Introduction
On the fourth part of my Oil&Gas series, I wanted to go deeper into formula investing using O&G firms only as an example. Dollar-Cost Averaging (DCA) is the term given to the process of buying a fixed dollar amount of stocks every week, month or quarter. DCA averages decline and upturn in stocks, building a portfolio more robust against fluctuations.
I first came across this concept in The Wealthy Barber, a book I found lying in my basement and put to good use. Today, I will be using R to perform DCA analysis of Suncor. The analysis will be performed from a worst-case perspective: buying stock as it increases in value - only to have oil crash and plateau at a price much lower than what you began with.
Suncor (SU) stocks will be brought on the first Monday of each month beginning on May 2014, when Suncor was showing a strong growth at $38, till May 2017, when it plateaued around $31. I assumed a monthly investment of USD $1,000 into Suncor with a $30 commission per trade. I found that using DCA yielded an average price per share of $30.18 by May 2017. If sold on May 15th, The net profit is $1,810. With commission, the profit is a modest $700. With dividends, we can expect a greater yeild.
If spread over time, even a loss can be profitable. Or, at the very least, safe.
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| 2015's Oil Glut caused a drop on Suncor's stock, which it is still recovering from |
Technique
All coding is done in R, a statistical programming language. First, I loaded the necessary packages. I used Quandl to extract historical financial data.
#load necessary libraries
library("Quandl")
library("sqldf")
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| Quandl captures financial data from a range of companies, as Suncor |
I defined necessary variables. Feel free to experiment with different values.
#define variables
start_date = "01-05-2014"
end_date = "15-05-2017"
ticker = "GOOG/NYSE_SU"
To simplify repetitive simulations, I put most of the computation in its own function. My secret ingredient was sqldf. Sqldf is a package that allows me to code in SQL within a R environment, opening the way for powerful querying and analysis.
#Dollar Cost Averaging Function
DCA_calc <- function(x){
names(x)[1]="date"
names(x)[2]="price"
x$date = as.character(x$date)
a<-sqldf("select min(substr(date,9,2)), date, price from x group by substr(date,1,7)")
b<-sqldf("select sum(1000/price) d,(select count(price) from a) e from a")
c<-sqldf("select (e*1000)/(d) AveragePricePerShare, d TotalNumberofStocks, e*1000 TotalCapitalSpent from b")
return (c)
}
#call Quandl function
Suncor_price=data.frame(Quandl(ticker,trim_start=start_date,trim_end=end_date,type="raw"))
result=DCA_calc(Suncor_price)
Running my code yields:
DCA only works when the stock has spent a considerable time at a much lower value than what it had originally began. Thus, even a slight increase in price can yeild a profit or dramatically cut losses. Additionally, this post does not factor in the dividends gained from the company. Factoring dividends can yeild higher profits.
References
Chilton, D. (1989). The Wealthy Barber .
Toronto: Stoddart Publishing Co. Limited.
