If you're even a bit serious about analyzing a stock -- whether you're going to use the Capital Asset Pricing Model (CAPM) or whether you're just trying to know the stock's beta to build up some intuition -- you should calculate the beta yourself. Calculating the beta yourself is easy to do and if you either aren't able to do it or are unwilling to do it, you should probably not even be thinking about analyzing stocks at all (instead, you should stick with a far more passive strategy that involves mutual funds and ETFs).
Any serious investor and financial market participants will always calculate the beta on his or her own even if just to do a double-check against a number provided from a third-party source. However, in case you need some reasons to calculate a stock's beta on your own, here are 4 good ones:
1. Calculating a stock's beta yourself will allow all relevant information (until the day of your beta calculation) to be factored in
By calculating the beta yourself, you can literally use all the relevant data available to you through today. A third-party provider will most likely have a time lag. This time lag can be a day our two at best, but it could be almost a quarter at worst. Do you really want to have a beta that is almost 3 months stale? That is a ridiculous proposition when you can easily calculate a beta that will capture every available data point - you can calculate a beta in the evening and capture that afternoon's market volatility in your calculation.
2. You'll get to choose your own time horizon for the beta calculation
The beta you want might vary depending on your investment time horizon. For long term investors who have higher risk tolerances, daily price movements might be irrelevant. For traders or more risk-averse investors, daily price changes might be very important. When you calculate your own beta, you can choose how far back you want to go in terms of obtaining your data (eg. your market and stock prices).
Deciding how far back to go is useful, but clearly more current data is more useful than old data - there's going to be a limit to how far back you'll ant to go. Regardless, choosing how far back you want to go gives you the ability to capture data points in idiosyncratic times that you might care about (eg. an earthquake, a recession, geopolitical conflict, an election, etc.)
3. Calculating a stock's beta yourself will allow you to decide on your own interval
The more important and interesting part of calculating your own beta is the ability to choose the tie interval between data points - you can use daily market and stock prices or you can go longer and choose weekly or even monthly prices. Going longer would likely require a longer time horizon so that enough data points exist to perform soldi statistical analysis, but you're really in control when you calculate your own beta and you can decide what you care about. If you think weekly price changes are more relevant to you than daily gyrations, you can easily use that when calculating your own beta instead of having to rely on the assumptions and desires of a third-party.
4. Calculating a stock's beta will build your intuition regarding stocks, return time series, risk, and finance in general
Finally, you should calculate your own beta because it's easy to do and it will build your intuition of what beta is and what it represents. The more intuition you have, the less likely you are to make foolish investing mistakes - the more intuition you have the less likely you are to be led astray.
Once you understand the what the beta of a stock is and what it represents from a finance and a risk perspective, the next step is calculating it. Reading the theory behind the beta is important, but at some point actually calculating a stock's beta for yourself will prove more useful than reading another paragraph of finance theory. Here, we'll walk through a basic example of how to calculate the beta using MS Excel. We'll use the S&P 500 as a proxy for the market and we'll calculate the beta of Facebook (FB) stock.
Step 1: Obtain Daily Stock and S&P 500 Prices for 1 Year
The first step is to obtain daily prices of both the S&P 500 and the stock we're looking at (Facebook in this case) for a period of 1 year. We'll want at least one year's worth of prices in order to capture a full year's economic cycle, include all of the seasons, holidays, and any unique things that might influence the market over the course of a year in our data set.
We'll want to make sure that the dates align - we want to make sure that for every day we have both an S&P 500 piece and an FB price. Basically, what you want to avoid is a situation where you have the S&P 500 price for a day but don't have the FB price for that same day (or vice versa). This should be easy if you're using stocks from the US as bank holidays will generally coincide.
A final point to note is that you'll want the adjusted closing price for each day (as opposed to the general closing price). The adjusted closing price takes things like stock splits into account. Imagine a stock split occurred for the stock you're analyzing halfway through your yearly timeframe - this would make it seem like there was a huge price drop. To avoid this, adjusted closing prices (which are readily available online alongside normal historical closing prices) take this into account and provide (usually) a post-split price for the entire timeframe.
As to where to obtain the data, that should be easy in today's world - you can go to any of the major finance websites to download historical data or you might use your own brokerage account's platform. You can also use a paid data provider, but that's not a necessary expense for most people.
Step 2: Get the Stock and S&P 500 Price Data Neatly into One Excel File
Next, you'll want to copy the data into the same Excel file so you can work with it. This should be easy. Take care to leave a few columns between the data so that you can do the next calculations we'll go over below.
Step 3: Calculate the Daily Returns for the Stock and the S&P 500
Next, you'll want to calculate daily returns (eg. daily price changes) for the S&P 500 and FB. This can be done in two ways:
simple return: (today - yesterday)/yesterday
log return: ln(today/yesterday)
For the most part, simple returns will suffice. Sometimes log returns are useful because they inherently assist with normalizing the data, but that's both beyond the scope of this discussion and unnecessary for us here.
It's easy to calculate simple daily returns in Excel (see the formula in the image below). Once you have a single cell filled out, you can drag the cell all the way to the bottom to create a time series of daily returns for both the S&P 500 and FB.
Note that for the very final day (here it will be the first day in our time series), you won't be able to calculate a return - you'll get an error message in Excel. This is because you won't have a price for the previous day and you'll effectively be dividing by zero. This is not relevant for our purposes and this can safely be ignored.
Recall that the beta can be calculated by using the following formula:
beta = cov(x,y)/var(x)
where x is the stock and y is the S&P 500 and where var(x) does not equal 0.
So, we must calculate two things:
You can see this in the below images - notice the highlighted formulas and the sections of the Excel sheet they reference.
Finally, you simply divide the two obtained numbers per the above formula - notice this in the image below where we divide the obtained covariance by the obtained variance.
We now have our beta for FB - it's 0.861 as of the end of February 2017 - remember that this can change as the market changes and as Facebook changes. We'll notice that the beta is less than 1 - this means that Facebook stock is less volatile than the market (as represented by the S&P 500).
Step 4: Calculate the Two Subcomponents of the Beta Formula
A Bit of Intuition Building - Let's Graph the Stock Movements
We now have our beta, but let's go even deeper to build some intuition around the number. Below, we have created two portoflios, each consisting of $10,000 - at the outset, we invest the full $10,000 in either the S&P 500 or Facebook. So, at the beginning of our time series (February 26, 2016), we have the following:
We're doing this because we need to somehow compare the prices - if we only look at the movement of one share of the S&P 500 vs one share of FB, we won't get a clear picture because the starting numbers are different. What we care about is not the absolute amounts, but the relative movements of both.
Below you'll see a graph of how the portfolio would have moved throughout the year - this is literally what would have happened had $10,000 been invested as we described above. Here we see some interesting things:
Systematic Risk (aka Undiversifiable Risk)
Systematic risk is a risk that cannot be diversified away - this is why it's often called diversifiable risk. That's a nice definition, but what does it really mean? Let's dig deep to understand this crucial term.
Systematic risk is a vulnerability to things that can occur at a macro or aggregate level - things that affect not only the size of a specific slice of the pie but the overall size of the entire pie. Systematic risks arise because the world is stochastic (random) in nature and as we move forward in time, things can possibly occur that will not only have micro effects (eg. affecting a city, a specific firm, a specific industry, etc.) but will affect the entire economy as a whole (eg. the entire nation or the entire globe). Stated another way, systematic risk is the risk that the overall size of the economic pie will be affected - instead of only affecting the distribution of it.
What can affect the size of the matter? That's easy to answer. Things that can affect the pie are generally events that have macro impacts:
All of these things would invariably affect everything - the overall economy would be affected. Yes, individual firms, businesses, cities, states, and counties would be affected, but they would only be affected because the entire global economy is affected and not because of their own foolishness or bad luck. Therefore, we can say that systematic risk is the kind of risk you can't really hide from - this is why it's called diversifiable risk.
Imagine a person or a firm tries to diversify away risks and protect themselves from all of the possible negative things that could occur. Say they make sure spread their money into different places, get income from different sources, be prudent about capital purchases and how they are financed, and investing in a vast variety of things (things such as precious metals, stocks, bonds, real estate, private equity, etc.). all to these things would clearly protect whoever is doing them - a drop in gold wouldn't affect them, nor would the drying up of a certain source of income, and nor would the bankruptcy of an individual firm. The person or firm engaging in the above actions would be so diversified that they would hardly feel the effects of a small catastrophe. However, they are still totally exposed to the risks we described above - a major war, a comment, an alien invasion, or deep geopolitical troubles would impact them regardless because everything they own would lose value. They would be affected not because of the loss of a single slice, but because the entire pie would now be smaller than before.
Imagine a probability distribution - the x-axis represents wealth and the y-axis represents the number of people who have that amount of wealth. The area under the curve would be the total wealth in the society in effect. You're unsure of where you'll end up - maybe in the middle but you hope to end up on the far right. Systematic risk is the risk that you'll be affected no matter where you are - it's the risk that the entire distribution will get smaller (that the overall area under the curve will be smaller).
Unsystematic Risk (aka Diversifiable Risk)
Unlike systematic risk, unsystematic risk is a specific type of risk that is present only at a micro level. This type of risk can be that:
All of the above risks are obviously severe (and will obviously be unpleasant to those experiencing their realization), but they only affect a small number of firms and a small number of people. A person who owns no gold, hasn't invested in that stock, or didn't buy that bond won't care about any of the above risks - they'll be totally fine no matter any of the above potential scenarios. The risks, therefore, are not systematic in nature but are rightly called unsystematic or specific risks.
These risks are also called diversifiable risks. We stated above that you can't diversify away systematic risks - no matter what you do you're exposed to those large-scale risks that could make the entire pie smaller (that would affect the overall amount of resources instead fo just affecting the place in the distribution). You can, however, diversify away unsystematic risk - you diversify in investing, for example, by:
You will always be exposed to systematic risks, but you don't have to be exposed to unsystematic risk at all - you can simply diversify it away. By not diversifying, however, you are exposed to both systematic and unsystematic risk - not only are you exposed to the macro systematic risks, but you're also exposed to the risk of the particular investment you're holding.
It ain't what you don't know that kills you, it's what you know for sure that just ain't so (Mark Twain)
I love the above quote - it's so elegant and so true. What you don't know won't really be the thing that hurts you in life. We all don't know something; we're all foolish in our own ways. However, when we're aware of our own foolishness and ofebruary-20th-2017.htmlf our own weaknesses, we can prevent bad situations by not going too deep into things without taking precautions. When we're sure of something, however, we often act without taking many (or any) precautions - we go all in with confidence. Hopefully that works out, but sometimes it doesn't - and when it doesn't it can be bad because you've likely not put the proper risk management practices and controls in place. Had you not been so confident, however, you might have still been wrong, BUT you likely would be wrong in a much more subdued way.
Gold and silver might be good ways to diversify your portfolio and guard against risk and uncertainty (strategic portfolio risk management and mitigation), but you are mistaking if you make precious metals a very significant part of your portfolio in most cases.
As ultra-successful investor Warren Buffett has stated and as any good investor knows, gold and silver (and pretty much every such precious metal and commodity) are not productive assets - they just sit there and look pretty. Unlike a successful business, they don't make more money, as illustrated by the below example.
If you had $1000 to invest today and you purchased silver bullion with the entire sum, the value of your initial $1000 investment in 10 years would depend solely on the supply and demand for silver. That's it. If you invested that money into buying a share of a successful and profitable business, however, the value of your $1000 investment in 10 years would depend on a lot more than the supply and demand for a share of that business. It would depend on that of course, but it would also depend on the business's management, the innovation that occurred within the business, how effectively the business model and the business's plans were carried out, etc. Over those 10 years, your silver would just be sitting there but the business would be working hard to sell products or services, grow, and become more efficient. It may turn out that the investment in the business was a bad idea, but that wouldn't be because of the nature of the investment. It might be because of other factors such as a recession, depression, poor management, an act of God, etc. If your investment in the silver bullion turned out to be extremely profitable, however, it would only be because of the speculative nature of investing. You would have guessed correctly that market forces would cause an increase in the price of silver. Over those ten years, you silver bullion would have been sitting there, but the business will have been hard at work creating value and serving its customers.
Investing in precious metals and commodities is not a bad idea by any measure, it is just important that you do not place your hard-earned money into an investment or speculative venture without fully understanding what you are doing. It isn't wise, for example, to have 100% of your portfolio in precious metals.
Below is a quote by Warren Buffet that should more eloquently and more effectively illustrate the above lesson:
And now, given the rise of cyrptocurrencies and crypto assets to quasi-mainstream financial assets, we're dedicated to providing quality, relevant, and interesting material on cryptocurrencies and cryptoassets. Articles on Bitcoin, Ethereum, Ripple, Cardano, and many more cryptocurrencies and cryptoassets can be found on Pennies and Pounds - all that in addition to a plethora of information on what cryptoassets are, how the entire crypto industry came to be, blockchain/immutable ledge technology, mining, proof of work, proof of stake, and how to prudently invest in crypto if you are so inclined (based on your risk tolerance and ability to withstand the volatility that will come with a crypto portfolio).