The Industrials Sector is on a Tear

 The Vanguard Industrials Index ETF (VIS) touched a 52-week high of $202.86 on Friday, June 16 (Exhibit 1).  

Exhibit 1:

Vanguard Industrial Index ETF (VIS) 5-Day Performance
Source: Seeking Alpha

The top performer in this ETF was Vertiv Holdings, returning 133% over the past year (as of June 16, 2023). The company describes itself as follows:

"Vertiv is a global leader in the design, manufacturing and servicing of critical digital infrastructure for data centers, communication networks, and commercial and industrial environments. Our customers operate in some of the world's most critical and growing industries, including cloud services, financial services, healthcare, transportation, manufacturing, energy, education, government, social media, and retail." (Source: SEC.GOV)

This renewed interest in data centers is not surprising, given the popularity of artificial intelligence (AI) and the investments in new applications powered by AI. 

In the Q1 2023 Earnings call, Vertiv's management said the following about AI:

"We are distinctly seeing the first signs of the AI investment cycle in our pipelines and orders. Vertiv is uniquely positioned to win here, given our market leadership and deep domain expertise in areas like thermal management and controls, which are vital to support the complexity of future AI infrastructures." (Source: Seeking Alpha)

"Let me go back to the investments in AI. You may have heard it as generally characterized as the next infrastructure arms race, Vertiv benefits from this race and is an agnostic partner of choice to the risk participants. The acceleration in investment in AI will turn into a net infrastructure capacity demand acceleration, and this starts to be visible in our pipeline. AI applications’ demand and net capacity increase in the industry, higher power density, a gradual migration to an air and liquid hybrid cooling environment and a transition to liquid-ready facility designed." (Source: Seeking Alpha)

 Here's the list of the top 10 performers in the Vanguard Industrials Index ETF over the past year (Exhibit 2).  

Exhibit 2:

Top 10 Performers in the Vanguard Industrials Index ETF
Source: Barchart.com, Data Provided by IEX Cloud

 Here's the list of the bottom 10 worst performers in the Industrials ETF over the past year (Exhibit 3):

Exhibit 3:

Bottom 10 Worst Performers in the Vanguard Industrials Index ETF
Source: Barchart.com, Data Provided by IEX Cloud

Monthly Return Analysis of Conagra Brands

Conagra Brands owns many iconic brands in the food business (Exhibit 1). The company is categorized as a consumer staple. 

Exhibit 1:

 

Here's the histogram of monthly returns of Conagra Brands between June 2019 and November 2022 (Exhibit 2). Please click on the image to see an enlarged version.  

Exhibit 2:

Conagra Brands Histogram of Monthly Returns (Source: Data Provided by IEX Cloud, Author Calculations using Excel)

The average monthly returns of Conagra Brands (Exhibit 3) are very similar to that of the Vanguard S&P 500 Index ETF (Exhibit 4).

Exhibit 3: 

(Source: Data Provided by IEX Cloud, Data Calculations Using Excel)

Exhibit 4:

(Source: Data Provided by IEX Cloud, Data Calculations Using Excel)

The monthly returns of Conagra Brands and the Vanguard S&P 500 Index ETF have a mild positive correlation of 0.27 (Exhibit 5). 

Exhibit 5:  

A 12-month rolling correlation of the monthly returns yielded a very high positive correlation of 0.8 between April 2020 and March 2021 (Exhibit 6).

Exhibit 6:

(Source: Data Provided by IEX Cloud, Correlation Calculations Using RStudio)

A 12-month rolling correlation of the monthly returns yielded the highest negative correlation of 0.37 between July 2021 and June 2022 (Exhibit 7).

Exhibit 7:

(Source: Data Provided by IEX Cloud, Correlation Calculations Using RStudio)

A linear regression model estimates Conagra's Beta at 0.34, which is not statistically significant at the 95% confidence interval. The p-value is 0.083, suggesting that the correlation is not statistically significant.

Here's the output of the linear model:

Call:

lm(formula = CAG_Monthly_Return ~ VOO_Monthly_Return, data = VOOandCAG)

Residuals:

      Min        1Q    Median        3Q       Max 

-0.168638  -0.044057  -0.004737   0.045175  0.170379 

Coefficients:

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

(Intercept)        0.007141   0.011079   0.645   0.5229  

VOO_Monthly_Return 0.342593   0.192981   1.775   0.0835 .

---

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

Residual standard error: 0.07047 on 40 degrees of freedom

Multiple R-squared:  0.07303, Adjusted R-squared:  0.04986 

F-statistic: 3.152 on 1 and 40 DF,  p-value: 0.08346

The adjusted R-squared is 0.049, meaning that just 4.9% of Conagra's monthly returns can be explained by the monthly returns of the Vanguard S&P 500 Index ETF.    

  

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

 

Welch Two Sample t-test of Daily Price Returns of J.M. Smucker and Mondelez

J.M. Smucker and Mondelez are consumer staples companies with similar financial performance. Both companies compete in similar product categories, especially when it relates to snacks. But, Mondelez has a vast international operation, while J. M. Smucker is more focused on the U.S. with very little So, I wanted to see if the mean daily returns are the same between the two stocks.

First, I downloaded the daily price return data for the past six months for both J.M. Smucker (SJM) and Mondelez (MDLZ) from IEX Cloud. 

I did a correlation between the daily price returns of the two companies using R:

> cor(SJM_6Month_ClosingPrice_Daily_Return_Data$changePercent, MDLZ_6Month_ClosingPrice_Daily_Return_Data$changePercent) 

[1] 0.5815403

The correlation in the daily returns between the two companies was 0.58 for the past six months. It is a positive correlation, but I would not consider this as a strong correlation in returns between the two companies. I would consider it a strong correlation if it was at or above 0.70. This came as a surprise given that both the companies are in the consumer staples sector and that sector has performed very well since December 2021. 

Next, I wanted to calculate the p-value for the Welch two-sample t-test between means of the daily returns.  The null hypothesis is that the difference in mean daily returns between J.M. Smucker and Mondelez is zero. The alternate hypothesis is that the difference is not zero. The p-value is 0.9035, which means the null hypothesis can be resoundingly rejected. The t-test command in R and the result set is presented below. The J. M. Smucker had a mean daily return of 0.0893% and Mondelez had a daily return of 0.0717%. There is a 1.7 basis points difference in returns with J. M. Smucker having a slightly larger daily return than Mondelez.       

> t.test(SJM_6Month_ClosingPrice_Daily_Return_Data$changePercent,         MDLZ_6Month_ClosingPrice_Daily_Return_Data$changePercent)

Welch Two Sample t-test

data:  SJM_6Month_ClosingPrice_Daily_Return_Data$changePercent and MDLZ_6Month_ClosingPrice_Daily_Return_Data$changePercent

t = 0.1214, df = 239.4, p-value = 0.9035

alternative hypothesis: true difference in means is not equal to 0

95 percent confidence interval:

 -0.002682718  0.003035099

sample estimates:

   mean of x    mean of y 

0.0008936508 0.0007174603 

Finally, here's the scatter plot of daily price change of J.M. Smucker (SJM) and Mondelez (MDLZ).

(Data Source: IEXCloud.io, Plot Created using RStudio)

  

Can Salesforce (CRM) continue growing to justify its valuation?

Salesforce (CRM) grew at a breakneck speed over the past two decades. The is hoping that the growth will continue in this decade.

The company's free cash flow yield is very similar to that of Microsoft (MSFT) and Adobe (ADBE). Salesforce's free cash flow yield has been consistently around the 2% level over the past decade. Microsoft and Adobe have seen their market capitalization and earnings multiple expand over the years causing their free cash flow yield to drop. I might have to look into their number more closely. 

Exhibit: Free Cash Flow Yield

(Source: Seeking Alpha)

Salesforce is lagging behind Microsoft (MSFT) and Adobe (ADBE) on return on equity. Both those companies have more than 8x more return on equity than Salesforce.  

  Exhibit: Return on Equity

(Source: Seeking Alpha)

Microsoft and Adobe have 6x and 8x more return on invested capital (ROIC) compared to Salesforce. 

Exhibit: Return on Invested Capital 

(Source: Seeking Alpha)

Salesforce's EBITDA margin is much lower than that of Microsoft and Adobe.  

Exhibit: EBITDA Margin

(Source: Seeking Alpha)

Salesforce's EV to EBITDA multiple is higher than that of Microsoft and Adobe.  

                               Exhibit: EV to EBITDA Multiple for Salesforce, Microsoft, and Adobe.  

                                        

   (Source: Seeking Alpha)                                         

Salesforce's year-over-year quarterly revenue growth (See Exhibit: Year-over-Year Revenue Growth) has converged with Microsoft and Adobe.  

    Exhibit: Year-over-Year Revenue Growth

(Source: Seeking Alpha)

Salesforce's price to earnings growth ratio (See Exhibit: Salesforce, Microsoft, and Adobe PEG Ratio) was attractive during the past decade compared to Microsoft and Adobe. If the company's growth can continue, that would justify its higher valuation multiple compared to Microsoft and Adobe. Salesforce's revenue is already in the high $20 billion, so for it to grow at a 20% rate would take some work.  

                                        Exhibit: Salesforce, Microsoft, and Adobe PEG Ratio

(Source: Seeking Alpha)

MongoDB: Number of $100,000 or greater ARR Customers

Here's how MongoDB (NASDAQ: MDB) defines Annualized Recurring Revenue (ARR):

"We define ARR as the subscription revenue we would contractually expect to receive from customers over the following 12 months assuming no increases or reductions in their subscriptions."

Number of  $100,000 ARR Customers each year:

• 2016: 164
• 2017: 246
• 2018: 354
• 2019: 557
• 2020: 751
• 2021: 975

42.8% Compound Annual Growth Rate in $100K or greater ARR customers.  

(Source: MongoDB.com)

The company also closely monitors ARR expansion rate and here's is what they have to say how that:

"We also examine the rate at which our customers increase their spend with us, which we call net ARR expansion rate. We calculate net ARR expansion rate by dividing the ARR at the close of a given period (the “measurement period”), from customers who were also customers at the close of the same period in the prior year (the “base period”), by the ARR from all customers at the close of the base period, including those who churned or reduced their subscriptions. For Direct Sales Customers included in the base period, measurement period or both such periods that were self-serve customers in any such period, we also include annualized MRR from those customers in the calculation of the net ARR expansion rate. Our net ARR expansion rate has consistently been over 120%."

The ARR expansion rate is another critical metric for software subscription companies. It shows whether the customer is willing to buy more of the product and/or expand its use within the company.  

Sources:

• Form 10-K (Annual Report) FY 2021
• Form 10-K FY 2018