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I believe Peter Lynch actually coined this term in the first place. ( ten bagger 10x)
>***"Peter Lynch explained investing in the simplest way possible. His core philosophy was built on a simple premise: the downside is strictly limited, but the upside can be extraordinary if you remain patient. He shared that if you invest $1,000 in a stock, the absolute maximum you can lose is that $1,000. But with time and discipline, that same amount could grow into $10,000 or even $50,000.***
>***His message was clear wealth in the stock market comes not from perfect timing, but from patience, consistency, and the undeniable power of long-term compounding.***
>***His core philosophy was simple: before buying a stock, you should be able to explain exactly why you like the product. Lynch believed that if you love using a product, chances are millions of other people will too, which translates to booming sales and profits."***
Lately, I have been thinking a lot more about this brilliant approach, which I believe is more valid today than ever before. /Looking back at the market cycles since 2000, almost all the major price movements (the true 10x to 100x ones) were driven by companies like Amazon, Google, Apple, Nvidia, Netflix, or Tesla just to name a few well known examples whose products create extreme added value.
Right now, AI infrastructure and semiconductor stocks are keeping the markets busy with quite above above above average returns (and that is still a bit of an understatement). Industry insiders certainly had an easier time assessing the impact of AI demand in this sector. But I am sure that most investors do not understand in detail what products are behind every company they hold in their portfolio. They don't have to either / I often feel the exact same way!
But couldn't it be that this is exactly the key? The missing puzzle piece to holding onto true winners for longer?
Alongside the technical analysis of daily candles, risk management, and healthy fundamentals, a genuine understanding of the product is perhaps just as important a factor in being able to better assess product cycles and actually endure the volatility at times.
I would be interested to know: How do you see this?