FAANG+M Stock Dynamics Analysis and Investor Portfolio Formation

FAANG+M Stock Dynamics Analysis and Investor Portfolio Formation

FAANG (Facebook, Amazon, Apple, Netflix, and Google) and MSFT (Microsoft) stocks have gained immense popularity among investors due to several reasons.

  1. Firstly, these companies are giants in the tech industry and have proven their ability to innovate consistently. Their products and services have become a part of everyday life for billions of people around the world.
  2. Secondly, they boast impressive financial performance and strong growth prospects. With their huge user bases, these companies generate significant revenues through advertising, subscriptions, hardware sales, and cloud services. Moreover, they enjoy global reach and diversification across multiple sectors within the tech industry.
  3. Thirdly, FAANG stocks offer stability during economic downturns as people continue to rely on technology for various purposes. Lastly, investors are attracted to the long-term potential of these firms as they continuously invest in research and development to stay ahead of competition.

Overall, their dominance in the tech landscape coupled with consistent growth make FAANG and MSFT stocks highly appealing to investors seeking stable and lucrative investment opportunities.

So, an investor of the 21st century should definitely buy such shares. But there is one BUT. They are very expensive!

The price of just one share of these high-tech companies ranges from $120 to $440. And that means that with a modest budget of 5-10 thousand dollars, we can afford to buy only a dozen shares of these companies. And we will have no money left for other sectors, which means that our investor portfolio will not be balanced and if the whole high-tech sector goes down, the portfolio will also go down.

So how do we solve this problem? Obviously, we don’t need to buy all of these stocks, but choose only 2-3 of them. But which ones? To do this, we should conduct a comparative analysis of the dynamics of these stocks, their volatility and correlation with each other. Below you will find a Python code on how I do it.


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