Valuing FAANG and BAT stocks - Global Thematic Research

Valuing FAANG and BAT stocks - Global Thematic Research


Facebook, Amazon, Apple, Netflix, Google and Microsoft (FAANGM)

Apple, Microsoft and Google: These are the safest companies to hold in this group today because they are not particularly overvalued considering their technology leadership in the most important next generation tech cycles, as shown by our thematic screen on page 8.

Facebook and Amazon: These look a little expensive according to our valuation screen on page 9 and therefore may be more volatile over the coming days if investors get nervous over the tech sector. Yet both are likely to continue benefiting from their exposure to a multitude of next generation tech cycles.

Netflix: This is the most exposed within the group because it is a one-trick pony - it is the market leader in a single theme (internet TV). It is less able to benefit from the next generation of tech cycles (such as voice, internet of things, AI, augmented reality, blockchain, mobile payments, ecommerce, big data, robotics, cybersecurity, cloud, etc.) to which all the other FAANG companies are exposed. It will also see an escalation in competition from Amazon and other tech titans in the coming months, though its first mover advantage remains strong enough for us to believe that it is too early to sell a stake in this company.

Baidu, Alibaba and Tencent (BAT)

Baidu: Baidus sagging share price reflects a string of setbacks - from malfunctioning advertising algorithms to the loss of its AI head, Andy Ng. However, our research indicates thematic strength ahead.

Alibaba: Thematically, Alibaba is extremely strong. But our avoid rating reflects the investor risks.

Tencent: The strongest tech platform in China, in our view.

In this report we will look at the two groups FAANGAM and BAT.


- This report is part of our ecosystem of thematic investment research reports, supported by our thematic engine.
- About our Thematic Research Ecosystem
- GlobalData has developed a unique thematic methodology for valuing technology, media and telecom companies based on their relative strength in the big investment themes that are impacting their industry. Whilst most investment research is underpinned by backwards looking company valuation models, GlobalDatas thematic methodology identifies which companies are best placed to succeed in a future filled with multiple disruptive threats. To do this, GlobalData tracks the performance of the top 600 technology, media and telecom stocks against the 50 most important themes driving their earnings, generating 30,000 thematic scores. The algorithms in GlobalDatas thematic engine help to clearly identify the winners and losers within the TMT sector. Our 600 TMT stocks are categorised into 18 sectors. Each sector scorecard has a thematic screen, a risk screen and a valuation screen. Our thematic research ecosystem has a three-tiered reporting structure: single theme, multi-theme and sector scorecard. This report is a Single Theme report, offering in-depth research into a specific theme. It identifies winners and losers based on technology leadership, market position and other factors.

Reasons to buy

- Our thematic investment research product, supported by our thematic engine, is aimed at senior (C-Suite) executives in the corporate world as well as institutional investors.
- Corporations: Helps CEOs in all industries understand the disruptive threats to their competitive landscape
- Investors: Helps fund managers focus their time on the most interesting investment opportunities in global TMT.
- Our unique differentiator, compared to all our rival thematic research houses, is that our thematic engine has a proven track record of predicting winners and losers.
The FAANGM and BAT companies keep rising
Why should investors still hold most of the FAANGM and BAT companies?
What our thematic engine is telling us
You must be big to take advantage of the next generation of tech cycles
Artificial intelligence is the biggest single theme driving tech sector growth
Machine learning requires intensely heavy investment
Screen : Whos who and best ideas
Screen : Thematic screen
Screen : Valuation screen
Screen : Risk screen

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