In this assessment, you will be implementing, testing, and documenting Predictive Analytics models

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Overview

In this assessment, you will be implementing, testing, and documenting Predictive Analytics models for one of following data sets:

Dataset 1: The capital asset pricing model (CAPM) is an important model in the field of finance. It explains variations in the rate of return on security as a function of the rate of return on a portfolio consisting of all publicly traded stocks, which is called the market portfolio. Generally, the rate of return on any investment is measured relative to its opportunity cost, which is the return on a risk- free asset. The resulting difference is called the risk premium since it is the reward or punishment for making a risky investment. The CAPM says that the risk premium on security j is proportional to the risk premium on the market portfolio. That is,

𝑟𝑗 − 𝑟𝑓 = 𝛽𝑗(𝑟𝑚 − 𝑟𝑓 )

 

where rj and rf are the returns to security j and the risk-free rate, respectively, rm is the return on the market portfolio, and 𝛽𝑗 is the jth security’s ‘‘beta’’ value. A stock’s beta is important to

investors since it reveals the stock’s volatility. It measures the sensitivity of security j’s return to variation in the whole stock market. As such, values of beta less than 1 indicate that the stock is ‘‘defensive’’ since its variation is less than the market’s. A beta greater than 1 indicates an

‘‘aggressive stock.’’ Investors usually want an estimate of a stock’s beta before purchasing it. The CAPM model shown above is the ‘‘economic model’’ in this case. The ‘‘econometric model’’ is obtained by including an intercept in the model (even though theory says it should be zero) and an error term,

𝑟𝑗 − 𝑟𝑓 = 𝛼𝑗 + 𝛽𝑗(𝑟𝑚 − 𝑟𝑓) + 𝑒𝑗

 

Attribute Information:

In the data file “capm” are data on the monthly returns of six firms (Microsoft, GE, GM, IBM, Disney, and Mobil-Exxon), the rate of return on the market portfolio (MKT), and the rate of return on the

 risk free asset (RISKFREE). The 132 observations cover January 1998 to December 2008. Estimate the CAPM model for each firm, and comment on their estimated beta values.

Dataset 2: Please use the given data to make predictions on housing prices. The target variable

name is “SalePrice”. For the rest of the variables, the variable name is mostly self-explanatory.

Deliverables

1- Final report. MS Word document for your report. Add this declaration to your file:

I, (mention your name), declare that the attached assignment is my own work in

accordance with the Seneca Academic Policy. I have not copied any part of this assignment, anually or electronically, from any other source including web sites, unless specified as references. I have not distributed my work to other students.

2- SAS or Python project files.

3- Prepare a presentation and explain your findings in less than 12 slides. You will need to record a talk describing your work and The talk should not take longer than 10 minutes.

Note: Upload report file (Word or PDF), SAS/Python script project files (Zipped) and presentation(recording) to Blackboard. Do not zip all three deliverables together.

Evaluation Rubric

Exploratory Data Analysis/Summary Statistics/Graph Analysis [30%]

Apply Exploratory Data Analysis (EDA) to discover patterns, spot anomalies, test hypotheses and check assumptions with the help of summary statistics and graphical representations. Explain your understanding of the data and present insights using appropriate graphs. Elaborate on the requirement of any transformations and feature engineering needed for your project.

Predictive models [30%]

- Train at least two models using appropriate algorithms. Then validate your models.

- Assess, analyze, and compare the performance of your models

 

Report [20%]

Your report (less than 10 pages) should include the following:

a) The goal and summary of your work on the data set and your choice of algorithms.

b) Explain the results of EDA. What business decision should be made based on your data analysis?

c) Explain the training and validation methodology along with evaluation results. Use appropriate metrics for evaluation.

 

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