data)## [1] "Symbol" "Volatility" "VolatilityClass"
## [4] "No" "Company Name" "Sector"
## [7] "Market Cap" "% Change" "Volume"
## [10] "Revenue" "Market Cap Numeric" "MarketLeader"Random Selection of 3 stocks for each section.
There are 24 sections.So, it is assumed 24*3 = 72 stocks.
# Set the seed for reproducibility
set.seed(42)
# Load required libraries
library(dplyr)
# Make sure your column names are correct (e.g. MarketLeader is logical or character)
full_data <- full_data %>%
mutate(
MarketLeader = as.character(MarketLeader), # or as.factor if you prefer
VolatilityClass = as.character(VolatilityClass),
Sector = as.character(Sector)
)
# Group by Sector, MarketLeader, VolatilityClass and sample 3 from each group
selected_stocks <- full_data %>%
group_by(Sector, MarketLeader, VolatilityClass) %>%
slice_sample(n = 3) %>%
ungroup()
# View the result
print(selected_stocks, n = Inf)## # A tibble: 69 × 12
## Symbol Volatility VolatilityClass No `Company Name` Sector `Market Cap`
## <chr> <dbl> <chr> <dbl> <chr> <chr> <chr>
## 1 INGR 0.248 Stable 58 Ingredion Incorp… Consu… 8.74B
## 2 TR 0.244 Stable 91 Tootsie Roll Ind… Consu… 2.23B
## 3 FLO 0.206 Stable 73 Flowers Foods, I… Consu… 4.27B
## 4 STKL 0.511 Volatile 123 SunOpta Inc. Consu… 879.90M
## 5 JJSF 0.284 Volatile 82 J&J Snack Foods … Consu… 2.87B
## 6 WILC 0.387 Volatile 162 G. Willi-Food In… Consu… 226.03M
## 7 TSN 0.220 Stable 32 Tyson Foods, Inc. Consu… 20.15B
## 8 CHD 0.192 Stable 30 Church & Dwight … Consu… 25.07B
## 9 GIS 0.220 Stable 24 General Mills, I… Consu… 34.15B
## 10 ADM 0.272 Volatile 31 Archer-Daniels-M… Consu… 24.10B
## 11 EL 0.529 Volatile 29 The Est茅e Laude… Consu… 27.15B
## 12 HSY 0.268 Volatile 25 The Hershey Comp… Consu… 33.98B
## 13 NFG 0.206 Stable 68 National Fuel Ga… Energ… 5.53B
## 14 SBR 0.232 Stable 156 Sabine Royalty T… Energ… 960.27M
## 15 NRT 0.519 Volatile 233 North European O… Energ… 42.84M
## 16 LEU 0.962 Volatile 141 Centrus Energy C… Energ… 1.31B
## 17 REPX 0.486 Volatile 165 Riley Exploratio… Energ… 693.68M
## 18 SHEL 0.230 Stable 3 Shell plc Energ… 202.80B
## 19 TTE 0.231 Stable 4 TotalEnergies SE Energ… 132.32B
## 20 XOM 0.240 Stable 1 Exxon Mobil Corp… Energ… 477.57B
## 21 EOG 0.289 Volatile 10 EOG Resources, I… Energ… 73.82B
## 22 WMB 0.259 Volatile 12 The Williams Com… Energ… 72.62B
## 23 VLO 0.376 Volatile 31 Valero Energy Co… Energ… 44.20B
## 24 MTG 0.248 Stable 178 MGIC Investment … Finan… 5.97B
## 25 RLI 0.226 Stable 162 RLI Corp. Finan… 7.22B
## 26 ORI 0.209 Stable 147 Old Republic Int… Finan… 8.62B
## 27 PFBC 0.315 Volatile 364 Preferred Bank Finan… 1.14B
## 28 UVSP 0.326 Volatile 401 Univest Financia… Finan… 834.84M
## 29 GL 0.323 Volatile 140 Globe Life Inc. Finan… 9.39B
## 30 RY 0.190 Stable 12 Royal Bank of Ca… Finan… 171.61B
## 31 CM 0.215 Stable 43 Canadian Imperia… Finan… 59.41B
## 32 AFL 0.217 Stable 46 Aflac Incorporat… Finan… 57.31B
## 33 SAN 0.337 Volatile 36 Banco Santander,… Finan… 71.59B
## 34 ALL 0.256 Volatile 55 The Allstate Cor… Finan… 49.41B
## 35 BLK 0.254 Volatile 14 BlackRock, Inc. Finan… 153.97B
## 36 LH 0.237 Stable 60 Labcorp Holdings… Healt… 20.58B
## 37 UTMD 0.191 Stable 558 Utah Medical Pro… Healt… 213.40M
## 38 HOLX 0.224 Stable 71 Hologic, Inc. Healt… 16.03B
## 39 INCY 0.345 Volatile 78 Incyte Corporati… Healt… 13.98B
## 40 CYTH 0.948 Volatile 918 Cyclo Therapeuti… Healt… 23.62M
## 41 ASRT 0.718 Volatile 720 Assertio Holding… Healt… 80.49M
## 42 SYK 0.220 Stable 13 Stryker Corporat… Healt… 150.32B
## 43 NVS 0.195 Stable 11 Novartis AG Healt… 199.45B
## 44 ABT 0.206 Stable 8 Abbott Laborator… Healt… 216.86B
## 45 CNMD 0.409 Volatile 228 CONMED Corporati… Healt… 2.12B
## 46 NVO 0.426 Volatile 3 Novo Nordisk A/S Healt… 389.38B
## 47 RDNT 0.451 Volatile 142 RadNet, Inc. Healt… 4.74B
## 48 NPK 0.248 Stable 379 National Presto … Indus… 674.53M
## 49 MSA 0.234 Stable 166 MSA Safety Incor… Indus… 6.30B
## 50 RGR 0.248 Stable 387 Sturm, Ruger & C… Indus… 579.79M
## 51 WCC 0.444 Volatile 128 WESCO Internatio… Indus… 8.95B
## 52 FSTR 0.544 Volatile 439 L.B. Foster Comp… Indus… 289.71M
## 53 MTRX 0.501 Volatile 426 Matrix Service C… Indus… 345.54M
## 54 GGG 0.227 Stable 91 Graco Inc. Indus… 13.88B
## 55 UNP 0.232 Stable 5 Union Pacific Co… Indus… 140.40B
## 56 HON 0.235 Stable 4 Honeywell Intern… Indus… 143.98B
## 57 LECO 0.336 Volatile 112 Lincoln Electric… Indus… 10.49B
## 58 FIX 0.603 Volatile 79 Comfort Systems … Indus… 15.99B
## 59 MMM 0.334 Volatile 18 3M Company Indus… 72.06B
## 60 DOX 0.202 Stable 157 Amdocs Limited Techn… 9.52B
## 61 MKSI 0.608 Volatile 174 MKS Instruments,… Techn… 7.69B
## 62 PRGS 0.315 Volatile 282 Progress Softwar… Techn… 2.76B
## 63 UPBD 0.386 Volatile 337 Upbound Group, I… Techn… 1.61B
## 64 PAYX 0.227 Stable 53 Paychex, Inc. Techn… 50.17B
## 65 FI 0.246 Stable 28 Fiserv, Inc. Techn… 116.64B
## 66 GIB 0.219 Stable 78 CGI Inc. Techn… 24.48B
## 67 ADBE 0.375 Volatile 15 Adobe Inc. Techn… 188.72B
## 68 HPQ 0.395 Volatile 65 HP Inc. Techn… 31.96B
## 69 INTU 0.334 Volatile 18 Intuit Inc. Techn… 173.34B
## # ℹ 5 more variables: `% Change` <chr>, Volume <chr>, Revenue <chr>,
## # `Market Cap Numeric` <dbl>, MarketLeader <chr>It is assumed to select 72 stocks however, the selected stocks became 69 because 1 section has 1 stocks, and 1 section has 2 stocks although, it is assumed to have 3 for each section.
library(dplyr)
# 1. See all unique company names
unique_companies <- unique(selected_stocks$`Company Name`)
print(unique_companies)## [1] "Ingredion Incorporated"
## [2] "Tootsie Roll Industries, Inc."
## [3] "Flowers Foods, Inc."
## [4] "SunOpta Inc."
## [5] "J&J Snack Foods Corp."
## [6] "G. Willi-Food International Ltd."
## [7] "Tyson Foods, Inc."
## [8] "Church & Dwight Co., Inc."
## [9] "General Mills, Inc."
## [10] "Archer-Daniels-Midland Company"
## [11] "The Est茅e Lauder Companies Inc."
## [12] "The Hershey Company"
## [13] "National Fuel Gas Company"
## [14] "Sabine Royalty Trust"
## [15] "North European Oil Royalty Trust"
## [16] "Centrus Energy Corp."
## [17] "Riley Exploration Permian, Inc."
## [18] "Shell plc"
## [19] "TotalEnergies SE"
## [20] "Exxon Mobil Corporation"
## [21] "EOG Resources, Inc."
## [22] "The Williams Companies, Inc."
## [23] "Valero Energy Corporation"
## [24] "MGIC Investment Corporation"
## [25] "RLI Corp."
## [26] "Old Republic International Corporation"
## [27] "Preferred Bank"
## [28] "Univest Financial Corporation"
## [29] "Globe Life Inc."
## [30] "Royal Bank of Canada"
## [31] "Canadian Imperial Bank of Commerce"
## [32] "Aflac Incorporated"
## [33] "Banco Santander, S.A."
## [34] "The Allstate Corporation"
## [35] "BlackRock, Inc."
## [36] "Labcorp Holdings Inc."
## [37] "Utah Medical Products, Inc."
## [38] "Hologic, Inc."
## [39] "Incyte Corporation"
## [40] "Cyclo Therapeutics, Inc."
## [41] "Assertio Holdings, Inc."
## [42] "Stryker Corporation"
## [43] "Novartis AG"
## [44] "Abbott Laboratories"
## [45] "CONMED Corporation"
## [46] "Novo Nordisk A/S"
## [47] "RadNet, Inc."
## [48] "National Presto Industries, Inc."
## [49] "MSA Safety Incorporated"
## [50] "Sturm, Ruger & Company, Inc."
## [51] "WESCO International, Inc."
## [52] "L.B. Foster Company"
## [53] "Matrix Service Company"
## [54] "Graco Inc."
## [55] "Union Pacific Corporation"
## [56] "Honeywell International Inc."
## [57] "Lincoln Electric Holdings, Inc."
## [58] "Comfort Systems USA, Inc."
## [59] "3M Company"
## [60] "Amdocs Limited"
## [61] "MKS Instruments, Inc."
## [62] "Progress Software Corporation"
## [63] "Upbound Group, Inc."
## [64] "Paychex, Inc."
## [65] "Fiserv, Inc."
## [66] "CGI Inc."
## [67] "Adobe Inc."
## [68] "HP Inc."
## [69] "Intuit Inc."# 2. Create the summary table
company_table <- selected_stocks %>%
select(Sector, MarketLeader, VolatilityClass, `Company Name`, Symbol) %>%
mutate(
`Market leadership status` = ifelse(MarketLeader == TRUE, "Leader", "Non-Leader"),
`Volatility status` = VolatilityClass
) %>%
select(Sector, `Market leadership status`, `Volatility status`, `Company Name`, Symbol) %>%
distinct() # remove duplicates if any
# View the summary table
print(company_table, n = Inf)## # A tibble: 69 × 5
## Sector Market leadership st…¹ `Volatility status` `Company Name` Symbol
## <chr> <chr> <chr> <chr> <chr>
## 1 Consumer St… Non-Leader Stable Ingredion Inc… INGR
## 2 Consumer St… Non-Leader Stable Tootsie Roll … TR
## 3 Consumer St… Non-Leader Stable Flowers Foods… FLO
## 4 Consumer St… Non-Leader Volatile SunOpta Inc. STKL
## 5 Consumer St… Non-Leader Volatile J&J Snack Foo… JJSF
## 6 Consumer St… Non-Leader Volatile G. Willi-Food… WILC
## 7 Consumer St… Leader Stable Tyson Foods, … TSN
## 8 Consumer St… Leader Stable Church & Dwig… CHD
## 9 Consumer St… Leader Stable General Mills… GIS
## 10 Consumer St… Leader Volatile Archer-Daniel… ADM
## 11 Consumer St… Leader Volatile The Est茅e La… EL
## 12 Consumer St… Leader Volatile The Hershey C… HSY
## 13 Energy Sect… Non-Leader Stable National Fuel… NFG
## 14 Energy Sect… Non-Leader Stable Sabine Royalt… SBR
## 15 Energy Sect… Non-Leader Volatile North Europea… NRT
## 16 Energy Sect… Non-Leader Volatile Centrus Energ… LEU
## 17 Energy Sect… Non-Leader Volatile Riley Explora… REPX
## 18 Energy Sect… Leader Stable Shell plc SHEL
## 19 Energy Sect… Leader Stable TotalEnergies… TTE
## 20 Energy Sect… Leader Stable Exxon Mobil C… XOM
## 21 Energy Sect… Leader Volatile EOG Resources… EOG
## 22 Energy Sect… Leader Volatile The Williams … WMB
## 23 Energy Sect… Leader Volatile Valero Energy… VLO
## 24 Financial S… Non-Leader Stable MGIC Investme… MTG
## 25 Financial S… Non-Leader Stable RLI Corp. RLI
## 26 Financial S… Non-Leader Stable Old Republic … ORI
## 27 Financial S… Non-Leader Volatile Preferred Bank PFBC
## 28 Financial S… Non-Leader Volatile Univest Finan… UVSP
## 29 Financial S… Non-Leader Volatile Globe Life In… GL
## 30 Financial S… Leader Stable Royal Bank of… RY
## 31 Financial S… Leader Stable Canadian Impe… CM
## 32 Financial S… Leader Stable Aflac Incorpo… AFL
## 33 Financial S… Leader Volatile Banco Santand… SAN
## 34 Financial S… Leader Volatile The Allstate … ALL
## 35 Financial S… Leader Volatile BlackRock, In… BLK
## 36 Healthcare … Non-Leader Stable Labcorp Holdi… LH
## 37 Healthcare … Non-Leader Stable Utah Medical … UTMD
## 38 Healthcare … Non-Leader Stable Hologic, Inc. HOLX
## 39 Healthcare … Non-Leader Volatile Incyte Corpor… INCY
## 40 Healthcare … Non-Leader Volatile Cyclo Therape… CYTH
## 41 Healthcare … Non-Leader Volatile Assertio Hold… ASRT
## 42 Healthcare … Leader Stable Stryker Corpo… SYK
## 43 Healthcare … Leader Stable Novartis AG NVS
## 44 Healthcare … Leader Stable Abbott Labora… ABT
## 45 Healthcare … Leader Volatile CONMED Corpor… CNMD
## 46 Healthcare … Leader Volatile Novo Nordisk … NVO
## 47 Healthcare … Leader Volatile RadNet, Inc. RDNT
## 48 Industrials… Non-Leader Stable National Pres… NPK
## 49 Industrials… Non-Leader Stable MSA Safety In… MSA
## 50 Industrials… Non-Leader Stable Sturm, Ruger … RGR
## 51 Industrials… Non-Leader Volatile WESCO Interna… WCC
## 52 Industrials… Non-Leader Volatile L.B. Foster C… FSTR
## 53 Industrials… Non-Leader Volatile Matrix Servic… MTRX
## 54 Industrials… Leader Stable Graco Inc. GGG
## 55 Industrials… Leader Stable Union Pacific… UNP
## 56 Industrials… Leader Stable Honeywell Int… HON
## 57 Industrials… Leader Volatile Lincoln Elect… LECO
## 58 Industrials… Leader Volatile Comfort Syste… FIX
## 59 Industrials… Leader Volatile 3M Company MMM
## 60 Technology … Non-Leader Stable Amdocs Limited DOX
## 61 Technology … Non-Leader Volatile MKS Instrumen… MKSI
## 62 Technology … Non-Leader Volatile Progress Soft… PRGS
## 63 Technology … Non-Leader Volatile Upbound Group… UPBD
## 64 Technology … Leader Stable Paychex, Inc. PAYX
## 65 Technology … Leader Stable Fiserv, Inc. FI
## 66 Technology … Leader Stable CGI Inc. GIB
## 67 Technology … Leader Volatile Adobe Inc. ADBE
## 68 Technology … Leader Volatile HP Inc. HPQ
## 69 Technology … Leader Volatile Intuit Inc. INTU
## # ℹ abbreviated name: ¹`Market leadership status`The selected stocks are assumed 72 however, because of the data groupping 3 stocks is not revelead and avoided.The following part of the analysis is cocducted in Python such as Data preparation for Modeling, Modeling, Evaluation of the Models, and Results parts.