The uneven benefits arising from political certainty: A bad political environment will hurt your portfolio, but a healthy and beneficial political situation is only a prerequisite for investing success
A lot of people in finance and the investing world love to talk about politics and the geopolitical environment. They say how important it is for there to be a stable geopolitical environment for economies to perform well. This is true, but it leaves out too many vital details - a more useful understanding can easily be obtained.
Geopolitical certainty is less a beneficial item for investing and economic success - it's more of a prerequisite for a stable economy and an attractive environment for business. Geopolitical certainty is more of a necessary condition for good things to happen, NOT a sufficient condition. This means you need political certainty and a healthy geopolitical environment for business and the economy to be strong, BUT having a great political climate won't really help your portfolio.
The reason for this uneven distribution of benefits is because stocks aren't valued based on the geopolitical landscape - they are valued based on anticipated future cash flows, discounted at appropriate discount rates. Politics can influence this a bit, but far more so in the negative direction than in the positive one.
So, don't overdo it on how much you focus on politics, the global political landscape, and how this might impact your portfolio. A better approach is to monitor for signs of a terrible geopolitical situation on the horizon while prudently picking excellent investments to put your money in.
The evolving nature of homeownership in the modern world: A more-complex and expensive existence and the proliferation of boomerang children
The American Dream: Owning your own home
Buying a house is a critical step in most adults' lives in the western capitalist world. Still, we've seen a lot of changes related to homeownership and when homeownership happens over the last century. The way things are now from a home-buying perspective is not how they've always been. In fact, the way things are today is very different than they've ever been in human history.
Family life and real estate have changed after the Industrial Revolution
Before the Industrial Revolution, the world was very different than it is no. People lived in small communities, traveled very little, and interacted with less than a few hundred people over the course of their entire lives. A man leaving his home go out on his own would rarely have seemed like a prudent decision - how would he and his family survive without kin? A woman leaving her home alone to go out and make something of herself in the world? This was a non-starter in a non-capitalist and agricultural world when kinship and ancient communal ties kept you safe, fed, and busy.
In the 21st century, real estate traditions and family life evolved further: The rise of the boomerang child
Over the last century, as the western world (especially the US) modernized, it became typical for young men and women to leave their parents' home when they turned 18 - this usually coincided with the start of a college education. Not everyone had the opportunity or the desire to leave home in this way, but many did, and this phenomenon increased over time.
In the early 21st century, further shifts and refinements to this novel paradigm occurred. A combination of factors, including the following
In today's world, students leave at 18 for college, but typically either return home or need some extra support from their families even after they graduate. The idea of being able to leave home at 18 and have an economy that is able to provide enough income for you to do that in a separate housing unit is a bit wild - only economies that are growing substantially can support that sort of lifestyle. A more reasonable lifestyle is staying within the parental unit, at least in part, until you develop enough skills, income, and savings to be able to go out on your own and be a productive part of the global economy.
A lot of people, however, never develop the needed skills or the required savings and income to go out on their own. This is something that has been increasing. You can now easily find people in their late 20s living at home and even people well into their 30s. In 1950, if a 35-year-old man was living at home without a family of his own (and he came from a typical middle-class family), it would be a pretty big negative for him.
Maybe boomerang children aren't so bad? Towards a healthier perspective on modern real estate and modern family life.
Many people talk about the second shift (the one from leaving at 18 for good to the more complex current situation), but not a lot of people talk about the first. You won't see articles in the financial news and financial media talking about how interesting it is that society has changed so much. Still, you'll read about boomerang children and college-education coffee makers all the time on popular financial websites.
We've got to do a better job of putting things into perspective and understand that just because things were a certain way for a bit of time (maybe a few decades), doesn't mean that's how they've always been or how they should be. Perhaps it makes more sense for families to be tighter knit from a financial perspective. Maybe it doesn't. Either way, assuming something without taking a broader perspective is both narrow-minded and will prevent you from making some interesting and potentially useful insights.
Some of these insights might include the following:
Economic recoveries and bull markets move slowly; recessions and downturns move fast. So, be prepared...
Do you think that once a recession looms on the horizon, you'll be able to make preparatory moves to sustain your financial investments and your portfolio? If so, you're probably wrong. You're taking on too much risk and not effectively managing the risk within your portfolio and your financial life if you naively think that pre-recession prep isn't necessary.
Recessions come too quickly for most people - it'll probably be the same for you
Too often, people fail to take prudent steps to prep for recessions, market corrections, or economic downturns - they think they'll see the signs close to it and will be able to make the needed financial adjustments. This is incredibly hard to do, however, and most people fail at it.
The main reason it's hard to do is that, unlike economic recoveries and expansions, recessions come quickly and don't tend to give warning signs until after things are already bad (so, they're not really warning signs in this case).
There's too much prep work to do pre-recession
There is too much to be done to prep for a recession, and there might not be enough time to do it if you wait for some sort of warning sign to begin. These things may include the following:
The above items are essential if you want to both protect your financial and non-financial worlds during a recession. It's also important if you're going to thrive post-recession because of recessions, market corrections, and economic downturns bring asset prices down. The smartest investors are those who are eagerly awaiting a recession with a list of quality firms and other assets to buy up at low prices.
It's hard to find great firms at any time
Generally, quality firms are those who have healthy balance sheets, strong and growing earnings, proper management, and are engaged in businesses that are not readily open to competitive entrants. This is an entire field of study, however, and there isn't enough room in a single article to even begin to delve into this topic.
Sometimes luck gets in the way of us achieving our financial goals. Other times, we get in our own way. Don't make these classic financial mistakes - they have significant long-term consequences to your financial life.
1. Buy into the financial markets during market euphoria and sell out during the middle of an economic downturn: Far too often, people buy high and sell low instead of the often-stated "buy low, sell high" mantra. This has a lot to do with behavioral psychology and cognitive biases. A surefire way to take substantial steps backward in your financial life is to buy stocks (or any other asset) during a boom only to sell during a recession - this will result in capital losses, which are devastating to your financial portfolio.
2. Keep no cash cushion for the unexpected: Without a cash cushion (often called an emergency or rainy day fund), you'll need to sell less-liquid assets during harsh times for you and your family. Harsh times almost always do come, so neglecting to have a proper emergency fund in place will likely mean you'll end up selling assets at non-ideal times. Non-ideal is a good scenario. In fact, you could end up having to sell during a market correction or a full-blow recession (where asset prices can quickly drop by 50%). You can't afford to sustain such capital losses - having a cushion of money set aside as an emergency fund will protect you from this.
3. Fail to invest your savings: The first step to building wealth is saving - if you can't put money aside, you're going to have a hard time building real wealth in a sustainable and lasting way. Some people can save well, but they are afraid of investing. These people are often diligent and reasonably hard-working adults who don't like to take perceived risks. What they don't realize, however, is that for the most part, saving alone is not enough - you must invest your money at reasonable rates of return so that it may grow. Without growth, your wealth will only depend on your ability to earn income, and every year your wealth will slowly be eaten away by inflation.
When thinking about risk and probabilistic outcomes, potential gains and potential losses aren't the same things. Only hyper-academic people who aren't actually engaging with the real world or putting something at risk (eg. money, health, life, friends, family, reputation, dignity, etc.) would argue that downside potential should be regarded in the same way as upside potential.
Only entities that
Let's say you're a casino or a major corporation and have a ton of time and a ton of money – in that case, a binary 49% vs. 51% loss-gain distribution (49% chance of total loss; 51% of 2x gain) might make sense because you've got a 2% edge. In the long run, this edge will play itself out so that you come out ahead, assuming you stay around for the long run.
Most retail investors, small or medium-sized businesses, or other smaller orgs won't be around, however, especially if they keep sustaining losses. They should definitely consider the 49% vs. 51% probabilities, but they should also consider another key item: whether or not they think they'll survive for long if they keep engaging in these types of bets . Because, even if the odds are in their favor, a small entity has to be around in order to see those odds actually play out. If you're not around, it doesn't matter how good the odds are – to win the game, you've got to survive long enough first.
Thinking about risk, therefore, isn't a binary operation where you can simply compare probabilities – it's a far more complex exercise that has to take things such as the following into account:
Too often, people mix up the ideas of risk (or riskiness) and complexity - they aren't the same and they shouldn't be mixed up. When you don't understand the difference between the two, you're liable to make errors in judgment and/or decision-making related to your investing portfolio, your career, and even your overall life.
Complexity in our current context is when things are complicated, multifaceted, and difficult to grasp mentally in an all-encompassing way. Complex things need to be broken down to be understood, but they aren’t necessarily probabilistic (or stochastic) in nature.
Risky things, however, could be complex or they could be simple – there is an overlapping area where things are both risky and complex, but risky things definitely don’t need to be complex. Risk arises when there is a probabilistic (or stochastic) distribution of outcomes, some of which are unpleasant or detrimental in some way.
The reason people often mix the two up is because understanding and dealing with complex things requires mental energy – this means a human needs to exert real mental energy to deal with, process, or utilize complex things/concepts. This can be inherently unpleasant, since the conscious mind seems to want to be in a constant state of pleasant ambivalence and rumination (this rumination has a way of often becoming quite unpleasant, but that’s for a different day). Our minds might simply shut down in a sense when dealing with complex things, similar to how they’d shut down when working through risky things from a probabilistic sense.
The problem arises in that it makes a lot of sense to break risky things down into their underlying probabilistic components (assuming these components can even be understood), but it doesn’t always make sense to break complex things down in such a way if you’re only concerned about risk. It only makes sense when the complex things are risky; if they aren’t risky, there’s no reason to break things down to understand risk. Some mental discipline and clarity could help you take a few steps back and see the bigger picture. Ask yourself if this complex thing is in fact risky – if it is, analyze; if it isn’t, step back and don’t get bogged down in unnecessary details if your primary concern is risk-related.
The simplest things in the world (for example, whether the stock price will be above or below a certain strike price X) can be incredibly difficult to understand from a risk perspective. But, things that appear incredibly complex in their operation nature (for example, the fault rate of complex self-driving software) might be very simple from a risk perspective. In the self-driving example, maybe you only care about the error rate, which can be easily determined (and probably already has been by the firm producing the software) and utilized in downstream analyses, calculations, etc.
How to use historical stock market data to build your investing intuition, and move one step closer to becoming an investing superhero
On Monday, October 17, 1987, the S&P 500 dropped a little over 20%. Going back to 1950, this was the single worst one-day drop in the stock market. It showed and taught a lot then, and it can still teach market participants and investors today if they are willing to listen.
Historical financial data can be magical - it can help you travel into the past and see the forests from the trees. By listening to historical data, we can more easily understand that a single-day drop of 20% in an index such as the S&P 500 is possible. By knowing that a 20% stock market drop is possible -- and by seeing the number present itself to us in the data -- we can better understand the risks we face by investing and participating in the financial world.
How do we know whats or of stock market drops are possible?
What exactly does possible mean in the context of investing and considering severe market declines? Sure, we know it's "possible" for the S&P 500 to drop by this amount or that amount. But until you ground your understanding in some historical data, you're not going understand this possibility at a deep level.
Observing the S&P 500's daily price movements will help you learn what sort of severe drops are possible for your portfolio
First, a quick note on using the S&P 500: The S&P 500 is a great place to start because it's a reasonable proxy for the market. The S&P 500 surely doesn't represent the entire market of equities, financial assets, and let alone of all assets; but, it's a reasonable and easily-manipulatable proxy for "the market."
If we care about how bad things can get in a single day (eg. an extremely severe yet plausible one day decline in your portfolio), we'll want to look at daily price data. This type of data is readily available online. There's a lot of free data, but you'll have to pay to access longer time horizons or more esoteric data.
Below, we have S&P 500 data April 3, 1950 to September 6, 2019. This represents an almost 70-year time horizon, a bit less than the expected lifespan of a person today. You can see this in the screenshots below (bottom and top of the table shown; table ordered earliest to latest).
The data has three columns - one shows the date, the other the closing price of the S&P 500 on that date, and the last column is the day-over-day change int he S&P 500 stated as a percent. The day-over-day change is easy to calculate - it's merely the one day change divided by the previous day's closing price. Declared as a formula, it is: [Day 2 - Day 1]/Day 1
This is called the arithmetic return. More complex return types -- namely the geometric return -- exist, but they are outside the scope of this discussion. The simple arithmetic return above is sufficient for our purposes.
Observing financial market data can teach you a lot and help to build a bit of market-related intuition
Just observing stock market data like this can be useful. Exploring data visually without graphing it can give us some interesting and potentially-valuable preliminary insights. This is especially true for people who haven't done this sort of data analysis before. For example, we can observe that in the very beginning of our data set (the first few days of April 1950), the S&P 500 was around $18. This is in sharp contrast to the almost $3000 S&P 500 level we observe below for September 2019. This plainly shows us that there has been some dramatic growth over the last 70 years in absolute metrics.
Although we can see some things by observing the financial data, it's hard to determine summary statistics about the S&P 500 data set visually. Stats like mean, median, minimum, and maximum are hard to see because all of the data needs to be taken into account. Taking all of the data into account can't easily be done relying on the human mind - it's just not what it's made to do. The data set has almost 17,500 rows - that's simply too much to comprehend without the use of computing devices/methods (or without a considerable amount of time to devote to this endeavor). Luckily, such devices/methods are easily available for free (eg. Google Sheets) or cheap (eg. Microsoft Excel) in the form of software. More complex options are available that are both free and paid (eg. R, Matlab, Tableau, etc.). Something like Microsoft Excel would be enough for the vast majority of use cases, however.
Finding the minimum, or the worst stock market day over the last 70 years
Some relatively easy functions can be used in Excel -- the tool of choice for most finance people -- to get some stats on the data. In the screenshot below, you can see the average, the max, and the min. The min is what we care about most here - it represents the lowest day-over-day S&P 500 change; it represents the most severe single-day drop in the S&P 500 over the last 70 years.
We can see that the worst drop is -20.47%. That means that in one single day, the stock market effectively dropped by over 20%.
Black Monday - an over 20% one day drop in the stock market
We can see the drop occurred on Monday, October 19, 1987, by examing our data set in greater detail. By filtering the data from largest to smallest, we can see what date corresponds to the worst stock market drop. Once we have the date, we can observe what happened around it in the days before and after Black Monday, which is what Monday, October 19, 1987is called in the financial industry.
The image below is the copied and pasted data from around Black Monday. It's interesting and useful to observe what happened around that time. We can see in the 10 days around Black Monday, 7 out of 10 days were losing days. We can also combine the losses to see what the cumulative loss over the 10-day period would have been. We can see that it's even worse - over the 10 days, the stock market dropped over 26%.
That means you could wake up one day over the course of your investing life and see that your portfolio is down 20% in a single day. That event would be tough to deal with - you'd be in for a very rough day and rough week. It would take some time to recover from the loss, but recovery would definitely be possible. A mistake, however, would be to panic and deviate form your long term investment strategy for no real reason beyond the fact that you're freaked out.
Use this knowledge to avoid panic sales and other forms of freaking out during the next inevitable stock market disaster
We all get freaked out in investing - it's your money that's on the line, and you don't want to lose it. Even small drops can seem bad. Even times where there's no movement could be perceived as bad if you were anticipating gains. You can't let these investing difficulties make you make investing mistakes, however. You've got to do your best to maintain a long-term perspective on investing. Something that helps us do that is exercises like the one we just went through. Looking at historical market data, understanding how the markets have moved over time, and understanding how markets may tend to move int he future are all essential things that will buffer you from foolish investing mistakes made out of fear.
If you'd like to explore the Excel file form which the above screenshots originated, you can download the file here. You'll be able to copy and paste the S&P 500 data to do your own data analysis work, like finding the maximum one-day increase.
One of the most significant risks related to house flipping is holding period market risk - it's the risk that during the time in which you're holding the property you intend to "flip," the property value will decline.
The decline in property value can be caused by a variety of reasons (macro recessions, localized events, etc.), but that's not the point of this short piece. The point being made here is that house flipping exposes the "flippers" to significant market risk.
Not taking this real estate market risk to which you're exposed to when pursuing a house flipping strategy into proper account and consideration may have some serious negative consequences. The negative consequences are rare - they only arise in market downturns, which happen once every number of years. But, although the chances of the risk coming to fruition are small, the severity of the negative consequences (should there be a real estate market decline) are severe. The consequences can be severe enough to wipe out investors that are not well-capitalized and in positions of strong liquidity.
This is pretty easy to see if we think about a hypothetical example. Let's say you're doing house flipping and you buy a $200,000 property. The timeline might look something like this:
The risk exposure continues until you sell the house. So, in the above example with the relatively rapid renovation and resale (likely in a good real estate market; very unlikely in a real estate downturn), the investor or flipper would be exposed to market risk arising from adverse moves in the real estate market for at least a few months. If the investor is new, inexperienced, or doesn't have a lot of capital/liquidity in reserve, things might be over in one serious real estate or economic downturn.
If you're holding a property that's worth less than you bought it for -- even with the improvements you made or might make -- you'll have to (1) either accept a loss on this investment or (2) you'll have to continue making mortgage payments on the note until the market recovers.
In the first case, you'd lose real money - you'd possibly lose your entire down payment and in the worst scenarios you might be so underwater that you'd have to add additional funds to be able to get rid of it. This isn't far-fetched. Many people all across the United States experienced this during the Great Recession that started in 2007/8.
In the second case, you'd avoid having a severe capital loss, but you'd have to outlay money every month to keep the mortgage note current. This can be costly, especially if this is done for many months or even many years.
Of course, you might have bought the house in cash - in that case, you still may experience a severe loss (you'll just never be underwater on the mortgage). Renting might also help mitigate the risk - if there's a downturn, you might abandon your initial house flipping strategy and put a tenant(s) in the property for several months or years to help with the mortgage payments.
A prudent house flipper or potential house flipper would take these risks into account. Everything is exposed to risk, so this article isn't attempting to say that real estate investing in general, or house flipping specifically, are imprudent investments or that there's undue risk in a house flipping strategy. The article simply attempts to highlight a particular type of risk that house flippers are and will be exposed to.
From a mathematical perspective, those in Finance can clearly show you how not being diversified -- in an economy that allows for diversification -- is not prudent. Why isn't it prudent? Because, for most investors, not having all of their eggs in one basket will prevent them from devastating loss should some baskets break. Baskets break all the time.
Although diversification is an ancient concept, the modern idea of financial diversification in the context of creating an effective investment portfolio can be attributed to Harry Markowitz. Markowitz published his seminal paper titled Portfolio Selection in 1952. Check it out here, and other places online.
Some investors, however, feel that they don't need this rule. Some investors think the rule, or more precisely, the nature of the world in the investing space, doesn't apply to them. They feel that they know more than the typical investor or investing firm knows - they think they're somehow better at picking stocks or making investment decisions. These people -- and they are everywhere -- believe that they're just different. It's a common thing in humanity, and it might not change.
In the context of investing -- and specifically retail or family office investing -- portfolio concentration risk is the risk that you are overly exposed to something. That something can be any of the following and more:
Inappropriate portfolio concentrations are those that expose your portfolio to more risk than you would like or more risk than would be prudent. As such, assessing the concentration levels within your investment portfolio and taking steps to ensure that they are in line with your goals is a smart thing that should be done every so often.
The good thing is that it’s pretty easy and straightforward to determine the concentration levels for a lot of things like stocks, sectors, and countries (things like determining the concentration to strategies and assumptions is a bit more complex).
Step 1: Compile your entire investment portfolio
This might be the most difficult part as modern investors often have portfolios spread out amongst different account or different institutions. For example, you might have a brokerage account, a savings account for an emergency fund, some random savings accounts, and a 401k plan at work – this isn’t unreasonably complex but it does mean you’ll need to do a bit of work compiling things initially.
In fact – you should have done this already; the info should already be complied! If you’re investing and you don’t have a single source that is updated at least occasionally where you can get a high-level picture of your portfolio, you’re making a mistake. Spending some time on this will be beneficial in many ways, beyond just understanding concentrations and concentration risk.
Step 2: Pick a concentration category (eg. stocks, countries, sectors, etc.)
Next, pick a category against which you'd like to determine concentration levels in your portfolio. Don't start with complex things - start with basic things and move towards more complexity as you slowly get a better understanding of the risk nature of your portfolio.
For example, a great place to start would be sectors - you don't want to be exposed to a particular sector too much. If you're only in tech stocks or only in blue chips, you might want to diversify at bit more, depending on your risk tolerance and investing horizon. At the very least you'll want to know that you're heavily concentrated in particular sectors.
Other key concentrations are for individual stocks (eg. the investor who's absurdly exposed to one particular stock they love at the detriment to proper portfolio risk management and diversification).
Step 3: Simply make a list
For a retail investor doing simple portfolio concentration risk analysis, once you have your portfolio in one place and once you decide what you want to examine, it's very simple to proceed.
All you need to do is make a list with two columns - the particular investment product in the right column and the percent of the portfolio that the investment product represents. This is best illustrated by the table below.
As you can clearly see, this isn't a healthy portfolio. The vast majority of the portfolio is concentrated on
The portfolio has home country bias and seems to biased toward popular or newsworthy tech stocks and friend/family tips. Only 30% (broad market ETF + global ETF) of the portfolio is in a broad, well-diversified, investment product while 55% of the portfolio is in just 3 stocks. That's simply absurd for most investors - unless you're an excellent/skilled investor with a very long time horizon and a high risk tolerance, that sort of exposure is unacceptable.
Step 4: Take prudent risk-mitigating steps to reduce the concentration risk within your portfolio
Finally, after the analysis, you would take action - you'd act in ways to adjust your portfolio to reduce concentration risk. Of course, in doing this you'd want to be prudently confident in the insights on which you base your decisions and you'll want to take other factors into account - these other factors might include tax implications and macroeconomic assumptions.
In our example above, a prudent investor would sell off some of the tech stock exposure and re-assess weather the family member's energy stock tip was actually a good tip (eg. is the stock worth owning). Then, the investor might take the proceeds from these sales and invest them into more well-diversified products like ETFs, focusing both on foreign and domestic ETFs. The investor would also want to make sure to focus on both small cap and large cap ETFs, keeping in mind their risk tolerance and adjusting appropriately.
Finally, the investor might see that they are only in equities - this might make sense but putting some money in bonds or alternatives might make sense for some investors. These decisions are all individual - one needs to act prudently based on their own circumstances.
Concentration risk is only one type of investment portfolio risk, but it's an easy one to spot and fix. A lot of investors are prone to taking on too much concentration risk. They don't do it intentionally - they just lack an investing plan or approach and instead buy stocks here and there based on emotions. This is hard to remedy - not everyone is going to create an investing approach and monitor it over time. But, people easily -- and enjoyably -- do the above exercise once in a while (at least once a year) to see if their portfolio is too concentrated on one stock, one sector, or one economy.
Devastating portfolio declines and what it takes to recover from them – the math isn’t in your favor
Everyone thinks about gaining money when they invest, but too often we neglect how important it is to not lose money. Not losing money is so important, in fact, that one of the greatest financial investors in history (and very likely the greatest one alive today) espouses the following as his Rule No 1:
Don’t lose money.
What’s Rule No 2?
Remember Rule No 1.
Rule No 2 is obviously meant to be a little humorous, but Buffet is a serious man when it comes to investing and his rules are meant to illustrate a fundamental truth about investing – that truth is that it’s very hard to recover from a loss and that it gets harder and harder the deeper the loss.
This is all best illustrated with examples. Sometimes, a good set of examples can do more for contributing to understanding than pages and pages of text. So, let’s go over three examples, each with increasing levels of severity of initial losses.
In each example, we’ll break things down into 3 time periods – Time 0, Time 1, and Time 2:
A 25% Loss – Somewhat severe, but recoverable
With a 25%loss, your $1000 declines to $750 – this represents a one-quarter decline in your portfolio and would obviously be an unwelcome occurrence. Now, let’s take a look at what sort of returns you’ll need to recover by Time 2; let’s see what sort of returns in the subsequent time period you’ll need to make you whole again.
As you can see from the table, a 33.33% gain is required in order for you to recover and get back to the initial $1000. A 10% return, 20% return, or even a strong 30%return in one time period simply won’t do it.
That means if each time period is 1 year, even a 30% return in the year subsequent to your 25% loss won’t be enough. 30% is a solid return. The fact that it’s not enough should be the first hint that getting back to whole is a lot harder than dropping, from a mathematical/percentage perspective. It’s only going to get worse.
A 50% Loss – Very severe, but you can recover if you stay prudent over the long term
With a 50% loss, it’s a lot harder to recover. Now, it takes a 100% gain (doubling your post-loss portfolio value) to get back to whole again. If you halve your portfolio, you’ll need to double it to bring it back to its original value.
So, if a time period is one year, you’ll need to double your post-loss portfolio value to get back to your Time 0 initial value. That’s very hard. You’d be far better off having avoided such a decline because it’ll be an uphill climb getting back to baseline again. This is what Warren Buffet’s Rule No 1 points to.
A drop of 50% in your portfolio value is very severe and detrimental to your long term investing goals. It will take a 100% increase -- doubling your portfolio -- to recover from a 50% loss. This is tough, but it's doable - it might not happen in a single time period but over time a prudent and disciplined investor stands a chance at recovery.
A 75% Loss – A devastating blow to a portfolio that will take some time to recover from
With a 75%, things get really bad. Now, in order to get back to whole, you’ll need a 300% gain. A 300% gain is the same as quadrupling your money (4x return). As any investor knows, a 300% return is very hard to get – it usually takes years to achieve such returns in a well-diversified portfolio.
Let’s think about this some more. As we keep increasing out Time 1 losses by 25% increments, the return needed to get back to whole by Time 2 goes up by way more than 25%. This is based on the underlying mathematics of portfolio returns, but we don’t need to get deep into that here. The above examples should clearly show how each time the loss gets more severe, the needed gain to get back to baseline gets more and more astounding.
If you lose 75% of your portfolio’s value in a single year due to a very severe recession or, far worse, due to investing blunders, you’re going to have to make some incredible returns (300%) to recover. What makes you think you’ll beable to do that? It’ll likely take a number of years and some serious investing discipline to be able to recover in this way.
A 75% portfolio decline is devastating to any portfolio. It will take a 300% return (quadrupling your money; a 4x return) to get back to whole again. This is very hard to do in a single time period. It might take years of prudent and disciplined long term investing to recover. This demonstrates why large portfolio declines are so detrimental and should be avoided.
A bit more mathematical, for the mathematically inclined
For those that are more mathematically inclined, let’s dig a bit deeper into the portfolio maths.
Let’s assume an initial portfolio value of a – this is your Time 0 value
For any portfolio change (decline or increase) d, where is greater than -1 but less than 1, the portfolio value in the immediately subsequent period (Time 1) will be a x (1 + d)
To get back to the initial portfolio value by Time 2, we’ll need to do something to the Time 1 value to get it back to a (which we stated above was our initial value)
We can simply divide the Time 1 value by (1 + d) to get back to a – that’s [a x (1 + d)]/(1 + d)
Dividing by (1 + d) is the same as multiplying by 1/(1 + d) – that’s the amount, no matter what our initial a is and what the change d ends up being, that we have to multiply the Time 1 portfolio value by
Now, notice that if d is less than 0, 1/(1 + d) will be larger than 1. So, if d is -0.25 (corresponding to a 25% decline in our first example above), then 1/(1 + d) is 1/(1 – 0.25) which is 1/0.75. What’s 1/0.75? It’s 1.3333. That means you’ll need 1.3333 times the Time 1 value – this exactly represents an aprox 33% increase.
Let’s do a 75% decline as in the third example above – now 1/(1 + d) is 1/0.25. That’s equal to 4, which represents a 300% increase over the Time 1 value.
We can see that as d approaches -1 (moving towards a total loss), 1/(1 + d) gets bigger, but by a disproportionate amount.
Can we derive a simple way to see how our 1/(1 + d) factor changes with changes in d? Yes – it’s easy using Calculus:
d/dx[1/(1 + d)] = d/dx[(1 + d)^-1] = [-(1 + d)^-2] x d/dx(1 + d) = [-(1 + d)^-2] x (0 + 1)
so, the derivative is -1/(1 + d)^2
Calculus can be applied to lots of situations to better understand how things change. F
We can see that by squaring the (1 + d) term, we’re increasing the effects of both positive and negative portfolio changes. If d < 0, then squaring (1 + d), which will be less than 1, will only make the factor smaller. By making that factor smaller, the entire factor gets bigger because dividing 1 by smaller and smaller numbers makes the result bigger and bigger.
This should be very discouraging – the numbers tell us that negative effects are magnified when we think about the returns needed to recover.
You can't predict when a recession will hit, but you can be sure that a recession will come at some point. Speculation on the exact timing is a fool's proposition, but indicators exist to indicate when the overall market is overvalued and when a recession is more likely. Preparation for a recession is wise and simply ignoring overall market valuations will cause you to (1) not be ready to take advantage of investing opportunities a recession presents and (2) be potentially exposed in troubling ways due to improper diversification. The below 3 strategies are excellent ways to prepare for a recession.
1. Start Piling Away Cash for Cheap Stock Purchases When the Recession Hits
Cash is dry powder to investors and without some set aside you simply won't be able to take advantage of a recession. Cash will allow you to buy good stocks at deep discounts when the market falls during a recession.
Do you notice how the first thing we're advocating in terms of recession preparation is something that will allow you to buy more stocks instead of something that is meant to protect you? Obviously, you want to be protected in severely adverse market circumstances as an investor, but the most important thing about a recession isn't what it does to your portfolio in the short term, but the potential it has to boost your portfolio in the long term. A recession allows you to buy a lot of good quality companies at deep discounts - sometimes you see a price to earnings (P/E) ratios of indices such as the S&P 500 can drop below 10, indicating an extremely undervalued market overall.
Without having cash piled up ready to toss into good companies -- it's key that you only buy good companies -- you will miss out on potentially outsized gains due to inevitable market recoveries. The great thing about recessions is that you don't even have to pick individual stocks - something that is not recommended for novice investors or those with low-risk tolerances. Purchasing indexes (eg. S&P 500 or the Dow Jones) via broad mutual funds or ETFs will allow you to at once diversify and benefit from future recoveries. Purchasing the Dow Jones at the bottom of the 2007/2008 Great Recession would have created a 300% + return over the course of a decade without having to take on the risks of owning single stocks or having to put in the effort to pick them.
When the market seems overvalued in terms of market P/E ratios, in terms of timeframe since the last recession, or in terms of high-quality research/opinions, it might be a good idea to slightly pull back on some of the more speculative investing you're doing to put aside cash. You'll want enough cash so that you can comfortably enter positions at lows and then continue buying more and more if markets continue to drop. This abundance of cash will allow you not to think about timing the market -- something that you will not be able to do -- but will instead allow for an aggressive dollar cost averaging strategy once things start to decline until things start to turn up again.
2. Properly Diversify Your Portfolio so it Can Withstand a Recession
To make your portfolio more resilient to recession declines you'll want to diversify across:
You don't want to hold just US firm and you don't want to hold firms only in a single industry (eg. tech). Instead, you want to hold a broad portfolio of high-quality firms from around the world and from different industries. Some regions and industries will be more resilient than others and this will affect your portfolio. additionally, some firms will go bankrupt in recessions - hopefully, you don't own any such firms because you've done proper due diligence but some things are very hard to predict. You'll want to not be tied to a single industry, a single location, or a single firm when the market turns downward so that a single disastrous event will not affect more than a small portion of your portfolio and so that you can survive as an investor into the recovery.
A great way to diversify is through the use of mutual funds and ETFs, but diversification is also achievable through simply building your own high-quality stock for experienced investors - moderately experienced investors should not try this (novice investors shouldn't even think about this).
3. Buy Protective Puts on the Market (ONLY FOR ADVANCED INVESTORS)
If you don't know what a protective put is, this section is not for you at all and you should skip tot he end of the article.
If you do know what a protective put is but wouldn't be properly considered an advanced or experienced investor, you can read this section but you should not engage in this activity because it could cause a needless drain on your portfolio and a false sense of security.
If you're an advanced investor, you probably already know this strategy, but we'll remind you again. Protective puts are simply put options - they are called protective because of the context they are being used in. You can buy such protective puts on the overall market via market proxy (eg. S&P 500) in order to profit from market declines.
One way to execute such as strategy is to buy monthly out-of-the-money puts on the market proxy via a mutual fund or more likely an ETF. These should be out-of-the-money because what you're buying here is a form of insurance in case the market drops significantly - you're not trying to speculate. Out-of-the-money puts will be worthless if the market goes up or doesn't move much but will increase in value in a significant market decline. You can buy them for a reasonable term - monthly, quarterly, yearly but shorter repetitive purchases might be better because you're less exposed to the option's time decay.
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.
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