In this video, I’ll look at whether higher education is worth the cost.
Tuition in the United States is soaring. Look at this chart from the American Enterprise Institute using data from the Bureau of Labor Statistics:
[Chart shown at 0:16.]
Naturally, student debt is soaring as well.
The February 2017 Quarterly Report on Household Debt and Credit from the New York Fed:
[Report cover shown at 1:10.]
Reported: “Outstanding student loan balances increased by $31 billion, and stood at $1.31 trillion as of December 31, 2016.” The $31B jump was in Q4 alone.
It’s a growing percentage of overall debt:
[Chart shown at 1:36.]
And its delinquency rate is rising:
[Chart shown at 2:15.]
Why might this be? Presumably, the cost of college is rising because the value of the education it bestows is worth more, right? Wrong.
The Washington Post reported in December 2013 that between 2003 and 2012, the median income of US college graduates with bachelor’s degrees dropped from almost $52,000 to just above $46,000, both in 2012 dollars.
Four years later, how do things stand now?
According to the Class of 2016 Student Survey Report from the National Association of Colleges and Employers, “just over 46% of 2016 graduates received a job offer before graduation, down from 51% from the Class of 2015.” The median salary offer was $47,358, which is 4.9% less than the inflation-adjusted 2012 figure of $48,388.
High cost, high debt, falling compensation = no good.
Television host Mike Rowe put it aptly:
[Mike Rowe quote shown at 4:07.]
Why don’t the jobs exist anymore?
The short answer is that they’ve been outsourced or automated, and the latter trend is picking up steam. Algos and bots are the workforce of tomorrow.
Knowledge is never a waste, but we need to stop thinking about higher education as job training, and reprice it accordingly.
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In this video, I’ll explore whether China can lead the world economy.
I recently spent time in Shanghai and Guangzhou, observing China in action, taking notes and photos.
I reported to Kelly Letter subscribers in early March 2017, and have condensed that report into this video.
China is routinely presented in media as the world’s next economic leader. Its growth has been impressive:
[China’s historical GDP growth shown at 0:44.]
By comparison, the 2016 nominal GDP of the United States was $18.6T, Japan’s was $4.4T, Germany’s was $3.5T, and the United Kingdom’s was $2.8T.
According to The Economist Intelligence Unit, China’s current path will make it the largest economy in the world by nominal GDP in 2026.
Three problems with the idea that China will supplant the United States as the leading economy of the world:
1. It’s not as developed as its image in media suggests.
2. It does not innovate.
3. Its growth may not continue as expected due to the rise of automated manufacturing.
We’ll take each in turn.
1. China is not as developed as its image in media suggests.
Pictures of China invariably show the skylines of Shanghai, Beijing, and Guangzhou as testament to the country’s rapid growth and modernity:
[Photo of Shanghai skyline shown at 2:20.]
That’s the Pudong skyline east of the Huangpu River, dominated by the Oriental Pearl Radio & TV Tower.
[Photo of Beijing skyline shown at 2:44.]
That’s the 3rd Ring Road into the city center past the oddly shaped and iconic CCTV building.
[Photo of Guangzhou skyline shown at 2:58.]
That’s the Canton Tower along the Pearl River.
All three are beautiful and deservedly admired, but there’s no depth to the image they present. Upon closer inspection, the advancement falls apart.
For example, the Shanghai Maglev Train is the fastest commercial electric-train in the world, with a top speed of 268 mph. I was excited to ride it.
What a letdown: The person selling the ticket at a counter looked tired and unkempt. Seat covers rumpled. Digital clock and speed readout broken.
Within the impressive city centers it’s easy to find outdated methods of construction using woodstick scaffolding.
[Photo of Guanghzhou woodstock scaffolding shown at 4:23.]
I also saw rundown streets with people carrying baskets of slaughtered chickens on bicycles, and urban wastelands of empty dormitories flanking wide concrete lots covered in dust.
[Photos of Guangzhou urban wasteland and Shanghai slums shown at 5:11.]
I looked up at the towers and wondered who in the world works in them, and where they live. Of course there are fine parts of each city, but they’re never far from third world conditions.
These real-life experiences in China are borne out in the data. GDP per capita in the United States was $57,300 in 2016. It was $48,200 in Germany, $46,200 in Canada, $42,500 in the UK, and $38,900 in Japan. In China, it was $14,300.
2. China Does Not Innovate
China’s economy grew through manufacturing. It makes more than any other country.
Apple’s iPhone is assembled by Foxconn and Pegatron, both Taiwanese. Most iPhones from Foxconn are assembled at its Shenzen, China location.
The iPhone is a good example of the problem for China. It’s designed elsewhere. China is just the factory floor.
[Photos of Foxconn shown at 7:50.]
A March 2014 article in Harvard Business Review, “Why China Can’t Innovate,” placed blame on the restrictive environment created by state control, a force weighing down on universities and companies. The Communist Party requires placement of a representative in every company with more than 50 employees. Talk about a status-quo magnet.
This does not explain the rip-off culture, though. Knock-off products everywhere.
[Fake Apple Store images shown at 9:13.]
Even in areas where Chinese innovation is slick, it’s derivative. For example: Jack Ma’s Alibaba Group.
[Photo of Jack Ma shown at 10:51.]
It runs a set of web portals and e-commerce services and retail operations descended directly from their original counterparts in the United States, such as Amazon, eBay, PayPal, and Wal-Mart using technology created in the West, such as the internet, cloud computing, mobile operating systems, and so on.
It has succeeded spectacularly, employing nearly 50,000 people and generating revenue of $21B, but hasn’t broken much new ground along the way.
Except in creative financial reports.
[Alibaba fraud news story shown at 11:47.]
It claimed to have shipped 278 million orders on a single day in November 2014, more than 7.5x greater than the 37 million orders Amazon shipped on that year’s Cyber Monday, and even more than Amazon’s 244 million users.
We could say it’s not clear where the “40 thieves” fit into China’s modern Ali Baba tale.
3. China’s growth may not continue as expected due to the rise of automated manufacturing.
Robots are coming.
[Photo of robot assembly line shown at 13:15.]
Scenes like this one will become common in all industries, not just automotive.
Foxconn plans to replace workers in its factories with “Foxbots.” It set a benchmark of 30% company-wide automation by 2020.
[Photo of Foxbot shown at 13:41.]
Already, one of its factories has replaced 60,000 workers with robots. It’s bringing 10,000 new robots online per year.
Why can’t Apple build its own robot-run factories?
China is behind the times on this front.
As of last summer, it employed just 36 robots per 10,000 manufacturing workers, compared with 164 in the US, 292 in Germany, 314 in Japan, and 478 in South Korea. This measurement is called robotic density, and is one to watch in the years ahead.
Understandable panic. If automation creates a mass exodus of manufacturing, lacking innovation results in no new ideas to fill the gap, how much impact?
According to China’s National Bureau of Statistics, manufacturing provided 20% of urban employment in 2014. While China’s services sector is gradually growing in importance, industry comprised 41% of China’s economy in 2015, according to Statista. A significant slowdown in manufacturing would greatly harm China’s economy.
I do not believe China can lead the world economy.
It will be held back by its largely undeveloped status, its lack of innovation, and the rise of automation.
In this video, I’ll show you how to keep a long-term perspective in the stock market.
I’m a believer in stock market systems, particularly my Signal system. However, no system beats the stock market in every time frame.
To work over time, a stock system needs to beat this:
[100-year Dow chart shown at 0:40.]
This is from Macrotrends.net. It shows that for the vast majority of the time, the goal is full exposure to stocks.
It’s why buy and hold works — but few will do it.
Why? Because sometimes the market does this:
[Chart of 1929 crash shown at 2:20.]
Looking at this, you think it’s best to avoid stocks at times. Wrong. It’s best to get buying big time.
Systems need to be ready for this, which means not reaching full exposure to stocks except after big crashes, but not reaching full exposure means missing out on the long-term rise of the last chart.
Our short-term perspective misleads us to miss out on the long-term profit power of stocks. It’s why bears have missed everything since the crash of 2008.
The long term is, well, long. We’re bad at it.
Momentum plans with great long-term performance have not beaten the S&P 500 in even one year since 2008.
My Sig system has lost to the market in past time frames. It’s done well this century thanks to high volatility, but a long enough rise would set it back.
When you think about it, what you’d really like is a market to go utterly sideways for 29 of the 30 prime investing years of your life, then rocket higher.
Everybody would complain along the way, though, wouldn’t they?
Find a plan that works in most time frames, and stick with it. Let periods of underperformance go. Keep putting more money in, and let the magic work.
The long term will contain many bad short terms, which will seem long at the time, but will disappear on the truly long time line.
Of course, most people will ignore this advice, but it needed to be said anyway.
As with everything human, emotions will rule. Which is another reason you need a system!
In this video, I’ll explore whether stock prices are too high, and therefore likely to crash.
Bearish pundits frequently say the stock market is overvalued. What does this mean?
Usually that prices are higher than they have been in the past when compared with another measurement, such as earnings or sales or even as a percentage of GDP.
[CNBC story and chart shown at 0:48.]
[John Hussman excerpt shown at 1:55.]
What’s the purpose of such observations? To indicate whether a crash is imminent. If it is, we should sell.
Are valuation measurements reliable indicators of this? No. They’re sometimes right, sometimes not, just like everything else in the coin-toss environment of stocks.
Look at the CNBC chart of market cap to GDP.
[CNBC chart shown at 3:34.]
The straight line shows the long-term average of the measure, where it’s almost never at.
Compare it with SPY since 1996:
[SPY chart shown at 4:09.]
1. No overvaluation into the dot-com crash.
2. Rising as stocks fell after the crash.
3. Crashing with stocks in 2008.
4. Rising with stocks since 2009.
So what? There’s no usable pattern in there.
How about Hussman’s ominous-sounding warning? He doesn’t share his valuation measurements, calling them just “the most reliable” ones he identifies, but are they reliable? Not recently.
Here’s an excerpt from this year’s Kelly Letter Note 11 sent to subscribers on March 19, 2017:
[Summary of six false alarms about overvaluation from Hussman since July 2014, shown at 5:55.]
Stock market valuation measurements are not reliable. They cannot be used to time entries and exits.
The market can keep rising above where people think it’s expensive, and fall below where they think it’s cheap.
I pay no attention to valuation, and suggest you ignore it as well. Nobody knows whether stock prices are too high or, frankly, what that notion even means.
In this video, I’ll show you one way to improve your stock market backtesting results.
This method is the one used by my research partner Roger Crandell and me when refining The 9% Signal.
One common problem with backtesting is curve fitting, which is finding a system in retrospect that would have maximized returns in the past market. For example, getting the most out of this two-year TQQQ chart:
[Chart shown in the video, at 1:09.]
The problem is that how the market moved in any period of the past is not guaranteed to repeat. What worked then, probably won’t work now.
We can extract elements from past price behavior, however, to construct a market characteristic.
For instance, this a seven-year scatter plot of TQQQ’s daily percentage price changes:
[Scatter plot shown in the video, at 3:20.]
We can defining this price behavior profile.
Then put it into a restricted randomizer that generates daily changes within this profile to simulate many markets.
This is key: Why is the real market of the past any more valid than a simulated one of the same profile?
We believe it’s not, and put the 9Sig plan through a rigorous test bed of not just a lengthy real-life backtest of 30 years, but 100 simulated 30-year markets.
Listen to this pull quote from this year’s Note 1 from The Kelly Letter:
[Quote shown in the video, at 5:42.]
From this, we zeroed in on the parameters that delivered the best results most of the time.
Could still be wrong, but offers better odds than just the one test bed of actual historical market data.