Introduction

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Scenario

I want to invest $1,000 in Stocks for the last quarter of 2021.

As the use of a CRM software seems increasingly important for every company that has an online presence, I want to look at the Stocks of some of the most popular CRM software companies such as:

  1. Salesforce
  2. Microsoft
  3. SAP
  4. Oracle

I will be analyzing the financial performance of each of these Stocks in 2020 before making a decision.

Key Indicator Analysis

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Financial Indicators/Metrics

  • P/E Ratio - It is the ratio of a company's share price to the company's earnings per share. The more the P/E, the more the price one has to pay to earn a dollar from the stock. Hence, companies with more P/E are less attractive for investors.
  • Forward P/E Ratio - It is the projected P/E Ratio of a company for next year.
  • Dividend Yield Ratio - is a financial ratio that shows how much a company pays out in dividends each year relative to its stock price.
  • Market Cap - the total value of a company's shares.

Row

Key Indicator Analysis at a glance

Although Microsoft has the highest Market Cap of $2T, Oracle has the best P/E ratio out of the four followed by SAP.

Salesforce has the worst P/E ratio out of the four.

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Individual Stock Analysis

Stocks

[1] "CRM"
[1] "MSFT"
[1] "SAP"
[1] "ORCL"

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Salesforce

Microsoft

SAP

Oracle

Comparison of Monthly Returns and Conclusion

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Comparison of Monthly Returns

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Conclusion

Oracle's Monthly Returns have been the most consistent followed by Microsoft. Oracle not only has the best P/E Ratio but it also has the best Forward P/E Ratio.

Although, SAP has the second best P/E and Forward P/E Ratios, it's Monthly Returns have not been as consistent as that of Microsoft or Oracle.

Looking at the Forward P/E Ratio of both Microsoft and Oracle, Oracle's Forward P/E ratio is 40% less than that of Microsoft. Hence, I want to invest 60% of my money in Oracle, which is $600 and 40% of my money in Microsoft, which is $400.

---
title: "ANLY 512 Lab1"
author: "Nischal Bondalapati"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source: embed
    vertical_layout: fill
  html_document:
    df_print: paged
  pdf_document: default
---

# **Introduction**

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### **Scenario**

I want to invest $1,000 in Stocks for the last quarter of 2021.

As the use of a CRM software seems increasingly important for every company that has an online presence, I want to look at the Stocks of some of the most popular CRM software companies such as:

1. Salesforce
2. Microsoft
3. SAP
4. Oracle

I will be analyzing the financial performance of each of these Stocks in 2020 before making a decision.

# **Key Indicator Analysis**
Row {data-height=200}
-------------------------
### **Financial Indicators/Metrics**

+ `r kableExtra::text_spec("**P/E Ratio**", color = "#5c5c5c")` - It is the ratio of a company's share price to the company's earnings per share. The more the P/E, the more the price one has to pay to earn a dollar from the stock. Hence, companies with more P/E are less attractive for investors.
+ `r kableExtra::text_spec("**Forward P/E Ratio**", color = "#5c5c5c")` - It is the projected P/E Ratio of a company for next year.
+ `r kableExtra::text_spec("**Dividend Yield Ratio**", color = "#5c5c5c")` - is a financial ratio that shows how much a company pays out in dividends each year relative to its stock price.
+ `r kableExtra::text_spec("**Market Cap**", color = "#5c5c5c")` - the total value of a company's shares.

Row {data-height=200}
-------------------------
### **Key Indicator Analysis at a glance**

Although Microsoft has the highest Market Cap of $2T, Oracle has the best P/E ratio out of the four followed by SAP. 

Salesforce has the worst P/E ratio out of the four.

```{r, echo = TRUE, include = FALSE, message = FALSE}
install.packages("xts",repos = "http://cran.us.r-project.org")
install.packages("dygraphs",repos = "http://cran.us.r-project.org")
install.packages("lubridate",repos = "http://cran.us.r-project.org")
install.packages("DT",repos = "http://cran.us.r-project.org")
install.packages("pdfetch", repos = "http://cran.us.r-project.org")
install.packages("PerformanceAnalytics", repos = "http://cran.us.r-project.org")
install.packages("stocks", repos = "http://cran.us.r-project.org")
install.packages("flexdashboard", repos = "http://cran.us.r-project.org")
library(xts) 
library(pdfetch)
library(DT)
library(lubridate)
library(dygraphs)
library(quantmod) 
library(dplyr)
library(knitr)
library(ggplot2)
library(tidyr)
library(plyr)
library(PerformanceAnalytics)
library(stocks)
library(kableExtra)
library(flexdashboard)
```

Row
-------------------------
```{r}
library(quantmod)
library(plyr)
what_metrics <- yahooQF(c("Price/Sales", 
                          "P/E Ratio",
                          "Price/EPS Estimate Next Year",
                          "PEG Ratio",
                          "Dividend Yield", 
                          "Market Capitalization"))
tickers <- c("CRM", "MSFT", "SAP", "ORCL")
metrics <- getQuote(paste(tickers, sep = ",", collapse = ";"), what = what_metrics)
metrics <- data.frame(Symbol = tickers, metrics[,2:length(metrics)])
names(metrics)[names(metrics) == "P.E.Ratio"] <- "P/E Ratio"
names(metrics)[names(metrics) == "Price.EPS.Estimate.Next.Year"] <- "Forward P/E Ratio"
names(metrics)[names(metrics) == "Dividend.Yield"] <- "Dividend Yield Ratio"
names(metrics)[names(metrics) == "Market.Capitalization"] <- "Market Cap"
DT::datatable(metrics)
```

# **Individual Stock Analysis**

#### **Stocks**

```{r, echo=FALSE}
getSymbols("CRM", from = "2020-01-01", to = "2021-01-01")
CRMlog <- CRM %>% Ad() %>% dailyReturn(type = 'log')
getSymbols("MSFT", from = "2020-01-01", to = "2021-01-01")
MSFTlog <- MSFT %>% Ad() %>% dailyReturn(type = 'log')
getSymbols("SAP", from = "2020-01-01", to = "2021-01-01")
SAPlog <- SAP %>% Ad() %>% dailyReturn(type = 'log')
getSymbols("ORCL", from = "2020-01-01", to = "2021-01-01")
ORCLlog <- ORCL %>% Ad() %>% dailyReturn(type = 'log')
```

Row {.tabset .tabset-fade}
-------------------------------------
### Salesforce
```{r}
CRM %>% Ad() %>% chartSeries()
CRM %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2020')
```

### Microsoft
```{r}
MSFT %>% Ad() %>% chartSeries()
MSFT %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2020')
```

### SAP
```{r}
SAP %>% Ad() %>% chartSeries()
SAP %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2020')
```

### Oracle
```{r}
ORCL %>% Ad() %>% chartSeries()
ORCL %>% chartSeries(TA = 'addBBands();addVo();addMACD()',subset='2020')
```

Comparison of Monthly Returns and Conclusion
===================================== 
Row {data-height=700}
-------------------------------------
### **Comparison of Monthly Returns**

```{r}
CRMmr <- monthlyReturn(CRM)
MSFTmr <- monthlyReturn(MSFT)
SAPmr <- monthlyReturn(SAP)
ORCLmr <- monthlyReturn(ORCL)

mg.return <- merge.xts(CRMmr, MSFTmr, SAPmr, ORCLmr)
colnames(mg.return) <- c("Salesforce", "Microsoft", "SAP", "Oracle")

dygraph(mg.return, main = "Monthly Return") %>%
  dyAxis("y", label = "Return") %>%
  dyOptions(colors = RColorBrewer::brewer.pal(4, "Dark2")) %>%
  dyHighlight(highlightSeriesBackgroundAlpha = 0.3,
              highlightSeriesOpts = list(strokeWidth = 3)) %>%
  dyRangeSelector(height = 30)
```

Row {data-height=300}
-------------------------------------
### **Conclusion**

Oracle's Monthly Returns have been the most consistent followed by Microsoft. Oracle not only has the best P/E Ratio but it also has the best Forward P/E Ratio.

Although, SAP has the second best P/E and Forward P/E Ratios, it's Monthly Returns have not been as consistent as that of Microsoft or Oracle.

Looking at the Forward P/E Ratio of both Microsoft and Oracle, Oracle's Forward P/E ratio is 40% less than that of Microsoft. Hence, I want to invest 60% of my money in Oracle, which is $600 and 40% of my money in Microsoft, which is $400.