---
title: "Lab_1 ANLY 512-90-O_Assignment_Hemant"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)

library(ggplot2)

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 9))


```


# **LAB -1 INSTRUCTIONS**

## Column {data-width =275}

### **OVERVIEW** 

Due to the inherent growth in the electronic production and storage of information, there is often a feeling of “information overload” or inundation when facing the process of quantitative decision making. As an analyst your job will often be to conduct analyses or create tools to support quantitative decision making.

A principle tool used in industry, government, non-profits, and academic fields to compensate for the information overload is the information dashboard. Functionally, a dashboard is meant to provide a user with a central resource to present in a clear and concise manner all the information necessary to support day-to-day decision making and support operations.



### **DECISION & RULES**

You make investments for an organization; your objective is to purchase securities/commodities for the key objective of maximizing profits. You want to make an investment in securities/commodities to make some short-term gains. You are considering investing in one of any four companies, for example: Twitter (TWTR), Microsoft (MSFT), or Apple (AAPL) (don’t use these). 

Choose 4 companies or commodities and determine which one of the four will produce the most short-term gains. Use your imagination.


## Column {data-width =725}

### **OBJECTIVE** 

The objective of this laboratory is to plan, design, and create an information dashboard to support quantitative decision making. To accomplish this task, you will have to complete a number of steps:

1. Delineate the necessary decision (I will do that below).

2. Identify what information will be relevant to decision making.

3. Find and collect the data necessary to create your visualization plan.

4. Organize and summarize the collected data.

5. Design and create the best visualizations to present that information.

6. Finally organize the layout of those visualizations in a way that conforms to 
   the theory of dash boarding.

7. Write a summary about what decisions you made based on the visualizations that 
   you developed.


### **METHODS HELP** 

Getting data
There are lots of places we can get financial data to support these decision. The simplest would be to go to for instance to the Yahoo Finance (https://finance.yahoo.com/) for data on the Hershey Company (HSY) the URL would be: (https://finance.yahoo.com/quote/HSY/history?p=HSY) and collect historical price data, and other financial and company information.

Alternatively, and more professionally, there are tons of packages that allow you to access data from R. See here quick examples. This is not a complete list but will get you started.



# **FINANCIAL INDICATORS OVERVIEW**


## Column {.tabset}

### **KEY FINANCIAL INDICATORS-OVERVIEW**


**OVERVIEW:**

Financial ratios are basic calculations using quantitative data from a company’s financial statements. They are used to get insights and crucial information on the company’s performance, profitability, and financial health.

The "Analysis summary" tab from this dashboard section presents some of the key findings from the perspective of financial indicators ratio for four of our dashboards select Japanese motor corporations i.e., Honda Motors (HMC), Toyota Motors (TM), Nissan Motors (NSANY), and Mazda Motors (MZDAY).

--------------------------------------------------------------------------------

**KEY FINANCIAL INDICATORS RATIOS:**

**1. PEG Ratio:** The price/earnings to growth ratio (PEG ratio) is a stock's price-to-earnings (P/E) ratio that is used to determine a stock's value while also factoring in the company's expected earnings growth, and it is thought to provide a more complete picture than the more standard P/E ratio.


**2. Dividend Yield:** The dividend yield, expressed as a percentage, is a financial ratio (dividend/price) that shows how much a company pays out in dividends each year relative to its stock price.


**3. P/E Ratio:** The price-to-earnings ratio is the ratio for valuing a company that measures its current share price relative to its earnings per share (EPS).


**4. Market Capitalization:** Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. The investment community uses this figure to determine a company's size instead of sales or total asset figures.


**5. EPS Estimate:** Earnings per share (EPS) is calculated as a company's profit divided by the outstanding shares of its common stock. The higher a company's EPS, the more profitable it is considered to be.


### **FINANCIAL INDICATORS ANALYSIS TABLE**

**FINANCIAL INDICATORS ANALYSIS TABLE:**



```{r}

library(quantmod)

library(plyr)

library(DT)

what_metrics <- yahooQF(c("Price/Sales", 
                          "P/E Ratio",
                          "Price/EPS Estimate Next Year",
                          "PEG Ratio",
                          "Dividend Yield", 
                          "Market Capitalization"))

tickers <- c("HMC", "TM", "NSANY", "MZDAY")

metrics <- getQuote(paste(tickers, sep="", collapse=";"), what=what_metrics)


metrics <- data.frame(Symbol=tickers, metrics[,2:length(metrics)]) 

#Change colnames
colnames(metrics) <- c("Symbol", "P-E Ratio", "Price EPS Estimate Next Year", "Div Yield", "Market Cap")

#write this to the csv file


DT::datatable(metrics)


```


### **ANALYSIS SUMMARY**

**FINANCIAL INDICATORS ANALYSIS SUMMARY:**


**1.**	The **Price-to-Earnings (P/E) key financial ratio** for company valuation depicted Nissan Motors and Toyota Motors as the top organization for the highest P/E Ratio.

**2.**	**Market capitalization financial indicator** referring to the total dollar market value of a company’s outstanding shares of stock too presented Nissan Motors and Toyota Motors as a top organizations.

**3.**	**Earnings per share (EPS) financial indicator**, calculated as a company’s profit divided by the outstanding shares of its common stock presented Toyota Motors and Honda Motors as the top organization for the highest EPS Ratio.

**4.**	**The Dividend Yield Financial indicator ratio**, expressed as a percentage, shows how much a company pays out in dividends each year relative to its stock price indicating Honda Motors and Mazda Motors as top candidates.



## Column {.tabset}


### **P/E RATIO-MARKET CAP PLOT**

```{r}

library(DT)

Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio

Metrics_Data_Plot_c<- ggplot(data, aes(x=P_E_Ratio, y=Market_Cap)) +
  
  geom_point(aes(color = Symbol, shape = Symbol), size = 3) +
  
  labs(title = "ORGANIZATIONS PRICE-EARNING RATIO & MARKET CAP COUNT PLOT", 
       x = "PRICE/EARNING (P/E) RATIO COUNT SCALE ", y = "MARKET CAP COUNT SCALE") + facet_wrap(~Symbol, nrow = 4) + scale_color_discrete("Motor Organization Legend") + scale_shape_discrete("Motor Organization Symbols") + cleanup


Metrics_Data_Plot_c



```


### **DIVIDEND YIELD - EPS RATIO PLOT**


```{r}

Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio


Metrics_Data_Plot_b<- ggplot(data, aes(x=Div_Yield, y=Price_EPS)) +
  
  geom_point(aes(color = Symbol, shape = Symbol), size = 3) +
  
  labs(title = "ORGANIZATIONS DIVIDEND YIELD & PRICE EPS RATIO COUNT PLOT", 
       x = "DIVIDEND YIELD SCALE ", y = "PRICE EPS COUNT SCALE") + facet_wrap(~Symbol, nrow = 4) + scale_color_discrete("Motor Organization Legend") + scale_shape_discrete("Motor Organization Symbols") + cleanup


Metrics_Data_Plot_b


```


### **DIVIDEND YIELD PLOT**

```{r}

Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio


Div_Yield_Plot <- 
  ggplot(data, aes(x= Symbol,y=Div_Yield, fill=Symbol)) +
  geom_bar(stat = "identity") + 
  labs(title = "DIVIDEND YIELD VALUE PLOT FOR MOTOR COMPANIES") +  
  labs(y = "Dividend Yield Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Div_Yield_Plot


```

### **PRICE/EARNING RATIO PLOT**

```{r}


Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio


Price_Earning_Plot <- 
  ggplot(data, aes(x= Symbol,y=P_E_Ratio, fill=Symbol)) +
  geom_bar(stat = "identity") + 
  labs(title = "PRICE/EARNING RATIO VALUE PLOT FOR MOTOR COMPANIES") +  
  labs(y = "Price/Earning Ratio Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup


Price_Earning_Plot


```

### **PRICE EPS ESTIMATE PLOT**


```{r}


Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio


Price_EPS_Plot <- 
  ggplot(data, aes(x= Symbol,y=Price_EPS, fill=Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "PRICE EPS ESTIMATE VALUE PLOT FOR MOTOR COMPANIES") +  
  labs(y = "Price EPS Estimate Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup


Price_EPS_Plot

```


### **MARKET CAP PLOT**

```{r}

Fin_Ratio = read.csv(file="~/Desktop/Anly_512_Course/Fin_Ratio.csv" , header = TRUE)

data<-Fin_Ratio


Market_Cap_Plot <- 
  ggplot(data, aes(x= Symbol,y=Market_Cap, fill=Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MARKET CAPITALIZATION VALUE PLOT FOR MOTOR COMPANIES") +  
  labs(y = "Market Cap Value in Billions (USD)") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup


Market_Cap_Plot


```




# **STOCK TREND**

## Column {.tabset}

### **DAILY STOCK TREND** 

```{r}


library(tidyquant)
library(highcharter)
library(viridisLite)
library(broom)
library(xts)
library(zoo)

start <- as.Date("2022-01-01") 
end <- as.Date("2023-02-28")

getSymbols(tickers, src = "yahoo", from = start, to = end)

stocks = as.xts(data.frame(A = HMC[, "HMC.Adjusted"], 
B = TM[, "TM.Adjusted"], C = NSANY[, "NSANY.Adjusted"], 
E = MZDAY[,"MZDAY.Adjusted"]))


# names(stocks) <- c("HMC", "TM", "NSANY","MZDAY")
index(stocks) <- as.Date(index(stocks))

Stocks_1 <- tidy(stocks) %>% 
  
  ggplot(aes(x=index,y=value, color=series)) +
  labs(title ="STOCK ADJUSTED PRICE COMPARISON:01/2022-02/2023",
       
       subtitle = "HONDA MOTORS, TOYOTA MOTORS, NISSAN MOTORS, MAZDA MOTORS",
       caption = " Source: Yahoo Finance",
       color = "Stock",
       x = "Date Range",
       y = "End of Day Adjusted Stock Price ($)") +
  scale_color_manual(values = c("Darkgrey", "Orange", "DarkBlue","Red"))+
  geom_line()

Stocks_1


```

### **DAILY STOCK TREND (FACET GRID VIEW)** 

```{r}

Stocks_2 = tidy(stocks) %>% 
  
  ggplot(aes(x=index,y=value, color=series)) + 
  geom_line() +
  facet_grid(series~.,scales = "free") + 
  labs(title = "STOCK ADJUSTED PRICE COMPARISON:01/2022-02/2023",
       
       subtitle = "HONDA MOTORS, TOYOTA MOTORS, NISSAN MOTORS, MAZDA MOTORS",
       caption = " Source: Yahoo Finance",
       color = "Stock",
       x = "Date Range",
       y = "End of day Adjusted Stock Price ($)") +
  scale_color_manual(values = c("Darkgrey", "Orange", "DarkBlue","Red"))

Stocks_2


```

### **STOCK TREND ANALYSIS SUMMARY**

**STOCK TREND ANALYSIS SUMMARY:**

The **Daily end-of-day adjusted stock price trend analysis** findings (Jan-2022 - Feb 2023 Time period) for four of our dashboards select Japanese motor corporations i.e., Honda Motors (HMC), Toyota Motors (TM), Nissan Motors (NSANY), and Mazda Motors (MZDAY), presented Toyota Motors as the top organization (Highest: Approx. 212.5 USD, Q1-2022, Lowest: Approx. 127 USD, Q-4 2022) and followed by Honda Motors (Highest: Approx. 32.5 USD, Q1-2022, Lowest: Approx. 22 USD, Q-4 2022) with the highest End of day Adjusted Stock Price in the dollar and Mazda Motors followed by Nissan Motors having the lowest End of day Adjusted Stock Price value between this time frame.


## Column {.tabset}


```{r}
library(tidyquant)

library(tidyverse)

options("getSymbols.warning4.0"=FALSE)
options("getSymbols.yahoo.warning"=FALSE)

# Determination of function:
Candle_Stick_plot<-function(symbol,from,to){
  tq_get(symbol,
        from = from,
        to = to,
        warnings = FALSE) %>% 
    mutate(greenRed=ifelse(open-close>0,
                           "Red",
                           "Green")) %>% 
    ggplot()+
    geom_segment(aes(x = date,
                     xend=date,
                     y =open,
                     yend =close,
                     colour=greenRed),
                 size=3)+
    theme_tq()+
    geom_segment(aes(x = date,
                     xend=date,
                     y =high,
                     yend =low,
                     colour=greenRed))+
    scale_color_manual(values=c("Blue","Red"))+
    ggtitle(paste0(symbol," (",from," - ",to,")"))+
    labs(y = "Stock Price ($)",
        x = "Date") +
    theme(legend.position ="none",
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
          plot.title= element_text(hjust=0.5))
}

```


### **HONDA MOTORS (HMC) DAILY ADJUSTED STOCK TREND PLOT**


```{r}

Candle_Stick_plot("HMC",from = start,to = end)

```


### **TOYOTA MOTORS (TM) DAILY ADJUSTED STOCK TREND PLOT** 


```{r}

Candle_Stick_plot("TM",from = start,to = end)

```

### **NISSAN MOTORS (NSANY) DAILY ADJUSTED STOCK TREND PLOT**


```{r}

Candle_Stick_plot("NSANY",from = start,to = end)

```

### **MAZDA MOTORS (MZDAY) DAILY ADJUSTED STOCK TREND PLOT**

```{r}

Candle_Stick_plot("MZDAY",from = start,to = end)

```


# **COMPANY INSIGHT SCORES**

**OVERVIEW:**

**1. INSIGHT SCORE OVERVIEW** : Overall an Insight score is calculated based on proprietary scores based on sector averages in key company indicators: fair value, dividends, innovation, hiring, and insider sentiment.

**2. DIVIDEND SCORE OVERVIEW**: Dividend Safety Scores™ predict dividend risk over a full economic cycle by analyzing the most important metrics for dividends.

**3. INNOVATION SCORE OVERVIEW**: The American Innovation Index™ (Aii) scores and ranks the innovativeness of U.S.companies defined as Innovation scores based on their customers' perceptions.


## Column {.tabset}

**COMPANY INSIGHT SCORES GAUGE COMPONENTS:**


### **HONDA MOTORS CO LTD (HMC) INSIGHT SCORE: SUCCESS**

```{r}

gauge(96, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```

### **HONDA MOTORS CO LTD (HMC) DIVIDEND SCORE: WARNING**

```{r}

gauge(88, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```


### **HONDA MOTORS CO LTD (HMC) INNOVATION SCORE: SUCCESS**


```{r}

gauge(99, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```


### **TOYOTA MOTORS CO LTD (TM) INSIGHT SCORE: WARNING**


```{r}

gauge(53, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))

```


### **TOYOTA MOTORS CO LTD (TM) DIVIDEND SCORE: WARNING**


```{r}

gauge(56, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))

```


### **TOYOTA MOTORS CO LTD (TM) INNOVATION SCORE: SUCCESS**

```{r}

gauge(99, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))

```


### **MAZDA MOTORS CO LTD (MZDAY) INSIGHT SCORE: WARNING**

```{r}

gauge(71, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```

### **MAZDA MOTORS CO LTD (MZDAY) DIVIDEND SCORE: WARNING**

```{r}


gauge(64, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```


### **MAZDA MOTORS CO LTD (MZDAY) INNOVATION SCORE: WARNING**

```{r}

gauge(67, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))


```


### **NISSAN MOTORS CO LTD (NSANF) INSIGHT SCORE: DANGER**


```{r}

gauge(9, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```


### **NISSAN MOTORS CO LTD (NSANF) DIVIDEND SCORE: DANGER**

```{r}

gauge(12, min = 0, max = 100,
      gaugeSectors(success = c(90,100),
                   warning = c(50, 89),
                   danger = c(0,49)))
```

## Column

### **COMPANY INSIGHT SCORE ANALYSIS SUMMARY**

The overall company insight score calculated based on the proprietary scores based on sector averages in key company indicators such as dividends, innovation, and fair value presented the following insight score ranking and critical findings.

This section presents some of the crucial analysis findings from the perspective of the organization's insight score values for four of our dashboards select Japanese motor corporations i.e., Honda Motors (HMC), Toyota Motors (TM), Nissan Motors (NSANY), and Mazda Motors (MZDAY) as under:

**1.** **Insight Score** was highest for Honda Motors with the highest and topmost Insight score of 96, followed by Mazda Motors with an overall Insight Score of 71, and Nissan Motors (Insight Score: 9) and Toyota (Insight Score: 53) have the lowest Insight Score values.

**2.**	**Dividend Safety Score** predicting the dividend risk over the full economic cycle depicted Honda Motors with the highest and topmost Dividend score of 88, followed by Mazda Motors with an overall Dividend Score of 64, and Nissan Motors (Dividend Score: 12) and Toyota (Dividend Score: 56) having some of the lowest Dividend Score values.

**3.**	**Company Innovation Score** ranking the innovativeness of U.S companies based on their customer perceptions depicted Honda and Toyota Motors with the highest and identical Innovation score of 99, and Mazda Motors (Innovation Score: 67) having the lowest Innovation Score values.


# **KEY VALUATION MEASURES**


## Column {.tabset}


### **VALUATION MEASURES OVERVIEW**:

**OVERVIEW:**

Company valuation, also known as business valuation, is the process of assessing the total economic value of a business and its assets. During this process, all business aspects are evaluated to determine the current worth of an organization or department.

The "Analysis summary" tab from this dashboard section presents some of the crucial analysis findings from the perspective of the organization's valuation measures for four of our dashboards select Japanese motor corporations i.e., Honda Motors (HMC), Toyota Motors (TM), Nissan Motors (NSANY), and Mazda Motors (MZDAY).

--------------------------------------------------------------------------------
**KEY VALUATION MEASURES:**

**1. MARKET CAP** : 
Market capitalization refers to the total dollar market value of a company's outstanding shares of stock.

**2. ENTERPRISE VALUE**: 
Enterprise value (EV) measures a company's total value, often used as a more comprehensive alternative to equity market capitalization.

**3. TRAILING P/E VALUE**: 
The trailing price-to-earnings ratio looks at a company's share price in the market relative to its past year's earnings per share.

**4. PRICE/SALES (ttm) VALUE**: 
The price-to-sales (P/S) Trailing Tweleve month (ttm) ratio shows how much investors are willing to pay per dollar of sales for a stock.

**5. PRICE/BOOK (mrq) VALUE**: 
The price-to-book (P/B) ratio measures the market's valuation of a company relative to its book value.

**6. ENTERPRISE VALUE/REVENUE**: 
A measure of the value of a stock that compares a company's enterprise value to its revenue.

**7. ENTERPRISE VALUE/EBIDTA**: 
Enterprise multiple, also known as the EV-to-EBITDA (Earning before Interest, Tax, Deductions, Amortization) multiple, is a ratio used to determine the value of a company.

### **VALUATION MEASURES ANALYSIS TABLE**

**ANALYSIS TABLE:**

```{r}

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

DT::datatable(Valuation_Measure)

```

### **ANALYSIS SUMMARY**

**KEY VALUATION MEASURES SUMMARY:**

**1.**	**Enterprise value (EV)** measuring a company’s total value depicted Toyota Motors with the Highest Enterprise Value (Approx. 342.86 USD in Billions), Honda Motors with the second highest Enterprise Value (Approx. 69.36 USD in Billions), and Mazda with the lowest EV of 4.38 USD in Billions.

**2.**	The **trailing Price-to-Earnings (P/E) ratio** considering the company’s share price in the market relative to its past year’s earnings per share revealed Nissan Motors (P/E Value [Highest]: 14.68) and Toyota Motors (P/E Value: 10.01) with the highest P/E value and in comparison, with Mazda Motors (P/E Value [Lowest]: 4.39) and Honda Motors (P/E Value: 7.92) respectively.

**3.**	The **Price-to-Sales (P/S) Trailing ratio** depicting how much investors are willing to pay per dollar of sales for a stock revealed Toyota Motors (P/S Value [Highest]: 0.74) and Honda Motors (P/S Value: 0.33) with the highest P/S value and in comparison, with Nissan Motors (P/S Value [Lowest]: 0.18) and Mazda Motors (P/S Value: 0.19) respectively.

**4.**	The **Price-to-Book (P/B) ratio** measuring the market’s valuation of a company relative to its book value revealed Toyota Motors (P/B Value [Highest]: 0.88) and Mazda Motors (P/S Value: 0.45) with the highest P/B value and in comparison, with Nissan Motors (P/B Value [Lowest]: 0.32) and Honda Motors (P/B Value: 0.44) respectively.

**5.**	The **Enterprise value-to-revenue (Enterprise value/Revenue or EV/R)** measuring the value of a stock comparing a company’s enterprise value to its revenue revealed Toyota Motors (EV/R Value [Highest]: 0.04) and Honda and Nissan Motors (EV/R Value: 0.02) with the second highest EV/R values, and in comparison, with Mazda Motors (EV/R Value [Lowest]: 0.00) having the lowest EV/R value respectively.

**6.**	The **EV-to-EBITDA (EV/EBIDTA) value** used to determine the valuation of the company revealed Toyota Motors (EV/EBIDTA Value [Highest]: 0.22) and Nissan Motors (EV/EBIDTA Value: 0.19) with the second highest EV/EBIDTA values, and in comparison, with Mazda Motors (EV/EBIDTA Value [Lowest]: 0.1) and Honda Motors (EV/EBIDTA Value: 0.13) having the lowest EV/EBIDTA value respectively.



## Column {.tabset}


### **1. MARKET CAP PLOT**



```{r}

library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

Market_Cap_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=Market_Cap, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES MARKET CAP PRICE IN BILLIONS") +  
  labs(y = "Market Cap Price in Billions (USD)") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Market_Cap_Plot


```


### **2. ENTERPRISE VALUE PLOT**


```{r}


library(reshape)

cleanup = theme(panel.background = element_blank(), 
                panel.grid.major = element_blank(),
                panel.grid.minor = element_blank(),
                axis.line.x = element_line(colour = "black"),
                axis.line.y = element_line(colour = 'black'),
                legend.key = element_rect(colour = "white"),
                text = element_text(size = 8))

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

Enterprise_Value_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=Enterprise_Value, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES ENTERPRISE VALUE PRICE IN BILLIONS") +  
  labs(y = "Enterprise Value Price in Billions (USD)") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Enterprise_Value_Plot


```

### **3. TRAILING P/E VALUE PLOT**


```{r}

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

Trailing_PE_Value_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=Trailing_P_E, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES TRAILING P/E RATIO VALUES") +  
  labs(y = "Trailing P/E ratio Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Trailing_PE_Value_Plot



```

### **4. PRICE/SALES (ttm) VALUE PLOT**


```{r}

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

P_S_Trailing_Value_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=Price_Sales, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES PRICE/SALES TRAILING TWELEVE MONTH (ttm) VALUES") +  
  labs(y = "Price/Sales (ttm) Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

P_S_Trailing_Value_Plot


```


### **5. PRICE/BOOK (mrq) VALUE PLOT**


```{r}

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

P_B_Value_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=Price_Book, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES PRICE/BOOK MOST RECENT QUARTER (mrq) VALUES") +  
  labs(y = "Price/Book (MRQ) Value") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

P_B_Value_Plot


```


### **6. ENTERPRISE VALUE/REVENUE PLOT**


```{r}


Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

Ent_Value_Rev_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=EnterpriseValue_Revenue, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES ENTERPRISE VALUE/REVENUE VALUES") +  
  labs(y = "Enterprise Value/Revenue") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Ent_Value_Rev_Plot


```

### **7. ENTERPRISE VALUE/EBIDTA PLOT**


```{r}

Valuation_Measure = read.csv(file="~/Desktop/Anly_512_Course/Valuation_Measure.csv" , header = TRUE)

data<- Valuation_Measure

Ent_Value_EBITDA_Plot <- 
  ggplot(data, aes(x= Company_Name_Symbol,y=EnterpriseValue_EBITDA, fill=Company_Name_Symbol)) +
  geom_bar(stat = "identity") +
  labs(title = "MOTOR COMPANIES ENTERPRISE VALUE/EBITDA VALUES") +  
  labs(y = "Enterprise Value/EBITDA") +
  scale_fill_discrete("Company_Name_Symbol") + cleanup

Ent_Value_EBITDA_Plot

```


# **COLLECTIVE ANALYSIS SUMMARY REVIEW**


## Column {.tabset}


### **A. COMPANY KEY VALUATION MEASURES ANALYSIS SUMMARY**



**A1.**	**Enterprise value (EV)** measuring a company’s total value depicted Toyota Motors with the Highest Enterprise Value (Approx. 342.86 USD in Billions), Honda Motors with the second highest Enterprise Value (Approx. 69.36 USD in Billions), and Mazda with the lowest EV of 4.38 USD in Billions.

**A2.**	The **trailing Price-to-Earnings (P/E) ratio** considering the company’s share price in the market relative to its past year’s earnings per share revealed Nissan Motors (P/E Value [Highest]: 14.68) and Toyota Motors (P/E Value: 10.01) with the highest P/E value and in comparison, with Mazda Motors (P/E Value [Lowest]: 4.39) and Honda Motors (P/E Value: 7.92) respectively.

**A3.**	The **Price-to-Sales (P/S) Trailing ratio** depicting how much investors are willing to pay per dollar of sales for a stock revealed Toyota Motors (P/S Value [Highest]: 0.74) and Honda Motors (P/S Value: 0.33) with the highest P/S value and in comparison, with Nissan Motors (P/S Value [Lowest]: 0.18) and Mazda Motors (P/S Value: 0.19) respectively.

**A4.**	The **Price-to-Book (P/B) ratio** measuring the market’s valuation of a company relative to its book value revealed Toyota Motors (P/B Value [Highest]: 0.88) and Mazda Motors (P/S Value: 0.45) with the highest P/B value and in comparison, with Nissan Motors (P/B Value [Lowest]: 0.32) and Honda Motors (P/B Value: 0.44) respectively.

**A5.**	The **Enterprise value-to-revenue (Enterprise value/Revenue or EV/R)** measuring the value of a stock comparing a company’s enterprise value to its revenue revealed Toyota Motors (EV/R Value [Highest]: 0.04) and Honda and Nissan Motors (EV/R Value: 0.02) with the second highest EV/R values, and in comparison, with Mazda Motors (EV/R Value [Lowest]: 0.00) having the lowest EV/R value respectively.

**A6.**	The **EV-to-EBITDA (EV/EBIDTA) value** used to determine the valuation of the company revealed Toyota Motors (EV/EBIDTA Value [Highest]: 0.22) and Nissan Motors (EV/EBIDTA Value: 0.19) with the second highest EV/EBIDTA values, and in comparison, with Mazda Motors (EV/EBIDTA Value [Lowest]: 0.1) and Honda Motors (EV/EBIDTA Value: 0.13) having the lowest EV/EBIDTA value respectively.




### **B. COMPANY KEY FINANCIAL INDICATORS SUMMARY**

**B1.**	The **Price-to-Earnings (P/E) key financial ratio** for company valuation depicted Nissan Motors and Toyota Motors as the top organization for the highest P/E Ratio.

**B2.**	**Market capitalization financial indicator** referring to the total dollar market value of a company’s outstanding shares of stock too presented Nissan Motors and Toyota Motors as a top organizations.

**B3.**	**Earnings per share (EPS) financial indicator**, calculated as a company’s profit divided by the outstanding shares of its common stock presented Toyota Motors and Honda Motors as the top organization for the highest EPS Ratio.

**B4.**	**The Dividend Yield Financial indicator ratio**, expressed as a percentage, shows how much a company pays out in dividends each year relative to its stock price indicating Honda Motors and Mazda Motors as top candidates.


## Column {.tabset}


### **C. COMPANY INSIGHT SCORE ANALYSIS SUMMARY**

The overall company insight score calculated based on the proprietary scores based on sector averages in key company indicators such as dividends, innovation, and fair value presented the following insight score ranking and key findings:

**C1.**	**Insight Score** for Honda Motors was observed to be the highest and with an Insight score of 96, followed by Mazda Motors with an overall Insight Score of 71, and Nissan Motors (Insight Score: 9) and Toyota (Insight Score: 53) have the lowest Insight Score values.

**C2.**	**Dividend Safety Score** predicting the dividend risk over the full economic cycle depicted Honda Motors with the highest and topmost Dividend score of 88, followed by Mazda Motors with an overall Dividend Score of 64, and Nissan Motors (Dividend Score: 12) and Toyota (Dividend Score: 56) having some of the lowest Dividend Score values.

**C3.**	**Company Innovation Score** ranking the innovativeness of U.S companies based on their customer perceptions depicted Honda and Toyota Motors with the highest and identical Innovation score of 99, and Mazda Motors (Innovation Score: 67) having the lowest Innovation Score values.

### **D. COMPANY DAILY STOCK TREND ANALYSIS SUMMARY**

**D1.** The **Daily end-of-day adjusted stock price trend analysis** findings (Jan-2022 - Feb 2023 Time period) presented Toyota Motors as the top organization (Highest: Approx. 212.5 USD, Q1-2022, Lowest: Approx. 127 USD, Q-4 2022).

**D2.** Followed by **Honda Motors (Highest: Approx. 32.5 USD, Q1-2022, Lowest: Approx. 22 USD, Q-4 2022) with the highest End of day Adjusted Stock Price** in the dollar and Mazda Motors followed by Nissan Motors having the lowest End of day Adjusted Stock Price value between this time frame.



# **CONCLUSION**

## Column

**CONCLUSION SUMMARY:**

Taking into consideration the obtained quantitative and visual analysis results findings collectively from the perspective of various crucial financial segments and indicators such as "Key Financial Indicators" summary results, "Company's Daily Stock Trend Analysis" summary results, "Company's Insight Score Analysis" result summary findings, and Company's Key Valuation Measures Analysis result finding's, for all of the four, depicted major Japanese Motor company organization's such as Toyota Motors (TM), Honda Motors (HMC), Nissan Motors (NSANY), and Mazda Motors (MZDAY), **it is considered to be a most financial profitable proposition to invest in the Toyota Motors organization** and, investing by the mechanism of purchasing securities or commodities and with the objectivity of maximizing our short term profit gains, **since to a large proportion and all of the four majorly analyzed financial sections i.e., "Company Key Valuation Measure Analysis (EV, P/E, P/B, EV/R, and EV/EBIDTA) presented Toyota Motors as the most profitable organization for investement** from the perspective of some of the key valuation measures, further in addition even the result findings from the perspective of some of the crucial financial indicator ratios results such as for Price-to-Earnings (P/E) ratio, Earnings per share (EPS) financial-indicator ratio, and Market capitalization-related financial-indicator ratio further presented Toyota Motors as a more potential candidate to invest in comparison to some of the other select major Japanese motor organization, besides even the company's daily stock trend analysis depicting the daily end-of-day adjusted stock price trend analysis findings (Jan-2022 - Feb 2023 Time period) presented Toyota Motors as the top organization (Highest: Approx. 212.5 USD, Q1-2022, Lowest: Approx. 127 USD, Q-4 2022) and highest among the other select motor organizations, and followed by also a reasonably good company overall insight score and having an impressive company innovation score of 99.

**Thus by collectively taking into all the above critical depicted factors into account, it would be therefore considered to be a most financial profitable proposition to invest in the Toyota Motors organization.**