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", conf.int = 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

summary(lmVOOCMI)

Call:

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

Residuals:

      Min        1Q    Median        3Q       Max 

-0.123268 -0.051037  0.004537  0.047062  0.115727 

Coefficients:

                   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.  

#

typeof(CMI_Monthly_Return_Col)

# 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))

typeof(y_CMI_Monthly_Return_Col)

# Let’s check if the data comes from a normal distribution 

# using a normal quantile-quantile plot.

# Source: https://cran.r-project.org/web/packages/distributions3/vignettes/one-sample-t-test.html

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

#

qqnorm(y_CMI_Monthly_Return_Col)

qqline(y_CMI_Monthly_Return_Col)

# 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 

0.01495693 

    

Beta of Various Stocks Listed in the US

I have calculated the beta of a few more stocks. Here's the latest list as of November 22, 2022. The one-year returns of Timken and Newell Brands are based on closing prices as of November 22.  

Exhibit: Beta of Various Stocks Listed in the U.S. 

Click on the image to see beta values. 
(Source:  Data Provided by IEX Cloud, Author's Analysis Using RStudio, Yahoo! Finance)

Stocks in this list: 

• Cisco
• Colgate
• Lennox
• Sealed Air
• Boeing
• Newell Brands
• Timken

  

Volatility of Monthly Returns of Newell Brands Compared to the Vanguard S&P 500 Index ETF

The monthly returns of Newell Brands and the Vanguard S&P 500 ETF have a positive correlation of 0.44, as calculated using the Pearson method. The data used in this study is range from June 2019 to October 2022 (41 months of data). 

Newell Brands is a company that owns some very famous brands across multiple consumer and commercial product lines. 

Exhibit: The Brands Owned by Newell Brands

(Source: Newell Brands)

      

Here's the R command and the output from R-Studio

> # Calculate the Monthly Return Correlation between Newell Brands 

> # and Vanguard S&P 500 Index using the Pearson method 

> cor(VOOandNWL['NWL_Monthly_Return'], VOOandNWL['VOO_Monthly_Return'], method = c("person"))

                        VOO_Monthly_Return

NWL_Monthly_Return          0.4434957 

Here's the plot of the S&P 500 and the Newell Brands' monthly returns:

Exhibit: S&P 500 Index Monthly Returns VS. Newell Brands Monthly Returns

                       S&P 500 Index Monthly Returns against Newell Brands' Returns

                          (Source: Data Provided by IEX Cloud, Correlation and Graph on RStudio)

When the correlation is calculated for the months when the S&P 500 Index had positive returns, the correlation drops to 0.28. 

> # Calculate the Monthly Return Correlation between Newell Brands 

> # and Vanguard S&P 500 Index using the Pearson method

> # for only those months when the Vanguard S&P 500 Index ETF 

> # had positive returns.

> cor(VOOandNWLPositiveReturns['NWL_Monthly_Return'], VOOandNWLPositiveReturns['VOO_Monthly_Return'], method = c("person"))

                        VOO_Monthly_Return

NWL_Monthly_Return           0.284022

Here's the plot of the S&P 500 Index against Newell Brands' monthly returns for months when the S&P 500 index had a positive return. 

           Exhibit: S&P 500 Index Monthly Positive Returns VS. Newell Brands Monthly Returns

S&P 500 Index Monthly Returns (Positive Months) against Newell Brands' Returns

                          (Source: Data Provided by IEX Cloud, Correlation and Graph on RStudio)

The linear regression of the monthly returns of the S&P 500 index and Newell Brands is used to estimate the average change in the monthly return of Newell Brands for a 1% change in the S&P 500 index. The coefficient of the independent variable (VOO_Monthly_Return) is the beta of Newell Brands.  In this case Newell Brands has a beta of 0.79. For every 1% monthly change in the S&P 500 index, Newell Brands is estimated to change by 0.79%. Yahoo Finance has calculated a beta of 0.84 for Newell Brands.      

> # Conduct the Linear Regression of the Monthly Returns Between $VOO and $NWL

> lmVOONWL = lm(NWL_Monthly_Return~VOO_Monthly_Return, data = VOOandNWL)

> # Present the summary of the results from the linear regression

> summary(lmVOONWL)

Call:

lm(formula = NWL_Monthly_Return ~ VOO_Monthly_Return, data = VOOandNWL)

Residuals:

     Min       1Q     Median       3Q      Max 

  -0.14372  -0.06818 -0.01767    0.06086  0.19915 

Coefficients:

                      Estimate   Std. Error   t value  Pr(>|t|)   

(Intercept)          -0.001988   0.014756     -0.135   0.89352   

VOO_Monthly_Return    0.793454   0.256769      3.090   0.00368 **

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.09306 on 39 degrees of freedom

Multiple R-squared:  0.1967, Adjusted R-squared:  0.1761 

F-statistic: 9.549 on 1 and 39 DF,  p-value: 0.003681

 

Volatility of Monthly Returns of Timken Compared to the Vanguard S&P 500 Index ETF

Here is the graph of monthly returns between June 2019 and September 2022 of Timken (TKR) plotted against Vanguard S&P 500 Index ETF (VOO):

Exhibit: Monthly Returns of VOO and TKR between June 2019 and September 2022

Monthly Returns of VOO and TKR between June 2019 and September 2022
(Source: Data Provided by IEX Cloud, Monthly Returns Calculated in Microsoft Excel, Graph Plotted in R Studio using ggplot package)
Click on the image to enlarge it.

The monthly returns of Timken have a very strong positive correlation of 0.77 with the S&P 500 Index. The very low p-value (p = 6.2e-09) indicates that the monthly returns of the S&P 500 Index have an effect on Timken's monthly returns.  

The Beta value indicates the monthly return volatility of Timken compared to the S&P 500 Index. Yahoo Finance provides a Beta value of 1.59 based on monthly returns over the past five years. A linear regression of the monthly returns between June 2019 and September 2022 yields a Beta of 1.48. The coefficient of Vanguard's monthly return is the volatility of Timken. The coefficient is the linear regression line's slope and Timken's Beta value. In other words, as the monthly return of the Vanguard S&P 500 Index ETF changes by 1%, Timken's monthly return can change by an average of 1.48%.  

Timken's Beta value is one of the highest I have seen. Here are the Beta values of some of the stocks in another post on this blog.  

Here's the output of the linear regression between the monthly returns of Vanguard S&P 500 Index ETF and Timken:

Call:

lm(formula = TKR_Monthly_Return ~ VOO_Monthly_Return, data = VOOandTKR)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.132602 -0.047815 -0.000585  0.059694  0.137770 

Coefficients:

                   Estimate Std. Error t value Pr(>|t|)    

(Intercept)        0.001198   0.011348   0.106    0.916    

VOO_Monthly_Return 1.489061   0.199966   7.447 6.17e-09 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.07103 on 38 degrees of freedom

Multiple R-squared:  0.5934, Adjusted R-squared:  0.5827 

F-statistic: 55.45 on 1 and 38 DF,  p-value: 6.168e-09  

  

Beta for Boeing, Sealed Air, Lennox International, Colgate-Palmolive & Cisco

I write about various companies on Seeking Alpha, covering Sealed Air, Lennox, Colgate-Palmolive, and Cisco Systems. I calculated the Beta of the monthly returns for these stocks compared to the Vanguard S&P 500 Index.  Here's the table showing the beta:  

Exhibit: Beta of Cisco, Colgate-Palmolive, Lennox International, Sealed Air, and Boeing

The beta of Cisco, Colgate-Palmolive, Lennox International, Sealed Air, and Boeing

You can read my articles on Seeking Alpha, which needs a subscription, by following the links below:

•  Sealed Air: Misclassified and Misunderstood
• Cisco Systems: Great Margins, But Lacks Growth
• Colgate-Palmolive Stock is Overvalued
• Lennox International May be Fully Valued

   

Boeing's Monthly Return Volatility Compared to the Vanguard S&P 500 Index ETF From June 2019 to August 2022

 Given its dominant position in the aerospace market, one would think Boeing's (BA) monthly returns would be less volatile than the S&P 500 index (VOO). But, Boeing has endured a lot in the past few years. First came the trade war with China that froze Boeing out of the second-largest aerospace market in the world.  Then came the COVID-19 pandemic that grounded airlines worldwide and brought Boeing to its knees. We did not even talk about the 737 Max plane crash in Ethiopia that kicked off the disastrous few years for Boeing.  

Boeing has never fully recovered from either the trade war or the pandemic. Boeing remains frozen out of the Chinese market, and airlines are only now seeing air travel return close to pre-pandemic levels (Exhibit 1).

Exhibit 1: TSA Checkpoint Travel Number September 17, 2022 - September 26, 2022

TSA Checkpoint Travel Numbers (Source: TSA.GOV)

Now, the world is grappling with slowing growth due to high inflation and interest rates, which is putting further pressure on Boeing. By the looks of it, Boeing stock may take a decade or more to recover its losses if it ever recovers. Boeing's stock has dropped from $440 in March 2019 to $127 as of September 27 - a loss of 71%.  

Due to these massive crises, Boeing's stock returns have become unhinged from that of the S&P 500 index. A linear regression of the monthly returns of the Vanguard S&P 500 Index and Boeing yields a very high beta of 1.35 (slope of the regression line). The value of 1.35 is the coefficient of the monthly returns of the Vanguard S&P 500 Index ETF (VOO).  Yahoo Finance displays a beta of 1.36 based on 5-year monthly returns.  One can expect any change in the Vanguard ETF to be magnified by Boeing.  For every 1% change in monthly returns of the S&P 500 index, Boeing's monthly returns are expected to change by 1.35%. Also, just 25% (Adjusted R-Squared in the RStudio output below) of Boeing's returns are explained by the monthly returns of the S&P 500 Index.  

Exhibit: Vanguard S&P 500 Index ETF and Boeing Monthly Returns [June 2019 - August 2022]

Vanguard S&P 500 Index ETF and Boeing Monthly Returns [June 2019 - August 2022]
(Source: Data Provided by IEX Cloud, Author Calculations Using RStudio)

Here's the output from the linear regression conducted on RStudio: 

> lmBAVOO = lm(BA_Monthly_Return~VOO_Monthly_Return, data = VOOandBA)

> summary(lmBAVOO)

Call:

lm(formula = BA_Monthly_Return ~ VOO_Monthly_Return, data = VOOandBA)

Residuals:

     Min       1Q   Median       3Q      Max 

-0.26036 -0.07433 -0.00562  0.07323  0.33452 

Coefficients:

                   Estimate Std. Error t value Pr(>|t|)    

(Intercept)        -0.02348    0.02007  -1.169 0.249682    

VOO_Monthly_Return  1.35442    0.36247   3.737 0.000628 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1229 on 37 degrees of freedom

Multiple R-squared:  0.274, Adjusted R-squared:  0.2543 

F-statistic: 13.96 on 1 and 37 DF,  p-value: 0.0006279

      

    

 

  

 

 

Linear Regression of Monthly Returns of Sealed Air Corp and the Vanguard S&P 500 Index ETF

Sealed Air Corporation is a global provider of packaging solutions to various industries. The company provides various packaging products to pack red meat, medical and life science products, cheese, electronics, and other products.  

Exhibit: Sealed Air Corporation Revenue by Region and Product Type

(Source: Sealed Air Q2 FY 2022 Investor Presentation on August 2,2022)

Sealed Air has very high volatility. This high volatility may be due to heavy dependency on consumer spending. If consumer spending is weak, they may buy less packaged red meat or packaged cheese, resulting in reduced revenue for the company.  

Here is the graph of monthly returns of Sealed Air (SEE) plotted against Vanguard S&P 500 Index ETF (VOO):

Exhibit 1: Monthly Returns of Sealed Air Corp. and Vanguard S&P 500 Index ETF [June 2019 -  August 2022]

Monthly Returns of Sealed Air Corp. and Vanguard S&P 500 Index ETF [June 2019 -  August 2022]

(Source: RStudio, ggplot, Data Provided by IEX Cloud)

Results of the linear regression of monthly returns of Sealed Air Corporation against Vanguard S&P 500 Index ETF:

> lmSEEVOO = lm(SEE_Monthly_Return~VOO_Monthly_Return, data = VOOandSEE)

> summary(lmSEEVOO)

Call:

lm(formula = SEE_Monthly_Return ~ VOO_Monthly_Return, data = VOOandSEE)

Residuals:

     Min       1Q   Median       3Q      Max 

-0.13572 -0.03866  0.01319  0.03375  0.14587 

Coefficients:

                    Estimate Std. Error t value Pr(>|t|)    

(Intercept)        -0.002498   0.009056  -0.276    0.784    

VOO_Monthly_Return  1.144479   0.163536   6.998 2.85e-08 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.05547 on 37 degrees of freedom

Multiple R-squared:  0.5696, Adjusted R-squared:  0.558 

F-statistic: 48.98 on 1 and 37 DF,  p-value: 2.849e-08

Sealed Air Corporation has a higher volatility than the S&P 500 Index, with a Beta of 1.144. The company's monthly returns positively correlate with the Vanguard S&P 500 Index ETF. The correlation is 0.75.  

The linear regression yields an adjusted R-squared of 0.55, indicating that about 55% of Sealed Air's monthly returns can be explained by the monthly returns of the S&P 500 Index. 

Sealed Air stock may be risky due to its high volatility compared to the market. But, if the stocks are bought at a reasonable or low valuation, they may yield returns that exceed the market's return.   

     

Linear Regression of Monthly Returns of Cisco Systems and the Vanguard S&P 500 Index ETF

Here is the graph of monthly returns of Cisco Systems (CSCO) plotted against Vanguard S&P 500 Index ETF (VOO):

Exhibit 1: Monthly Returns of Cisco Systems and Vanguard S&P 500 Index ETF [June 2019 -  August 2022]

Monthly Returns of the Vanguard S&P 500 Index ETF and Cisco Systems Inc.

(Source: RStudio, ggplot, Data Provided by IEX Cloud)

Results of the linear regression of monthly returns of Cisco Systems against Vanguard S&P 500 Index ETF:

VOOandCSCO <- read_excel("/CSCO_VOO_LM_September_2022.xlsx", sheet = "Sheet1")

lmCSCOVOO = lm(CSCO_Monthly_Return~VOO_Monthly_Return, data = VOOandCSCO)

summary(lmCSCOVOO)

Call:

lm(formula = CSCO_Monthly_Return ~ VOO_Monthly_Return, data = VOOandCSCO)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.156924 -0.031998 -0.008248  0.038045  0.127839 

Coefficients:

                   Estimate Std. Error t value Pr(>|t|)    

(Intercept)        -0.01062    0.01049  -1.012    0.318    

VOO_Monthly_Return  0.91708    0.18946   4.841 2.31e-05 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06426 on 37 degrees of freedom

Multiple R-squared:  0.3877, Adjusted R-squared:  0.3712 

F-statistic: 23.43 on 1 and 37 DF,  p-value: 2.306e-05

The slope of the regression corresponds to the beta of the stock. In this case, Cisco Systems has a beta of 0.91. 

The adjusted R-squared is 0.37. About 37% of Cisco's monthly return is explained by the returns of the S&P 500 index.  

Cisco Systems cannot protect a portfolio against market volatility since it has a beta value close to 1. Cisco's stock will almost entirely reflect the volatility in the market.