Predicting the Future Stock Prices of ConAgra

Running Head: PREDICTING THE FUTURE STOCK PRICES
Predicting the Future Stock Prices of ConAgra
Name of the Student
Name of the University
Author Note

1PREDICTING THE FUTURE STOCK PRICES
Table of Contents
1.0 Executive Summary.............................................................................................................2
2.0 Description of the Data........................................................................................................2
3.0 Variance Inflation Factor.....................................................................................................3
4.0 Residual Analysis.................................................................................................................3
5.0 Analysis of Variance (ANOVA)..........................................................................................4
6.0 Coefficient of Determination (R2)........................................................................................5
7.0 Testing of Hypothesis..........................................................................................................6
8.0 Coefficients..........................................................................................................................6
9.0 Prediction of Share Prices....................................................................................................7
10.0 Conclusion..........................................................................................................................8
Appendix....................................................................................................................................9

2PREDICTING THE FUTURE STOCK PRICES
1.0 Executive Summary
The main aim of this report is to analyse and evaluate the past data and assess whether it can
be used suitably to predict the future changes in the prices of the stocks of the company
ConAgra. The stock market data is on various products from April 2016 to March 2018. The
company deals mainly with packaged food services and is one of the largest packaged food
services across the globe.
The daily data will be used to conduct the analysis. The relationship between the changes in
the prices of Euro with the help of a multiple regression model. The multiple regression
model will include the variance inflation factor (VIF) and the analysis of variance (ANOVA)
along with the value of the adjusted R-square.
2.0 Description of the Data
The dataset contains data on 378 trading days over a two-year time period from 4th
April 2016 to 26th March 2018. There are 6 measurements of inputs from selected days which
are considered as independent variables and one output variable which is considered as the
dependent variable. The dependent variable in this paper is the future prices of ConAgra. The
daily changes in prices of different assets are measured in each of the columns of the dataset.
These financial assets include:
 Gold
 Aluminium
 The Baltic Dry Index
 Canada Intermediate Oil
 The index of Stock Prices of Standard and Poor 500 (S&P500)
 The future prices of ConAgra

3PREDICTING THE FUTURE STOCK PRICES
In the dataset, it can be observed that most of the variables that have been used for the
prediction of the future changes in the stock prices are interaction variables. The original data
has been transformed to this dataset where the changes in the prices vary between 0 and 1.
The changes in the prices that have occurred originally has been ranked and sorted after that.
After the sorting, the percentage changes are divided by 378. Thus, 0 indicates the highest
decrease in the price, 1 indicates the highest increase in the price and the median change in
the price is indicated by 0.5.
3.0 Variance Inflation Factor
Before analysing a data, it is important to test the existence of multi-collinearity to the
dataset. Multi-collinearity indicates the existence of high correlation between two or more
variables. It is difficult to fit a model to the existing data in the presence of multi-collinearity.
In order to test for multi-colinearity, the variance inflation factor (VIF) test is conducted.
The PHStat add-in has been used in Excel to do the analysis. The VIF values obtained
as a result of the analysis for each of the independent variables are less than 5. Thus, it can be
said that the independent variables are uncorrelated or very little correlation. Thus, the
independent variables are also independent of each other and there is thus no need to delete
any variables from the study.
4.0 Residual Analysis
Residual analysis gives an idea of the presence or absence of outliers to the data. This is
estimated with the help of the normal probability plot. The normal probability plot given in
figure 1 shows that the distribution of the data is normal. Thus, calculations can be done on
this data and the results will be valid as the regression and ANOVA analysis follows the
assumption of normality. The histogram given in figure 2 also shows that the residual is also