---
title: "ANLY 512: Dashboard Laboratory"
author: "Yang Lu & Jiayi Zhang"
Date: "October 31 2019"
output:
flexdashboard::flex_dashboard:
orientation: rows
source_code: embed
vertical_layout: scroll
---
```{r message=FALSE, include=FALSE, warning=FALSE}
library(flexdashboard)
library(dygraphs)
library(quantmod)
library(dplyr)
library(DT)
library(xts)
library(tidyquant)
library(knitr)
library(dygraphs)
library(lubridate)
library(ggplot2)
library(tidyr)
library(tidyverse)
```
Sidebar {.sidebar}
-----------------------------------------------------------------------
### Financial Data Lab
1. From Stock Price charts, we could see that:
+ All four stocks have upward trends during past 1 year.
+ Microsoft has been more stable compared to the other three.
+ Google has been more volatile in contrast.
+ Apple shows two big drops in recent months, and Facebook shows big drops in earlier months during past 1 year.
2. Based on 30-day rolling Sharpe Ratio, we could see that:
+ All Sharpe Ratios are below zero. They are within a similar range.
+ However, Apple shows a decreasing trend recently, and all the other three show upward trend. Google, especially, shows a steady increasing trend.
3. The Basic Metrics show that:
+ Apple has the lowest Revenue Multiple and Earnings Multiple.Facebook is the highest, and Google is second highest.
+ Apple and Microsoft pay dividends while the other two do not.
4. The Monthly Return chart shows that.
+ All four stocks fluctuate within a similar range.
+ For the most recent month, Facebook has second lowest Monthly Return.
5. The Daily Return charts show that:
+ Apple has the highest daily loss and lowest daily return in all four stocks.
In conclusion, we recommend selling Apple stocks. If capital is not of a concern, Google is recommended. Or else, Microsoft is an alternative.
Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Stock Price - BAC
See stock price trend to get a genral sense of the performance of the four stocks.
Apple is steadily increasing in recent month, Microsoft is also steady in a long run, while Twitter see downward trend in recent.
```{r}
tickers <- c("BAC", "JPM", "C", "WFC")
symbol <- getSymbols(tickers, src = 'yahoo', auto.assign = TRUE, warnings = FALSE, from = '2018-10-01')
AAPL <- BAC[,1:4]
MSFT <- C[,1:4]
GOOGL <- JPM[,1:4]
FB <- WFC[,1:4]
dygraph(AAPL, main = "Bank of America Corporation (BAC)") %>%
dyCandlestick() %>%
dyAxis("y", label="Price") %>%
dyOptions(colors= RColorBrewer::brewer.pal(5, "Set2")) %>%
dyHighlight(highlightCircleSize = 4,
highlightSeriesBackgroundAlpha = 1) %>%
dyRangeSelector(height = 55)%>%
dyLegend(show = "onmouseover")
```
### Stock Price - Citi
```{r}
dygraph(MSFT, main = "Citigroup Inc. (C)") %>%
dyCandlestick() %>%
dyAxis("y", label="Price") %>%
dyOptions(colors= RColorBrewer::brewer.pal(5, "Set2")) %>%
dyHighlight(highlightCircleSize = 4,
highlightSeriesBackgroundAlpha = 1) %>%
dyRangeSelector(height = 55)%>%
dyLegend(show = "onmouseover")
```
### Stock Price - JPM
```{r}
dygraph(GOOGL, main = "JPMorgan Chase & Co. (JPM)") %>%
dyCandlestick() %>%
dyAxis("y", label="Price") %>%
dyOptions(colors= RColorBrewer::brewer.pal(5, "Set2")) %>%
dyHighlight(highlightCircleSize = 4,
highlightSeriesBackgroundAlpha = 1) %>%
dyRangeSelector(height = 55)%>%
dyLegend(show = "onmouseover")
```
### Stock Price - WFC
```{r}
dygraph(FB, main = "Wells Fargo & Company (WFC)") %>%
dyCandlestick() %>%
dyAxis("y", label="Price") %>%
dyOptions(colors= RColorBrewer::brewer.pal(5, "Set2")) %>%
dyHighlight(highlightCircleSize = 4,
highlightSeriesBackgroundAlpha = 1) %>%
dyRangeSelector(height = 55)%>%
dyLegend(show = "onmouseover")
```
```{r, message = FALSE, warning= FALSE, ecol = FALSE}
getSymbols(c("BAC", "JPM", "C", "WFC"), from = '2018-10-01', to = '2019-10-29')
getSymbols('USD1MTD156N', src = 'FRED')
AAPL.ret <- periodReturn(BAC, period = 'daily')
GOOGL.ret <- periodReturn(JPM, period = 'daily')
MSFT.ret <- periodReturn(C, period = 'daily')
FB.ret <- periodReturn(WFC, period = 'daily')
ret <- merge(AAPL.ret, GOOGL.ret, MSFT.ret, FB.ret)
colnames(ret) <- c('AAPL_ret', 'GOOGLE_ret', 'MSFT_ret', 'FB_ret')
rf <- mean(USD1MTD156N['2018/10 to 2019/10'], na.rm = T) / 100
# Average 1-M LIBOR as Risk-Free Rate for simplification
```
Row {}
-----------------------------------------------------------------------
### Rolling Sharpe-Ratio for Three Stocks
```{r, message=FALSE, warning=FALSE}
mean_roll_30 <- rollapply(ret, 30, FUN = mean)
mean_roll_30 <- mean_roll_30[complete.cases(mean_roll_30), ]
sd_roll_30 <- rollapply(ret, 30, FUN = sd)
sd_roll_30 <- sd_roll_30[complete.cases(sd_roll_30), ]
sharp.ratio <- (mean_roll_30 - rf) / sd_roll_30
colnames(sharp.ratio) <- c('BOA_SR', 'Chase_SR', 'Citi_SR', 'WFC_SR')
sharp.ratio.df <- data.frame(date=index(sharp.ratio), coredata(sharp.ratio))
sharp.ratio.melt <- gather(sharp.ratio.df, key = 'company', value = 'Sharp_Ratio', -date)
ggplot(data = sharp.ratio.melt, aes(x = date, y = Sharp_Ratio)) +
geom_line(alpha = 0.3) +
geom_hline(yintercept = 0) +
facet_grid(company ~.) +
ggtitle('30-Day Rolling Sharp Ratio for BOA, Chase, Citi & S&P500 \n (2018.10-2019.10)') +
xlab('Time') +
ylab('Shart Ratio')
count.sharp <- apply(sharp.ratio > 0, 2, sum)
# From the result we found that TWTR has the most days with positive sharpe ratio
```
Row {}
-----------------------------------------------------------------------
### Current Basic Metrics
Basic metrics for Four stocks, in order to frame a general picture and details of the four stocks.
Apple is having the biggest market cap, which Microsoft second, while Twitter is the smallest according to this.
```{r}
what_m <- yahooQF(c("Price/Sales",
"P/E Ratio",
"Price/EPS Estimate Next Year",
"PEG Ratio",
"Dividend Yield",
"Market Capitalization"))
ms <- getQuote(paste(tickers, sep="", collapse=";"), what=what_m)
ms <- data.frame(Symbol=tickers, ms[,2:length(ms)])
# colnames(ms) <- c("Symbol", "Revenue Multiple", "Earnings Multiple",
# "Earnings Multiple (Forward)", "Price-to-Earnings-Growth", "Div Yield", "Market Cap")
colnames(ms) <- c("Symbol", "Price-to-Earnings-Growth","Earnings Multiple (Forward)", "Div Yield", "Market Cap")
DT::datatable(ms)
```
Row {}
-----------------------------------------------------------------------
### Monthly Return
Monthly return analysis, to see if it's possible to get the highest monthly return for short term investment.
```{r}
m.rt.APPL <- monthlyReturn(AAPL)
m.rt.GOOGL <- monthlyReturn(GOOGL)
m.rt.MSFT <- monthlyReturn(MSFT)
m.rt.FB <- monthlyReturn(FB)
mg.return <- merge.xts(m.rt.APPL,m.rt.GOOGL, m.rt.MSFT, m.rt.FB)
colnames(mg.return) <- c('BOA','Chase','Citi','WFC')
dygraph(mg.return, main = "Monthly Return") %>%
dyAxis("y", label = "Return") %>%
dyOptions(colors = RColorBrewer::brewer.pal(5, "Set2")) %>%
dyHighlight(highlightSeriesBackgroundAlpha = 0.5,
highlightSeriesOpts = list(strokeWidth = 4)) %>%
dyRangeSelector(height = 55)
```
Row {}
-----------------------------------------------------------------------
### Daily Returns
```{r, message=FALSE, warning=FALSE}
tickers <- c("BAC", "JPM", "C", "WFC")
symbol <- getSymbols(tickers, src = 'yahoo', auto.assign = TRUE, warnings = FALSE, from = '2018-10-01')
AAPL_return<-dailyReturn(BAC$BAC.Adjusted)
plot(AAPL_return, xlab="Time",ylab = "Return",main = "BOA Daily Return in October 2018")
GOOGL_return<-dailyReturn(JPM$JPM.Adjusted)
plot(GOOGL_return, xlab="Time",ylab = "Return",main = "Chase Daily Return in October 2018")
MSFT_return<-dailyReturn(C$C.Adjusted)
plot(MSFT_return, xlab="Time",ylab = "Return",main = "Citi Daily Return in October 2018")
FB_return<-dailyReturn(WFC$WFC.Adjusted)
plot(FB_return, xlab="Time",ylab = "Return",main = "WFC Daily Return in October 2018")
```
