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")

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

getSymbols("JNJ")

[1] "JNJ"

#chartSeries(JNJ, subset='2015-01::2019-01', theme = chartTheme('white',up.col='green',dn.col='red'))
chartSeries(JNJ, subset='2015-01::2019-01', 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")))

##### 1B) Select a particular crossover point to explain how you would implement a trading strategy (ref p184)

There is a crossover point of the two moving average lines in August of 2016, and this could signal that it would be wise to sell at that time.

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)

getSymbols("JNJ")

[1] "JNJ"

chartSeries(JNJ, subset='2015-01::2019-01', theme = chartTheme('white',up.col='green',dn.col='red'), TA=c(addBBands(n=20,sd=2)))

In July of 2016, the stock price peaks around $125 and approaches the top of the Bollinger Bands. As expected, the price begins to decrease after and falls to $110 by January of 2017. By taking the Bollinger Bands into consideration, one could recognize that the price was expected to fall and shorted the stock at any point in July.

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)

getSymbols("JNJ")

[1] "JNJ"

chartSeries(JNJ, subset='2015-01::2019-01', theme = chartTheme('white',up.col='green',dn.col='red'), TA=c(addBBands(n=20,sd=2),addRSI(n=14)))

Going back to July 2016, the RSI reach a level of about 80. It is suggested to go short when the RSI > 100 - ET, and a standard ET value is 30. In this case, the RSI of 80 is above the threshold and going short would have been the proper decision as the stock price began to fall soon after.

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 available (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

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.

##### 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/TRY") 

[1] "USDTRY"

ar1 = arima(USDTRY[1:175], order = c(1,0,0))
print(ar1) 

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

Coefficients:
         ar1  intercept
      0.9815     5.4736
s.e.  0.0130     0.3217

sigma^2 estimated as 0.009135:  log likelihood = 160.9,  aic = -315.8

\(X_t = .98185X_{t-1} + W_t\)

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.

USDTRYPredict = predict(ar1,n.ahead=4)
newpredict=arima.sim(list(order=c(1,0,0),ar=.9815),n=4)
print(USDTRYPredict)

$pred
Time Series:
Start = 176 
End = 179 
Frequency = 1 
[1] 5.348904 5.351214 5.353481 5.355705

$se
Time Series:
Start = 176 
End = 179 
Frequency = 1 
[1] 0.09557911 0.13392302 0.16251872 0.18595190

The best forecaster of an AR(1) process is \({\phi}X_t + W_t\), where \(W_t\) is \(N~(0,{\sigma}^2)\). The first predicted value can be calculated as (.9815 * 5.346551) + \(W_t\) = 5.348904. \(W_t\) is within the range of .10126.

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.

Actual 5.328380 5.328538 5.331789 5.342132 Calculated 5.348904 5.351214 5.353481 5.355705 The covariance of these two sets are \(1.6743*10^{-5}\) and the standard deviations are .006474 and .002928. The correlation coefficient r for this data is .8835, so there is a reasonable level of fitness for this.

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.

Johnson & Johnson, JNJ has a P/E of 19, P/B of 5.32, and Debts/Assets of .220. Abbott Labs, ABT has a P/E of 37.5, P/B of 3.99, and Debts/Assets of .366. Both have fiscal years ending 12/31 with the most recent financial reports releasing in January.

##### 3B) Based on solely the three reported ratios which company has a stronger financial condition. Explain your logic.

Johnson and Johnson has the lower debt/asset ratio of the two, so they are less leveraged and in a position to take on more debt for larger operations. JNJ has a Graham’s number of 47.69 with a current market price of 128.8 and Abbott Labs has a number of 58.02 with a current market price of 69.91. Both companies satisfy that portion of Graham’s criteria, but some of the other conditions cannot be determined solely from these numbers. I think that JNJ has the stronger financial condition because they do not seem to be as overvalued while being in a better position to grow further.

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