Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Big-Data PCA: 50 Years of Stock Data
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Big-Data PCA: 50 Years of Stock Data
AnalyticsR Programming Language

Big-Data PCA: 50 Years of Stock Data

DavidMSmith
DavidMSmith
4 Min Read
SHARE

In this post, Revolution engineer Sherry LaMonica shows us how to use the RevoScaleR big-data package in Revolution R Enterprise to do principal components analysis on 50 years of stock market data — ed.

In this post, Revolution engineer Sherry LaMonica shows us how to use the RevoScaleR big-data package in Revolution R Enterprise to do principal components analysis on 50 years of stock market data — ed.

Principal components analysis, or PCA, seeks to find a set of orthogonal axes such that the first axis, or first principal component, accounts for as much variability as possible and subsequent axes are chosen to maximize variance while maintaining orthogonality with previous axes. Principal components are typically computed either by a singular value decomposition of the data matrix or an eigenvalue decomposition of a covariance or correlation matrix; the latter permits us to use the RevoScaleR function rxCovCor with the standard R function princomp.

More Read

Alteryx 8.5 and the Data Artisan: Focusing on the User Experience of the “New Boss”
Applying Big Data to 2011 Holiday Shopping Information
Social Data on the Top 4 Social Media Channels: How They Use Each Other
Big Data: A Kick in the Business Intelligence Expert’s Habits
Can Big Data and Hadoop Feed the World?

Stock market data for open, high, low, close, and adjusted close from 1962 to 2010 is available from InfoChimps. As you might expect, these data are highly correlated, and principal components analysis can be used for data reduction. We read the original data (a set of 26 comma-separated text files, where each file is represented by a letter in the alphabet) into an .xdf file, NYSE_daily_prices.xdf:

nyseDataDir <- "C:/Users/Sherry/Downloads/NYSE"
dataSourceName <- file.path(nyseDataDir, "NYSE_daily_prices")
dataFileName <- "NYSE_daily_prices.xdf"
append <- "none"
for (i in LETTERS)
{
       importFile <- paste(dataSourceName, "_", i, ".csv", sep="")
       rxTextToXdf(importFile, dataFileName, stringsAsFactors=TRUE,
                append=append)
       append <- "rows"
}

The full data set includes 9.2 million observations of daily open-high-low-close data for some 2800 stocks:

> rxGetInfoXdf(dataFileName)
File name: NYSE_daily_prices.xdf
Number of observations: 9211031
Number of variables: 9
Number of blocks: 34 

We will use the rxCor function to calculate the Pearson’s correlation matrix for the variable specified, and pass this to the princomp function:

stockCor <- rxCor(~ stock_price_open + stock_price_high +
stock_price_low + stock_price_close +
stock_price_adj_close, data="NYSE_daily_prices.xdf")
stockPca <- princomp(covmat=stockCor)
summary(stockPca)
loadings(stockPca)
plot(stockPca) 

This yields the following output:

> summary(stockPca)
Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4
Standard deviation 2.0756631 0.8063270 0.197632281 0.0454173922
Proportion of Variance 0.8616755 0.1300327 0.007811704 0.0004125479
Cumulative Proportion 0.8616755 0.9917081 0.999519853 0.9999324005
Comp.5
Standard deviation 1.838470e-02
Proportion of Variance 6.759946e-05
Cumulative Proportion 1.000000e+00
 
> loadings(stockPca)
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
stock_price_open -0.470 -0.166 0.867
stock_price_high -0.477 -0.151 -0.276 0.410 -0.711
stock_price_low -0.477 -0.153 -0.282 0.417 0.704
stock_price_close -0.477 -0.149 -0.305 -0.811
stock_price_adj_close -0.309 0.951
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
SS loadings 1.0 1.0 1.0 1.0 1.0
Proportion Var 0.2 0.2 0.2 0.2 0.2
Cumulative Var 0.2 0.4 0.6 0.8 1.0

The default plot method for objects of class princomp is a screeplot, which is a barplot of the variances of the principal components. We can obtain the plot as usual by calling plot with our principal components object:

> plot(stockPca)

Pcp
Between them, the first two principal components explain 99% of the variance; we can therefore replace the five original variables by these two principal components with no appreciable loss of information.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

AI role in medical industry
The Role Of AI In Transforming Medical Manufacturing
Artificial Intelligence Exclusive
b2b sales
Unseen Barriers: Identifying Bottlenecks In B2B Sales
Business Rules Exclusive Infographic
data intelligence in healthcare
How Data Is Powering Real-Time Intelligence in Health Systems
Big Data Exclusive
intersection of data
The Intersection of Data and Empathy in Modern Support Careers
Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Data Visualization Doesn’t Need to be Biased

4 Min Read
online cookies and privacy concerns
Analytics

The Role of Cookies in Analytics and Privacy Issues they Raise

8 Min Read
Image
Predictive Analytics

Big Data: The Secret Snacking Ingredient

6 Min Read

The State of Play in Automating Insurance Claims

4 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?