FINC621 Winter 2018-19 Lab Worksheet 05

Technical Analysis & Fundamentals (finc621-lab05)

About

This worksheet looks at some common trading rules, modeling and forecasting, and some fundamental analysis.

Setup

Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.

Note

Always read carefully the instructions on Sakai. For clarity, tasks/questions to be completed/answered are highlighted in red color (visible in preview) and numbered according to their particular placement in the task section. Quite often you will need to add your own code chunk.

Execute all code chunks, preview, publish, and submit link on Sakai follwoing the naming convention. Make sure to add comments to your code where appropriate. Use own language!

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Task 1: Technical Trading Rules

This task follows the example in the book R Example 6.1/p 185 A new package will be required for this worksheet. All packages are included, for your convenience, in the code chunks below.

#Install package quantmod 
if(!require("quantmod",quietly = TRUE))
  install.packages("quantmod",dependencies = TRUE, repos = "https://cloud.r-project.org")

#Install package fArma for modelling ARMA time series processes
if(!require("fArma",quietly = TRUE))
  install.packages("fArma",dependencies = TRUE, repos = "https://cloud.r-project.org")

there is no package called ‘fArma’

Warning in install.packages :
  package ‘fArma’ is not available (for R version 3.5.1)

The following questions are for a stock of your choice from S&P500 (other than AAPL and your colleague!) and for the time-period from Jan 1, 2015 to present.

Observing the crossings or intersections between two simple moving averages can provide signals for buying or selling.

##### 1A) Create a chart of close prices and add the two simple moving averages over the periods n=20 and n=50. Clearly label the plots or provide a legend

# gain data
getSymbols("JD",src = "yahoo")

[1] "JD"

# create close price of JD chart,MA(20) is black line; MA(50) is blue line.
chartSeries(JD$JD.Close,subset='2017-01::2018-01',
            theme = chartTheme('white'),
            TA=c(addSMA(n=50,col="blue"),
                 addSMA(n=20,col="black")))

close price of JD chart MA(20) is black line; MA(50) is blue line.

##### 1B) Select a particular crossover point to explain how you would implement a trading strategy (ref p184) The crossover point happened at price 39.4 on 2017-12-19 when the MA(20) line surpassed the MA(50) line. I would buy my total 10% postion JD stock at this point since \(X_t=MA(20)_t-MA(50)_t, X_t>0\). I would long 20% more postion JD stock when price broke through Bollinger band with 2 standard deviation, which means the price broke ceiling and formed new trend. Finally, I would sell all JD postion when RSI reached 80.

Bollinger bands are a kind of a trading bands or moving average envelopes. The high and low limits of a Bollinger band are calculated based on a x-number of standard deviation (usually 2) away from the moving average. A standard deviation is also a measure of volatility, as such the band will widen when volatility increases and contract when volatility decreases. As with bands and envelopes the idea of Bollinger band is that prices tend to stay within the band. When prices reach the top band it is assumed that resistance will be encountered and the price will fall. Opposite true when the lower band is reached. When the price moves outside the bands, a continuation of the current trend is expected.

##### 1C) Chart the Bollinger Bands based on the 20-days moving average and two standard deviations. Select a particular point to explain how the BB can be used in a trading strategy (ref p185)

# gain data
getSymbols("BIDU",src = "yahoo")

[1] "BIDU"

# create close price of JD chart,MA(20) is black line; MA(50) is blue line.
chartSeries(BIDU$BIDU.Close,subset='2017-01::2018-01',
            theme = chartTheme('white',up.col='green',dn.col='red'),
            TA=c(addBBands(n=20,sd=2)))

On 2017-03-23, the price of Baidu reached lower bollinger bands in which i would long the Baidu stocks, based on statistics data that represent lowest price during period of 20 trading days.

RSI measures the velocity of price changes. Rapid price increases is an indication of stock overbought conditions, and rapid price decreases result in oversold conditions. When used properly RSI can assist in optimizing proper entry and exit decisions.

##### 1D) Chart the Relative Strength Index (RSI) based on 14-days moving average. Select a particular point to explain how RSI can be used in a trading strategy (ref p206)

# gain data
getSymbols("AAPL",src = "yahoo")

[1] "AAPL"

# create close price of JD chart,MA(20) is black line; MA(50) is blue line.
chartSeries(AAPL$AAPL.Close,subset='2017-01::2018-01',
            theme = chartTheme('white',up.col='green',dn.col='red'),
            TA=c(addBBands(n=20,sd=2),addSMA(n=50,col="blue"),
                 addSMA(n=20,col="black"),addRSI(n=14)))

When the RSI is 30.6 round point at 2017-06-15, the stock is worthy to buy at lower price since it almost reached ET=30. The stock should sell until RSI reached 78.32 on 2017-11-08,since RSI rises above 70.

Task 2: Modeling & Forecasting

Similar to lab03 pick a currency exchange rates of your choice. Follow R Example 4.2/p 119. Pay special attention to Remark 4.1/p 112 and class notes for the correct interpretation and representation of results. Depending on the R version, the package fArma may not be avlibraailable (true for 3.5 and up). One can use instead the function arima() to fit an AR(1) and the print to obtain the model coefficients. Check the illustrated example below and read the Help in R, as needed, for more insights on how to use commands.

#ar1 = arima(dataset, order = c(1,0,0)) to obtain the equivalent of an AR(1) model fit.  dataset is in reference to your particular currency exchange pair like RUBUSD for example.
#print(ar1) to print the coffecients of the model.  Pay attention to the interpretation of intercept
# install package astsa and package xts
install.packages("astsa")

Error in install.packages : Updating loaded packages

library(astsa)

First we derive an autoregressive model of order 1, excluding the out-of-sample points. To exclude ppints check the example below

#ar1 = arimadataset[1:n], order - c(1,0,0)).  This will build an AR(1) model based on the time series dataset [1:n] where n is the length of the tie series minus the last four points.  For example EURUSD[1:175] is for the case where the total time series for the EURUSD is 179 observation points.
install.packages("jsonlite")

Error in install.packages : Updating loaded packages

library(jsonlite)

##### 2A) Derive an AR(1) model excluding the last four points from your time series (the out-of-sample). Note the values of the model coefficients, and write down the correspondng mathematical representation of the model.

getFX("USD/CNY") # why do i have to download package jsonlite?\

[1] "USDCNY"

nrow(USDCNY)

[1] 179

arima(USDCNY[1:175],order = c(1,0,0))

Call:
arima(x = USDCNY[1:175], order = c(1, 0, 0))

Coefficients:
         ar1  intercept
      0.9709     6.8573
s.e.  0.0181     0.0316

sigma^2 estimated as 0.0001827:  log likelihood = 503.42,  aic = -1000.84

The format above is \(X_t-6.8615=0.9733(X_{t-1}-6.8615)+W_t\),{\(W_t\)}~N(0,\(\sigma^2\))

Next we apply the derived model to predict the rates, within confidence levels, for the out-of-sample points.

##### 2B) Using the fitted model, predict the rates for the excluded points corresponding to the four days ahead. Select one particular forecasted day to illustrate manually how the reported predicted rate can be reproduced using the derived AR(1) model.

sarima.for(USDCNY[1:175],n.ahead = 4,1,0,0)

$pred
Time Series:
Start = 176 
End = 179 
Frequency = 1 
[1] 6.779188 6.781463 6.783672 6.785817

$se
Time Series:
Start = 176 
End = 179 
Frequency = 1 
[1] 0.01351730 0.01884012 0.02274423 0.02589080

date rate 2019-01-16 6.779188 2019-01-17 6.781463 2019-01-18 6.783672 2019-01-19 6.785817

\(6.762943084 =0.9709(6.760115-6.8573)+ 6.8573+W_t\),date:2019-01-16

Finally we want to assess the goodness of the model by conducting a quantitative analysis.

##### 2C) Formulate and execute a way to test the fitness of the model. Share your results.

    factua data predictable data  

2019-01-16 6.760347 6.779188 2019-01-17 6.769953 6.781463 2019-01-18 6.776844 6.783672 2019-01-19 6.777850 6.785817

\(Mean Squared Error(MSE)=((6.760347-6.779188)^2+(6.769953 -6.781463)^2+(6.77684-6.783672 )^2+(6.777850-6.785817)^2)/4=0.000149403\) MSE=0.000149403

Task 3: Fundamental Analysis

Consider two stocks of your choice. Unfortunately the code in the R Example 6.2/p 202 is no more valid as Google stopped providing such data as of March 2018. Instead you are advised to use Bloomberg or other sources to extract the needed information. The information in Bloomberg is available in the security description page of a stock.

##### 3A) For each stock report the P/B, P/E, and Assets/Debts ratios. Note also the fiscal calendar date associated with the reported ratios.

library(readxl)
path<-file.path("/Users/hangxigudeaoqi/Desktop/Book1.xlsx")# Book1.xlsx
pop_1<-read_excel(path,sheet = 1)

-
/
                                                                                

pop_1

##### 3B) Based on solely the three reported ratios which company has a stronger financial condition. Explain your logic. Based on P/E ratio, TIF paying for $1 of a TIF’s earning is higher than SIG. Relatively speaking companies,SIG stock is cheaper than TIF stock. But i cannot tell which one has a stronger financial condition. Even if TIF has higher EPS based on current stock price, it doesn’t mean higher profitability because it growth of EPS is slower.

Price to book value=stock price/[total asset-liabilities]. It means how much it cost to buy company’s asset. Compared with SIG P/B ratio, TIF’s asset is more valuable than SIG’s asset. In the other word, from inverstor’s perspective, TIF’s asset is more expensive than SIG’s asset. But i can’t decide whic on has a stronger financial condition.

The debts/Assets ratio presents how much debts in operation of company and what kind of strategy company use to finance itself. The TIF’s higher debts demostrates it uses high financial leverage to finance itself within control. But i still can’t say which one has a stronger financial condition. If i know cost of debt both company, i can synthetically consider which one has a stronger condition.

*http://computationalfinance.lsi.upc.edu