Bill Miller is another excellent, but not someone as widely known as Benjamin Graham outside of financial circles. Miller spent 35 years at Legg Mason Capital - his last role at the asset management firm was as Chairman and Chief Investment Office (CIO).
During his time there, Miller was able to beat the S&P 500 (in after-fee returns) for 15 consecutive years (from 1991 to 2005). This spectacularly consistent and exceptional performance is considered highly improbable per well-known financially theory that says the market is efficient and that above-market returns will most likely arise to do chance.
If there is a 50-50 chance of beating the market (the S&P 500) on any given year (as the Efficient Market Hypothesis would lead us to believe), the chance of beating the market for 15 consecutive (eg. flipping heads 15 times in a row) is 0.0031%. Miller's approach, therefore, seems to be more than just pure luck and many investors believe that his deep value-oriented approach to picking stocks can consistently produce market-beating returns if applied in a disciplined and knowledgeable way.
Market Cap Less than 3X Free Cash Flow (FCF) for Next 5 Years
The first screen wants us to only allow those firms whose market capitalization is less than three times the total estimated free cash flow (FCF) over the next 5 years. Here we are clearly looking for undervalued firms in terms of earnings, but we're not looking at the typical price to earnings (P/E) ratio that most investors look at - Bill Miller is concerned not with profits but with free cash flow (FCF), an important measure that is much harder to manipulate than is profit by the firm's bookkeeper.
A firm can make a profit but lose cash. A firm can lose money but be raking in cash (this is the case with Amazon). The reason for this has to do with accounting principles and how they have to be applied for publicly-traded firms reporting their quarterly earnings. Without getting into the weeds here, the nature of financial reporting leads to quite unintuitive representations of things - profit on the books might not translate into real cash every quarter and losses might not really be as bad as they might sound if cash if rolling into the firm's bank accounts.
By eschewing profit nad focusing gon cash, Miller moves toward a more realistic and intuitive measure. By looking at those firms that have a market cap less than three times the esteemed free cash flow (FCF) over the next 5 years, we are effectively putting a maximum multiple over free cash flow (FCF) on the firm. This means that we expect the full market cap to be repaid within the next 5 years in free cash (not in profit). This is a powerful criterion that will leave relatively solid value plays in terms of free cash flow (FCF).
Price Earnings to Growth (PEG) Ratio Under 1.5
As with Peter Lynch and Phillip Fisher, Miller also focused on the P/E to Growth (PEG) ratio. However, unlike Lynch whose screen includes a filter to eliminate PEG ratios greater than 1 and Fisher whose screen only seeks to include PEG ratios between 0.1 and 0.5, Miller is more aggressive in terms of accepting a higher PEG ratio of 1.5.
In this screen, although a PEG of 1.5 still is reasonable, the PEG filter can best be understood as eliminating overly expensive items rather than being a hard screen for deep value plays. If that was the case, the PEG ratio would likely be lower - around 1 or less.
Long-Term Debt Ratio Below Industry Average
Finally, if we're looking at value plays in terms of market cap to free cash flow, we want to make sure that the deep value present isn't because the firm is over-levered - we want to make sure the firm isn't burdened by excessive debt as debt can be a killer both to the ability to effectively use the cash the firm generates and because it creates a lot of risks.
By looking at firms with debt ratios below the industry average, we can be sure that we are being conservative in our stock pick. Combined with a reasonable PEG ratio and a low market cap relative to estimate future free cash flow (FCF) over the next 5 years, we can paint a full picture of the firm as a reasonably conservative value play.
Philip Arthur Fisher is going to be on the fringe of investors' knowledge - only those that are truly serious and deep in investing and stock analysis will likely know this man's name in our era. Everyone should know his name, however, as Philip Fisher is one of the greatest investors of all time.
Starting his career after dropping out of Stanford in 1928 to work in a San Francisco bank. Think about how astonishing this is - the likes of Bill Gates, Steve Jobs, and other Silicon Valley wunderkinds would follow suit (likely without even knowing who Fisher was) half a century or more later.
Fisher's seminal work Common Stocks and Uncommon Profits is a foundational piece of investing theory and writing that was published in 1958 but has remained in publication ever since, demonstrating how relevant Fisher still is to this day.
Fisher's investing approach was focused on purchasing growth at incredible discounts. Let's take a look at what to screen for if you want to perform stock screening in a manner aligned with Philip Arthur Fisher's investing principles.
Increase in Year-over-Year (YoY) Sales Over Last 5 Years
Here we can already see that we are not going to be playing games with the typical price to earnings (P/E) ratios and similar metrics most investors focus on too much - Fisher isn't going to play in that field but will instead be looking at metrics that evince a strong and growing business.
Year over year sales growth simply means that the current year's sales are greater than last year's sales - we want to see such growth for the last 5 years. Seeing a dip (or even a plateauing) of sales indicates that the business model is either (1) quite mature, (2) is experiencing cyclical difficulties, or (3) the firm's management isn't doing a good job.
Clearly, 1 and 3 above are not good, but many investors would accept 2 and say that the sales decline is simply due to the business cycle or the general cyclicality that the firm's business is exposed to. By required year-over-year sales growth for 5 years, Fisher implicitly answers the investors by saying that if the business is capable of being affected by this type of cyclicity, it isn't the type of business we want to invest in - we want businesses that thrive in good time and do well in bad times (we want robust businesses that can thrive in almost any economic environment).
Price Earnings to Growth (PEG) Ratio Between 0.1 and 0.5
The P/E to Growth (PEG) ratio is simply the P/E divided by the earnings growth rate - it shows you how much you're paying relative to a firm's earnings growth.
In the piece on The Peter Lynch Stock Screen we looked at a PEG ratio of less than 1 - here we take that even further and require an almost astoundingly low PEG ratio between 0.1 and 0.5. We can see that Fisher's approach is to find not just deeply undervalued companies, but deeply undervalued companies in terms of the growth they are exhibiting. In effect, the key in Fisher's approach is to pay as little as possible for as much steady and reliable growth you can get.
Research and Development (R&D) as a Percent of Sales Greater than Industry
Again we are focusing on things that will demonstrate intense growth or growth potential. Research and development is a good indication of a firm's belief of its ability to innovate - generally speaking, if a firm invests in R&D it believes that the benefits derived from the initial capital outlays (eg. the returns) will be higher than other potential capital uses (eg. the opportunity cost) - if a firm invests in R&D, it generally means that they think they have an ability to innovate.
Additionally, successful R&D generally results in growth. Therefore, a firm that is heavily investing in research and development is more likely to be a firm that is either already growing at a strong pace or will do so down the line. By choosing those firms that have a higher research and development expense compared to sales than others in the industry, you have a greater chance to look at firms that are Horwitz and creating new and innovative products/services.
However, it is important to be aware that only looking at research and development expenses as a percentage of sales is far from sufficient - looking only at R&D can deeply mislead you if that's all you look at. For example, imagine a firm that has sales of $1 and R&D expenses of $10 - this firm would have a tremendous R&D budget compared to sales, but we can clearly see that this firm is doomed because it's sales are too low in absolute terms and its R&D is excessively high in relative terms.
Growth in Sales Greater than Growth in Research and Development (R&D) Expenses
Here we see Fisher again focusing on research and development - this time, however, we're focusing on R&D growth. We want R&D growth to be less than sales growth - this will help prevent the plant scenario ($1 sales vs. $10 R&D) discussed above because a growing R&D budget doesn't by itself mean that much. A growing R&D budget that is accompanied by growing sales, however, does mean a lot - sales growth even greater than R&D growth means even more because it implies that the R&D expenses are producing great returns and that the firm is ultimately becoming more efficient in terms of the percentage of sales required for R&D.
Peter Lynch is one of the greatest investors of all time - any investor (or anyone involved in the financial markets for that matter) likely has heard of Peter Lynch.
Lynch managed the Magellan Fund at Fidelity from 1977 to 1990 during which the funds assets under management grew from about $18 million to about $14 billion dollars - this is an increase of about 777x, meaning that $1000 invested in the Magellan Fund under Lynch's helm in 1977 would yield about $777,000 in 1990 - an absolutely astounding return that skyrocketed Lynch into the top echelon of investors not only in his generation but in the history of investing.
In case the above numbers aren't enough to convince you of Peter Lynch's investing genius, let's compare the Magellan Fund's performance from 1977 to 1990 with the performance of the Dow Jones Industrial Average over the same time period. The Dow Jones Industrial Average managed an increase of about 3x over the same period - $1000 invested in the Dow would yield a comparably paltry $3000 in 1990.
Clearly, any investor should at least be interested in the general methods employed by Peter Lynch. Although Lynch articulates some general principles regarding his investing philosophy in the now classic One Up on Wall Street, we will look at what can be called a Peter Lynch Stock Screen - a stock screen that generally uses his principals to screen the universe of potential stocks for a small number of potentially lucrative stock picks.
Price Earnings (P/E) Ratio Lower than Industry
The common price to earning (P/E) ratio is often used in stock screening and Peter Lynch was no stranger of this classic and often used metric. By screening for firms that have a lower P/E ratio than the industry, an investor can find potentially undervalued equities.
In order to perform this screen, one would first need to accurately identify the industry. It's key that the industry classification is not too broad - this will create a more accurate comparison. For example, a luxury car company such as BMW might be better grouped with other similar luxury firms (eg. Mercedes Benz, VW Group, etc.) instead of as part of the car industry as a whole (eg. Ford, GM, etc.).
Once an industry P/E ratio is identified all stocks that have a P/E ratio at or above it can be screen out. More conservative investors might even choose a slightly lower P/E in order to more aggressively target deep value plays.
Price Earnings to Growth (PEG) Ratio Less than 1
The Price Earnings to Growth (PEG) ratio is an excellent metric and is especially useful for high-growth firms. The ratio compares the P/E ratio to the growth of earnings per share (EPS) - clearly, firms that have earnings per share (EPS) growth might allow for greater accommodation of higher P/E ratios because you are paying for future growth.
A PEG ratio allows investors to take the P/E into full account by also looking at EPS - it's possible that a relatively high P/E will be viewed in a much better light when the PEG ratio is looked at.
A PEG ratio below one is a low PEG ratio - it can be said that "growth is being purchased cheaply" with a low PEG ratio.
Insider Buying to Selling Ratio Greater than 1.5
This is an interesting thing to look at and it gives us a glimpse into Peter Lynch's thinking. Who has more knowledge of the firm, random investors or insiders? Clearly, insider buying implies optimism about the future prospects of the firm - relying on this easy to see metric requires no real analysis or calculation and is simply based on an understanding of the nature of knowledge and human society.
Sequential stock screening involves reducing the universe of stocks to a manageable size. For example, sequential stock screening might involve reducing all US publicly-traded stock to 10 stocks for use in a portfolio by eliminating stocks, one by one, based on filtration criteria (usually with the most important criteria first.
Although there's no absolutely best way to perform sequential stock screening, using criteria articulated by the famous Benjamin Graham is both a good way to approach stock screening and an excellent way to better understand some fundamentals that this man (and his successful investing successors) feel are important.
Benjamin Graham -- Warren Buffet's professor and mentor at Columbia University and author of such foundational books in investing as Security Analysis and The Intelligent Investor -- had a stock-picking approach that fundamentally could be described as looking for deep value. He effectively advocated looking for those stocks that were pretty much sure bets but whose current prices were deeply undervalued. The main components of a Graham-style stock screen would consist of the following criteria when performing a stock screen.
Adequate Size - Avoid Overly Small Firms
Adequate size can mean different things for different investors and there's not a hard and fast rule that Graham articulated that we can apply in today's market, but an investor can do one of two things in order to screen for size:
Sufficiently Strong Financial Conditions
This is another slightly ambiguous criterion and can be interpreted in different ways - it can mean having enough cash or not having too much debt (eg. various leverage-related metrics). Things to look at can be:
Non-stop Dividend Payments for Last 20 Years
This is the heart of Graham-style investing - you're focusing on a company that has done well over a very long period of time in the investing world. This would remove:
No Losses for Last 7 Years
This is similar to the dividend requirement - it shows that the firm has been doing well for a significant period of time. Not having losses for the past 7 years (or however many years you might choose - 5 years, 10 years, etc.) will provide some sort of assurance over the quality, resiliency, and robustness of the businesses the firm is in.
As above, this filter will rule out companies where you can't actually observe profits or losses for the last 7 years or companies that haven't existed for at least 7 years.
Increase in Per-Share Earnings of at Least 33% in Last 10 Years
As with the above, this shows long-term stability of the firm's business model - you want to see that the business is at least keeping up with (or beating) inflation in terms of its profits.
Current Price Less than 1.5x Book Value
This is a deep-value filter - we're looking for first that that are trading at only 1.5x book value. Book value can be thought of as liquidation value - book value is different from the market value in that it literally show the value on the "books" of the firm. This filter tells us that we want to buy firms whose market value is only 1.5x the firm's book value - this usually means the firm is reasonably valued.
To compare the 1.5x market to book value we're looking at here and to better understand it in the context of the overall market, let's look at the market to book value of the S&P 500. As of late 2016, the price to book of the S&P 500 was right around 3x, meaning the overall S&P 500 was trading at a price three times higher than the combined book value of all firms that comprise the S&P 500. Near the Dot Com Bubble the price to book of the S&P was about 5x.
Current Assets Worth at Least 1.5x Current Liabilities
This is a classic leverage filter where you look at whether your current assets are sufficient to provide current liability coverage if necessary. The world "current" in finance (for both assets and liabilities) generally means that one year or less - so current liabilities are liabilities (eg. debts) that can reasonably be expected to need to be repaid within one year.
This filter prevents a very unpleasant situation where an otherwise profitable firm might possibly be forced to liquidate productive assets or be forced into bankruptcy due to an inability to cover short-term debts due to adverse economic conditions or a change in the business cycle.
It's important to note, however, that not all of the above criteria have to be used - Benjamin Graham didn't articulate a particular sequential screening strategy but instead articulated principles that are represented in the above filters.
When people think of stock screening, they usually think of sequential stock screening - that is they think of taking the stock universe and narrowing it down by applying filters one after the other until a final list of stocks that each meet the criteria is obtained.
Sequential Stock Screening is Binary
One key point is that sequential stock screening is binary - this means that at each point on the screen, the stocks in the stock universe (or the remaining stock universe) either continue or don't continue. Unlike simultaneous stock screening -- which screens stocks simultaneously based on a z-score -- sequential stock screening either keeps a stock in or tosses is out at each junction.
This binary nature of sequential stock screening is considered a weakness by some because a stock may be disqualified early on that would otherwise prove to be excellent given the overall screen (given the upcoming filters that will occur on the screen).
The Initial Investment Universe is the Starting Point for Sequential Stock Screening
The starting point of a stock screen is the initial investment universe - this is simply the totality of stocks that will enter the filtering system. Usually, an investment universe is comprised of publicly traded equities on the NYSE and the NASDAQ.
It is possible to include stocks that aren't traded on exchanges, but this isn't recommended for new investors or those new to stock screening as this can add complexity and can hinder liquidity.
Screen Criteria - What to Look at in Sequential Stock Screening
You can literally look at almost anything in a stock screen - there really is not set requirement. Things such as
can all be looked at in stock screens. There are many sequential stock screening strategies out there but they are beyond the scope of the current discussion.
Many Free Sequential Stock Screening Resources Exist Online
Here are a few popular free online stock screeners (that don't require a brokerage account to use):
Other sequential stock screeners exist -- some of which are more powerful than the ones above -- but they are tied to specific brokerage and require you to have an account with them.
Check out the video below for a basic example of how to use a stock screener
This is one of the better YouTube videos available on how to use a basic stock screener. The creator is a reputable individual in the investing community and is known to produce high-quality material.
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:
A precursor to truly understanding the concept of Beta is an understanding of the difference between systematic (unverifiable) and unsystematic (diversifiable) risk. These terms sound complicated, but they really aren't.
Here's a very brief review of the difference between these two types of risk:
Keeping in mind the above definitions, it would be useful to know how much systematic risk you are being exposed to with a given security (eg. a stock) relative to the market as a whole. Stated another way (and hopefully more simply), if you're going to hold a stock, it's useful to know how risky that stock is relative to the market - this knowledge will allow you to understand how the stock will fit into an already diversified portfolio and it will also allow you to use the important Capital Asset Pricing Model (CAPM) down the line. Stated yet another way for the purposes of clarifying a possibly confusing topic, the beta of a stock will allow you to know the market risk of the stock (the risk arising from general market factors) - it is not, however, a measure of the idiosyncratic risk fo the stock.
Boats, Water, and Rain: An Example to Help Clarify the Meaning of Stock's Beta and Why We Care About It
A question that arose for me in studying finance was in this effect:
If we are only looking at systematic (eg. market risk), why would any stock have a different exposure to market risk? If finance theory says that there is a certain risk called diversifiable market risk that you're still exposed to even if you have a diversified portfolio, why would betas be different? Shouldn't all betas be the same?
Stated another way, this question might sound like:
Why does beta tell you about systematic (market) risk and not unsystematic (idiosyncratic) risk - and why does that even really matter?
This question evinced a deep lack of understanding of finance and further study clarified things for me enough so that the question itself seemed foolish. I will attempt to provide context here so that such foolish questions don't arise.
Let's leave finance altogether and travel to a port. In that port, there are wooden boats of all shapes, sizes, and designs on the shore. The port is open and you can take any one of them and go out into the water. The port has constant clouds overhead and there is constant rain. This is a unique port b/c the rain is different in different places - if you're standing on the beach and you walk just 10 feet to another direction, the amount and strength of the rain will change.
Now, imagine you take a boat out into the water. You'll feel the rocking and the swaying of the water. No matter where you are in the port area, you're going to feel the water. If a heavy wind storm comes in, all boats will be affected. If it's calm today, all boats will be calm. The way your boat feels, however, is going to be based on the design of your boat - a large heavy boat will sway less than a small boat and a swiftly designed boat that can cut through the water will react differently than a rugged boat. Before you go onto the water, in anticipation of the chaotic rain in this unusual port, you can easily construct a quasi-roof over your boat to totally protect you from the rain. You can choose to go out with no roof at all and be totally exposed to the rain. You can choose to go out with a poorly made roof and just have limited protection against the rain. You can also choose to go out with a fully-built roof and be totally protected from the rain.
You go out into the water now and some wind comes in. No matter what you do, your boat will be affected by the water moving. This is like systematic risk - all boats are affected by it. This risk is not diversifiable - no matter what you do, if you're in the water (eg. if you're in the market), you are exposed to this general risk of moving waters (eg. general market risk). So, if a systematic risk is moving water, a unysystematic risk is the rain - you can choose to diversify that risk away by simply putting up the roof we discussed earlier. You don't have to be exposed to it and many people on the water probably aren't because they've put up roofs - this risk is idiosyncratic to each boat and is diversifiable.
In choosing a boat, therefore, you might want to think about a few things. For one, you might want to decide if you want a roof up. Another important thing to think about is the shape of the boat. Since you know the water will move and you will always be exposed to that movement, you'll want to know how your boat will move relative tot he movement of the overall water. You'll want to look at each boat and think about whether or not it will move calmly or forcefully whenever the water moves. Forceful movement isn't necessarily bad, but you'll still want to know what kind of a journey you're about to have.
This looking at the boat and seeing how it will react to movements of the water is exactly what the beta is about - knowing a stock's beta will allow you to know how the price of that stock will move relative to moves in the market overall. Clearly, the beta then will not tell you about idiosyncratic risks (just like the shape of the boat will not tell you whether or not you'll be exposed to the rain - that's something for you to decide based on your diversification). It's not up to you to control as stock's beta just like it's not up to you to control how the boat will move - the boats are there laid out for you and built already, you can simply choose a pre-built one.
Going further, we now can see that just because the water moves a certain way (eg. just because the market goes a certain way), it does not at all imply that each stock will move the same way. It is obvious when we think of our port - no one would ever question that boats designed differently would move differently in the water. By the same token, it should be easy to see that firms (which are comprised of different people, processes, assets, liabilities, products, knowledge, etc.) will move differently when the overall market shifts and changes.
Why do firms move differently with the market?
Going a bit further, we can ask what the underlying causes are for different betas (for different movements relative to market risk). We know why the boats move different (because they are designed differently), but understanding why firms move differently is a far more complicated matter.
Firms are different in many ways. Some of these ways include:
These things, and much more, clearly influenced the way a firm's cash flows and stock price (which is dependent on both cash flows and overall market sentiment int the short run) will change based on changes in the market. A firm located in a single US state in Middle America that sells basic goods to people of that state exclusively is clearly exposed to less market risk (eg. less geopolitical risk, less market risk, exchange rate risk, economic downturns, etc) than a multinational firm that produces services that businesses generally purchase in prosperous times but can do without in difficult times. Clearly, one firm's beta would be less than the other if the beta is a measure of a firm's relative exposure to market risk.
Quick Note on the Beta of the Market
The beta of the market (eg. the beta of the S&P 500) is said to be 1. This will make sense further down the line because we will see that the beta is calculated by seeing how a stock's prices more relative to the S&P 500. A beta greater than 1 indicates a stock more volatile than the S&P 500 and vice versa - clearly, then the S&P 500 will have a beta of 1 because it moves with perfect correlation to itself.
Another Quick Note on What Diversification Means
Now that we've gone through the basics, we must define diversification. True diversification when discussing beta and when saying that an investment is diversified involves holding the entire market - meaning all stocks, all bonds, all real estate, etc. (or a portion of a basket of them). Holding just a diverse portfolio of stocks still exposes the holder to idiosyncratic risk - they are exposed to shocks that only or primarily affect the stock market.
Now, it is difficult and cumbersome to use as total market basket - one doesn't really exist because it's hard to value things that don't have regular market prices like stocks. Therefore, most finance texts use a proxy for the market - that proxy is the S&P 500 most of the time. We'll note that this is a weak proxy because it only focuses on 500 major US stocks - ignoring all of the other stocks and asset classes that one could invest in. Keeping that in mind, we can proceed with using the S&P 500 due to the fact that it is commonly used and that it will still produce reliable results and metrics for our use and understanding.
And Finally...How to Actually Calculate the Beta
We've spent a lot of words and sentences discussing what the beta is, but without an actual walkthrough of the calculation, the entire concept is likely to still be obscure to those who have not studied finance before. Let's dive right into the calculation.
Another way to take the beta is the correlation of price movements of a stock to price movements of the S&P 500 (eg. the market). We talked about risk before this, but in order to actually quantify these concepts, we must move from a world of words to a world of numbers. We can do that by talking about prices - risk can be represented by volatility (by price movements). We can look at the price movements of a stock and the price movements of the S&P 500 side by side and see how the move - they might move in tandem, the stock might move more aggressively than the S&P 500 (more volatile - higher beta), or it might move more calmly than the S&P 500 (lower volatility - lower beta).
In order to get the numbers (both the stock you're looking at and the S&P 500), you can simply use the internet to obtain historical prices - it's a relatively simple exercise. You'll want daily prices and you'll want to make sure that the data lines up in terms of day and the exclusion of weekends - you'll want a day-to-day match up. Additionally, if any stock splits occurred over the period you're looking at, you'll want to take the post-split stock price for the entire time period - this will avoid adding a lot of error because if you don't adjust the price it will seem like the price dropped significantly on the day of the split. It's pretty easy to use a post-split number for the entire period (post and pre-split) because most online repositories of historical stock data will do this for you automatically.
Next, you'll have to calculate daily returns - this is simply done with the follwoing formula and should intuitively makes sense:
return = (today's price - yesterday's price)/(yesterday's price)
This is a simple percentage change over a single day and this should be done for both your stock and for the S&P 500. You'll now have a list of daily price changes for both the stock and the S&P 500. The reason we look at price changes is because we want to see how the movements of the stock related to movements of the s&P 500 - in effect, we don't really care about the absolute prices of either the stock or the S&P 500 but are only concerned with their movements over time.
One important thing to note is that you'll want to capture enough time within your analysis - you'll likely want a full year's worth of data.
Finally, you'll compare the price movements of the stock to the S&P 500 using the most commonly used formula for calculating the beta of a stock:
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.
This formula might seem complicated, but it really isn't - it can be easily explained and easily performed din a program such as MS Excel using the functions COVAR() and VAR() over a list of price changes.
Covariance (which is represented by cov in the above formual) is simply a measure of the joint variability of two random variables - it's a measure of the degree two random variables (here the random variables are the price changes) move in tandem with each other. Variance (which is represented by var in the above formula) is simply a measure of how much a random variable (here the random variable in question is S&P 500 price changes) moves about its mean.
Once you have the covariance of the stock and the S&P 500 and the variance of the S&P 500, you simply divide the two numbers per the above formula to obtain the stock's beta - you now have a very powerful piece of information telling you how a stock moves relative to the S&P 500 (which is a proxy for the market). You now know how risk the stock is relative to the market and how much risk the stock would add to a diversified portfolio - you now know the systematic (undiversifiable) risk of the stock.
We touched on this above, but let's formally review the possible betas:
There are No "Bad" Betas
Remember, in no place did we say that any beta measures are bad. A beta of less than 1 is not bad. A beta of greater than 1 is not bad. Beta simply tells you how much the price of a stock varies relative to the market, it does not imply anything beyond that. A beta of less than one might be desirable for a conservative investor while a high beta might be desirable for a more aggressive investor who is looking for more return. A stock's beta tells us a bit about the expected turn of the stock with higher betas indicating higher expected returns - this makes sense because more risk should entail more reward. However, this discussion about translating beta measures into an understanding of a stock's expected return is beyond of the scope of this present discussion.
1. You're Taking too Little Risk
Proper finance goes far beyond the cliche risk vs. reward thinking, but there is some truth in that oversimplistic expression of financial theory - you must expose yourself to at least some risk in order to obtain returns. A portfolio that is without any risk will only earn the risk-free rate. Portfolios that aren't exposed to less risk than other, all else equal, will generally earn less than more risky portfolio.
Obviously, prudence would dictate that the proper amount of risk be taken, proper risk mitigation tactics should be used, and preferably deep value investments will be made in order to create extremely low-risk higher return investments. However, shying away from any risk or taking too little risk will usually lead to subpar returns.
Investors should examine things such as the following in order to better understand their risk tolerance:
This means that a single 30 years old earning $100,000 a year with a healthy emergency fund is likely not exposing himself or herself to enough risk if they have the vast majority of their money in CDs. They would do themselves a big service by prudently moving some money into the stock market so that far higher returns over the long run can be gained. Such a move could move the return of the portfolio from 2% to 8% - a difference that will likely mean millions of extra dollars over the course of a full investing life.
2. You Are Churning Your Portfolio Too Much
Too many investors buy and sell and buy and sell and buy and sell. They spend ridiculous amounts of mental energy, precious time, and precious money on trading fees attempting to:
Churning your portfolio will cause damage for the following reasons:
You're not a hedge fund or a trader. You don't have a supercomputer sitting near Wall St. You don't have PhDs working for you. Play the game where you have an advantage, not the game where you are deeply handicapped. Buying calmly and sitting is usually better than heavy coughing for most investors.
3. You're Trying to Pick Stocks, BUT You're a Terrible Stock Picker
Top investors such as Warren Buffet and Monish Pabrai can pick stocks - they have a true talent for it and they spend their lives doing it. What makes you think you can compete with them? Would you enter an Olympic swimming competition just because you enjoy taking laps at the local YMCA? No - that would be ridiculous. So, why do you think that you are capable of picking stocks when the cards are deeply stacked against you?
Of course, some small time investors are great at picking stocks. They are talented and lucky. If this is you, you don't really need this advice. But if you keep putting in time and energy tiring to pick the next five bagger or ten bagger only to see your portfolio trail indexes such as the Dow Jones or the S&P 500, you must ask yourself why you are wasting so much time. Why not just sit back, buy the index, and relax?
Take a hard look at your portfolio if you're in this camp and be honest with yourself. You can leave a bit of play money to mess around with, but the majority of your money might be better off in excellent mutual funds and ETFs that track both US and global indexes. These mutual funds and ETFs can be matched to your risk tolerance and time horizon and they offer market returns at almost no effort to the investor.
Out of the many questions a new investor can ask, this question is by far a ridiculous one:
How much is the stock?
This question wouldn't be so ridiculous if it wasn't followed by statements to the effect of:
These and similar statements, when combined with the first statement above, evince a fundamental misunderstanding of what it means to buy a share or a stock. A person who can ask such a question with a straight face and who proceeds to enter the equities market despite his or her lack of knowledge is asking for trouble - they'll likely end up losing over time until they get a better grasp of finance and the financial markets.
The reason the person who asks the above question is deluded is because they don't seem to understand that in purchasing a stock, you are purchasing a piece of a company - in purchasing a stock, you literally become the owner (along with many other owners) of a real business. The price of the stock, therefore, should be somewhat related to the business - specifically, it should be related to the cash flows the business produces.
A seasoned and knowledge investor may ask the same question -- he or she might ask how much a stock costs of course -- but the seasoned investor will only do so at the end of a thought process and analysis that will allow him or her to put the price in its appropriate context. The seasoned investor won't just look at the price and arbitrarily decide whether it's cheap or expensive based on how much the stock costs relative to other goods in the market - he or she will think about how much the stock costs relative to the profits of the firm (and/or the momentum of the price if he or she is a technical investor).
The seasoned investor won't care if he or she can only purchase a single share of the stock at it's current price because he or she will understand that the price of the stock is determined by how the firm is divided - if you divide up the firm into more shares each stock (which will now represent a smaller piece of the firm) will be worth less. The seasoned investor instead focuses on the profits (or cash flows) that each stock represents - in a simple sense he or she is focused on how profitable the entire firm is.
A seasoned investor thinks of buying a single share of a stock as equivalent to buying the entire firm - they are one and the same with the only difference being the percentage of the firm that is owned.
Knowing the above, what sort of questions might a seasoned investor ask? He or she might ask something to the effect of:
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 ledger 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).