J.M. Smucker's Low Correlation With The Vanguard S&P 500 Index ETF

 J.M. Smucker is known for its iconic and timeless consumer staples brands (Exhibit 1). 

Note: Click on each image in this blog post to view an enlarged version

Exhibit 1:

Brands Owned by J.M. Smucker & Co. (Source: J.M. Smucker)

Here's the histogram of monthly returns for J.M. Smucker between June 2019 and November 2022 (Exhibit 2).

Exhibit 2:

J.M. Smucker (SJM) Histogram of Monthly Returns (Source: Data provided by IEX Cloud, author calculations & graph using Microsoft Excel)

The average monthly return for J.M. Smucker is less than the Vanguard S&P 500 Index ETF (Exhibit 3).

Exhibit 3:

 J.M. Smucker Monthly Return Statistics - Average, First Quartile, Third Quartile, Standard Deviation, Highest Monthly Return, Lowest Monthly Return (Source: Data provided by IEX Cloud, author calculations & graph using Microsoft Excel)

J.M. Smucker had a lower standard deviation than the Vanguard ETF during this period (Exhibit 4). A company with a lower standard deviation than the well-diversified ETF, a measure of volatility, is an infrequent occurrence. 

Exhibit 4:

Vanguard S&P 500 Index ETF Monthly Return Statistics - Average, First Quartile, Third Quartile, Standard Deviation, Highest Monthly Return, Lowest Monthly Return (Source: Data provided by IEX Cloud, author calculations & graph using Microsoft Excel)

Here's a graph of the monthly returns of the Vanguard ETF (x-axis) and J.M.Smucker (y-axis) with the fitted regression line (Exhibit 5).

Exhibit 5:

Monthly Return Graph of the Vanguard S&P 500 Index ETF and J.M. Smucker (Source: Data provided by IEX Cloud, author calculations & graph using Microsoft Excel & RStudio) 

The correlation of the monthly returns between June 2019 and November 2022 between the Vanguard ETF and J.M. Smucker is a low 0.19 (Exhibit 5). The fitted linear regression line has a p-value of 0.23, indicating that the relationship is insignificant at the 95% confidence interval. 

The fitted linear regression line has a p-value of 0.23, indicating that the relationship is insignificant at the 95% confidence interval. Here's the output from the linear regression:

> summary(lmVOOSJM)

Call:

lm(formula = SJM_Monthly_Return ~ VOO_Monthly_Return, data = VOOandSJM)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.089842 -0.034389  0.002403  0.022171  0.118077 

Coefficients:

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

(Intercept)        0.005011   0.007476   0.670    0.507

VOO_Monthly_Return 0.157911   0.130216   1.213    0.232

Residual standard error: 0.04755 on 40 degrees of freedom

Multiple R-squared:  0.03546, Adjusted R-squared:  0.01135 

F-statistic: 1.471 on 1 and 40 DF,  p-value: 0.2324

The beta for J.M. Smucker is 0.15, but the high p-value is a concern. This beta (the coefficient of VOO_Monthly_Return) may not be the true value. Yahoo Finance has calculated a beta of 0.24.    

  

Corning's Volatility Compared to the Vanguard S&P 500 Index ETF

Here's the histogram of Corning's (GLW) monthly returns between June 2019 and October 2022 (Click on the image to see a larger version):

Exhibit: Corning's Monthly Returns Fell at or below 2.56% for the Majority of the Months

Data Provided by IEX Cloud, Author Calculations

There were 14 months between June 2019 and October 2022 when Corning's monthly returns were greater than 2.56%.  There were seven months when the monthly returns were greater than or equal to 11.5%.    

Corning's monthly returns have a high positive correlation of 0.66 with the monthly returns of the Vanguard S&P 500 Index ETF (VOO). 

Exhibit: The Vanguard S&P 500 Index and Corning Monthly Returns [June 2019 - October 2022]

Data Provided by IEX Cloud, Monthly Returns Calculated by the Author, Graph using RStudio 

A linear regression of the Vanguard S&P 500 Index ETF and Corning's monthly returns yields a beta of 1.03 for Corning. Corning's average monthly return will mirror the Vanguard S&P 500 Index ETF. Corning may not help diversify a portfolio and will not protect against the market's volatility.  

Here's the linear regression model:

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

> lmVOOGLW = lm(GLW_Monthly_Return~VOO_Monthly_Return, data = VOOandGLW)

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

> summary(lmVOOGLW)

Call:

lm(formula = GLW_Monthly_Return ~ VOO_Monthly_Return, data = VOOandGLW)

Residuals:

      Min        1Q      Median     3Q       Max 

   -0.118363 -0.050928 -0.009998  0.041106  0.187435 

Coefficients:

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

(Intercept)        -0.003778     0.010818    -0.349    0.729    

VOO_Monthly_Return  1.039148     0.188256     5.520    2.4e-06 ***

---

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

Residual standard error: 0.06823 on 39 degrees of freedom

Multiple R-squared:  0.4386, Adjusted R-squared:  0.4242 

F-statistic: 30.47 on 1 and 39 DF,  p-value: 2.403e-06

The coefficient for VOO_Monthly_Return [1.039148] is the beta for Corning.  The p-value is significant at a 95% confidence interval. The adjusted R-squared is 0.42, which means about 42% of Corning's average monthly return is explained by the Vanguard S&P 500 Index ETF returns.