Negative Beta Coefficients in the Stock Market

This report is a statistical analysis of the validity of using stocks with negative beta coefficients as hedges against economic downturns. The scope of this report does not include financial instruments which use short positions to artificially create inverse relationships.

Research Question

Does an equity with a negative beta coefficient in a bull market truly make a worthwhile hedge in a bear market?

Conventional stock market wisdom suggests that stocks which have inverse relationships with overall market performance may have a place in an investor's portfolio if said investor believes the overall market is going to experience a moment of decline (Streissguth, 2019). Mathematically, this theory makes sense; however, it is unclear if the theory holds water in real-life applications. A majority of stocks with negative beta coefficients may simply be underperforming or non-performing assets that continue to perform poorly in a downturn.

The hypotheses and null hypothesis are as follows:

H0: Stocks with negative beta coefficients perform as expected during distressed market conditions (there is no statistically significant change in beta coefficients).

H1: Stocks with negative beta coefficients perform better than expected relative to market indices during distressed market conditions (beta coefficients decrease).

H2: Stocks with negative beta coefficients perform worse than expected relative to market indices during distressed market conditions (beta coefficients increase).

Data Collection

Data collection for this research paper began by finding samples to test the hypotheses. Ideally, one would be able to analyze complete data of historical prices for every equity that has ever traded publicly; however, this paper will be analyzing sample sets instead.

A representative sample began to be gathered by manually scraping mentions of stocks with negative betas from the web. To obtain a valid representation, examples were taken from different time periods, different industries, and different beta ranges. Various search terms were used when scraping the web to try and mitigate confirmation bias. There was a total of 55 observations recorded with 49 of them being distinct.

As some of the observations were American Depositary Receipts (ADRs), it also became necessary to retrieve historical currency exchange rates to isolate for interactions between the equity and the index rather than interactions between the U.S. Dollar and the native currency. This data was retrieved from ofx.com (“Historical Exchange Rates Tool & Forex History Data”, 2020).

The next step was to retrieve historical price data for each of these observations so that beta coefficients could be calculated. To do this efficiently, a node.js script was developed which used the Alpha Vantage API. The code is as follows:

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const https = require('https');
const csv = require('csv-parser');
const fs = require('fs');
const rp = require('request-promise');
let URL = "https://www.alphavantage.co/query?function=TIME_SERIES_MONTHLY_ADJUSTED&symbol=";
let URLEnd = "{API_KEY}";
let output = [];
let tickers = [
{ Ticker: 'ARNA' },{ Ticker: 'FGP' },{ Ticker: 'AEM' },{ Ticker: 'DPS' },{ Ticker: 'TRMD' },{ Ticker: 'ENEVY' },
{ Ticker: 'ZM' },{ Ticker: 'GRSXY' },{ Ticker: 'GEGYY' },{ Ticker: 'COE' },{ Ticker: 'CCOEY' },{ Ticker: 'FLRAF' },{ Ticker: 'DRCSY' },
{ Ticker: 'VIRT' },{ Ticker: 'SAFE' },{ Ticker: 'FRHC' },{ Ticker: 'IHICY' },{ Ticker: 'CUYTY' },{ Ticker: 'PGENY' },{ Ticker: 'DHT' },
{ Ticker: 'PYPTF' },{ Ticker: 'AHCO' },{ Ticker: 'VIAAY' },{ Ticker: 'SLP' },{ Ticker: 'PLMR' },{ Ticker: 'AWR' },{ Ticker: 'CTWS' },
{ Ticker: 'TCO' },{ Ticker: 'CHCJY' },{ Ticker: 'OLCLY' },{ Ticker: 'KGDEY' },{ Ticker: 'HRL' },{ Ticker: 'DNLMY' },{ Ticker: 'WDFC' },
{ Ticker: 'PSO' },{ Ticker: 'DCMYY' },{ Ticker: 'VGZ' },{ Ticker: 'SO' },{ Ticker: 'SO' },{ Ticker: 'SO' },{ Ticker: 'SO' },
{ Ticker: 'SO' },{ Ticker: 'SO' },{ Ticker: 'SO' },{ Ticker: 'ASPS' },{ Ticker: 'GILD' },{ Ticker: 'GLPG' },{ Ticker: 'RHHBY' },
{ Ticker: 'HAS' },{ Ticker: 'ROST' },{ Ticker: 'WMT' },{ Ticker: 'AMGN' },{ Ticker: 'BUD' },{ Ticker: 'HRB' },{ Ticker: 'DLTR' }
];

function timer(ms) {
return new Promise(res => setTimeout(res, ms));
}

function make_api_call(Ticker){
return rp({
url : URL + Ticker + URLEnd,
method : 'GET',
json : true
})
}

async function processUsers(){
let result;
for(let i = 0; i < tickers.length; i++){
result = await make_api_call(tickers[i].Ticker);
outputTwo = result["Monthly Adjusted Time Series"];
var keys = Object.keys(outputTwo);
let csvText = "";
let n = 0;
for (x in outputTwo) {
csvText += "<tr><td>" + tickers[i].Ticker + "</td><td>" + keys[n] + "</td><td>" + outputTwo[x]['5. adjusted close'] + "</td></tr>";
n++;
}
output[i] = csvText;
var index = i + 1;
var time = 20 * (tickers.length - i + 1) / 60;

console.log("Retrieving " + index + "/" + tickers.length);
console.log("Estimated Time Remaining: " + time + " minutes");
await timer(20000);
}
return output;
}

async function doTask(){
let result = await processUsers();
console.log(result);
let html = "<table>" + result + "</table>";
fs.writeFile('newfile.html', html, function (err) {
if (err) throw err;
console.log('Saved!');
});
}

doTask();

Finally, the data for the S&P 500 (NYSE: SPY) was retrieved using the node.js script.

Data Extraction and Preparation

The first step in the data-preparation process was to put all historical price for each ticker into a single, readable table where the data can be compared easily. This step exposed fourteen observations which did not contain enough data to be used in the analysis (DPS, ENEVY, GRSXY, GEGYY, DRCSY, PYPTF, CTWS, CHCJY, KGDEY, TRMD, ZM, FRHC, AHCO, PLMR). This was a relatively easy process, so it was performed manually in Excel.

The resulting table begins as follows and continues through 08/31/2000:

The advantage of preparing this data in Excel is that it isn't necessary to spend time creating a script to prepare the data; however, if the amount of data were larger, it may make sense to spend the time writing a script rather than spending time manually manipulating the data. Setting the data up in a table like this will allow for each column to be read in as a variable in R for regression to be performed. Additionally, the regression can be performed directly in Excel. This format also sets the data up nicely for Excel-based regression to be executed.

The data for the independent variable (S&P 500) was then appended to the table:

For quick reference, ADRs were then color-coded by country. Each country was then given a corresponding Excel tab with a matching color which contains the historical exchange rates for the country's native currency. Setting the data up this way allows for easy access to desired information through tools like Excel's “VLOOKUP()” function.

These steps are illustrated by the following images:

Analysis

While linear regression is how the beta statistic is commonly calculated within the industry, it can also be calculated simply by dividing covariance by variance (Nickolas, 2019). However, this analysis will be using complete linear regression models to dive more deeply into the strength of the relationships and evaluate more fully the null hypothesis that the difference between beta coefficients is actually zero. Additionally, linear regression is easier to use when evaluating models with multiple independent variables. Out all of the tools I've used throughout this program, I've found that R is the easiest to use when performing regression methods on larger data sets

This analysis will be focusing on two “inflection points” from which the market turned from a positive trend to a negative trend. Specifically, these two inflection points are October 31st, 2007 and December 31st, 2019. The next step was to calculate the Beta Coefficient (3Y monthly) from each of those points for every one of the dependent variables. This was done using linear regression in R with the following code:

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library(readxl)
output <- read_excel("C:/Users/Frenc/OneDrive/Desktop/output.xlsx")
#DETERMINE BETA (3Y MONTHLY) FROM INFLECTION POINT
inflectionOne <- output[8:44, ]
#DECLARE VARIABLES
spy <- inflectionOne$SPY
arna <- inflectionOne$ARNA
fgp <- inflectionOne$FGP
aem <- inflectionOne$AEM
coe <- inflectionOne$COE
ccoey <- inflectionOne$CCOEY
flraf <- inflectionOne$FLRAF
virt <- inflectionOne$VIRT
safe <- inflectionOne$SAFE
ihicy <- inflectionOne$IHICY
cuyty <- inflectionOne$CUYTY
pgeny <- inflectionOne$PGENY
dht <- inflectionOne$DHT
viaay <- inflectionOne$VIAAY
slp <- inflectionOne$SLP
awr <- inflectionOne$AWR
tco <- inflectionOne$TCO
olcly <- inflectionOne$OLCLY
hrl <- inflectionOne$HRL
dnlmy <- inflectionOne$DNLMY
wdfc <- inflectionOne$WDFC
pso <- inflectionOne$PSO
dcmyy <- inflectionOne$DCMYY
vgz <- inflectionOne$VGZ
so <- inflectionOne$SO
asps <- inflectionOne$ASPS
gild <- inflectionOne$GILD
glpg <- inflectionOne$GLPG
rhhby <- inflectionOne$RHHBY
has <- inflectionOne$HAS
rost <- inflectionOne$ROST
wmt <- inflectionOne$WMT
amgn <- inflectionOne$AMGN
bud <- inflectionOne$BUD
hrb <- inflectionOne$HRB
dltr <- inflectionOne$DLTR
#PERFORM REGRESSIONS
arna1 <-lm(arna ~ spy)
fgp1 <- lm(fgp ~ spy)
aem1 <- lm(aem ~ spy)
coe1 <- lm(coe ~ spy)
ccoey1 <- lm(ccoey ~ spy)
flraf1 <- lm(flraf ~ spy)
virt1 <- lm(virt ~ spy)
safe1 <- lm(safe ~ spy)
ihicy1 <- lm(ihicy ~ spy)
cuyty1 <- lm(cuyty ~ spy)
pgeny1 <- lm(pgeny ~ spy)
dht1 <- lm(dht ~ spy)
viaay1 <- lm(viaay ~ spy)
slp1 <- lm(slp ~ spy)
awr1 <- lm(awr ~ spy)
tco1 <- lm(tco ~ spy)
olcly1 <- lm(olcly ~ spy)
hrl1 <- lm(hrl ~ spy)
dnlmy1 <- lm(dnlmy ~ spy)
wdfc1 <- lm(wdfc ~ spy)
pso1 <- lm(pso ~ spy)
dcmyy1 <- lm(dcmyy ~ spy)
vgz1 <- lm(vgz ~ spy)
so1 <- lm(so ~ spy)
asps1 <- lm(asps ~ spy)
gild1 <- lm(gild ~ spy)
glpg1 <- lm(glpg ~ spy)
rhhby1 <- lm(rhhby ~ spy)
has1 <- lm(has ~ spy)
rost1 <- lm(rost ~ spy)
wmt1 <- lm(wmt ~ spy)
amgn1 <- lm(amgn ~ spy)
bud1 <- lm(bud ~ spy)
hrb1 <- lm(hrb ~ spy)
dltr1 <- lm(dltr ~ spy)
#CREATE LIST OF BETA COEFFICIENTS
x <- list(as.numeric(arna1$coef[2]),
as.numeric(fgp1$coef[2]),
as.numeric(aem1$coef[2]),
as.numeric(coe1$coef[2]),
as.numeric(ccoey1$coef[2]),
as.numeric(flraf1$coef[2]),
as.numeric(virt1$coef[2]),
as.numeric(safe1$coef[2]),
as.numeric(ihicy1$coef[2]),
as.numeric(cuyty1$coef[2]),
as.numeric(pgeny1$coef[2]),
as.numeric(dht1$coef[2]),
as.numeric(viaay1$coef[2]),
as.numeric(slp1$coef[2]),
as.numeric(awr1$coef[2]),
as.numeric(tco1$coef[2]),
as.numeric(olcly1$coef[2]),
as.numeric(hrl1$coef[2]),
as.numeric(dnlmy1$coef[2]),
as.numeric(wdfc1$coef[2]),
as.numeric(pso1$coef[2]),
as.numeric(dcmyy1$coef[2]),
as.numeric(vgz1$coef[2]),
as.numeric(so1$coef[2]),
as.numeric(asps1$coef[2]),
as.numeric(gild1$coef[2]),
as.numeric(glpg1$coef[2]),
as.numeric(rhhby1$coef[2]),
as.numeric(has1$coef[2]),
as.numeric(rost1$coef[2]),
as.numeric(wmt1$coef[2]),
as.numeric(amgn1$coef[2]),
as.numeric(bud1$coef[2]),
as.numeric(hrb1$coef[2]),
as.numeric(dltr1$coef[2])
)
#PRINT OUT LIST OF BETA COEFFICIENTS
print(x)

The results are presented below:

As can be observed, the first inflection point narrowed our data down to only eight observations with negative beta coefficients. These eight stocks would likely move forward to the next step of analysis; however, two of the stocks were ADRs which meant that the currency exchange rate had to be factored into the regression model.

To accomplish this, the following code was executed:

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ADR <- read_excel(“C:/Users/Frenc/OneDrive/Desktop/ADR.xlsx”)

flrafRegression <- lm(ADR$FLRAF ~ ADR$SPY + ADR$GBP)
summary(flrafRegression)
ihicyRegression <- lm(ADR$IHICY ~ ADR$SPY + ADR$JPY)
summary(ihicyRegression)

ADR.xlsx combined the previously gathered exchange rate data with the relevant stock variables and was limited to only the desired timeframe. This code produced the following results:

As can be observed, FLRAF's beta changed from -0.00595 to -0.007924 and IHICY's changed from -0.02372 to -0.02170 after taking exchange rates into account.

Further review and verification revealed two more ADRs in the list: BUD and COE. As such, the following code was executed to adjust for currency interactions:

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budRegression <- lm(ADR$BUD ~ ADR$SPY + ADR$EURO)
summary(budRegression)
coeRegression <- lm(ADR$COE ~ ADR$SPY + ADR$CNY)
summary(coeRegression)

It is important to note that simply specifying the exchange rate in the model may not consider exogenous effects which occur when currencies strengthen and weaken against each other.

This left the analysis with the following eight observations:

The ADR dataset was then updated to include the post-inflection data and the flowing code was executed:

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#DETERMINE POST-INFLECTION BETA
postInfOne <- output[1:7, ]
postInfOneOne <- ADR[1:7, ]

p1flraf <- lm (postInfOne$FLRAF ~ postInfOne$SPY + postInfOneOne$GBP)
p1ihicy <- lm (postInfOne$IHICY ~ postInfOne$SPY + postInfOneOne$JPY)
p1bud <- lm (postInfOne$FLRAF ~ postInfOne$SPY + postInfOneOne$EURO)
p1coe <- lm (postInfOne$FLRAF ~ postInfOne$SPY + postInfOneOne$CNY)

p1fgp <- lm(postInfOne$FGP ~ postInfOne$SPY)
p1tco <- lm(postInfOne$TCO ~ postInfOne$SPY)
p1vgz <- lm(postInfOne$VGZ ~ postInfOne$SPY)
p1asps <- lm(postInfOne$ASPS ~ postInfOne$SPY)

y <- list(as.numeric(p1flraf$coef[2]),
as.numeric(p1ihicy$coef[2]),
as.numeric(p1bud$coef[2]),
as.numeric(p1coe$coef[2]),
as.numeric(p1fgp$coef[2]),
as.numeric(p1tco$coef[2]),
as.numeric(p1vgz$coef[2]),
as.numeric(p1asps$coef[2]))

print(y)

The final data for the first inflection point is as follows:

The same process was then performed for the second inflection point. After running the following code, only four data points were found to have a negative beta coefficient (3Y monthly) preceding the inflection point.

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inflectionTwo <- output[154:190, ]

pre2arna <- lm(inflectionTwo$ARNA ~ inflectionTwo$SPY)
pre2fgp <- lm(inflectionTwo$FGP ~ inflectionTwo$SPY)
pre2aem <- lm(inflectionTwo$AEM ~ inflectionTwo$SPY)
pre2slp <- lm(inflectionTwo$SLP ~ inflectionTwo$SPY)
pre2awr <- lm(inflectionTwo$AWR ~ inflectionTwo$SPY)
pre2tco <- lm(inflectionTwo$TCO ~ inflectionTwo$SPY)
pre2hrl <- lm(inflectionTwo$HRL ~ inflectionTwo$SPY)
pre2wdfc <- lm(inflectionTwo$WDFC ~ inflectionTwo$SPY)
pre2pso <- lm(inflectionTwo$PSO ~ inflectionTwo$SPY)
pre2dcmyy <- lm(inflectionTwo$DCMYY ~ inflectionTwo$SPY)
pre2vgz <- lm(inflectionTwo$VGZ ~ inflectionTwo$SPY)
pre2so <- lm(inflectionTwo$SO ~ inflectionTwo$SPY)
pre2gild <- lm(inflectionTwo$GILD ~ inflectionTwo$SPY)
pre2rhhby <- lm(inflectionTwo$RHHBY ~ inflectionTwo$SPY)
pre2has <- lm(inflectionTwo$HAS ~ inflectionTwo$SPY)
pre2rost <- lm(inflectionTwo$ROST ~ inflectionTwo$SPY)
pre2wmt <- lm(inflectionTwo$WMT ~ inflectionTwo$SPY)
pre2amgn <- lm(inflectionTwo$AMGN ~ inflectionTwo$SPY)
pre2hrb <- lm(inflectionTwo$HRB ~ inflectionTwo$SPY)
pre2dltr <- lm(inflectionTwo$DLTR ~ inflectionTwo$SPY)

z <- list(as.numeric(pre2arna$coef[2]),
as.numeric(pre2fgp$coef[2]),
as.numeric(pre2aem$coef[2]),
as.numeric(pre2slp$coef[2]),
as.numeric(pre2awr$coef[2]),
as.numeric(pre2tco$coef[2]),
as.numeric(pre2hrl$coef[2]),
as.numeric(pre2wdfc$coef[2]),
as.numeric(pre2pso$coef[2]),
as.numeric(pre2dcmyy$coef[2]),
as.numeric(pre2vgz$coef[2]),
as.numeric(pre2so$coef[2]),
as.numeric(pre2gild$coef[2]),
as.numeric(pre2rhhby$coef[2]),
as.numeric(pre2has$coef[2]),
as.numeric(pre2rost$coef[2]),
as.numeric(pre2wmt$coef[2]),
as.numeric(pre2amgn$coef[2]),
as.numeric(pre2hrb$coef[2]),
as.numeric(pre2dltr$coef[2])
)

print(z)

The four observations were DCMYY (-0.0038776), WMT (-0.03485316), AMGN (-0.1831979), and HRB (-0.03964931). DCMYY's regression model was adjusted to include currency interaction between the USD and JPY using the following code:

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ADR2 <- read_excel("C:/Users/Frenc/OneDrive/Desktop/ADR2.xlsx")

preADR2 <- ADR2[154:190, ]

adjpre2dcmyy <- lm(preADR2$DCMYY ~ inflectionTwo$SPY + preADR2$JPY)

summary(adjpre2dcmyy)

This resulted in the following output:

As the beta coefficient turned positive when the exchange rate was included, DCMYY was excluded from further analysis. The next step for the other three observations was to gather the post-inflection data. This was accomplished using the code displayed below:

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#GATHER BETA COEFFICIENTS FOR THE SECOND POST-INFLECTION POINT
postInfTwo <- output[119:155, ]
#View(postInfTwo)

p2wmt <- lm(postInfTwo$WMT ~ postInfTwo$SPY)
p2amgn <- lm(postInfTwo$AMGN ~ postInfTwo$SPY)
p2hrb <- lm(postInfTwo$HRB ~ postInfTwo$SPY)

z <- list(as.numeric(p2wmt$coef[2]),
as.numeric(p2amgn$coef[2]),
as.numeric(p2hrb$coef[2]))
print(z)

The final data for the second inflection point is included in this table:

Data Summary and Implications

Testing the hypotheses of this analysis was done by performing a Paired sample T-test, using T distribution (DF=10) (two-tailed) with the following code:

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before <- c(-0.05472,
-0.25392,
-0.12322,
-0.06046,
-0.007924,
-0.0217,
-0.21292,
-0.00336,
-0.1831979,
-0.03964931,
-0.03485316
)

after <- c(0.1471804,
-0.01966157,
-0.03566156,
0.003014498,
-0.05585217,
-0.02989029,
-0.190013,
0.005399438,
-0.03735994,
0.02661733,
-0.01906176
)

t.test(before, after, paired = TRUE, alternative = "two.sided")

The results are displayed below:

This demonstrates that the null hypothesis which states that there is no change in beta coefficients after an inflection point can be rejected at at least the 95% level. The mean difference between pre-inflection and post-inflection data was -0.07187598 meaning that, on average, beta coefficients increased by more than 0.07 after an inflection point. This shows preference for H2 which states that stocks with negative beta coefficients perform worse than expected relative to market indices during distressed market conditions (beta coefficients increase).

The limitations of this analysis include a relatively small sample size with possible bias in the selection process (a truly representative sample may not have been achieved). However, based on this analysis alone, it is recommended that investors seeking to use equities with negative coefficients as a hedge against overall market downturns expect their assets to become more correlated to the market in a downturn.

Two approaches which may be appropriate for future study would be to obtain an entire population to study rather than a sample and to separate observations by the reasons they might have an inverse relationship with the market such as the stock being counter-cyclical or under-performing. This would allow each regression model to be more correctly specified.

Sources

Streissguth, T. (2019, March 31). Is a Negative Beta Coefficient More Risky Than a Positive in the Stock Market? Retrieved June 27, 2020, from https://finance.zacks.com/negative-beta-coefficient-risky-positive-stock-market-7596.html

Nickolas, S. (2020, February 05). The Formula for Calculating Beta. Retrieved June 27, 2020, from https://www.investopedia.com/ask/answers/070615/what-formula-calculating-beta.asp

Historical Exchange Rates Tool & Forex History Data. (2020). Retrieved July 13, 2020, from https://www.ofx.com/en-us/forex-news/historical-exchange-rates/

Caplinger, D. (2012, December 12). Negative-Beta Stocks: Worth Buying? Retrieved July 14, 2020, from https://www.fool.com/how-to-invest/2012/12/12/negative-beta-stocks-worth-buying.aspx

Negative Beta Stocks. (2020). Retrieved July 10, 2020, from https://www.marketbeat.com/market-data/negative-beta-stocks/

Hacker, D. (2008, January 28). Can beta be negative? Retrieved July 10, 2020, from https://www.analystforum.com/t/can-beta-be-negative/5351

Carnevale, C. (2015, May 21). The Great Beta Hoax: Not an Accurate Measure of Risk After All. Retrieved July 10, 2020, from https://www.advisorperspectives.com/commentaries/2015/05/21/the-great-beta-hoax-not-an-accurate-measure-of-risk-after-all

Sanchez, CFA, L. (2020, January 15). This Countercyclical Stock Could Be a Good Hedge. Retrieved July 10, 2020, from https://www.fool.com/investing/2020/01/15/this-countercyclical-stock-could-be-a-good-hedge.aspx

Delaet, R. (2020, May 15). 3 Strong Hedges For A Second Market Crash. Retrieved July 10, 2020, from https://seekingalpha.com/article/4347970-3-strong-hedges-for-second-market-crash

Divine, J. (2020, March 18). 7 Stocks That Soar in a Recession. Retrieved July 10, 2020, from https://money.usnews.com/investing/slideshows/7-stocks-that-soar-in-a-recession?slide=2

Stock Overview: Uber Technologies (NYSE: UBER)

Uber Technologies Inc (Uber) went public on May 9, 2019 and has since been a divisive topic among investors. While some believe Uber is a disruptive force in transportation, others see the business as a highly commoditized service.

The company was launched in March of 2009 by co-founders Travis Kalanick and Garrett Camp as a simple mobile app which would theoretically allow a consumer to request a ride from gig workers rather than from a Taxi service. Since that point, Uber's product offerings have grown more sophisticated.

Products

While Uber continues to maintain its ride service business, the product has evolved to include various tiers of luxury and even a carpooling option. Ten years later, this business is continuing to experience rapid growth. The company also claims to have 76% of 2019’s gross bookings as it pertains to the “rides” business.

Growing in relevance is their food delivery service, Uber Eats, which seems to be spoken about just as much (if not more) than the core business. One of their chief competitors, Postmates, was even just acquired by Uber for $2.65 billion. Uber Eats teams up with restaurants to deliver meals to their customers. The delivery is executed by gig workers, much like their core business.

One of the company's newer segments is “freight”. Freight pairs ground carriers with shippers. This segment still has room to grow considering its recency and it only making up approximately 1% of Uber's gross bookings in 2019. Freight allows Uber to gain exposure to business spending and embed itself as a core piece of infrastructure.

Another new segment that emphasizes Uber's gig worker model is the “work” product which pairs freelancers with companies that are working for labor to meet short-term needs. This product is only available in Chicago, Miami, and Dallas as of writing this.

Uber is also targeting intracity transportation (also referred to as the micro-mobility segment) with a $170 million investment in the electric scooter company Lime. At the time of this investment, Uber also announced that their existing micro-mobility play, Jump (which operates both electric bikes and scooters), would be acquired by Lime.

Uber Elevate is a division which is operating and researching multiple product offerings. Currently, Uber Copter is offered through the Elevate division and currently allows helicopter rides between Manhattan and JFK International Airport to be booked directly from the Uber app. Additionally, the Elevate team is working to develop Vertical Takeoff and Landing (VTOL) vehicles that would be available for ridesharing within select markets. The company is projecting that this product will be available by 2023.

The company also recently announced Uber Money which allows a spectrum of financial products such as a debit card, wallet, and budgeting tools.

Finally, Uber has a Business division which builds on the Rides and Eats products through office meals, courtesy rides, commuting, etc. The Business division also leverages tools such as integration with their API to better serve their business partners.

They also have a spectrum of smaller offerings (such as healthcare and boat rides) and will likely continue to expand on their offerings.

Leadership

The leadership team features Dara Khosrowshahi as the CEO who was previously the CEO of Expedia, CFO of IAC Travel, and received a degree in Electrical and Electronics Engineering at Brown University. The team also notable features Eric Meyhofer as the Head of Advanced Technology Group (ATG). The ATG is responsible for innovative new technologies such as autonomous vehicles. Eric previously attended and was employed by Carnegie Mellon in addition to starting various robotics companies. It is also worth noting that neither co-founder works for the company anymore nor do they sit on the board.

Innovation

It seems as if Uber has at least three major innovative missions: Autonomous Vehicles, Uber Elevate, and the Gig Economy.

Autonomous vehicles have the potential to change Uber's business. Labor costs have the potential to be affected positively. Although, it is uncertain whether autonomous vehicles would be available in every market or if they would only be available on pre-determined routes with few variables and the lowest potential for error.

Not only does paying workers per job they complete make sense for a business (as you are converting labor to a true variable cost), it also has the potential to empower the workers. Finding a conventional job can be a relatively long process compared to signing up with Uber. This promotes short-term earning potential. Additionally, it allows for workers to create “side-hustles” on their own terms without giving up the security of their day job. Of course, it is a valid option for a worker to make a full-time income for themselves as well.

Financials

As of today (July 12, 2020) Uber (NYSE: UBER) is trading at $33.14. This represents a decrease of approximately 26.4% from its IPO price of $45. It has a market cap of $57.46 billion while trailing twelve months sales are about $14.59 billion putting it at a multiple of just 3.94. Admittedly, 3.94 is not necessarily a low P/S ratio generally speaking; however, Uber is trading more like a utility than a growth stock. The company is still growing with an average revenue increase of 34% for the past three years as well as emerging segments which have a long runway before they hit maturity.

On the other hand, the company still has not hit profitability but did have projections of profitability by 2021 before the pandemic hit. Of course, this guidance was pulled, but it does demonstrate the emphasis that is being placed on the matter. Additionally, the company has nearly $14 billion in liquid assets to tap until it hits profitability (not to mention huge equity stakes in leading international rides businesses) and no shortage of access to capital.

Uber has the first-mover advantage in an industry that is here to stay and continues to make strategic investments in new technologies as well as its long-term financial health and market position. Uncertainty around the company’s future profitability is keeping some investors away but it seems likely that profitability is around the corner. The stock may take a hit in the short-term while this pandemic continues; however, I am bullish on the company's future.

Rating: Strong Buy

Disclosure: I am long on Uber Technologies (NYSE: UBER) as of July 12, 2020.

Cryptocurrency & Smart Contracts: How the World is Changing

Image by mohamed Hassan from Pixabay

At this point, you have likely heard of cryptocurrencies paired with stories of wild speculation; however, there is much more to the phenomenon than just fortune-hunters. Intelligent developers across the globe are leveraging the underlying blockchain technology to create innovative new solutions to age-old problems.

One such solution is Smart Contracts. In the past, contracts have required a certain level of trust between parties and were enforced by the law of the land. Smart Contracts allow contract stipulations to be programmed directly into the blockchain.

One use case currently being implemented is in the peer-to-peer lending space. Developers can program loan terms to be automatically executed. For example, loan payments can be automatically withdrawn, and account minimums can be absolutely enforced.

Other uses include the exchange of property, allocation of resources, voting protocols, security roles and permissions, along with a potentially endless range of other possibilities.

Not only do these contracts eliminate the need for trust-based transactions, they also allow interested parties to enter into agreements quickly and cheaply from anywhere around the world.

Surely, this technology has the potential to change the world for the better if used correctly. The main obstacle to mass adoption remains to be accessibility, however.

Wyndham Hotels & Resorts: Is Worst Case Scenario Already Priced In?

Summary

  • WH is down 58.09% from recent highs of $60.94.

  • WH announced on Tuesday (03/17) that it would be withdrawing it's 2020 outlook announced on 02/13.

  • The previous outlook already had between -5.99% and -7.94% of revenue growth included in the forecast before even considering the impact the coronavirus would have.

The hotel industry is one of the most affected by the recent COVID-19 outbreak. Occupancy rates are negligible as people are self-quarantining to prevent the spread of this disease.

The industry uses a metric called “RevPAR” to evaluate performance which stands for “Revenue Per Available Room”. This is calculated by dividing revenue by the number of available rooms where revenue is essentially a function of the occupancy rate and the average daily rate. As demand destabilizes across the entire industry, occupancy rate and average daily rate become much more endogenous.

Wyndham Hotels and Resorts (WH) ended 2019 with 831,000 rooms and a RevPAR of $40.92 and previously suggested in their now void 2020 outlook statement that they expected RevPAR to increase by (2%)-0% and rooms to increase by 2%-4%. (WH) is a franchising company and it's fee structure is largely dependent on it's franchisees ability to maintain reasonable RevPAR figures.

For example, (WH) had an average royalty fee of 3.8% in 2019 which resulted in about $480 million dollars (almost 25% of it's revenue). This royalty fee is quite literally 3.8% of the average revenue of it's franchisees.

Despite most of it's revenue essentially being a variable cost to franchisees which are struggling to fill rooms, there may still be a bright side to this story. Many of (WH)'s expenses are structured as variable costs as well. It's possible that over half of the expenses on the 2019 income statement could be shed in dire circumstances and the company's half a billion dollars in current assets would come close to covering the remainder.

This suggests that (WH) is likely to come out of this situation alive. The question that remains is whether or not it's franchisees can come out of it alive. Losses in franchisees would mean less rooms for (WH) to collect fees on. Otherwise, the company would likely be unaffected in the long-term after suffering a loss in revenue in the short-term. The company is without a doubt going to have an earnings cut this year; however, if it can get through this crisis without shedding most of it's properties and rooms, it should be able to continue it's trajectory once this is all over.

Considering that this stock was trading at multiples of around 2.8 before this situation came up, and assuming that those valuations still hold up in this new market, the expectation of the market seems to be that the company would only make about $850 million dollars in revenue this year which would be over two quarters of completely lost revenue. However, if we are entering a true bear market, valuations are likely to be lower across the board and that revenue expectation would need to be adjusted upward.

In any case, this is probably a risky play for short-term investors but a value play for investors with a 3–7 year horizon as most of the long-term damage is likely already priced in.

Presidential Stock Market Returns

Can the performance of U.S. presidents be measured by stock market performance? How has the market performed throughout the tenure of various presidents?

The following representation displays the returns of the S&P 500 during each of the presidential terms for which there were data available.

Please note that the last U.S. president displayed is the incumbent and the data represented is as of today (03/21/2020).

Which Presidents Oversaw Negative Growth?

Herbert Hoover, the first president found in the data, saw the index decrease by 77.09% during his tenure. This is because the infamous “Great Depression” began six months after he took office and continued well after he was gone. The causes of this depression are still debated, making it difficult to assign blame with confidence.

Richard Nixon oversaw an index decline of 20.48% and was in office during the start of not just one, but two recessions. These recessions were caused in part by inflation, a decrease in government spending, and the 1973 oil crisis.

Finally, George W. Bush was in office during the “Great Recession”. This recession was caused by loose lending standards leading to a virtual collapse of the U.S. housing market.

Which President Oversaw the Most Growth?

Bill Clinton saw the index increase by 209.79% during his tenure. His administration's economic policy was characterized by a decrease in national debt, budget surpluses, tax reform, and free trade agreements. However, President Clinton also enacted measures which essentially deregulated multiple facets of the finance industry. While these measures may have resulted in short-term economic growth, some suppose that it may have been a contributing factor to the “Great Recession” mentioned before.

As the economy can be dramatically affected by such a wide variety of events (many of which are unforeseeable), it is difficult to judge a presidency solely on the performance of stock market indices. However, presidential administrations certainly have a huge role in anticipating and responding to events which could have harmful effects on the economy as well as encouraging actions which would improve the economy.

How I Completed Half of my M.S. in Two Months

The choice to pursue my Master's Degree was fueled by a desire to maintain a competitive edge, not to lose relevance in the job market, and to ensure I didn't get stuck in the first job I accepted after undergrad. However, I'm not sure I would have committed right away if it weren't for the program offering at Western Governors University (WGU).

WGU allows students to pace themselves through classes which permits them to work as quickly or as slowly as they would like (as long as they meet the minimum term requirements). Also, instead of paying per credit, students pay per six month “term”.

After hearing this, I knew I would be capable of completing courses quickly and potentially pay a lot less money than I would at a traditional program while saving a lot of time.

I finished my first few courses within a couple of weeks. My major was Data Analytics and the first three courses I tackled were: Fundamentals of Data Analytics, Programming in Python, and R for Data Analysts. These were easy for me as I had spent a lot of time working with statistics and learning how to program during undergrad.

The competency-based learning model helped a lot too. Instead of sitting through lectures pretending to learn things I already knew, I was able to almost immediately be tested and receive credit for all of the extracurricular learning I had already done in my academic career.

The next two classes took me about a month to complete. Advanced Data Visualization covered a lot of data visualization concepts I was already familiar with but it went in depth into how to create effective graphics in Tableau (a program I had used but not extensively), and Statistics for Data Analysis was a beast but I was ultimately able to complete it in a few weeks.

Prior knowledge played a role in these courses but effective study techniques were much more important (especially in statistics). For me, it was helpful to outline all of the core competencies and diagram the subcomponents that made them up. Then, make sure that each component was sufficiently understood, one-by-one.

Finally, I completed Data Mining and Analytics I in just under a month. This one took me longer as I began to venture into new topics that pushed me out of my comfort zone. For me, the biggest strategy for learning new topics is finding a way to relate the information to topics you already know and slowly build on that until you breach the gap.

Finishing these classes put me at an important milestone: finishing approximately half of my Masters in approximately two months. Since then, I have finished two more classes in about the same amount of time (SQL for Data Analysis and Data Mining and Analytics II) and have three more classes to go.

The structure of the program has allowed me to accomplish a lot in my personal life as well as my academic life. During this time I was able to maintain side projects (like my personal website), start a new job (sometimes working 70+ hours a week), and relocate for work.

I look forward to completing my degree this year and sharing what I've learned.

Robinhood is Losing to Webull

In the battle to create a better way to invest online, Robinhood is losing to Webull — Here's why:

Data
Not only does Webull provide a much more comprehensive data service (complete with financials, charting, and technical indicators), Robinhood often provides incorrect or incomplete data, especially when it comes to dividends.

This is huge for serious investors. When putting your money on the line you want to make sure you have all of the facts. Webull provides access to quarterly reports, a wide range of financial statistics, institutional holdings, ETF weighting's, insider activity, industry statistics (and the list literally keeps going on).

Not only is this service challenging online brokerages, it could potentially be a challenger to market data services such as Yahoo Finance, Morningstar, Seeking Alpha, and possibly even the mighty Bloomberg if it continues to expand its scope.

IRAs
Although many people choose to invest in regular brokerage accounts, most savvy investors leverage at least one tax-advantaged account. Webull currently allows you to create traditional IRAs, Roth IRAs, and Rollover IRAs while Robinhood supports none.

Additionally, supporting IRAs allows the organization to attract long-term customers who will make regular contributions to their accounts rather than novice investors looking to make a quick buck.

International Markets
Webull provides access to market data internationally which allows you to invest in ADRs (American Depositary Receipts) with confidence.

Social
One aspect that not a lot of people mention is that Webull has a robust social feature integrated into it’s Investment platform which allows you to comment on your favorite stocks and see the opinions of the community regarding any investment you view. This is huge for investors looking to gauge sentiment before making a purchasing decision.

Referral Program
Not only does Webull pay more per referral, it also has no caps on how much a person can earn whereas Robinhood caps referral commissions out at $500 per year (as of 02/15/2020).

This isn't a huge deal for the average person but it is meaningful when it comes to influencer marketing. Influencers on platforms such as YouTube are incentivized to talk about Webull over Robinhood as they could potentially earn a lot more money.

Currently, both platforms allow both the referrer and the referee to receive free stock when a person signs up using an affiliate link.

By the way here are my affiliate links for anyone looking to join either platform:

Webull: https://act.webull.com/i/yMBqYhTvytGP/s7d/

Robinhood: https://join.robinhood.com/trevorf343

The areas that Robinhood seems to be emphasizing in order to differentiate itself are fractional shares, cryptocurrency, and cash accounts. None of which suggest that it is trying to attract the mature investor. Sure, all of these features are nice to have but, should they come before the basics are nailed down?

As of now, Robinhood has better brand recognition and many of its customers have fond feelings towards it as it was the first major platform to challenge the status quo; however, it needs to keep innovating and evolving if it is going to maintain its position.

5 Reasons Why College Is Worth It

It seems as if the value of having a formal education is being thrown into question more and more often. Sure, there is not only a very literal price to pay but also a huge opportunity cost when it comes to the pursuit of higher education.

But still, there must be some reason why people aren't just continuing this pursuit but pursuing it in larger numbers than ever before.

Reason 1: Money
Having a formal education still has a very real and statistically significant impact on a person's earning potential both in the short-term and especially in the long run.

Reason 2: Knowledge
It's nice to gain hard skills which lead to higher wages but what a lot of people don't mention is that it just feels good to know and to learn more.

Reason 3: Relationships
The University setting is an excellent place to develop and nurture quality relationships. There is a special sort of camaraderie that can be found when people are tackling difficult tasks together in order to grow and become more.

Reason 4: Exploration
People have an innate sense of exploration which can be manifested through education. The same thrill that accompanies making discoveries for humankind can be found through discovering something new for yourself.

Reason 5: Differentiation
Maybe you don't have aspirations of higher earnings potential but, at the very least, you should know that having a formal education adds an extra layer of security to your lifestyle as you enter an entirely different, more exclusive class of labor.

Sure, our education system has it's flaws but that isn't to say that it is unable to adapt to our changing world. That also doesn’t mean it is going anywhere. Formal education will likely always exist in some shape or form as society passes down information and seeks ways to validate/accredit those who have done the work to acquire this information.

Is Microsoft (NASDAQ: MSFT) a BUY?

Microsoft stock closed out 2019 at $157.70 a share, achieving a 55.3% return and far outperforming the market, which ended 2019 at 28.9%.

Is this growth driven by market exuberance?
Maybe slightly- 2019 also saw MSFT's P/E ratio (Price to Earnings) climb from around 23 to nearly 30 by the end of the year. However, the growth was supported by a 10.9% dividend increase, which follows a trend of yearly dividend increases for more than a decade, as well as substantial revenue, EPS, and net income increases.

Why are people paying a premium in the form of higher P/E ratios?
One word: Azure. Only beaten by Amazon Web Services (AWS), Microsoft's Azure is leading an entire industry called cloud computing. An excerpt from their own website:

"Cloud computing is when you access computing services — like servers, storage, networking, software — over the internet (“the cloud”) from a provider like Azure. For example, instead of storing personal documents and photos on your personal computer's hard drive, most people now store them online: that's cloud computing."

To give an idea of how big this market is and how fast it's growing:

-In the first quarter of 2018 Microsoft, Amazon, and Google reported a combined revenue of $8.4 billion from cloud computing alone.

-In the first quarter of 2019 the three tech giants reported $13.3 billion in combined revenue from cloud computing (annualized, that’s a market worth over $50 billion).

-These statistics illustrate a market growth of over 58%.

How will MSFT perform in a downturn?
MSFT has a Beta (5Y Monthly) of 1.23, which means that for every dollar that the market increases or decreases MSFT will generally increase or decrease by $1.23 (this is a very watered-down explanation of beta, I suggest researching this topic more if you don’t understand it).

This statistic prevents the stock from technically being labeled a defensive stock; however, it's suite of business tools are integrated closely into the operations of many large companies and aren’t likely to be subject to discretionary cuts.

Additionally, Microsoft not only maintained it's dividend throughout the Great Recession in 2008, it actually posted a 10% dividend increase.

Overall, it might not be the most defensive stock available but might still be worth considering for an investor worried about recession.

Final Rating: BUY

Stock Overview: KeyCorp (NYSE: KEY)

KeyCorp is a holding company which owns the well-known retail bank KeyBank. As of today, KeyCorp is trading at $17.87. Here are a few things that investors should be paying attention to:

  • KEY has a dividend yield of 3.81% and an impressive dividend growth rate of 21.3% over the past five years and 24.9% over the past three years.
  • It is a relatively aggressive stock with a beta of 1.50 (3Y monthly) as of today. A bet on KEY may be a bet on the overall market.
  • It has a payout ratio of about 39% with Earnings Per Share currently at $1.73.
  • Although KEY continued to raise dividends throughout the 2001 recession, it did cut dividends massively during the 2008 financial crisis.
  • KEY still has not returned to pre-recession prices nor dividend yields.

The company has been in business since 1849. Although it may be seen by some as a relic of the old financial system, KeyCorp has been striving to innovate within it's retail banking services as well as it's commercial banking services with implementations of consumer products such as Zelle (a peer-to-peer payments application) and commercial products such as KeyNavigator (a commercial online banking application).

Rating: BUY