Are Fang Stocks overvalued?

(I originally wrote this article in September)
The biggest obstacle to financial independence and success in investing is often investor behavior.

It really is amazing how investors don’t learn from the past, and simply implement the academic data at hand. Technology stocks are a case in point. During the 1990s, the NASDAQ performed brilliantly, and rose about 85% during 1999 alone.

As most of us know, the bubble burst, and by 2002, the Nasdaq was down about 80% on its high.  In recent times, the NASDAQ has once again been outperforming the S&P and Dow Jones.

Should investors in the FANG stocks and tech more generally be worried? The S&P price-to-earnings index is now at around 23-24, less than the Nasdaq, which is trading at about 26.

Now let’s look at the P/E ratios of some major tech stocks as of July 26, 2018:

 

Stock – P/E Ratios

 

Apple– 19

 

Facebook– 36

 

Netflix– 236

 

Amazon– 234

It isn’t just some of the famous FANG stocks either. Microsoft, Google, Alphabet, Tencent and Alibaba all have high P/E ratios, no matter whether you use the trailing P/E ratios or any other matrix.

Warren Buffett’s favorite book is the Intelligent Investor. The book really differentiates between being a rational investor and a speculator.

What is interesting is that the vast majority of traders are speculators. Only about 20% of traders beat the market over 5 years, and about 2% manage it over a 50 year period.

Investing isn’t difficult, but that doesn’t mean it is easy to implement the evidence everyday, with incredible self-control. Going to the gym and being healthy is simple, but how many people reading this do that every day, every week and every year for decades?

But that is often the difference between success and failure in every domain. Those sports stars that do implement the evidence every day, like Christiano Ronaldo, benefit. Simplicity usually beats complexity long-term.

The book Outliers, also shows that intelligence and natural ability beyond a certain level, doesn’t really matter compared to hard work and getting 10,000 hours of practice under your belt.

Likewise, in investing, simplicity usually beats complexity. It is a fact that it is very hard to beat the market, but human nature gets in the way.  It is human nature to:

  • Think we are smarter than others (egoism), and we can use research to beat the market. So many doctors assume they can pick healthcare stocks, and teachers may feel they can pick education stocks. Many traders feel they can trade stocks in their home market or another areas where they have expertise. The evidence suggests the more you trade, the more you lose. Women beat men, on average, in investing, as they are less confident and tend to buy and hold.
  • Further, short-term over-performance, increases egos. I myself beat the market over a 5 year period, and this just encouraged me to trade and speculate more.
  • Prefer safety. So many investors have `home country bias` and only trade in their home index.
  • Get greedy when the going is good.
  • Panic when there is a market crash.
  • Listen to market gurus on CNBC and Bloomberg despite the fact that the advisory group CXO looked at forecasts from well known market gurus who appear on the show. They looked at their forecasts and Marc Faber was only right 44% of the time. The most successful was Ken Fisher, who was right 66% of the time. Research from Nobel Laureate William Sharpe in 1975 called “Likely Gains from Market Timing” found that a market timers must be accurate 74% of the time in order to outperform a passive portfolio.

Interestingly, in the time it has taken me to write this article, Facebook’s share has decreased by about 20%. Many investors are asking if this is the right time to buy. However, based on the above, people should be very careful before coming into the market and buying individual stocks.

If investors really want to stock price, they should try trading a small percentage of their portfolio (10%-15%) in the more risky and volatile small cap and emerging markets, where the academic data on the efficient market hypothesis is less clear.

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