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Dataset:

• twitter_ecommerce.csv contains information on tweets about four Chinese E-commerce
  stocks: BABA, JD, PDD, and VIPS.
• stock_price.csv contains the daily price information for the above four stocks.
  Tasks:

1. Calculate three sentiment scores: neg (fraction of negative words), pos (fraction of positive
   words), and vader for each stock at the daily level and assess the pairwise correlations among
   them using the Pearson Correlation tool. Comment on how these sentiment measures are
   correlated with each other.
2. Calculate the daily returns: Return is defined as (Price_t – Price_t-1)/Price_t-1. Consider the
   “Adj Close” column as the price. Use the Multi-Row Formula tool to generate the lagged price
   column with the following expression: IF [Row-1:Ticker] = [Ticker] THEN [Row-1:Adj Close]
   ELSE NULL() ENDIF. Make sure you sort the data appropriately before applying this expression.
3. Join the stock return and tweet data by Ticker and Date and then run the regression of the
   same-day stock return on daily tweet intensity (i.e., number of tweets) and three sentiment
   measures. Pool the data of the four stocks together for the regression. Given the distribution
   of tweet numbers, apply the logarithmic transformation to the number of tweets variable to
   increase model fit after adding 1 to it, i.e., log(1+tweet_num). Note that on certain trading
   days, there may not be any tweet on a specific stock on Twitter. Replace such missing values
   of tweet intensity and sentiments with 0. Comment on the regression results and summarize
   the key insights. Interpret the regression coefficients, p-values, and R-squared.
4. Run the regression of next-day stock return on daily tweet intensity and three sentiment
   measures, i.e., using yesterday’s tweet data to predict today’s stock return. To do this, you
   can use the Multi-Row Formula tool to generate a lagged date column (LaggedDate) first in
   the stock return dataset. Then in the existing Join tool, you can simply change the Field from
   Date to LaggedDate for the stock return side. Comment on the regression results and
   summarize the key insights. In your response, highlight the differences in regression results
   for next-day stock return from that of same-day stock return.
   Deliverables:

• You are required to finish this assignment in Alteryx only.
• Source code of your program in one file (.yxmd for Alteryx) containing the complete analysis,
  with annotations explaining each tool and step. Use relative path for workflow dependencies
  in Alteryx so that the grader can run your program without making any change. There should
  be one Join Tool in your program, and by changing the specific Field, the grader can run either
  same-day or next-day return regressions.
• A Word document (.docx) containing your responses to each requirement in the Tasks.
• Compress the above two files into a zip file named with your student ID, e.g., 123456.zip.
• You should not make any modifications to the two input files: twitter_ecommerce.csv and
  stock_price.csv. Also, DO NOT include these two input csv files in your zip file.
  Evaluation Criteria:
• Correctness and completeness of the feature engineering steps implemented in Alteryx.
• Accuracy and thoroughness of the model interpretation of results within Alteryx.
• Quality and clarity of the final report, including insights and conclusions drawn from the
  analysis

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