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 3 solid stock screeners to try 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 solid 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:
An introduction to Beta, a foundational concept in finance that measures exposure to general market risk (systematic risk)
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.
The 5 types of beta
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 too little risk in your portfolio, so you're going to have a very hard time beating the market
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 - excessive trades lead to poor investing outcomes
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.
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.
The Complete History of the Dow: The changing companies that made up the Dow Jones Industrial Average since the prominent stock index's inception
The Dow Jones Industrial Average is one of the longest-running stock market indexes in the world. Its components have changed since inception - they've changed 51 times since the inception of the index by Charles Dow.
Looking at the Dow Jones Industrial Average's (or simply the Dow's) components over time allows us to see how American business (and the world in general) has changed over the last century and a half.
We won't go into all 51 component changes here -- you can find them elsewhere if you'd like -- but we will focus on the most interest and relevant ones and discuss them in a bit more depth than you can find elsewhere on the internet. Instead of giving a cursory overview, we'll dig a bit deeper to see what underlying changes were the root causes of the changes and in the process, we'll gain the following benefits:
The Dow on July 3, 1884 (precursor)
The initial Dow (which wasn't properly the Dow Jones Industrial Average but was instead a creation of Dow called the Dow Transportation Average) consisted of the following:
As the original "Transportation Average" name should indicate, the original Dow components were heavily focused on transportation. We can clearly see that there are a lot of railroad companies represented in the initial Dow mix. In the 1880s, railroads had been around for a few decades, but they still represented the new and happening industry - similar to how technology is today fast growing and focused on thing in business even though computers have been around for a few decades already. Railroads represented Manifest Destiny and a new industrial era where lots of money was being made in the business of moving things from one place to another.
We see that 9 out of the initial 11 firms represented in the 1884 Dow were railroad companies - that's a very large representation and should clearly indicate the importance of transportation generally (and railroads specifically) in the pre-20th Century US economy. As the country moved westward and as more and more goods were in need of rapid transportation in the post-Industrial Revolution era, railroads were able to extract very healthy nominal and real profits.
Basically, a discussion of the early years of the Dow inherently is a discussion of railroads. The first public railways opened up in the US in 1830 using steam engine - by the 1880s, technology had improved as did ridership and a need for transporting goods in a new type of economy where self-reliance was beginning to give way to mass consumption and production.
The equivalent today in terms of industry would be seeing all tech firms dominating the Dow Jones Industrial Average - imagine seeing the Dow today composed of the likes of Google, Facebook, Microsoft, Oracle, Salesforce, Twitter, Apple, HP, Dell, Cisco, etc. An observer would think that the US economy was heavily dominated by tech. Luckily for us, today's economy is far more diverse than the industrial and transportation economy of the late 19th century - we have large industrial firms, firms involved in chemicals, firms involved in telecommunications, firms producing basic products, firms that primarily provide services (eg. consulting firms), etc. Today's economy is as diverse as any has been in human history.
May 26, 1896 (the first proper Dow Jones Industrial Average)
The first proper (non-transportation only) Dow Jones Industrial included the following firms:
This was the first real Dow Jones Industrial Average. Here we see many of the railroad companies replaced - only two of the firms (the Northern American Company and the Tennessee Coal, Iron and Railroad Company) are firms heavily involved in transportation.
We can see that the list has now moved away from transportation and is focused important necessities for late 19th Century America. Things like cotton, oil, tobacco, cattle feed, coal, iron, leather and rubber are all represented - these basic necessities were key to a life that was moving away from self-reliance on farms and into a mass-produce economy that required energy (in the form of gas, oil, and coal), straps, linens and other fabrics, heavy metal, electricity, etc.
If the Dow had existed 500 years prior in the Middle Ages, things like electricity, leather, cotton, coal, and rubber would not be there - most of life would consist of cattle feed and other types of feed.
Another interesting thing to note is that the names of these firms are quite basic - they are literally are names of what the company produces. Can there be any doubt that the Tennessee Coal, Iron, and Railroad Company is involved in the production of coal, iron, and railroads? Would you be surprised to find out that the United States Rubber Company produces rubber? These firms were the first of their kind - they are representations of commerce and big business in an era that had only recently exited the darkness of the Middle Ages via the Renaissance. The unique names we see that not only don't represent the firm's products or services but sometimes are not even traditional words that humans have used are only possible in a world that understands what firms are - world filled with people used to branding, buying things from companies instead of from friends or family, and have a lot of trust in business and capitalism in general. The ability of firms to market and brand themselves in order to educate the public about their products and services allows firms today to eschew the basic naming conventions of the past and to use innovative and obscure names such as Twitter or Exxon. A Twitter or an Exxon would be strange in the early years of the Dow - no one would have any idea what these firms produced. Without the ability to create an image of the firm through the use of advertising (which requires a lot - print ads, TV, radio, the internet, etc.), firms would who used strange names would find themselves at a deep disadvantage in the past. It was a far smarter idea to make sure people knew what your business did just by reading the name.
October 1, 1928 (Dow expanded to 30 firms)
As the index expanded to contain 30 firms (the size it's been ever since), the Dow was comprised of the following firms:
Here, the index was expanded to the 30 firms we have today. This was an interesting time in the history of the United States and especially its economic history. The Roaring Twenties were coming to a close and little did anyone knows that the Great Depression was right around the corner.
Here we can see the that we have a few automotive firms represented - we've got General Motors, American Car, Mack Trucks, and Nash Motors. Car companies have come on the market and are now some of the largest firms in the country. A car firm at this time would be similar to seeing the edition of technology and internet firms in the 2010s and 2020s - the firms came up over a few decades and finally took their place among the largest in the US by playing in a new and important industry.
We see that the names here are still those basic names that hearken back to an era before sophisticated marketing and advertising and before readily available means of communicated such as radio, TV, and mass color print.
July 3, 1956
This is the first Dow changes after the US entered WWII - the previous Dow adjustment occurred on March 4, 1939. Since we last saw the Dow above (1928), the US had plunged into a decade-long economic downturn called the Great Depression, entered WWII (which helped it recover), and saw droves of new babies being born in post-war America (the Baby Boom). Let's see how the Dow has been affected:
Here in 1956, we can say that we are in a totally different America. The last time we checked in was in 1928 - almost 30 years later the Depression-era youths fought a war abroad and came back home to have a ton of babies. Although key staples remain in the Dow, we can see the addition of many new firms.
We can see a big variety of firms represented here - car companies, companies producing basic materials, food-related companies, retailers, energy firms, and even a photography company in the form of Kodak. In 1950s America, technology has advanced far enough to make consumer products (photography, cars, retailing, toiletries, etc.) major parts of the economy. A Procter & Gamble wouldn't exist just 50 years prior - people didn't have the disposable incomes to shower often and use toiletries nor did they have a desire to in their mostly self-reliant forms of living. In 1950s America, a firm producing household necessities would make a lot of sense. In the same light, in 1950s America, big retailers, big tobacco, and big car companies all make sense - our conception of that era is of one that has now moved way past the agrarian roots of the United States and now is in the realm of post-WWII technology and sophistication. If you had told the people living through the Great Depression that a photography firm (Kodak) would be one of the biggest in the country, they would have scoffed and not understood why - photography was a luxury and the technology was not all there yet. The same can be said about many things represented above.
August 9, 1976
Jumping forward another twenty years, let's see where this journey has brought us:
In these 20 years, surprisingly little has changed. Only a 5 firms were replaced since the last time we checked in in 1956. By comparison, there over 30 changes from 1928 to 1956 (some back and forth). What you have in this period is a stable period of growth, some merging of firms, and a movement away from those classic self-descriptive names to the more unusual firm names we know of today.
Look above to see the firms that remained in the Dow but changed their names - International Nickel became Inco, Texas Incorporated became Texaco, Swift became Esemar, and Standard Oil of NJ became Exxon. These movements are away from names that clearly state what the firm produces to more esoteric and strange names that don't provide any indication whatsoever about the firm - clearly a reliance on marketing, adverting, and branding is required in order to educate the public and create a mental picture of the firm when such strange names are used. This is possible because we are no in a world of color television, radio, print magazines, and other forms of advertising.
This trend has continued today where almost all new and interesting firms have absolutely strange names that give zero indication of what the firm actually does - names such as Twitter, Facebook, Yelp, Apple, etc. If you took a person from 1850 and asked him to whet he or she tough a firm named Standard Oil or a firm named American Can do, he or she would very likely guess correctly. If you asked the same person to describe what he or she thought a firm like Exxon does (bear in mind this is Standard Oil with a name change), they would have no clue and rightly so because Exxon is a totally made up term. As stated above, such strange names can only work in a modern world filled with modern telecommunication systems and a general populace that is receptive to advertising and marketing.
March 17, 1997
About two more decades after our last stop, a lot has happened - the Cold War is over and we're at the apex of the 1990s economic boom. Here are the Dow components now:
Here we can see more name changes (eg. Chevron and AT&T), continuing the overall movement away from the simple names to the more strange and esoteric ones that require branding.
We can also see that some of the old components (eg. AT&T, Chevron, Exxon, Union Carbide, General Electric, General Motors, Minnesota Mining, Sears, and Union Carbide) still here - times have changed but these good firms have endured for a variety of reasons. Some endured because of good management (eg. General Electric), some because of early entry and the existence of various barriers to entry (eg. General Motors), and many because of luck.
It's interesting that even though we're in the heart of the proliferation of personal computing and the internet, there are few technology firms. This makes quite a bit of sense - it takes time for these new firms to grow to a size large enough that would put them in the Dow (the top 30 firms in the US). Firms like Apple, Cisco, Microsoft, and others might have been making big moves during this era, but they were still growing. IBM and HP are present because they were around longer - IBM was around for almost a century at this time.
November 1, 1999
About two more decades after our last stop, a lot has happened - the Cold War is over and we're at the apex of the 1990s economic boom. Here are the Dow components now:
With the addition of Intel, Microsoft, and SBC, we can see that in just about 2 years, the Dow has taken on some of the tech firms. These firms have grown in market cap by now (due in part to what would later be called the Tech Bubble) and had market caps large enough to allow placement within the Dow. The Dow here contains many old stalwarts but is filled with new firms that were either started within the last few decades or came to major prominence recently.
March 19, 2015
The last Dow Jones Industrial Average change happened in early 2015 - here is the current makeup of the Dow:
In today's Dow, we see the familiar firms that make up the market share and the mind share of the US economy today. We see an almost complete transition away from the simple self-descriptive business names to esoteric and strange names that require branding - compare this final list with the first list. Firms like National Lead, Tennessee Coal, United States Leather, and others are so clear in their descriptions while firms like Visa, Pfizer, Nike, and Apple would be totally obscure if not for branding and advertising.
Another interesting thing is the rise of big pharmaceutical and healthcare firms - firms such as Merck, Pfizer, and UnitedHealth have come to major prominence due to various large-scale factors. These factors include an aging population (Baby Boomers), a more wealthy economy that can spend more on healthcare, and the success of pharmaceutical research in producing new, innovative, and expensive drugs.
We also see the tech firms playing a bigger role - Cisco, Apple, Microsoft, and Verizon are all part of the overall technology economy, helping to provide hardware, software, and telecommunication services.
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 knowledgeable 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:
Rates of return play a tremendous role in investing performance - without adequate returns, it's difficult to build real wealth
A fundamental principle of investing is that rates of return are key - but most people don't really understand their profound importance. Of course, most savers and investors know that the rate of interest they get on their savings or the rate of return they get on their investments matters a lot, but they are too easily willing to give up valuable return to things such as the below.
The common thieves of investing returns
The common thieves of peoples' investing returns have proven to typically be the following:
It's important to note that not all of the above fees are bad - you're paying these for a reason. For example, you want the mutual fund to hire a good money manager - this person will need to be compensated well. You clearly understand that administrative fees are going to exist for mutual funds and ETFs. Trading fees obviously are required so that the brokerage is paid for the service they provide you - this is a small price to pay for being able to enter and exit positions with ease.
However, you still don't want to overpay. You will not want your mutual fund or ETF to spend excessively on hiring poor-performing managers, spring money on lots of useless advertising, or running thing so inefficiently that the administrative fees are too high relative to similar funds. You'll obviously want to shop around to find a reputable and high-quality broker, but not one that charges excessive fees relative to what's available on the market. You'll also want to be disciplined and not constantly enter or exit positions so you don't accumulate excessive trading fees that will eat away at your capital. Common sense will dictate that even if the fees are reasonable in principle, they could be unreasonable in practice (meaning in amount).
To illustrate this point well, let's use an example. Examples are often an excellent way to illustrate importance finance principles in ways that are easy to understand - a theory is good but seeing numbers and graphs often allows people to really visualize the concepts being presented and gives the motivation to use the new knowledge they gained.
Investing $10k at different rates of a return - a simple example
Let's start with $10,000 in our example and let's invest that money at different rates of return - the return rates will be from 0% to 8% in intervals of 2%. First, we'll break down the possible rates and understand where you might obtain them:
Now, let's see how $10,000 will grow at each of the above rates of return by taking a look at the graph below. From looking at the graph we can see that the 0% return stays constant throughout with all of the non-zero returns separating from it more and more over time. We can also see that each 2% increase does not bring a proportional increase in the final amount - the increase itself increases over time.
The 8% portfolio brings the initial $10,000 to almost $500,000 but the 6% doesn't even reach $200,000. We can say how important even a small increase in return can make over the long term. That 2% difference is sadly something too many investors ignore. It makes sense given the human mind's propensities that a person wouldn't be able to totally and intuitively grasp the importance of even a 0.25% difference in return, but through education, we can see that the small differences end up with very big differences in results.
How can a 2% difference result in a greater than 50% difference in the final portfolio value? This doesn't seem to make too much sense at first glances - the 2% difference is only 1/4 of 8%, so shouldn't it result in a 25% difference? The maths of finance don't work this way - this is an incorrect way of thinking through it. The way it works is that the 2% you forgot on the first year doesn't stop there - that 2% you would have gained is no longer able to be around in the second year to earn additional return. For example, by forgoing 2% on the $10,000 investment, you forgo $200 in your first year, BUT it doesn't end there - in the second year that $200 would have been working for you t earn a return. The same is true in the third year, the fourth year, and so on. In effect, the person who invests at 8% is able to not only bring along that extra amount every year but to also keep that amount invested and earning. In effect, changes in investment returns compound over time. This is the underlying principal as to why small differences in return can have tremendous impacts in final portfolio value.
We aren't going deep into the maths here, but you can reference a 2013 article titled "The Arithmetic of Investment Expenses" by William F. Sharpe. The article is accessible to most readers and the title should give you a hint at the complexity of the maths - it's not very complex to calculate nad understand the impact of fees on final returns.
Next, we'll present another graph - this time with the same $10,000 initial investment but now we'll look at a broader spectrum of return rates (0% to 18%).
As we did above, let's take a look at how each of the additional returns can be achieved:
As you can see from the graph, the initial investment returns we plotted on the first graph are made to look minuscule here. Although most investors shouldn't expect to obtain returns over 14% over the long term, this graph clearly represents how important every percentage point is to the final portfolio value.
Benjamin and Gerald - How final rates of return end up mattering a lot in the long run
Finally, to really bring this home, let's go over one more example - this time let's look at two men. One is Benjamin and one is Gerald. Both Benjamin and Gerald invest $10,000 on the birth of their first child - this could be a college fund or a sort of "start of life" fund so that their progeny is financially stable. Clearly, both Benjamin and Gerald are intelligent, prudent, and caring individuals and parents - most people don't do such things. Another thing that's clear is that their children are quite lucky - they have dad's who care enough to put away some money for them at their birth. Both Benjamin and Gerald have $10,000 ready for this investment - they are quite similar in this and many respects. But, let's now see how they're different?
The strange thing is that Benjamin and Gerald are far more similar than different - in the thing that matter (caring, prudence, planning ahead, etc.), they are clearly quite similar. Their differences, as we'll see shortly, will be quite small and trivial if it wasn't for the outcome those differences would lead to.
Benjamin takes his $10,000 and invests it in a fund over the course of one year in a series of 24 purchases, once every month. He shops around for a good brokerage - the makes sure they're reputable and reliable but keeps an eye on trade pricing too. Benjamin chooses a long-term growth fund but looks at expense ratios, loads, and the quality of management in order to make sure that he's choosing the best fund for his strategy.
Gerald takes his $10,000 and invests it in 60 purchases because he is attempting to time the market. Gerald doesn't shop around for a brokerage and chooses the first one he finds. Gerald doesn't shop around for a fund, but instead takes a recommendation from his friend or family member - this fund has the same strategy as Benjamin's fund but isn't managed as well, has a load, and has a higher expense ratio.
Both Benjamin and Gerald leave the money in their account after the first year and never touch it again - they pass it down to their children who also are wise enough to leave it alone and let it grow.
Take a look at the tables above to see the actual numbers Benjamin and Gerald are dealing with. In effect, Benjamin and Gerald end up with different starting amounts and different return rates (9.75% vs. 7.25%) due to their different choices. These small differences made in the first year have tremendous impacts on the final portfolio values after 50 years. While Benjamin's portfolio is valued at over $1 million in 50 years, Gerald's is valued at only a bit above $300,000 - this is approximately a 70% difference. This 70% was a result of about a $600 difference in initial investment and a 2.5% difference in return. Most people would probably ignore these differences, but they are clearly extremely important.
If you're interested in further reading, below is a paper titled "The Arithmetic of Investment Expenses" by William F. Sharpe - a paper published by the same William Sharpe who created the famous Sharpe Ratio on how fees and expenses can impact the terminal value of a portfolio.
Compound interest and returns is something we all know about, but not something we fully grasp. Sure, most people in the developed world in 2017 know what compound interest is and what that it's a pretty powerful thing, but few know exactly how powerful it is. There's a rumor that Einstein said something to the effect of "compound interest is the greatest force in the universe." If that's true, he was a smart man.
So, how can we approach a better understanding of compound interest? There seem to be two possible approaches here:
It seems that both are important, bu that the examples always are better in getting the ball rolling. Some examples are so astonishing that students of finance and financial theory are in awe both at the power of compound interest nad the previous disrespect for it. Here is one such example for your entertainment and pleasure.
Let's imagine 3 scenarios:
So, we've got 3 scenarios here, with each person separated by two main differences:
So, we know what's different, but what is the same? We have a few things that are the same for the people in all 3 scenarios:
So, we now have what we need - we have 3 babies born on the same day in the same hospital to different families. Let's take a look at how things turn out for them over the course of their lives.
We first notice that the baby in the Ideal scenario starts accumulating wealth - although not very much. In the first year, $1200 is saved. By Year 10, however, almost $20,00 has been accumulated thanks to the 10% growth. Another 10 years goes by and by Year 20 the Ideal baby has accumulated almost $69,000 - a very significant sum especially given the fact that the parents have only saved/invested $24,000 over the course of those 20 years.
Now, let's move to Year 25 - the Optimistic baby is now and adult and is joining the pack here. Unlike the Ideal adult who now has a lot of cash at Year 25 (about $118,000), the Optimistic baby has nothing. However, the Optimistic baby is saving 10x what the Ideal baby is saving - that's $1200 a year vs. $12,000 a year!
So, let's observe these two over time. At Year 30, the Ideal adult has roughly double the amount the Optimistic adult has (that's about $197,000 vs. $93,000). This might seem not astounding unless you realize that the Ideal adult has only saved/invested $36,000 over the course of his or her life while the Optimistic baby has already (over the course of just 5 years) invested $60,000.
Continuing through to Year 50, where the Typical adult finally joins us, we see an interesting situation - the Ideal and Optimistic adults have roughly caught up with each other. Each has about $1.3 million, BUT the astounding part is only revealed when we think about how much each has saved/invested over the course of their lives:
Now, we've got everyone in the game - the Ideal, the Optimistic, and the Typical adults. Let's follow through until 65 - they have 15 more years to save and growth their wealth.
Catching up with all of them at 65, what do we see? We see a few interesting things, but first, let's start with the numbers:
BUT, let's again sit in awe of the power of compounding by taking a look at how much each saved/invested:
So, we see that they're all very wealthy, but they've achieved their wealth in very different ways. While the Ideal person barely saved anything throughout their life, the Typical person saved a ton. Digging deeper, we can see that the Ideal person actually saved less in their entire 65 years than the Typical person saved in one single year. This is truly astounding and provides excellent evidence at the amazing power of compounding.
We can see that time matters a lot. In fact, this example clearly shows that in many cases, time matters more than money. The one who started first finished ahead of everyone and barely had to save anything. The Idel person could have earned $40,000 their entire lives with no raises at all but would still have more money at 65 than the Typical person who had to earn enough from 50 to 65 to be able to put away $120,000 per year (that's very hard considering that savings comes after expenses and after-tax for the most part). The Typical person would probably have to earn at least $400,000 per year to be able to reasonably save that much money - even at that level of income saving $120,000 per year would feel like an incredible sacrifice.
Take a look at the table above for a rundown of all the numbers. You'll see the age of our group on the left along with the savings rates and rates of returns at the top.
Hobby vs. Business - A business is a value-creating entity that receives some of the value it creates in the form of revenue
What is a business? This is a deep question that is rarely asked - possibly never asked. Everyone goes about their lives today talking about businesses, thinking about businesses, and dealing with businesses, but almost know one ever thinks about the definition of a business.
This likely stems from the fact that we seem to have an inherent understanding of what a business is - we guilty learn it growing up and see no need to ever define the term. Even MBAs in the world's greatest business schools - schools like Wharton, HEC Paris, Booth, or the London School of Economics - never seem to really discuss what business means. They (and everyone involved in any sort of human enterprise that attempts to create value) would be well-served by taking some time to dig deep and understand what business really it - having a workable conception beyond the mere imagery we currently use to understand the concept.
A business is an individual or an organization that is engaged in value-creating activities in order to earn remuneration for the value-creation at least equivalent to the costs of creating the value but attempting to charge enough to earn both a nominal and a real profit.
Let's dive deeper into our definition in order to flush out the meaning each of the definition's subcomponents:
You enterprise data is fundamental to your business's success - protect your business data well with these key tips
Data provides intelligence. It doesn't equal intelligence, but through the proper application of analysis, data can be turned into intelligence. Your business has data - whether you use that data now or not to create business intelligence, that data (assuming it is of reasonable quality) is quite valuable.
Data such as:
is crucial to your business. Even if you haven't implemented proper methods for turning that data into business intelligence, you can't afford to let your data go away.
If you're letting the life of your data ride on the functioning of a mechanical hard drive, you are making a big mistake. Big businesses understand the importance of data and invest time and energy in order to preserve it - it's time small and medium sized businesses and managers did the same. It's time that small and medium-sized business owners and managers took a few easy and important steps to add quite a bit of resilience to their business.
Local storage is one possible way to store key business data
The first part of creating a robust data resilience strategy is local storage - you have to have a local backup of your data. For most small businesses, data usually resides on one or a handful of computers. For medium sized businesses, data can reside on multiple computers and mobile devices. Wherever your data resides, you must have a robust local storage system set up to back up your data - although this will take a bit more time and effort for medium sized businesses.
For small businesses, there are two options here:
Either one of the above methods would work and should be looked at in light of:
Now, for medium sized businesses, things can get a bit more tricky. If a medium sized business doesn't thave many computers, they can approach it from the same way we outlined above. If, however, a medium sized business had data stored on multiple computers or mobile devices, a more thought-out strategy will save money and decrease headaches over the long-term. Hiring a consultant to assist with setting up a high-quality backup system might be a good investment here.
Whatever local storage method you choose, it is key to make sure that the physical storage is safe and secure both physically and electronically:
Cloud storage should be a part of most robust and well-developed data resilience strategies
In addition to local backup, cloud backup is key - local storage is exposed to various operational risks such as flooding, fire, theft, misplacement, mechanical failure (for typical spinning hard disks), or electronic failure (for solid state devices). To guard against the risk fo loss of locally stored data, a cloud backup system can be used.
There are many solutions tailored to both small and medium-sized businesses - we won't' go into them here but it's important to focus on a business solution and not on consumer-level solutions here. Additionally, free solutions should likely be avoided - "if you're not the customer, you're the product" is a relevant saying here that should deter you from storing valuable data in a free cloud storage solution where the provider fo the storage has little or no obligation to you or your business.
As with the local storage, you can choose whether you want a continuous backup of the entire system of ad-hoc backups of only relevant files and folders. Again, this depends on the same factors discussed above.
When choosing a provider of cloud storage, a few key things to look at are:
Supplementary Computing Systems
In additional to having your data backed up, you'll want to invest in a supplementary system to both access the data and operate your normal business software (be it MS Excel, MS Word, just accessing the internet, using some sort of CRM software, or using propriety software for your business) - an operational risk even might affect your computers and you'll want to be able to get up and running quickly again.
You'll need to access the level of your and your business's reliance on computing and software in order to determine how much to invest in supplementary computing. A business that jus needs access to the internet and email might be fine with a simple additional laptop or no supplementary system at all. A business that relies on persistent CRM software and propriety software to run the business might need to invest in maintaining a secondary computing system with up to date software and an ability to connect to a network quickly (eg. a wireless chip that can get online without needing a physical connection).
Additionally, supplementary batteries might be useful here depending on the business's reliance on mobile battery operated devices and the risk that a power outage poses.
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).