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Price Predictions Project

Overview and Rationale

This assignment is designed to give you hands-on experience in performing both

regression and time series forecasting. You will be given a particular real-life time series,

and are asked to perform regression for predictions and to perform a time series

forecasting. In addition, you are asked to perform a sensitivity analysis by using different

parameter values and calculating measures of error for each of those values.

Course Outcomes

This assignment is directly linked to the following key learning outcomes from the course

syllabus:

CO1: Use descriptive, Heuristic and prescriptive analysis to drive business strategies

and actions

CO3: Analyze the role of analytics in supporting decision making for various other

stakeholder groups within and outside of your organization

CO5: Utilize applied analytics and definitions of measures of success to provide a

strategic analytic roadmap for an organization

Assignment Summary

The Excel workbook Honeywell2020.xlsx contains the historical stock prices of the

Honeywell International Incorporated, an American multinational company that produces

a variety of commercial and consumer products, engineering services and aerospace

systems for a wide variety of customers, from private consumers to major corporations and

governments from 1/22/2019 to 1/17/2020 (courtesy of Yahoo Finance). This project

consists of three parts. Each part should be completed in a separate worksheet as

designated in the workbook.

Project Instructions:

1. Perform exponential smoothing forecasts on the Honeywell stock prices to forecast

the price for 1/21/2020. Use successive values of 0.15, 0.35, 0.55, and 0.75 for the

smoothing parameter α. Calculate the MSE of each forecast, Use the MSEs of your

forecasts to determine the value of α that has provided the most accurate forecast.

Describe qualitatively as to why such a value of α has yielded the most accurate

forecast.

2. Use your exponential smoothing forecast with 𝜶=𝟎.75, and perform adjusted

exponential smoothing forecasts on the Honeywell stock prices to forecast the price

for 1/21/2020. Use successive values of 0.15, 0.25, 0.45, and 0.85 for the trend

parameter β. Use the MSEs of your forecasts to determine the value of β that has

provided the most accurate forecast. Describe qualitatively as to why such a value of

β has yielded the most accurate forecast.

3. Perform a simple regression analysis of Honeywell stock prices versus periods (i.e.,

1, 2, 3,…) to forecast the Honeywell stock value for 1/21/2020. Calculate the MSE of

this forecast and compare its value with those obtained from parts (1) and (2)

above. The regression analysis should consist of the following additional details:

a. Coefficients of correlation and determination, and the interpretations of their

values

b. A histogram of the regression residuals, and the interpretation of its shape

c. A Chi-squared normality test of the residuals, and the interpretation of its

outputs

d. A Normal probability plot of the residuals

e. A scatter plot of residuals versus time to study their independency, and the

interpretation of the shape of the scatter plot

f. A scatter plot of residuals versus the predicted stock values to study their

homoscedasticity, and the interpretation of the shape of the scatter plot

4. Perform a research to find out the actual Honeywell stock value on 1/21/2020, and

compare this true value with your forecasts in this project. Among the forecasting

methods that you have used in this project, what method has shown to be actually

the most accurate method in predicting the Honeywell stock price for 1/21/2020?

Format & Guidelines

The report should follow the following format:

(i) Introduction

(ii) Analysis

(iii) Conclusion

And be 1000 - 1200 words in length, not including the title page, and presented in the APA

format.

Rubric

Category Above Standard Meets Standards Approaching

Standards Below Standards

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