Showing posts with label Cummins. Show all posts
Showing posts with label Cummins. Show all posts

Saturday, November 26, 2022

Beta of Cummins Included in the List

 Here's the latest list of beta values. I have included Cummins on this list, and the returns for Cummins and the Vanguard S&P 500 Index are based on November 25, 2022, closing prices. Click on the image to see an enlarged version. 

Exhibit: Beta Values for Cummins Included in the list

Companies in this list:

  • Cisco Systems
  • Colgate-Palmolive
  • Lennox International
  • Sealed Air
  • Boeing
  • Newell Brands
  • Timken
  • Cummins


Monthly Return Comparison Between Cummins and Vanguard S&P 500 Index ETF

The following chart shows the Vanguard S&P 500 Index ETF (VOO) monthly returns on the x-axis and Cummins (CMI) on the y-axis. The regression line on the graph shows a steep slope, and the Pearson correlation value is 0.7. This value shows a very strong correlation between the monthly returns of the Vanguard S&P 500 Index ETF and Cummins. The p-value is significant at a 95% confidence interval. Cummins has returned 13.9% in the past year, while the Vanguard S&P 500 Index ETF has returned a -12.4%.  

Exhibit: Monthly Returns of Vanguard S&P 500 Index ETF and Cummins [June 2019 - October 2022]

The following command was used to create this graph:

> # Create a new Graph of $VOO and $CMI Monthly Returns as Percentages
> ggscatter(df1, x = 'VOO_Monthly_Return', y = 'CMI_Monthly_Return', 
+           add = "reg.line", = TRUE, 
+           cor.coef = TRUE, cor.method = "pearson",
+           xlab = "VOO ETF Monthly Returns (%)", ylab = "CMI Monthly +           Returns (%)")

A linear regression of the monthly returns of the Vanguard S&P 500 Index ETF and Cummins shows the beta (coefficient of Vanguard S&P 500 Index ETF) for Cummins' monthly returns compared to the Vanguard ETF. 

# Conduct the Linear Regression of the Monthly Returns Between $VOO and $CMI
lmVOOCMI = lm(CMI_Monthly_Return~VOO_Monthly_Return, data = VOOandCMI)
# Present the summary of the results from the linear regression

lm(formula = CMI_Monthly_Return ~ VOO_Monthly_Return, data = VOOandCMI)

      Min        1Q    Median        3Q       Max 
-0.123268 -0.051037  0.004537  0.047062  0.115727 

                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.005104   0.009226   0.553    0.583    
VOO_Monthly_Return 0.993327   0.160541   6.187 2.84e-07 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05819 on 39 degrees of freedom
Multiple R-squared:  0.4954, Adjusted R-squared:  0.4824 
F-statistic: 38.28 on 1 and 39 DF,  p-value: 2.845e-07

Cummins has a beta of 0.99 (nearly 1). Given this beta, Cummins, on average, moves in line with the S&P 500 Index. The adjusted R-squared value of 0.48 shows that about 48% of Cummins' monthly returns can be attributed to the returns of the S&P 500 Index. The p-value of 2.84e-07 shows that the regression analysis results are significant at the 95% confidence interval.

The average monthly return for Cummins between June 2019 and October 2022 was 1.49%. A one-sample t-test shows that the monthly return falls between -1.05% and 4.04%. But, the p-value of 0.99 is much higher than 0.05. This t-test may not be significant in the 95% confidence interval.        

# One Sample t-test of $CMI average monthly returns.
# Step 1: 
# Copy Dataframe Column CMI_Monthly_Return into a List 
CMI_Monthly_Return_Col <- c(VOOandCMI['CMI_Monthly_Return'])
# CMI_Monthly_Return_Col is a list object, but needs to be numeric
# for qqnorm to work.  
# Convert List Object into a Column of doubles as as.numeric and unlist
y_CMI_Monthly_Return_Col <- as.numeric(unlist(CMI_Monthly_Return_Col))
# Let’s check if the data comes from a normal distribution 
# using a normal quantile-quantile plot.
# Source:

Exhibit: Check if Cummins' Monthly Returns are Normally Distributed before doing a t-test

# Conduct a t-test to see if the population mean is 1.49% [0.0149]
t.test(y_CMI_Monthly_Return_Col, mu = .0149)

One Sample t-test

data:  y_CMI_Monthly_Return_Col
t = 0.0045069, df = 40, p-value = 0.9964
alternative hypothesis: true mean is not equal to 0.0149
95 percent confidence interval:
 -0.01057188  0.04048574
sample estimates:
 mean of x 


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