#In ORDER TO FIND THE BEST MUTUAL FUND WE NEED TO KNOW FEW THINGS FIRST
#MIN SIP FOR INVESTMENT , AVERAGE EXPENSE RATIO, FUND MANAGER , SD-BETA RATIO, RETURNS IN 1,3 AND 5 YEARS
# Filtering out mutual funds with ratings 1, 2, 3, 4, and 5 stars
comprehensive_mutual_funds_data<-read.csv("D:\\LPU\\Sem 4\\Data Exploration\\archive\\comprehensive_mutual_funds_data.csv")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.3.3
## Warning: package 'ggplot2' was built under R version 4.3.3
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library(ggplot2)
filtered_data <- filter(comprehensive_mutual_funds_data, rating == 1 | rating == 2 | rating == 3 | rating == 4 | rating == 5)
ggplot(filtered_data, aes(x = factor(rating))) +
geom_bar(fill = "skyblue", color = "black") +
labs(x = "Rating", y = "Count", title = "Number of Mutual Funds by Rating") +
theme_minimal()
#What is the average expense ratio of the mutual funds in the dataset?
library(dplyr)
library(tidyverse)
average_expense_ratio <- comprehensive_mutual_funds_data %>%
summarise(avg_expense_ratio = mean(expense_ratio))
average_expense_ratio
## avg_expense_ratio
## 1 0.713231
# finding the best mutual funds from the data set
library(dplyr)
library(tidyverse)
best_mutual_fund <- comprehensive_mutual_funds_data %>%
arrange(desc(returns_5yr)) %>%
slice(1)
best_mutual_fund
## scheme_name min_sip min_lumpsum expense_ratio fund_size_cr
## 1 Quant Small Cap Fund 1000 5000 0.64 3301
## fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level
## 1 10 Sanjeev Sharma 3.71 19.16 24.75 0.99 2.11 6
## amc_name rating category sub_category returns_1yr
## 1 Quant Mutual Fund 5 Equity Small Cap Mutual Funds 5.4
## returns_3yr returns_5yr
## 1 71.4 23.2
#which are the best mutual funds in case some one wish to invest in sip and lumpsum and minimum amount
library(dplyr)
library(tidyverse)
filtered_data <- comprehensive_mutual_funds_data %>%
filter(min_sip <= 100 & min_lumpsum <= 100)
selected_data <- filtered_data %>%
slice(1:25)
print(selected_data)
## scheme_name min_sip min_lumpsum
## 1 Aditya Birla SL Active Debt Multi-Mgr FoF-Dir Growth 100 100
## 2 Aditya Birla SL Balanced Advantage Fund 100 100
## 3 Aditya Birla SL Corporate Bond Fund 100 100
## 4 Aditya Birla SL Credit Risk Fund 100 100
## 5 Aditya Birla SL Equity Hybrid ’95 Fund 100 100
## 6 Aditya Birla SL Flexi Cap Fund 100 100
## 7 Aditya Birla SL FP FoF – Aggressive – Direct Growth 100 100
## 8 Aditya Birla SL FP FoF – Conservative – Direct Growth 100 100
## 9 Aditya Birla SL FP FoF – Moderate – Direct Growth 100 100
## 10 Aditya Birla SL Frontline Equity Fund 100 100
## 11 Aditya Birla SL Global Emerging Oppt Fund 100 100
## 12 Aditya Birla SL Gold Fund 100 100
## 13 Aditya Birla SL Low Duration Fund 100 100
## 14 AXIS FTP – Series 104 – 1112Days 0 0
## 15 AXIS FTP – Series 109 – 111Days 0 0
## 16 HDFC Arbitrage Fund 100 100
## 17 HDFC Balanced Advantage Fund 100 100
## 18 HDFC Banking and PSU Debt Fund 100 100
## 19 HDFC Capital Builder Value Fund 100 100
## 20 HDFC Children’s Gift Fund 100 100
## 21 HDFC Corporate Bond Fund 100 100
## 22 HDFC Credit Risk Debt Fund 100 100
## 23 HDFC Dynamic Debt Fund 100 100
## 24 HDFC Dynamic PE Ratio Fund 100 100
## 25 HDFC Equity Savings Fund 100 100
## expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha
## 1 0.27 10 10 Kaustubh Gupta 0.32 2.24
## 2 0.61 6386 10 Mohit Sharma 3.69 1.99
## 3 0.31 12120 10 Kaustubh Gupta 2.28 2.38
## 4 0.69 1032 8 Sunaina da Cunha 1.82 3.01
## 5 1.01 7046 10 Mahesh Patil 2.54 1.43
## 6 0.86 15449 10 Anil Shah 3.4 -1.46
## 7 0.22 159 10 Vinod Narayan Bhat 3.13 1.65
## 8 0.34 14 10 Vinod Narayan Bhat 3.82 1.94
## 9 0.36 28 10 Vinod Narayan Bhat 3.09 1.69
## 10 1.03 21131 10 Mahesh Patil 3.36 0.76
## 11 0.70 261 10 Vinod Narayan Bhat 0.98 -
## 12 0.15 278 10 Lovelish Solanki 1.07 -0.57
## 13 0.39 10035 10 Mohit Sharma 4.76 3.33
## 14 0.13 110 4 Devang Shah 7.27 5.29
## 15 0.00 565 1 Devang Shah - -
## 16 0.43 5886 9 Arun Agarwal 0.99 1.24
## 17 0.88 51042 10 Gopal Agrawal 3.38 7.38
## 18 0.39 4805 9 Anil Bamboli 1.74 3.4
## 19 1.04 5227 10 Gopal Agrawal 3.26 3.69
## 20 0.96 6157 10 Chirag Setalvad 4.11 7.62
## 21 0.32 23486 10 Anupam Joshi 1.59 1.91
## 22 0.95 8437 9 Shobhit Mehrotra 1.5 2.63
## 23 0.49 527 10 Anil Bamboli 2.3 2.71
## 24 0.22 30 10 Srinivasan Ramamurthy 4.11 6.38
## 25 1.10 2524 10 Anil Bamboli 3.67 4.42
## sd beta sharpe risk_level amc_name rating
## 1 9.39 0.01 0.24 3 Aditya Birla Sun Life Mutual Fund 3
## 2 10.38 0.68 1.39 6 Aditya Birla Sun Life Mutual Fund 4
## 3 1.93 0.78 1.35 2 Aditya Birla Sun Life Mutual Fund 4
## 4 2.9 -0.95 1.47 4 Aditya Birla Sun Life Mutual Fund 4
## 5 12.84 0.85 1.33 6 Aditya Birla Sun Life Mutual Fund 2
## 6 16.6 0.96 1.27 6 Aditya Birla Sun Life Mutual Fund 3
## 7 11.73 0.8 1.4 6 Aditya Birla Sun Life Mutual Fund 3
## 8 5.49 0.36 1.57 4 Aditya Birla Sun Life Mutual Fund 4
## 9 8.56 0.58 1.44 5 Aditya Birla Sun Life Mutual Fund 3
## 10 16.02 0.95 1.39 6 Aditya Birla Sun Life Mutual Fund 3
## 11 16.64 - 0.7 6 Aditya Birla Sun Life Mutual Fund 0
## 12 13.89 0.97 0.45 5 Aditya Birla Sun Life Mutual Fund 2
## 13 0.97 1 2.05 3 Aditya Birla Sun Life Mutual Fund 4
## 14 - 2.36 2.19 2 Axis Mutual Fund 0
## 15 - - - 3 Axis Mutual Fund 0
## 16 0.7 0.5 0.82 1 HDFC Mutual Fund 2
## 17 14.82 0.88 1.6 6 HDFC Mutual Fund 4
## 18 2.02 1.06 0.98 2 HDFC Mutual Fund 4
## 19 16.41 0.95 1.53 6 HDFC Mutual Fund 2
## 20 11.58 0.76 1.87 6 HDFC Mutual Fund 0
## 21 2.19 0.94 0.99 3 HDFC Mutual Fund 3
## 22 2.56 -0.52 1.3 4 HDFC Mutual Fund 3
## 23 3.62 0.55 0.79 3 HDFC Mutual Fund 4
## 24 9.63 0.6 1.8 5 HDFC Mutual Fund 4
## 25 7.09 0.98 1.61 4 HDFC Mutual Fund 4
## category sub_category returns_1yr
## 1 Other FoFs Domestic 4.0
## 2 Hybrid Dynamic Asset Allocation or Balanced Advantage 4.5
## 3 Debt Corporate Bond Mutual Funds 4.9
## 4 Debt Credit Risk Funds 8.7
## 5 Hybrid Aggressive Hybrid Mutual Funds -5.7
## 6 Equity Flexi Cap Funds -2.1
## 7 Other FoFs Domestic 2.5
## 8 Other FoFs Domestic 4.7
## 9 Other FoFs Domestic 3.7
## 10 Equity Large Cap Mutual Funds 1.6
## 11 Other FoFs Overseas -5.7
## 12 Other FoFs Domestic 11.8
## 13 Debt Low Duration Funds 5.7
## 14 Debt Fixed Maturity Plans 11.2
## 15 Debt Fixed Maturity Plans 11.2
## 16 Hybrid Arbitrage Mutual Funds 5.6
## 17 Hybrid Dynamic Asset Allocation or Balanced Advantage 14.4
## 18 Debt Banking and PSU Mutual Funds 4.3
## 19 Equity Value Funds -0.7
## 20 Solution Oriented Childrens Funds 10.5
## 21 Debt Corporate Bond Mutual Funds 4.1
## 22 Debt Credit Risk Funds 4.6
## 23 Debt Dynamic Bond 4.6
## 24 Other FoFs Domestic 8.6
## 25 Hybrid Equity Savings Mutual Funds 5.1
## returns_3yr returns_5yr
## 1 6.5 6.9
## 2 18.6 9.7
## 3 7.1 7.4
## 4 9.0 7.2
## 5 20.4 7.7
## 6 25.0 10.4
## 7 21.7 10.7
## 8 13.3 8.2
## 9 17.3 9.4
## 10 26.3 10.6
## 11 18.8 8.3
## 12 12.1 13.2
## 13 6.5 7.0
## 14 14.5 14.0
## 15 14.5 14.0
## 16 4.5 5.2
## 17 29.3 12.9
## 18 6.8 7.2
## 19 33.7 11.3
## 20 26.8 12.9
## 21 6.9 7.3
## 22 8.0 7.7
## 23 7.3 6.1
## 24 21.9 11.1
## 25 16.0 8.8
# Create the scatter plot of 3yrs return and 5 yrs returns
library(ggplot2)
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.3.3
## corrplot 0.92 loaded
ggplot(comprehensive_mutual_funds_data, aes(x = returns_3yr, y = returns_5yr)) +
geom_point(color = "red") +
labs(x = "3 Years Returns", y = "5 Years Returns", title = "5 Years Returns vs. 3 Years Returns of Mutual Funds")
## Warning: Removed 167 rows containing missing values or values outside the scale range
## (`geom_point()`).
#mutual funds above 4 star
library(dplyr)
library(tidyverse)
library(ggplot2)
filtered_data <- filter(comprehensive_mutual_funds_data, rating >= 4)
ggplot(filtered_data, aes(x = factor(rating))) +
geom_bar(fill = "skyblue", color = "black") +
labs(x = "Rating", y = "Count", title = "Number of Mutual Funds with Rating > 4") +
theme_minimal()
#finding by category wise
library(dplyr)
library(tidyverse)
library(ggplot2)
category_counts <- table(comprehensive_mutual_funds_data$category)
category_data <- as.data.frame(category_counts)
colnames(category_data) <- c("Category", "Count")
ggplot(category_data, aes(x = Category, y = Count, fill = Category)) +
geom_bar(stat = "identity") +
labs(x = "Category", y = "Count", fill = "Category", title = "Distribution of Mutual Funds by Category")
#found some mutual funds by the mutual fund manager names
library(dplyr)
library(tidyverse)
selected_managers <- c("Kaustubh Gupta", "Dhaval Gala", "Lovelish Solanki")
filtered_data <- filter(comprehensive_mutual_funds_data, fund_manager %in% selected_managers)
print(filtered_data)
## scheme_name min_sip min_lumpsum
## 1 Aditya Birla SL Active Debt Multi-Mgr FoF-Dir Growth 100 100
## 2 Aditya Birla SL Arbitrage Fund 1000 1000
## 3 Aditya Birla SL Banking&Financial Services-DirGrowth 1000 1000
## 4 Aditya Birla SL Banking&PSU Debt Fund 1000 1000
## 5 Aditya Birla SL Corporate Bond Fund 100 100
## 6 Aditya Birla SL Dividend Yield Fund 1000 1000
## 7 Aditya Birla SL Floating Rate Fund 1000 1000
## 8 Aditya Birla SL Gold Fund 100 100
## 9 Aditya Birla SL Liquid Fund 500 500
## 10 Aditya Birla SL Nifty 50 Index Fund 1000 100
## 11 Aditya Birla SL Overnight Fund 0 5000
## 12 Aditya Birla SL Savings Fund 1000 1000
## 13 Aditya Birla SL Short Term Fund 1000 1000
## expense_ratio fund_size_cr fund_age_yr fund_manager sortino alpha sd
## 1 0.27 10 10 Kaustubh Gupta 0.32 2.24 9.39
## 2 0.36 4288 10 Lovelish Solanki 1.33 1.53 0.72
## 3 1.17 2384 9 Dhaval Gala 2.07 1.24 25.53
## 4 0.37 7994 10 Kaustubh Gupta 1.92 4.46 1.91
## 5 0.31 12120 10 Kaustubh Gupta 2.28 2.38 1.93
## 6 1.73 850 10 Dhaval Gala 3.78 5.04 15.77
## 7 0.23 12364 10 Kaustubh Gupta 2.81 3.73 1.14
## 8 0.15 278 10 Lovelish Solanki 1.07 -0.57 13.89
## 9 0.21 28071 10 Kaustubh Gupta 5.28 1.49 0.39
## 10 0.32 494 10 Lovelish Solanki 3.42 -0.87 16.65
## 11 0.07 13399 4 Kaustubh Gupta 0.11 0.58 0.35
## 12 0.34 14457 10 Kaustubh Gupta 4.23 2.64 0.76
## 13 0.38 5045 10 Kaustubh Gupta 3.7 4.91 2.05
## beta sharpe risk_level amc_name rating category
## 1 0.01 0.24 3 Aditya Birla Sun Life Mutual Fund 3 Other
## 2 0.56 1.1 1 Aditya Birla Sun Life Mutual Fund 3 Hybrid
## 3 0.96 0.97 6 Aditya Birla Sun Life Mutual Fund 2 Equity
## 4 1.78 1.09 3 Aditya Birla Sun Life Mutual Fund 4 Debt
## 5 0.78 1.35 2 Aditya Birla Sun Life Mutual Fund 4 Debt
## 6 0.85 1.58 6 Aditya Birla Sun Life Mutual Fund 0 Equity
## 7 1.37 1.66 2 Aditya Birla Sun Life Mutual Fund 5 Debt
## 8 0.97 0.45 5 Aditya Birla Sun Life Mutual Fund 2 Other
## 9 0.8 1.06 3 Aditya Birla Sun Life Mutual Fund 4 Debt
## 10 0.99 1.29 6 Aditya Birla Sun Life Mutual Fund 3 Other
## 11 0.43 0.02 1 Aditya Birla Sun Life Mutual Fund 3 Debt
## 12 0.86 1.96 3 Aditya Birla Sun Life Mutual Fund 4 Debt
## 13 1.27 1.57 3 Aditya Birla Sun Life Mutual Fund 4 Debt
## sub_category returns_1yr returns_3yr returns_5yr
## 1 FoFs Domestic 4.0 6.5 6.9
## 2 Arbitrage Mutual Funds 5.6 4.8 5.5
## 3 Sectoral / Thematic Mutual Funds 5.3 24.6 9.2
## 4 Banking and PSU Mutual Funds 4.5 6.8 7.3
## 5 Corporate Bond Mutual Funds 4.9 7.1 7.4
## 6 Dividend Yield Funds 6.2 30.5 10.0
## 7 Floater Mutual Funds 5.8 6.4 6.8
## 8 FoFs Domestic 11.8 12.1 13.2
## 9 Liquid Mutual Funds 5.7 4.4 5.4
## 10 Index Funds 0.7 25.5 11.6
## 11 Overnight Mutual Funds 5.4 3.9 NA
## 12 Ultra Short Duration Funds 5.5 5.8 6.5
## 13 Short Duration Funds 5.5 7.8 7.6
#best mutual funds according to fund size
library(dplyr)
library(tidyverse)
best_fund_size <- filtered_data %>%
arrange(desc(fund_size_cr)) %>%
slice(1)
print(best_fund_size)
## scheme_name min_sip min_lumpsum expense_ratio fund_size_cr
## 1 Aditya Birla SL Liquid Fund 500 500 0.21 28071
## fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level
## 1 10 Kaustubh Gupta 5.28 1.49 0.39 0.8 1.06 3
## amc_name rating category sub_category
## 1 Aditya Birla Sun Life Mutual Fund 4 Debt Liquid Mutual Funds
## returns_1yr returns_3yr returns_5yr
## 1 5.7 4.4 5.4
#best mutual funds in debt category
library(dplyr)
library(tidyverse)
debt_funds <- comprehensive_mutual_funds_data %>%
filter(category == "Debt")
best_debt_fund <- debt_funds %>%
arrange(desc(returns_5yr)) %>%
slice(1)
print(best_debt_fund)
## scheme_name min_sip min_lumpsum expense_ratio
## 1 AXIS FTP – Series 104 – 1112Days 0 0 0.13
## fund_size_cr fund_age_yr fund_manager sortino alpha sd beta sharpe risk_level
## 1 110 4 Devang Shah 7.27 5.29 - 2.36 2.19 2
## amc_name rating category sub_category returns_1yr returns_3yr
## 1 Axis Mutual Fund 0 Debt Fixed Maturity Plans 11.2 14.5
## returns_5yr
## 1 14
#find the best MUTUAL FUNDS
library(dplyr)
library(tidyverse)
filtered_funds <- comprehensive_mutual_funds_data %>%
filter(min_sip >= 100 &
min_lumpsum >= 100 &
fund_size_cr > 10000 &
returns_1yr > 6 &
returns_3yr > 7 &
returns_5yr > 8 &
rating >= 4 &
fund_age_yr >= 10 &
sharpe > 1.2) %>%
select(scheme_name, min_sip, min_lumpsum, fund_size_cr, returns_1yr, returns_3yr, returns_5yr, rating)
print(filtered_funds)
## scheme_name min_sip min_lumpsum fund_size_cr
## 1 HDFC Balanced Advantage Fund 100 100 51042
## 2 HDFC Flexi Cap Fund 100 100 31672
## 3 ICICI Pru Asset Allocator Fund 1000 5000 18393
## 4 ICICI Pru Balanced Advantage Fund 100 500 44515
## 5 ICICI Pru Equity & Debt Fund 100 5000 21232
## 6 ICICI Pru Multi-Asset Fund 100 5000 16449
## 7 ICICI Pru Value Discovery Fund 100 1000 27449
## 8 Kotak Emerging Equity Fund 100 100 23962
## 9 Kotak Equity Opportunities – Direct Growth 500 500 11608
## 10 Nippon India Small Cap Fund 100 5000 23910
## 11 SBI Small Cap Fund 500 5000 15395
## returns_1yr returns_3yr returns_5yr rating
## 1 14.4 29.3 12.9 4
## 2 12.4 34.5 14.0 4
## 3 8.8 21.6 12.8 4
## 4 6.7 19.7 10.4 4
## 5 6.8 30.6 14.3 5
## 6 11.3 31.3 14.2 4
## 7 8.9 36.9 14.9 4
## 8 6.6 34.0 15.2 4
## 9 6.2 28.8 13.9 4
## 10 8.7 47.6 16.8 4
## 11 8.2 38.3 15.3 4
#find whose sharp ratio is above 2.5
library(dplyr)
library(tidyverse)
filtered_funds <- comprehensive_mutual_funds_data %>%
filter(sharpe >= 2.5) %>%
select(scheme_name, fund_size_cr, returns_1yr, returns_3yr, returns_5yr, sharpe, risk_level)
print(filtered_funds)
## scheme_name fund_size_cr returns_1yr returns_3yr
## 1 Quant Liquid Plan – Direct Growth 1561 5.6 4.9
## returns_5yr sharpe risk_level
## 1 5.9 3.52 2
# corelation and corrplot between sd and beta
library(dplyr)
library(tidyverse)
library(corrplot)
data<-comprehensive_mutual_funds_data
data$sortino <- as.numeric(data$sortino)
## Warning: NAs introduced by coercion
data$beta <- as.numeric(data$beta)
## Warning: NAs introduced by coercion
data$sd <- as.numeric(data$sd)
## Warning: NAs introduced by coercion
data$alpha <- as.numeric(data$alpha)
## Warning: NAs introduced by coercion
data$sharpe <- as.numeric(data$sharpe)
## Warning: NAs introduced by coercion
data <- replace(data, is.na(data), 0)
correlation_coefficient <- cor(data$sd,data$beta)
print(correlation_coefficient)
## [1] -0.2730955
corrplot(cor(cor(data[, c("sd", "beta")]), method = "pearson"), method = "circle")
# count amc
library(dplyr)
library(tidyverse)
library(ggplot2)
amc_counts <- table(comprehensive_mutual_funds_data$amc_name)
amc_counts_df <- as.data.frame(amc_counts)
ggplot(amc_counts_df, aes(x = Var1, y = Freq)) +
geom_bar(stat = "identity", fill = "skyblue", color = "black") +
labs(x = "AMC Name", y = "Count", title = "Count of Mutual Funds by AMC") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#co relation of fund size and 5yrs return
library(dplyr)
library(tidyverse)
library(corrplot)
data<-comprehensive_mutual_funds_data
data$sortino <- as.numeric(data$sortino)
## Warning: NAs introduced by coercion
data$beta <- as.numeric(data$beta)
## Warning: NAs introduced by coercion
data$sd <- as.numeric(data$sd)
## Warning: NAs introduced by coercion
data$alpha <- as.numeric(data$alpha)
## Warning: NAs introduced by coercion
data$sharpe <- as.numeric(data$sharpe)
## Warning: NAs introduced by coercion
data <- replace(data, is.na(data), 0)
correlation <- cor(data$fund_size_cr,data$returns_5yr)
print(correlation)
## [1] 0.1569896
#finding the median of 3yrs return
library(dplyr)
library(tidyverse)
median_return_3yr <- median(comprehensive_mutual_funds_data$returns_3yr, na.rm = TRUE)
print(median_return_3yr)
## [1] 18.5