Hello, and welcome back to my blog. After spending quite some time learning, practicing, and gradually building my knowledge of finance, I have started to appreciate the ideas behind the numbers much more than I did before. So instead of keeping what I have learned in my notes and spreadsheets, I want to start writing some of it down here.
And for the first stop on this part of my financial journey, I want to begin with one small but interesting number: Beta.
I still remember the first time I came across Beta in a financial analysis class. My professor showed us how to find the number on Yahoo Finance, just like in the picture below, and explained why it was worth paying attention to. At that time, though, I didn’t really understand what was hidden behind that single number. I found it, wrote it down, used it where I needed it—and that was pretty much it.
Learning something once, however, doesn’t necessarily mean we will understand or remember it for long. I was no exception. Fortunately, pursuing my second bachelor’s degree gave me more time to encounter Beta again and again through different financial analyses. The more often I worked with it, the more I began to understand what the number actually represented—and, more importantly, why I shouldn’t simply accept it without asking where it came from.
That is what brings me to one of my previous analyses, where I decided to stop looking up Beta for a moment and try calculating it myself.
That led me to my first question: Could I calculate TSMC’s Beta myself?
To find out, I went back to the data. I collected monthly return data from 2020 to 2025, giving me six years of observations for TSMC and the market. I then used Excel to estimate Beta by examining the relationship between TSMC’s monthly returns and the corresponding market returns.
But before looking at the result, it is worth understanding what Beta actually means.
In simple terms, Beta measures how sensitive a stock tends to be to movements in the overall market. A Beta of 1 suggests that the stock has historically moved roughly in line with the market. A Beta above 1 indicates greater sensitivity to market movements, while a Beta below 1 indicates lower sensitivity.
What interested me even more was realizing that Beta does not simply exist somewhere waiting to be found. It is estimated from historical data. In my case, I compared each company’s monthly stock returns with the corresponding returns of the market. If a stock tended to react strongly when the market moved, its estimated Beta would generally be higher; if its movements were less sensitive to the market, its Beta would generally be lower.
One way to visualize this relationship is through a regression: market returns are placed on one axis and the stock’s returns on the other. The slope of the fitted line gives us the estimated Beta. In other words, Beta is not just a number on a financial website—it is the result of a relationship we estimate from data.
With that idea in mind, I calculated Beta not only for TSMC, but also for four other Taiwanese semiconductor companies that I had previously analyzed. Here is what I found:
| Company | Beta (calculated) | Beta from Yahoofinance (5Y monthly) |
| TSMC | 0.8479 | 1.26 |
| UMC | 0.7433 | 1.19 |
| MediaTek | 0.9499 | 1.63 |
| Nanya Technology | 1.1804 | 1.69 |
| ASE Technology Holding | 0.9551 | 1.4 |
Looking at the table, another question caught my attention. All five companies operate within Taiwan’s semiconductor industry, yet their estimated Betas are noticeably different. UMC had the lowest Beta in my sample at around 0.74, while Nanya Technology had the highest at approximately 1.18. TSMC sat in between at around 0.85, while MediaTek and ASE Technology Holding were both close to 0.95.
At first, it might seem reasonable to expect companies within the same industry to behave similarly. But “semiconductor” is a much broader label than it appears. These companies do not necessarily make money in the same way or occupy the same position in the industry’s value chain. TSMC and UMC operate primarily as foundries, MediaTek focuses on IC design, ASE Technology Holding operates mainly in semiconductor packaging and testing, while Nanya Technology is concentrated in the memory business.
This made me rethink what I was actually comparing. Perhaps belonging to the same industry does not automatically mean having the same exposure to market movements. Each part of the semiconductor value chain faces its own demand conditions, competitive environment, capital requirements, customers, and industry cycles. Those differences may contribute to how investors respond to changes in the broader market—and therefore to the historical relationship that Beta captures.
My results cannot prove that a particular business model caused a company’s Beta to be higher or lower. Beta is estimated from stock and market returns, not directly from a company’s business model. Still, the differences gave me a useful reason to look beyond the industry label. Two companies can both be called “semiconductor companies” while their businesses—and the way their stocks behave—remain quite different.
Continuously, the results also raised another question for me. My calculated Beta for TSMC was approximately 0.85, while the figure I had recorded from Yahoo Finance was 1.26. The differences were even larger for some of the other companies in the table.
My first reaction was simple: Why are they different?
At first, seeing two different numbers for the same company made me question whether I had calculated something incorrectly. But as I looked deeper into how Beta is estimated, I realized that there isn’t necessarily one single Beta that will remain the same everywhere. The result can change depending on several choices behind the calculation, including the time period, frequency of returns, market benchmark, and estimation methodology.
This changed the question I was asking. Instead of simply wondering, “Which Beta is correct?”, I started asking something more useful:
“How was this Beta calculated, and is it appropriate for the analysis I am trying to make?”
At this point, I found myself thinking back to something my professor once taught me: we can look at the numbers, study the charts, and apply different analytical methods, but what matters most is how intelligently we use them.
Financial analysis is rarely about finding one perfect number and treating it as the final answer. The same company can be examined from different angles, using different methods and assumptions, and each one may reveal a different part of the story. Beta is a good example. Instead of accepting a number simply because it appears on a well-known financial website, I began to realize that I should first understand how that number was produced and what it can—and cannot—tell me.
For me, this was an important shift in thinking. The goal was no longer simply to find Beta. It was to understand the number well enough to decide when it is useful, how it should be interpreted, and where its limitations begin.
This led me to one more question: If a company has a lower Beta, does that automatically make it a better investment?
Not necessarily. A lower Beta simply tells us that, based on the data and methodology used, the stock has historically been less sensitive to movements in the overall market. It does not tell us whether the company is financially healthy, whether its stock is fairly priced, or whether it will generate attractive returns in the future.
Looking back at my own results makes this distinction even clearer. Most of the Betas I calculated were lower than the figures I had recorded from Yahoo Finance. But that does not suddenly make those companies safer or better investments. It simply shows how much the measurement itself can depend on the assumptions behind it.
That is why I now see Beta as one piece of information rather than a final investment signal. It deserves attention, but it also needs context. A lower or higher Beta can tell us something useful about how a stock has historically responded to the market, but it cannot tell the entire story of a company—or make an investment decision for us.
Looking back, I find it interesting that Beta was once just a number I searched for, copied into my analysis, and moved on from. I didn’t question where it came from because, at that time, I didn’t know what questions I should be asking.
Calculating it myself years later changed that perspective. The difference between 0.85 and 1.26 didn’t teach me that one number was right and the other was wrong. Instead, it taught me to look beyond the result—to ask about the data, the time period, the methodology, and the assumptions that created it.
And perhaps that is the lesson I want to keep from this small experiment. Financial analysis is not only about knowing how to calculate a number. It is also about knowing how to question it.
So now, whenever I come across a financial number, I try not to stop at:
“What is the number?”
I want to go one step further and ask:
“Where did this number come from, and what is it actually telling me?”
Beta happened to be the first number that made me think this way. And I think it is a good place to begin this part of my financial journey.
A Note from Jai
When I first learned about Beta, I treated it as just another number I needed to find. Open Yahoo Finance, get the number, put it into my analysis, and move on. Simple.
But the more time I spent learning finance, the more I realized that the interesting part often begins after we find the number.
Why did I get 0.85 while another source showed 1.26? Why can companies in the same semiconductor industry have different Betas? And if a company has a lower Beta, does that really make it a better investment?
I don’t think learning finance means having an immediate answer to every question. For me, it has increasingly become about learning how to ask better questions.
Maybe that is what Analyzing will be about on Jai Is Here. I want this space to be somewhere I can take the numbers I come across, break them apart, test what I have learned, and sometimes discover that the answer is more complicated than I expected.
I’m still learning, and there will always be more numbers, more companies, and more questions to explore. But one lesson from Beta is staying with me:
Don’t just find the number. Question the number.
— Jai
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