Journey of Analytics

Deep dive into data analysis tools, theory and projects

Category: Machine Learning (page 2 of 2)

Stock Price Analysis – Linear Regression Model in 5 simple steps

In this post we are going to analyze stock prices for company Facebook and create a linear regression model.

 

Code Overview:

Our code performs the following functions. You can download the code here.

  1. Load original dataset.
  2. Add data to benchmark against S&P500 data.
  3. Create derived variables. We create variables to store calculations for the following:
    • Date values: split composite dates into day, month and year.
    • Daily volatility : Price change between daily high and low prices or intraday change in price. A bigger difference signifies heavy volatility.
    • Inter-day price volatility : Price change from previous day
  4. Data visualization to validate relationship between target variable and predictors.
  5. Create Linear Regression Model

 

Step 1&2 – Load Datasets

We load stock prices for Facebook and S&P500. Note, the S&P500 index prices begin from 2004, whereas Facebook was listed as a public company only in May 2012.
We specify “all.x = TRUE” in the merge command to indicate that we do not want dates which are not present in the Facebook file.

fbknew = merge(fbkdf, sp5k, by = “Date”, all.x = TRUE)

Note, we obtained this data from the Yahoo! Finance homepage using the tab “Historical Data”.

 

Step 3 – Derived Variables

a) Date Values:
The as.Date() function is an excellent choice to breakdown the date variable into day, month and year. The beauty of this function is that it allows you to specify the format of the date in the original data since different regions format it differently. (mmddyyyy / ddmmyy/ ..)

fbknew$Date2 = as.Date(fbknew$Date, “%Y-%m-%d”)
fbknew$mthyr = paste(as.numeric(format(fbknew$Date2, “%m”)), “-“,
as.numeric(format(fbknew$Date2, “%Y”)), sep = “”)
fbknew$mth = as.numeric(format(fbknew$Date2, “%m”))
fbknew$year = as.numeric(format(fbknew$Date2, “%Y”))

b) Intraday price:

fbknew$prc_change = fbknew$High – fbknew$Low

We have only calculated the difference between the High and Low, but since data is available for “Open” and “Close” you can calculate the maximum range as a self-exercise.
Another interesting exercise would be to calculate the average of both, or create a weighted variable to capture the impact of all 4 variables.

c) Inter-day price:
We first sort by date, using the order() function, and then use a “for loop” to calculate price difference from the previous day. Note, since we are working with dataframes we cannot use a simple subtraction command like x = var[i] – var[i-1]. Feel free to try, the error message is really beneficial in understanding how arrays and dataframes differ!

 

Step 4 – Data Visualization

Before we create any statistical model, it is always good practice to visually explore the relationships between target variable (here “opening price”) and the predictor variables.

With linear regression model, it is more so, to identify if any variables show a non-linear (exponential, parabolic ) relationship. If you see such patterns you can still use linear regression, after you normalize the data using a log function.

Here are some of the charts from such an exploration between “Open” price (OP) and other variables. Note there may be multiple explanations for the relationship, here we are only looking at the patterns, not the reason behind it.

 a) OP versus Trade Volume:

From chart below it looks like the volume is inversely related to price. There is also one outlier (last data point) . We use the lines() and lowess() functions to add a smooth trendline.
An interesting self-exercise would be to identify the date when this occurred and match it to a specific event in the company history (perhaps the stock split?)

Facebook Stock – Trading Volume versus Opening Stock Price

b) OP by Month & Year:

We see that the stock price has been steadily increasing year on year.

Facebook_stock_price_by_month&and year

Facebook price by month and year

c) OP versus S&P500 index price:

Clearly the Facecbook stock price has a linear relationship with S&P500 index price (logical too!)

FB_stock_S&P500

Facebook stock price versus S&P500 performance

d) Daily volatility:

This is slightly scattered although the central clustering of data points indicates this as a fairly stable.

Note, we use the sqldf package to aggregate data by monthyear / month for some of these charts.

Step 5 – Linear Regression Model

Our model formula is as follows:

lmodelfb = lm(Open ~ High + Volume + SP500_price + prc_change +     mthyr + mth + year + volt_chg,     data = fbknew)
summary(lmodelfb)

We use the summary function to view the intercepts and identify the predictors with the biggest impact, as shown in table below:

predictor variables

We see that price is affected overall by S&P500, interday volatility and trade volume. Some months in 2013 also showed a significant impact.
This was our linear regression data model. Again, feel free to download the code and experiment for yourself.

Feel free to add your own observations in the comments section.

Machine Learning Algorithms

In the last few posts, we saw standalone analytics projects to perform sentiment analysis, visually explore large datasets for insights  and create interesting Shiny applications.

In the coming months however, we will cover how to implement machine learning algorithms in depth. We will explore the underlying concepts behind the algorithm, (why and how the formula works) , implement using a real-world Kaggle dataset and also learn about limitations and advantages.

 

What algorithms will we cover?

There are many algorithms to choose from, and this infographic from ThinkBigData provides an excellent and comprehensive list. Feel free to use as a handout or print one for your cubicles! For our purposes, we will cover two algorithms from each category.

Categories of machine learning algorithms

Categories of machine learning algorithms. Source – ThinkBigData.com, by author Anubhav Srivastava.

 

Quick FAQ – selecting algorithm in practice

Many readers often ask, “how do I understand which algorithm to select? ” And this is also where new programmers often get stuck.

The long-winded answer is there is no secret sauce, and unfortunately often comes from experience or the problem definition itself.

The above answer is not very satisfying, so here are two “cheat-sheet” answers:

  1. A good approximation is given by this infographic by Microsoft Azure is a great example.  Download it from Link here. 
  2. Regression is a very common and flexible model, so the table below provides idea to create a base model based on whether your target variable is qualitative (numeric) or categorical (e.g gender or country)

    regression algorithms

    regression algorithms based on target variables

 

Newer posts

Thanks for reading so far! If you liked our content, please share!

Facebook
Google+
https://blog.journeyofanalytics.com/category/machine-learning/page/2
Pinterest
LinkedIn