Are Tech Firms Overvalued

Question:  The chart below has information on the trailing PE ratio, the forward PE ratio, the PEG ratio and 2014 and 2017 gross income figures for 9 tech firms.

How do the trailing and forward PE ratios differ for these 9 firms?

What is the implied growth rate in earnings from the trailing PE ratios and the PEG ratio?  How do these implied earning growth rates compare to actual earing growth rates?

What is the implied growth rate in earnings based on the forward PE ratio and the PEG ratio? How do these implied earning growth rates compare to actual earing growth rates?

Financial Statistics for Nine Tech Firms
Trailing PE Forward PE PEG Gross Income December 2017 Gross Income December 2014
AAPL 20.35 16.56 1.46 88.2 70.5
AMZN 159.84 79.55 2.51 65.9 26.2
MSFT 52.8 22.91 2.07 72 60.5
FB 27.73 21.49 1.1 35.2 10.3
NFLX 169.65 85.72 2.19 4.03 1.75
GOOG 53.96 26.03 1.75 65.27 40.69
NVDA 40.83 35.11 2.17 5.82 2.6
EA 52.57 20.61 1.68 3.87 3.09
TXN 26.17 18.41 1.41 9.61 7.43

Analysis:

Below is information on the trailing and forward PE ratios for the 9 tech firms.

Comparing Trailing & Forward PE Ratios
Trailing PE Forward PE Diff.
AAPL 20.35 16.56 3.79
AMZN 159.84 79.55 80.29
MSFT 52.8 22.91 29.89
FB 27.73 21.49 6.24
NFLX 169.65 85.72 83.93
GOOG 53.96 26.03 27.93
NVDA 40.83 35.11 5.72
EA 52.57 20.61 31.96
TXN 26.17 18.41 7.76
Average 30.83
Paired t test 0.018

The trailing PE ratio is larger than the forward PE ratio for all 9 firms.

This occurs because analysts are optimistic that forward earnings will exceed past earnings.

The paired t-test indicates we should reject the null hypothesis that the mean difference between the trailing and forward PE ratio is zero.

Note:   The implied earnings growth forecast used in a PEG ratio can be obtained by dividing the PE ratio by the PEG ratio.   The definition of a PEG Ratio is PE/G.   This means PE/PEG or PE/(PE/G) is equal to G.

The actual annual growth rate of earnings between 2014 and 2017 is ((E17/E15)(1/3) -1.

Below is the comparison of implied growth rates from reported trailing PE and PEG ratios to actual earnings growth rates.

Implied vs. Actual Growth Rates
Trailing PE Ratio PEG

Ratio

Implied Growth Rate Actual Average Annual Rate of Growth in Gross Profits 2014 to 2017
AAPL 20.35 1.46 13.9% 7.8%
AMZN 159.84 2.51 63.7% 36.0%
MSFT 52.8 2.07 25.5% 6.0%
FB 27.73 1.1 25.2% 50.6%
NFLX 169.65 2.19 77.5% 32.1%
GOOG 53.96 1.75 30.8% 17.1%
NVDA 40.83 2.17 18.8% 30.8%
EA 52.57 1.68 31.3% 7.8%
TXN 26.17 1.41 18.6% 9.0%

Implied growth rate based on trailing PE ratios.

For 8 of the 9 companies the implied growth rate from trailing PE ratio and reported PEG is larger than the actual growth rate in gross earnings.

Only NVDA had an implied growth rate lower than its actual growth rate.

Below is the comparison of the implied growth rates from reported forward PE ratios and PEG ratios to actual earnings growth rates

Implied vs Actual Growth Rates
Forward PE PEG Implied Growth Rate Actual Average Annual Rate of Growth in Gross Profits 2014 to 2017
AAPL 16.56 1.46 11.3% 7.8%
AMZN 79.55 2.51 31.7% 36.0%
MSFT 22.91 2.07 11.1% 6.0%
FB 21.49 1.1 19.5% 50.6%
NFLX 85.72 2.19 39.1% 32.1%
GOOG 26.03 1.75 14.9% 17.1%
NVDIA 35.11 2.17 16.2% 30.8%
EA 20.61 1.68 12.3% 7.8%
TSN 18.41 1.41 13.1% 9.0%

Implied Growth Rate based on forward PE ratios

For 5 of the 9 firms the implied growth rate based on forward PE ratio is larger than the actual growth rate.

The other 4 firms have higher actual growth rates than implied growth rates

Discussion:

 Are these tech stocks overvalued?

My view is there is a lot of unjustified optimism about these stocks.

Many of these firms had high actual growth rates 2014 to 2017.   This actual growth rate may be unsustainable.

Many of the implied growth rates calculated here are even higher than current unsustainable growth rates.

Perhaps analysts are using the growth rate of net taxes in their PEG estimates but tax cuts result in a one-time shift in earnings growth.  Soon the tax cut will define net earnings in the bae year of the earnings growth calculation.   This should decrease growth rates and cause the PEG ratio to rise.

Fixing Health Savings Accounts and High Deductible Health Plans

 Abstract:   The current rules governing health savings accounts and high deductible health plans favor the rich over the middle class, divert funds from 401(k) plans, and create an incentive for the chronically ill to forego prescription medicines.  These rules create a tradeoff — save for your retirement or take care of your health.   The tradeoff is most severe for middle class people with chronic health conditions.  This memo considers alternative rules for health savings accounts, which would lead to better financial and health outcomes.

Issue:   Health Savings Accounts coupled with high deductible health plans are gaining market share.   Proponents of this type of health insurance stress three advantages – (1) lower premiums, (2) incentives to economize on health care, and (3) a new source of retirement savings.

However, the increased use of health savings accounts has resulted in several problems.

First, the use of health savings accounts has resulted in low and middle income people with relatively low marginal tax rates paying more after taxes for health services.

Second, it appears that many mid and lower middle households have an incentive to contribute funds to a health savings account rather than a 401(k) plan.   All contributions from health savings accounts on qualified health care expenses are tax free.  After age 65, non health related expenses from health savings accounts are no longer subject to penalty and at that point health savings account and 401(k) health plans are fungible.   The creation of health savings accounts is funded in large measure by reduced contributions to 401(k) plans because of this fungibility in funds after age 65 and because many middle-income households can’t save more.  Moreover, most middle income taxpayers realize a modest tax savings from additional contributions to tax-deferred accounts.

Third, the high out-of-pocket expenses under high deductible health plans and the lack of funds in health savings accounts results in many people with chronic diseases choosing to forego needed prescription drugs. Studies have shown that 20% to 30% of prescriptions are never filled and that around 50% of prescriptions for chronic diseases are not taken as prescribed.   The research indicates that a lack of adherence to prescription drug prescriptions contributes to 125,000 deaths, at least 10 percent of hospitalizations, and increased annual health costs ranging from $100 billion to $289 billion.

Intuitively, the growing use of health savings accounts and high deductible health plans has exacerbated this problem.  Prior to the introduction of health saving accounts many insurance plans reimbursed expenses for prescription drugs prior to the deductible being met.  High deductible health plans generally do not provide any reimbursement for prescription drugs or for any service until health expenses exceed the deductible.

The greater use of health savings accounts and high deductible health plans will result in sicker people having lower levels of retirement savings than healthy people.   This occurs because sick people disburse more funds from health savings accounts and health savings accounts crowd out other tax deferred retirement accounts.   Some people may economize by foregoing use of prescription medicines.   This strategy may cause them to leave the workforce due to a chronic disease.

The increased substitutability of health care and retirement savings and expenditures insures that sicker people who cannot extend their careers are most likely to have insufficient retirement savings.

How should the rules governing health savings accounts and high deductible health plans be altered to reduce these unintended problems?

Alternative Rules:

The memo proposes modifications to current rules governing health savings accounts and high deductible health plans, which will maintain and strengthen existing positive incentives and will reduce the tradeoff between saving for retirement and spending to maintain health.

Modification One: Tax payers with family income less than 400 percent of the federal poverty line would be offered a refundable tax credit of $750 for individual plans or $1,500 for family plans to fund their health savings account.   Higher income households could continue to make untaxed contributions to their health savings accounts

Comments on modification one:

This modification directly reduces the economic disparities between high and mid or low-income households stemming from the greater ability of higher income households to place funds in health savings accounts.

The additional cash given to low-income households should encourage more people to adhere to their prescription drug instructions.

The tax credit would only be available to people who have active qualified plans.   The loss of the tax credit from a lapse in insurance coverage serves the same purpose as the recently repealed individual mandate for the state markets.

Modification Two:  Contributions to health savings accounts would be allowed for people with higher coinsurance rate plans even if their plan had a relatively low deductible.

Comments on modification two:

The partial payment for prescription drugs prior to the insured meeting the deductible will reduce the number of people who do not adhere to prescription instructions because they lack funds for their prescription.

High deductibles tend to be a highly effective way to reduce premiums.  In most cases the high-deductible plan will be less expensive than the high coinsurance rate plan.  The choice between a high coinsurance rate plan and a high deductible health plan may depend on who pays the premium.   When employers or government subsidies pay for the premium households are likely to prefer the more expensive plans.  Individuals may be indifferent or prefer the less expensive plan when they are responsible for premium payments.

High coinsurance rate plans can in some circumstances provide a greater incentive to economize on health care plans than high deductible plans.   Consider the example in the box below.

 

Consider a simple example comparing incentives to economize for a high deductible health plan and a high coinsurance rate health plan.

 

The first plan has a $5,000 deductible and no coinsurance for expenses over $5,000.   The insured individual may be reluctant to spend anything on health care unless he believes that total expenses will go over $5,000.   Once expenses exceed $5,000 the person has no reason to economize of covered expenses.

 

The second health plan has a $0 deductible and a 50% coinsurance rate.    The person does not lose his incentive to economize on health care until or unless total health expenses exceed $10,000.

 

Modification Three:  Regulations governing prescription benefit formulas for high-deductible plans should be modified to require partial payment of prescription drug costs prior to the deductible being met.

Comment on modification three:

Patients who receive no prescription drug benefits until a very large deductible is met have a strong incentive to forego prescribed medicines.  This incentive is especially large for diseases like diabetes where the patient does not have immediate symptoms.  However, failure to control blood and failure to treat other chronic conditions can lead to bad health consequences in the long or medium term.

The disadvantage of modification three is that higher prescription benefits will increase premiums.

Cost Considerations:

The tax credit for contributions to the health savings account will result in a loss of tax revenue.  However, a tax credit that induces people to purchase less expensive health care plan could also reduce government subsidies for premiums.  The outcome may depend on whether and how the government subsidies for premiums both in state exchange markets and for employer-based insurance are restructured.

The cost of this proposal may also be offset by proposals designed to strengthen state market places created by the affordable care act.

Concluding Thoughts:

 There is something perverse about the movement toward health savings accounts and high deductible health plans when one considers the totality of the impacts.

The innovation makes health expenditures more expensive for low and mid income people with lower marginal tax rates.

This innovation creates an incentive for people to make contributions to health savings accounts rather than 401(k) plan.

People who are sick are more likely to spend funds in their health savings accounts than people who are healthy decreasing funds available in retirement.

People who have limited funds and high deductibles will have a large incentive to economize on all health expenses especially prescription drugs for diseases like diabetes.

The decision to forego necessary prescriptions and treatments will lead many individuals to get diseases that cause them to leave the workforce.

The current rules governing health savings accounts and high deductible health plans create a tradeoff — save for your retirement or take care of your health.   The tradeoff is most severe for the middle class and people with chronic health conditions.

Some Additional Readings:

Recent research has shown that a high deductible health plan coupled with a health savings account will be the only health plan offered by four of ten employers.

https://healthpayerintelligence.com/news/high-deductible-health-plans-dominate-employer-offerings

 Readers interested in empirical work on health savings account balances can go to the following study:

How Health Savings Accounts are Being Used Over Time:

https://www.ebri.org/pdf/PR.1194.HSAs.11July17.pdf

Readers interested in learning more about failures to adhere to drug prescriptions can go here:

https://www.ncbi.nlm.nih.gov/pubmed/22964778

Some information on how diseases like diabetes affect workforce participation for people nearing retirement age can be found here.

https://financememos.com/2018/08/23/diabetes-and-employment/

Authors Note:  Please consider my book about ways to fix the student debt problem, available exclusively on Kindle and Amazon.

https://www.amazon.com/Innovative-Solutions-College-Debt-Problem-ebook/dp/B07D9VV8K7

 

 

Diabetes and Employment

How Does Diabetes Influence the Impact of Aging on the Probability of Employment?

David Bernstein

Bernstein.book1958@gmail.com202 413 5492

Objective:   The purpose of this study is to evaluate how diabetes and complications from diabetes impact the relationship between age and attachment to the workforce for people nearing retirement age.

Research Design and Methods: The study uses data from the 2015 Medical Expenditures Panel Survey to examine how the relationship between age and attachment to the workforce differs across three health groups – (1) people without diabetes, (2) diabetics with no diabetic complications, and (3) diabetics with complications impacting either eyes or kidneys.   The sample covers 3314 people between the age of 58 and 66.  The dependent variable in the logistical regression models is whether a person was currently employed or attached to an employer during the survey period.   The coefficients of the logistical regression models are used to obtain employment probabilities for a specific worker at ages ranging between the age of 58 and 66.   These probability estimates allow us to examine how the impact of aging on employment probability is affected by diabetes and complications from diabetes

Results: The basic logistical regression model, estimated over the entire sample, reveals that diabetes and complications from diabetes lead to a substantial reduction in the likelihood a person nearing retirement age remains employed.  Separate logistical regression models reveal people without diabetes are better able to remain in the workforce until they become eligible for Social Security at age 62 and to remain in workforce additional years to increase their monthly Social Security benefit.   The tendency to exit the workforce early is especially pronounced for diabetics with complications.  The employment probabilities for a white college educated male at age 58-59 are 83.6 % for the non-diabetic, 76.4 % for the diabetic with no complications, and 32.3 % for the diabetic with complications. Employment probabilities at age 65-66 for the college educated white male are 57.0 % for non-diabetics, 40.1 % for diabetics without complications, and 11.5 % for diabetics with complications.

Conclusions:  Differences in how health conditions affect the impact of age on employment probability have important implications for the adequacy of retirement income and proposals to modify Social Security.  Relatively few people who leave the workforce early delay claiming Social Security to increase retirement income. Any increase in the age of eligibility for Social Security benefits would have harmful financial impacts on diabetics who already have a greater tendency to leave the workforce prior to age 62.  Medical expenditures that cure diabetes or reduce diabetes related complications would allow people to stay in the workforce longer and this expansion in workforce participation would stimulate economic growth.  Perhaps proposals to expand spending for improvements in health care outcomes should be evaluated based on dynamic scoring techniques to account for higher economic growth attributable to expanded workforce participation.

Introduction:

Previous research indicates diabetes has a statistically significant impact on employment or other work related productivity measures (1-6).  Diabetes is not the only disease associated with lower employment levels.   A recent blog post presented preliminary results indicated several diseases (diabetes, complications from diabetes, complications for diabetes, stroke, arthritis, asthma coronary heart disease, emphysema and cancer) reduced the likelihood a person nearing retirement age would remain employed.

Previous research has not examined how diabetes affects the impact of age on employment probability.   The exact age at which a person leaves the workforce has a large impact on household financial security, retirement income workforce participation and the Social Security system.

The financial incentives from Social Security on employment and on the decision of when to claim Social Security benefits are complex.  The maximum Social Security benefit can only be received by workers with an income history of 35 years.   62 is the earliest age where workers can claim Social Security retirement benefits but some workers may be eligible for a disability benefit prior to age 62.  The full Social Security retirement benefit for workers born between the years of 1943 and 1954 is 66 and workers who delay their retirement until 70 will further increase their retirement benefits.

Studies typically find that most people do not delay claiming Social Security after leaving work.  One highly influential study found that around 10 percent of men who retired before their 62nd birthday delayed claiming Social Security (7).

The relatively small percentage of people who are can delay claiming Social Security benefits after leaving the workforce suggests that the ability of people to remain in the workforce may be the most important determinant of financial security during retirement.  Furthermore, incentives designed to persuade people to work longer may be ineffective if a person is in poor health.  The empirical work presented here attempts to provide insight on how diabetes and complications from diabetes impacts attachment to the workforce as people age.

Research Design and Methods:

The study employs data from the 2015 Medical Expenditures Panel Survey (MEPS).  The MEPS survey contains detailed information on a wide variety of health topics including insurance, expenditures, and diseases of respondents. Data from the survey can be used to obtain estimates of national totals and averages.   The data can also be used to estimate relationships between economic variables and health variables.

The survey contains information on respondent employment status, whether the respondent has been diagnosed as having diabetes, whether diabetes has impacted eyes or kidneys, the respondent’s age and respondent’s education level.

The information on employment was obtained from questions EMPST53.  The dependent variable was set to 1 if the person responded she was employed at the time (option 1), had a job to return to during the round (option 2), or had a job at some point during the round.   This employment measure does not correspond to the concept of workforce participation used by labor economists.   People who are unemployed but actively looking for a position are considered workforce participants.

The key health related variables used in this study were obtained from variables DIABDX, DSKIDN53, and DSEYPR53. DIABDX asks whether a person has been diagnosed as a diabetic.  DSKIDN53 provides information about whether diabetes has ever caused kidney problems.  DSEYPR53 provides information on whether diabetes has ever caused eye problems.  The complications from diabetes variable used in this study is defined as having either kidney problems or eye problems caused by diabetes.

The MEPS database had a variable SEX used to create a dummy variable set to 1 if the respondent was male. The dummy variables ba_deg (has a BA degree or higher) and no_college (has not attended college) were created from responses to question EDUYRDG.

The first model considered in this paper involves using the entire sample to estimate the impact of diabetes and complications from diabetes on employment probability.  This approach implicitly assumes that the impact of aging on employment probability does not depend on whether the respondent has or does not have diabetes or diabetic related complications.

The second model considered in this paper involves the estimation of logistical regression models for three samples – (1) people without diabetes, (2) people with diabetes but no complications, and (3) people with diabetic related complications.   This approach allows us to contain separate estimates of the impact of age on employment probabilities for the three groups.

The regression coefficients obtained from logistical regression models can be used to examine the employment probability of an individual with specific characteristics at different age levels.   These probability estimates are generated by the following formula.

P=Exp(XB)/(1+Exp(XB))

In this formula X is the vector of variable values and B is the vector of coefficients.

This formula was used to estimate the employment probability for a specific individual (a white male with a college degree) at the five age groups (58-59, 60-61, 62, 63-64 65-66). These estimates provide insight on when people with and without diabetes with and without complications from diabetes tend to leave the workforce.

In this model, employment probability estimates for females and people with educational background different than college educated would simply be a shifted version of the results for males with a college degree. The model specification used here does not allow for the impact of age on employment to vary with gender or sex.   The parsimonious model was selected due to the limited sample size in the MEPS database.

There is always room for additional sensitivity analysis of the model to alternative specification.  Am happy to look at specific suggestions from reviewers.

Results:

Most of the previous research motivating this paper involved an examination of how a disease impacted employment variables over a sample covering people in an age range.

The results of this approach for a model on how diabetes and diabetes related complications impacted employment based on people between the age of 58 and 66 from the MEPS database is presented below.

 

Logit Model of Disease on Employment, Entire Sample Age 58 to 66
Variable Coef. P>z
age5859 0.511 0.000
age6061 0.228 0.092
age6364 -0.225 0.101
age6566 -0.857 0.000
male 0.413 0.000
ba_deg 0.627 0.000
no_college -0.007 0.949
diabetes -0.505 0.000
complications -1.186 0.000
_cons 0.064 0.668
Number of obs 3314
LR chi2(9) 330.59
Prob > chi2 0
Pseudo R2 0.0722

The coefficient of the full-sample employment logit regression model reveal that both diabetes and complications from diabetes significantly decrease the likelihood that a person is employed.   This is consistent with other literature on the relationship between disease and employment.

The reported coefficients on the age variables are reflective of the difference in employment at specified age and the base group, which is people who are 62 years old.  The age coefficients for the model estimated with the full sample, people with and without diabetes, reveals that increases in age are generally but not always related to a decreased likelihood of being employed.

  • At age 58-59 the employment probability is significantly higher than at age 62.
  • At age 60-61 the difference in employment probabilities is not significant if one employs a one-tailed test with alpha equal to 0.05.
  • At age 63-64 the difference between employment probability at age 62 is not significant with a one-tailed test at alpha equal to 0.05.
  • At age 65-66 the employment probability is significantly lower than at age 62.

The impact of age on the employment probability may differ for people with diabetes and for people without diabetes.  Similarly, the impact of age on employment may differ between diabetics with no complications and diabetics with complications.   This issue can be considered by comparing logistical regression models for the three groups.   The results from the three logistical regressions are presented below

 

Employment Equation for Three Groups
No Diabetics Diabetics No Complications Diabetics with Complications
Variable Coef. P>z Coef. P>z Coef. P>z
age5859 0.585 0.000 0.248 0.458 0.014 0.985
age6061 0.274 0.069 0.118 0.726 -0.359 0.625
age6364 -0.190 0.212 -0.343 0.312 -0.470 0.551
age6566 -0.761 0.000 -1.327 0.000 -1.285 0.131
male 0.367 0.000 0.725 0.000 0.191 0.667
ba_deg 0.690 0.000 0.339 0.400 0.137 0.899
no_college 0.042 0.727 -0.307 0.277 -0.005 0.994
_cons -0.015 0.926 -0.138 0.714 -1.081 0.188
#  obs 2635 535 145
LR chi2(7) 181.58 53.98 4.43
Prob > chi2 0 0 0.729
Pseudo R2 0.05 0.0734 0.0305

 

The results presented here indicate that the impact of aging on employment probability differs sharply for the three groups of people.

The age589 coefficient is a measure of differences between employment probability at age 58-59 and age 62.   It is positive and significantly different from zero for people without diabetes but insignificantly different from zero for both diabetics with no complications and for diabetics with complications.   The lesson here is that diabetics with or without complications tend to leave the workforce early, often before they are eligible for any retirement Social Security benefits.

The age6566 coefficient is a measure of differences between employment probability at age 65-66 and age 62.      The point estimates are negative for all three groups.   The difference is significant for people without diabetes and for diabetics without complications.   The difference is not significant for people with diabetic related complications

Aging is not a statistically significant explainer of the employment probability for diabetics with complications.  None of the coefficients for the age variables are statistically different from zero (at alpha equal to zero) for the sample of 145 individuals with complications related to diabetes.   This occurs partially because the smaller sample size reduces the power of the statistical tests and possible because the employment probability is already lower at an earlier age.

Economists and health professionals could also benefit from information on the magnitude of differences in employment probabilities at different ages for different health profiles.

Separate employment probability estimates are presented for a male with a college degree for the three health condition groups.

Employment Probability Estimates for a

 College Educated Male

Age Not Diabetic Diabetic No Complications Diabetic with Complications
58-59 83.6% 76.4% 32.3%
60-61 78.8% 74.0% 24.8%
62 73.9% 71.6% 32.0%
63-64 70.1% 64.2% 22.8%
65-66 57.0% 40.1% 11.5%
% Change 58-59

 to 62

-11.6% -6.2% -0.9%
% Change 62

to 65-66

-22.9% -44.0% -64.0%

Results for a college-educated male.   Estimates for females and people with different education backgrounds would be a shifted version of this chart.

The estimates reveal that employment probability at age 58-59 is 7 percentage points higher for people without diabetes compared to people with diabetes and no complications.   The employment probability gap between people without diabetes and people with diabetic related complications is over 51 percentage point at age 58-59.

The decrease in employment probability from age 58-59 to age 62 is 11.6% for people without diabetes, 6.2 percent for diabetics with no complications, and -0.9% for diabetics with complications.   The lower decrease in employment probability for the two diabetic categories stems from the fact that many diabetics had already left the workforce at age 58-59.

The estimated employment probabilities at age 65-66 is 17 percentage points higher for people with no diabetes and diabetics with no complications.  The employment probability gap between diabetics with no complications and diabetics with complications at age 65-66 is around 29 percentage points.   Only 11.5% of diabetics with complications are employed at age 65-66.

The decrease in employment probability from age 62 to 65-66 is 22.9% for non-diabetics, 44.0% for diabetics with no complications, and -64.0 percent for diabetics with complications.

The examination of magnitudes in the shift of employment probability variable is especially important to better under the diabetes with complications group.   The age variables are not statistically significant.   However, the age 65-66 employment probability is only 11.5 percent, very low compared to other groups at this age.

Conclusions:

Diabetes substantially reduces the ability of people to stay in the workforce as they age.   The impacts of aging on employment are especially large for diabetics with complications impacting eyes or kidneys.  A substantial number of diabetics especially those with complications leave the workforce before becoming eligible for Social Security Retirement benefits.   Diabetics especially those with complications appear unable to prolong employment to increase their Social Security benefit.

Diabetes is not the only disease impacting the relationship between age and employment.  A recent blog post using the same database employed in this paper found that a 10-factor disease index also affects the impact of aging on employment probability (7).

Many financial economists are fearful that improvements in health which increase life expectancy could worsen the finances of the Social Security system.   The results presented here indicate that improved health could increase workforce participation and spur economic growth.

Bibliography:

 

  1. Tunceli, K, Bradley C. Nerenz D, Williams L., Pladevall, M and Lafata, J The Impact of Diabetes on Employment and Work Productivity
  2. Kahn ME: Health and labor market performance: the case of diabetes.J Labor Econ 16:878–899, 1998
  3. Bastida E, Pagan JA: The impact of diabetes on adult employment and earnings of Mexican Americans: findings from a community based study.Health Econ 11:403–413, 2002
  4. Kraut A, Walld R, Tate R, Mustard C: Impact of diabetes on employment and income in Manitoba, Canada.Diabetes Care 24:64–68, 2001
  5. Mayfield JA, Deb P, Whitecotton L: Work disability and diabetes.Diabetes Care 22:1105–1109, 1999
  6. Ng YC, Jacobs P, Johnson JA: Productivity losses associated with diabetes in the U.S.Diabetes Care 24:257–261, 2001
  7. Bernstein D, The Impact of Disease on Employment for People Nearing Retirement Age, http://financememos.blogspot.com/2018/04/impact-of-disease-on-employment-for.html
  8. Courtney, C., Diamond, P, Gruber, J,. Jousten, A.  Delays in Claiming Social Security Benefits, Journal of Public Economics, 84: 357-385, 2002

 

 

Short Term Stock Trends 8/17/2018

 

Question:   The chart below has information on number of times stock price has increased in the past 10 days for six ETFs.   The chart also has the median number of up days for a 10-day period over the past year for the six funds.

What funds have streaks that are above or below average?

Are there any obvious buy or sell signals?

The Data:

Short Term Stock Trends
Up days over last 10-day period Median Stock Up Days
Fund Sector 8/17/18 2017-2018
VDE Energy 6 5
VDC Consumer Staples 4 5
VFH Financial 6 6
VHT Health Care 6 6
VIS Industrials 5 6
VGT Information Technology 7 6

 

The Discussion:

Current number of stock wins out of 10 days is lower than median by 1 day for consumer staples and industrials.

Current number of stock wind out of 10 days is higher than median by 1 day for energy and technology,

I do not see any obvious buy or sell signals based on the analysis of number of recent up days.

This post relies on a previous Excel post on the use of the frequency function in Excel.

http://www.dailymathproblem.com/2018/08/frequency-function-in-excel.html

 

 

 

Impact of Market Changes and Interest Rates on Four Stocks

Question:   To what extent have the stock prices of four companies – Duke Power, Bank of America, IBM and Proctor and Gamble – moved with the overall stock market?   Do lagged movements in the overall stock market also impact the current period stock price?

How have changes in the 10-year government bond interest rate impacted the stock price for the four companies?

The Data:   The analysis presented here is based on monthly data starting with the fourth month of 1980 and ending with the seventh month of 2018.   N=456.

The Model:   The dependent variable is stock price at period t divided by the stock price at time t-1.   The explanatory variables are S&P 500 at period t divided by S&P at time t-1, three lags of this S&P ratio, and the ratio of the current 10-year government bond interest rate to the lagged 10-year government bond interest rate.

Results:   The results for the return equations for the four companies are presented below.

Duke Power
dukror Coef. P>t
spror 0.33556 0
lag1spror 0.0365767 0.546
lag2spror 0.058777 0.335
lag3spror -0.0211995 0.725
tenyrch -0.1645325 0.001
_cons 0.7624648 0
Bank of America
bacror Coef. P>t
spror 1.392815 0
lag1spror 0.0396445 0.68
lag2spror 0.1671838 0.085
lag3spror -0.0386275 0.686
tenyrch 0.1329733 0.079
_cons -0.6889676 0
IBM 
ibmror Coef. P>t
spror 0.9212106 0
lag1spror -0.051747 0.444
lag2spror -0.0875875 0.199
lag3spror -0.0776229 0.248
tenyrch 0.0725273 0.172
_cons 0.2299272 0.089
Proctor & Gamble
pgror Coef. P>t
spror 0.5961397 0
lag1spror -0.0131834 0.844
lag2spror 0.0276608 0.682
lag3spror -0.0264788 0.692
tenyrch -0.0543051 0.173
_cons 0.4844197 0

 

Observations on Beta or Systematic Risk

The short-term beta is the coefficient of the current mark variable.

The long-term beta is the sum of the coefficients for the current market variable and the three lagged market variables.

Below is a chart comparing betas (both short and long term) from this model to betas published by yahoo finance.

Different Measures of Beta
Yahoo Finance Beta One Period Beta from Model Long Term Beta from Model
Duke 0.03 0.336 0.410
BAC 1.67 1.393 1.561
IBM 1.04 0.921 0.704
PG 0.43 0.596 0.584

 

The order of betas from this model and from Yahoo finance across the four companies least to highest are identical.

Both estimates reveal that Duke Power has the lowest beta and BAC the highest one.

Even though Duke Power still has the lowest beta, the estimates presented here indicate that Duke Power has far larger systematic risk than the Yahoo finance beta estimates.

In two cases, (Duke and BAC) the long-term beta is smaller than the short-term beta. The long-term beta for IBM is lower than the short-term beta.   The long and short term betas for PG are similar.

Need to do more research on reasons difference in companies that might cause this result.

Observations on the Interest Rate Coefficient

The 10-year interest rate is negatively related to stock price for only one company Duke Power.   This coefficient is significantly different from zero for a two-tailed test with alpha equal to 0.05.

The interest rate coefficients for the other three companies are positive but not significantly different from zero at a two-tailed test with alpha equal to 0.05.

Concluding thoughts:   The impact of interest rates on firm returns can be very complicated.  First, the direct impact on the S&P 500 should be modeled.  Changes in short rates could have a much different impact on rates than changes in long rates but this impact could be difficult to separate because of collinearity.

More empirical work on how interest rates impact stock prices will follow.

 

 

Flatting Yield Curves, Mortgage Rates and Choice of Mortgage

Flatting Yield Curves, Mortgage Rates and Choice of Mortgage

Question:  Short and intermediate term government bond rates have risen substantially more than long-term bond rates in recent months.   This pattern of rates is called a flattening yield curve.

To what extent has the government bond yield curve flattened between January 4, 2018 and July 26, 2018?

Has the yield curve for the mortgage market also flattened between January 4, 2018 and July 26, 2018?

Calculate mortgage payments for a 15-year and 30-year Fixed Rate mortgages on these two dates.  Calculate equity after five years for the 15-year and 30-year mortgage rates on the two dates.

Most market analysts at the beginning of the year were advising clients to take out a 15-year mortgage rather than a 30-year mortgage if the client could obtain the necessary down payment.

Have changes in market conditions warranted a change in this advice?

Descriptive Statistics on Government Bondn and Mortgage Market Interest Rates

Below are calculations on government bond yields and mortgage market rates for the two dates.

Government Bond Yields
4-Jan-18 26-Jul-18
2-year U.S. Bond Rate 1.931 2.686
10-year U.S. Bond 2.463 2.975
30-year U.S. Bond 2.786 3.101
10-year minus 2-year 0.532 0.289
30-year minus 10-year 0.323 0.126
Mortgage Market Yields
4-Jan-18 26-Jul-18
5/1 Year ARM 3.45 3.87
15-year FRM 3.38 4.02
30-year FRM 3.95 4.54
15-Year mins 5/1 ARM -0.07 0.15
30-year minus 15-year 0.57 0.52

Observations:

The gap between 10-year and 2-year bond rates and the gap between 30-year and 10-year bond rate narrowed considerably between 1/4/2018 and 7/26/2018.

The spread between the 15-year FRM and the 5/! ARM went for negative to positive from January to the end of July.   This spread remained low.

The spread between the 30-year FRM and the 15-year FRM barely changed.

Below are calculations on mortgage payments and mortgage balance reduction after 5 years for the two mortgages on the two origination dates.

Mortgage Payments:

Mortgage 4-Jan-18 26-Jul-18
pmt 15-year loan $2,127.01 $2,222.07
pmt 30-year loan $1,423.61 $1,527.19
15-year pmt minus 30-year pmt $703.40 $694.88

 

Below are calculations on mortgage balance after 5 years of payments for the 145-year and 30-year FRM on the two origination dates.

Mortgage balance after 5 years of payments

Mortgage 4-Jan-18 26-Jul-18
15-year FRM $216,325.31 $219,268.50
30-year FRM $271,122.83 $273,639.10
Diff. ($54,797.52) ($54,370.60)

Observations:

The gap between monthly payments on 15-year versus 30-year FRM went down between January and late July 2018 despite the slight flattening in mortgage rate yields.  (This occurred because the higher rates had a larger impact on 30-year payments than 15-year payments.)

The mortgage balance reduction obtained by taking the 15-year FRM over the 30-year FRM remains around $53 k.

Conclusion.  The government bond yield curve has flattened quite a bit. The mortgage market yield curve has not changed much.   The 15-year FRM remains the preferred mortgage option for those who can afford higher payments.

 

Interested Readers can go here for some articles on mortgage math and choice.

http://www.dailymathproblem.com/p/top-real-estate-posts.html

Impact of Gender on Annuity Payments

Impact of Gender on Annuity Payments

 Introduction Females have longer life expectancy than males in virtually all countries.   Gender related differences in life expectancy make it more likely that females will out-live their retirement resources than will males. Females, because of their longer life expectancy, might choose to purchase a longer-term annuity.

Question:   A 75-year old person has $100,000 to spend on an annuity., which makes monthly payments for a fixed period.  She or he wants to reduce the probability of outliving the annuity to below 10 percent.

What annuity term would accomplish this goal for a male and for a female?

How does the longer life expectancy of the female affect the size of the monthly annuity payment?

Data Source: This analysis is based on the United States Life Tables, 2008 published on September 24, 2012 by the National Center for Health Statistics of the Centers for Disease Control and Prevention

http://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_03.pdf

Table Two of the report has life statistics for males and Table Three of the report has life statistics for females.   Both tables can be downloaded directly into an EXCEL Spreadsheet.The data in the Table below is from the CDC life tables. 

 

Age Total number of females alive at age x Proportion of 75-year-old females who survive to age X Total number of Males Alive at age X Proportion of 75-year Males surviving until age X
75 73,974 61,980
76 71,973 97.3% 59,531 96.0%
77 69,831 94.4% 56,962 91.9%
78 67,539 91.3% 54,268 87.6%
79 65,080 88.0% 51,437 83.0%
80 62,448 84.4% 48,469 78.2%
81 59,647 80.6% 45,390 73.2%
82 56,688 76.6% 42,227 68.1%
83 53,563 72.4% 38,994 62.9%
84 50,253 67.9% 35,694 57.6%
85 46,782 63.2% 32,360 52.2%
86 43,166 58.4% 28,996 46.8%
87 39,414 53.3% 25,650 41.4%
88 35,567 48.1% 22,372 36.1%
89 31,677 42.8% 19,213 31.0%
90 27,805 37.6% 16,223 26.2%
91 24,017 32.5% 13,448 21.7%
92 20,380 27.6% 10,928 17.6%
93 16,962 22.9% 8,691 14.0%
94 13,821 18.7% 6,754 10.9%
95 11,006 14.9% 5,122 8.3%
96 8,549 11.6% 3,783 6.1%
97 6,467 8.7% 2,719 4.4%
98 4,754 6.4% 1,898 3.1%
99 3,392 4.6% 1,286 2.1%
100 2,345 3.2% 844 1.4%

Analysis:

 Calculation the Annuity Term:

Based on this cohort of 100,000 females, 73,974 females have survived to page 75.   Around 90% of these females are still alive until somewhere between age 95 and 96.

In a cohort of 100,000 men 61,980 are still alive at age 75 and the 90% survival mark for these 75-year olds is reached somewhere between age 94 and 95.

Let’s interpolate to get an exact number of months for our annuity formula.

For females we get 96-75 (21) years plus (11.6-10)/(11.6-8.7) x 12 or (7) months.  The 75-year old female must buy an annuity of 259 months  to reduce the probability that she will outlive the annuity to 10 percent.

For males we get 94-75 (19) years plus (10.9-10)/(10.9-8.3) x 12 or 5 months (I am rounding up to fulfill the contract.)   The 75-year old male must buy an annuity of 233 months to reduce the probability that he will outlive the annuity to 10 percent.

Calculating the Impact on Annity Payments:

So now we calculate the annuity payments for the female and the male with the PMT function.   The only input that differs is the duration of the contract – 259 months for females and 233 months for males.

The annuity payment calculations were obtained from the PMT function in Excel.

 

Female Male
Rate 0% 0 0
Rate 3% 0.03 0.03
Rate 6% 0.06 0.06
NPER 259 233
PV $100,000 $100,000
Type 1 1 DIFFERENCE % DIFFERENCE WITH FEMALE AS BASE
PMT rate=0 $386.10 $429.18 ($43.08) -11.2%
PMT rate =3% $524.96 $566.77 ($41.81) -8.0%
PMT rate=6% $689.45 $727.62 ($38.17) -5.5%

 

Conclusions:   Females must buy a longer-term annuity to obtain the same reduction in longevity risk as a male.   This reduces their monthly annuity payment.

This annuity calculator on the web confirms that females receive lower annuity payments and or pay higher prices for a comparable annuity.  I am not familiar with the specific formulas used by this calculator or the product that it pertains to.   My sole interest here is to provide some insight on how gender determines longevity risk.

http://www.annuityfyi.com/immediate-annuities/

 

 

 

 

 

 

Survivor Bias and Stock Market Risk

 

Survivor Bias and Stock Market Risk

Issue:

Shortly after the large stock price decline of Facebook on July 26,2018, CNBC posted an article with a chart of other large market-cap declines of U.S. stocks.   Every single company in the chart still exists as an ongoing company.   It appears as the chart was compiled from a database that only contain companies that are still listed.

What no longer actively traded companies might have made a list of the largest declines in equity value for large-cap companies?

Why does the exclusion of these companies from the chart create a misleading picture of the risk of investing in equity?

The chart lists three companies with large cap declines in 2018?  Why is the occurrence of so many recent large declines in big-cap equity a concern?

The article:

https://www.cnbc.com/2018/07/26/facebook-on-pace-for-biggest-one-day-loss-in-value-for-any-company-sin.html

The Chart:

Market Cap Losses in Big Cap Stock

Company

Date Decrease In Equity ($B)

Facebook

Jul 26 2018 $114.50

Intel

sep 22 2000

-90.7

Microsoft

Apr 3 2000

-80

Apple Jan 24 2016

-59.6

Exxon Mobile

Oct 15 2008

-52.6

General Electric

Apr 11 2008

-46.9

Alphabet

Feb 2 2018

-41

Bank of America

Oct 7 2008

-38.4

Amazon

Apr 2,2018

-36.5

Wells Fargo

Feb 5 2018

-28.9

Citigroup

Jul 23 2002

-25.9

JP Morgan Chase Sep 29 2008

-24.9

 

Discussion

What no longer actively traded companies might have made a list of the largest declines in equity value for large-cap companies?

Companies no longer actively traded, which experienced large one-day drops in equity value include   — Enron, Worldcom, the old General Motors, Lehman Brothers, Bear Stearns, and Merill Lynch.   There are probably many more.

Why does the exclusion of these companies from the chart create a misleading picture of the risk of investing in equity?

 First, this list suggests large drops in equity values occur less frequently than a chart composed from all firms that ever existed.

Second, a list of large declines composed of stocks that survived understates the potential loss of wealth from buying and holding stocks after a large decline.   In fact, all the stocks on the CNBC list have recovered nicely.

Third, an anaysis of risk, which includes bankrupt firms would encourage investors to seek greater diversification in terms of number of equity holdings, sector, and asset classes.

The chart lists three companies with large cap declines in 2018?  Why is the occurrence of so many recent large declines in big-cap equity a concern?

Facebook, Alphabet, and Amazon all had their large declines in 2018.   These are three of the four FANG stocks.   The smallest FANG company also has had a recent large percentage decline.

The most popular trendy sector of the stock market is now realizing extremely large one-day changes.   This appears to be happening with greater frequency.

The disproportionate number of 2018 large-cap declines in this chart convinces me that people who have made a lot of money in FANG and in other high-tech stocks need to take some money off the table when the stock prices reach new highs.

Of course, in the case of Facebook  this advise was more valuable prior to July 26.

The volatility of FANG names and other Tech stocks does not mean this is a good time to buy value stocks because the pending  increase in interest rates might hurt value stocks more than growth stocks

I would be alarmed by the large number of 2018 events even if the chart contained firms that were no longer actively traded.

The Democratic Split

Democrats are optimistic that the Russian and corruptions scandals will propel the party to victory in 2018.  Perhaps they are correct but I fear that they are wrong for two reasons.

First, President Trump continues to give weekly rallies to his enthusiastic base.  There are no figures on the Democratic giving large robust rallies.

Second, aside from Russia and corruption (admittedly two big asides) the entire debate today centers around Trump priorities – basically immigration, trade and low taxes.

The Democrats do much better when the conversation involves health care, student debt, pensions and Social Security.   The common theme linking these issues is that despite the currently strong economy many Americans are financially insecure and are having trouble saving for the future or for education.

Why aren’t Democrats out there rallying their base the way Trump is?

Why aren’t Democrats talking more about financial security — health care, student debt and retirement – issues of concern to working-class households?

The basic answer is that there is a huge split in the Democratic party both on economic priorities and appropriate policies.  This split is paralyzing the party and impeding the delivery of a viable progressive agenda.

 The liberal wing of the Democratic Party offers transformative radical change.  The centrists believe that the liberal wing’s policy proposals are unrealistic and could make many people worse off.

The centrists offer relatively modest policy changes and appear to only reluctantly embrace left-wing proposals when they need votes.   The current message offered by the centrists will not rouse the base.

The liberals correctly believe the current centrist agenda would at best maintain the status quo and at worse could implement compromises with Republicans that will weaken the existing safety net.

This conflict can be illustrated by a discussion of three issues – health care, student debt and retirement savings.   These issues allow for the Democrats to create a theme for 2020.   The theme is how do we make Americans more financial secure and what party do you trust to do this?

This is a good place for me to state my bias.   I am a centrist not a liberal.  However, many of the current policy proposals offered by centrists are insufficient.

Here is an outline of some policy ideas, which could bridge the gap between centrists and liberals.

Policy Priorities

Discussion of Health Care:

It is hard to understand why Democrats aren’t spending more time talking about health care.   The number of uninsured Americans without health insurance rose by 3.2 million people during President Trump’s first year in office.   This issue and other problems with health insurance markets received more coverage in CNBC than by MSNBC.

The liberals want single payer health care.   The fact that single-pay works well in some countries, which have only known single payer, does not mean that the United States could transition from the current system to a single payer system.

The adoption of a single-payer system would make some people more financially secure but would make many people worse off than the current situation.   Many health care providers and doctors would have substantially lower compensations.   This would be a disaster for new doctors with substantial medical school debt.  The single-payer would be more generous than some private plans but might deny or provide insufficient reimbursement for expensive procedures.

Many centrists have endorsed and voted for a Medicare for all plan, even though their more detailed thoughts are more in line with ACA modification proposals.   They argue their vote is a symbolic one in support of universal coverage.   I would argue this vote is a pander.

The centrists want to repair the ACA but their discussions do not fully acknowledge that problems with the ACA existed even before the Trump Administration weakened the program. The ACA was a big step forward but also a disappointment compared to where we want to be.

Despite the enactment of the ACA, insurance premiums and out-of-pocket expenses have continued to rise.  There is some evidence that state exchange insurance has narrower networks and entail higher costs than some employer-based insurance.  Moreover, people who take ACA coverage are required to take employer-based insurance if they obtain a job with an employer that offers coverage.  Rules and tax incentives favoring employer-based insurance over state exchange insurance weaken state market places.

There is no doubt that Trump Administration polices – ending the individual mandate, reducing funds for ACA enrollment, potential new rules allowing alternatives to ACA policies and a freeze on ACA reinsurance payments – have weakened state exchanges and are responsible for the increase in the number of uninsured.

However, centrists need to do more than offer a patch to the ACA.   They need to offer a vision on how we can move to a system that provides more coverage, lower out-of-pocket expenses, and access to all procedures and drugs.

Authors Note: I am currently working on a health care proposal.   It will be published by the end of 2018.

Discussion of student debt:  It is hard to believe that the Democrats aren’t talking a lot more about student debt, especially given recent Trump Administration proposals.   Recent actions or positions taken by the Trump Administration on student debt include:

  1. The proposed elimination of the public service loan program,
  2. The proposed elimination of subsidized student loans,
  3. The end of compensation for student borrowers who are victims of fraud,
  4. The reduced enforcement of rules allowing enrollment in Income Based Replacement loan programs

Why aren’t Democrats talking more about these student debt issues?

The liberal wing wants free college for people attending public universities.   This does work in some countries.  However, free public college is very expensive and the transition would adversely impact private institutions.

The centrists need to come up with innovative pragmatic solutions to the college debt problem and incorporate these proposals in their standard speech.

I have published a working paper, which lists 12 pragmatic ways to reduce the growth of student borrowers with excessive student debt and mitigate debt burdens for overextended borrowers.

One of my proposals involves providing additional financial assistance and reduce student loans to first-year students who do not have an extensive credit history or proven academic record.

A second proposal reduces interest payments after 15 years.   This approach is likely to be more effective than current loan forgiveness programs, which are designed to discharge debt.

My student debt proposals are available in my working paper on Amazon.

https://www.amazon.com/Innovative-Solutions-College-Debt-Problem/dp/1982999446

Discussion of retirement security issues:   Retirement security entails Medicare, Social Security and 401(k) plans.

The discussion on this issue should start with a recognition that under current law there will be automatic cuts to Social Security and Medicare benefits unless Congress acts.   Democrats need to ask whether Republicans can be trusted to act given their performance on ACA repeal or replace.

In the 1980s, President Reagan and Democrats in Congress came together to fix Social Security.   The Democrats need to ask whether the current Republican party can be trusted to compromise on this issue.

The other aspect of the retirement security problem is that many workers have insufficient retirement savings. Today few companies provide workers with traditional pension plans.   Many 401(k) plans have high fees and most lack an annuity option during retirement.   Many workers are unable or unwilling to maximize their contribution to 401(k) plans and often the money in these plans is disbursed prior to retirement.

The Democrats then need to provide detailed proposals on how to improve retirement security.   These proposals should recognize two important goals – (1) Social Security needs to be placed on a sound finance system and (2) the entire retirement savings system needs to be improved.

The Democrats need to advance policies that achieve both these goals and must reiterate opposition to Social Security benefit cuts that worsen retirement security.   The proposed solution is likely to include either additional revenue or use of some general tax revenue to maintain current Social Security income.

The Best Dialogue for Democrats

The current political dialogue is being set almost entirely by Trump and the Republicans.   Their focus is on trade and tariffs, immigration, and standing for the national anthem.

My thesis in this essay is that Democrats need to put financial security — health care, student debt, and retirement income at the center of the national discussion.

Centrists must move forward with a pragmatic progressive approach to these issues.  Many key centrists are spending more of their time either pandering to liberals or bashing liberals for being too radical than proposing polices that solve problems.

Some Democrats, both progressives and centrists, are more focused on tax issues and/or inequality.  The centrists are concerned that proposals by liberals will explode the deficit.  They should be more focused on revenue losses from the recently enacted Trump tax cut.

Elizabeth Warren recently pointed out the America was more prosperous when marginal tax rates were over 50 percent.  This defense of high tax rates is a losing argument both substantively and politically.  The decrease in prosperity or the increase in inequality was more likely caused by loss of jobs from factories moving overseas and from technological changes which reduced wages of low-skilled workers.

Tax policy is complicated.   A case can be made for changes in the tax code to obtain more revenue.   However, economists have found that high marginal tax rates change distort behavior.   Most notably, high marginal tax rates can decrease work, especially in two-worker households.

An economic agenda stressing reforms that will improve household financial security will resonate more with voters than an agenda focused on taxes and income inequality.

We live in a world where every one of Trump’s utterances and farts dominates the news cycle. Democrats seem totally incapable of prioritizing the farts.   In my view, Trump’s treasonous approach to Russia and separation of children from parents at the border are more important than Stormy Daniels

Democrats must respond to the moral dilemma created by the Trump Presidency.   However, at the end of the day a person struggling with student debt, worried about health care coverage for her family, and worried about whether she will be able to retire with decent income is more concerned about financial security than the scandal of the day.

Democrats need to get out there and reset the nation’s agenda.  To modify Bill Clinton said it was the economy stupid.   My view is that the economy must be broadly defined to include financial security.

Holdings of Ten Emerging Market Funds

Holdings of Ten Emerging Market Funds

This post started with a list of emerging market funds found at the site below.

http://etfdb.com/etfdb-category/emerging-markets-equities/

I examine and describe the holding and recent returns on the 10 largest of these funds.

I comment on the holdings of these funds, the risk of these funds and the risk of some of the holdings.

List of Funds, Discussion of Geographic Diversifications, and YTD returns:

Some Information on Ten Emerging Market Funds
Symbol Fund Geographic Concentration YTD Returns
VWO Vangarud FTSE Emerging Market 7 of top ten holdings are in China -7.32%
IEMG I Shares Core MSCI Emerging Market 7 of top ten holdings are in China -6.95%
EEM I Shares MSCI Fund 7 of top ten holdings are in China -7.44%
SCHE Schwab Emerging Markets Fund 6 of top ten holdings are in China -7.34%
FNDE Schwab Fundamental Large  Firm Emerging Market Index Largest holding is from Korea and second and third largest holding are Russian.  Top 10 holdings also include companies from China, Brazil and Taiwan -6.71%
DEM Wisdom Tree Emerging Markets Equity Income Fund Top two holdings are Russian.   Most other top ten holdings are form China or Taiwan. -4.54%
RSX Van  Eck Vectors Russia ETF All holdings appear to be in Russia. 4.36%
GEM Goldman Sachs ActiveBeta Emerging Markets Equity ETF 7 of top ten holdings are in China -6.75%
SPEM SPDR Portfolio Emerging Markets ETF 6 of top 10 holdings are in China -6.54%
DGS Wisdom Tree Emerging Markets Small Cap ETF Top 10 holdings are less than 9 percent of all holdings.   Highly diversified, smaller companies. -7.34%

 

Some Observations:

Many of the funds have a very large share of their funds in China.   In fact, some of the emerging market funds could accurately be called China plays.

Three of the funds have substantial issues in Russia.  Also, most but not all, of the Russian holdings are in the oil and gas sector.  Important to research holding If concerns about corruptions and sanctions would deter you from investing in Russia.   Energy funds that are not in Russia may be preferable to Russian energy plays.

Nine of the Ten funds have negative YTD returns.  The fund that exclusively invests in Russia has a +4.36% return. This fund performed really poorly in prior years.  The YTD return on U.S. large cap stocks is around 6.0%, largely because the market has been up for the past week or so.

Can investments in emerging market ETFs provide insurance against a major downturn in the U.S. market?

 Short answer is probably not:   The downturn in emerging markets in the 2007 to 2009 crisis was larger than the downturn in large-cap U.S. stocks.

Go here for discussion of emerging market funds during the financial crisis

http://financememos.com/2018/07/23/emerging-markets-during-the-financial-crisis/

Thoughts on specific holding of emerging market funds:

Emerging market funds have some well know reputable holding and some firms with dubious reputations, and some firms with little name recognition.

The most successful holdings of the funds in this sector include: (1) Alibaba, (2) Tencent and (3) Baidu Inc.

Investors who want a taste of advanced emerging markets or China may be better off directly investing a small fraction of their wealth in these companies.