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Data Analytics and Machine Learning Problem Set 6

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Data Analytics and Machine Learning
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Problem Set 6
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Use the business_insider_text_data.csv and vix_data.csv datasets available on CCLE for this exercise. The
Business Insider data is downloaded from www.kaggle.com (a really cool website for data and code, if
you haven’t seen it). You can and should look at the raw data using Excel before starting this exercise.
The vix_data.csv file contains two columns where “label” indicated whether the closing value of the VIX
is higher (1) or lower (0) than the closing value of the VIX for the previous trading day. The Business
Insider data contains various headlines downloaded from www.businessinsider.com.
Creating a sentiment index from text data
Before you start, please download the script “PS6_data_cleaning.R” from CCLE. Run it on the data to do
some pre-pre-processing and merge the two datasets.
1. Use Corpus(VectorSource(assignment_data)) to load the corpus. VectorSource makes each line a
document, so now each document corresponds to a different date in the dataset.
2. Pre-process the data as in the lecture notes. Feel free to use the code from Code Snippets Topic
6 on CCLE. That is, remove numbers, make all lower case, remove stopwords, stemming, etc.
3. As in the lecture note, create a DocumentTermMatrix, call it dtm. Run the line “inspect(dtm)”
Notice that the matrix is quite sparse (a lot of zeros).
4. As in the lecture note, create a freq matrix as the column sums of dtm. Show in a bar plot the
frequency of words that occur more than 25 times.
5. Create a wordcloud of the 20 most frequent words. Based on this (and 4.), how would you
characterize the typical headline in terms of the news subject? Are there words that, intuitively,
can matter for the stock market returns that day?
6. Create the data “y_data <- as.factor(assignment_data$Label)” and “x_data <- as.matrix(dtm)”.
You will try to construct an index based on the words in dtm that predicts the direction of stock
returns.
7. Split the data into a training data-set, based on data up to and including 2016-12-31. The
remaining data should be used for actual out-of-sample testing.
8. We will first let the logistic regression create the word-based index. That is, try to fit a regular
logistic regression using y_data and x_data and the training dataset. Explain why this doesn’t
work.
9. Next, run a logistic regression with an elastic net constraint (let alpha = 0.5) using crossvalidation and the training dataset. Why does the regression routine work now (ie, why does it
give an answer (a coefficient vector; no meltdown))? Explain.
10. Using lambda.min, what (if any) are the words chosen and their associated coefficients?
Comment on your results.
11. Now, create instead a pre-defined sentiment word list:
dtm_sentiment <-
dtm[,c( “trump”,”invest”,”growth”,”grow”,”high”,”strong”,”lead”,”good”,”risk”,”debt”,”oil”,”loss
“,”war”,”rate”,”hous”,”weak”)]
Run the elastic net with x_data_pre <- as.matrix(dtm_sentiment)” using cross-validation and the
training sample. Create a bar plot with the words on the x-axis and the coefficients on the y-axis.
Comment on differences and similarities to the case in 10. Again, get the coefficients using
lambda.min.
12. Create the ROC curves for the sentiment model in 11, using lambda.min to get coefficient
vector. Is it better than random? You likely want to use the predict function to get the model
predictions.
13. Now, using the test sample and the model in 11, what is the proportion of days the model
would have made the right prediction in this new sample? Is it better than random (50/50)?

PlaceholderData Analytics and Machine Learning Problem Set 6
$30.00
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