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Top Five Investing Articles for Individual Investors Read in 2019

09 Monday Dec 2019

Posted by wmosconi in asset allocation, Average Returns, behavioral finance, beta, bond yields, confirmation bias, correlation, correlation coefficient, economics, finance theory, financial advice, Financial Advisor, financial advisor fees, financial advisory fees, financial goals, financial markets, Financial Media, Financial News, financial planning, financial services industry, gross returns, historical returns, Individual Investing, individual investors, investing, investing advice, investing information, investing tips, investment advice, investment advisory fees, investments, market timing, personal finance, portfolio, reasonable fees, reasonable fees for financial advisor, reasonable fees for investment advice, reasonable financial advisor fees, risk, risk tolerance, risks of stocks, S&P 500, S&P 500 historical returns, S&P 500 Index, speculation, standard deviation, statistics, stock market, Stock Market Returns, stock prices, stocks, time series, time series data, volatility, Warren Buffett, yield, yield curve, yield curve inversion

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As the end of 2019 looms, I wanted to share a recap of the five most viewed articles I have written over the past year.  The list is in descending order of overall views.  Additionally, I have included the top viewed article of all time on my investing blog.  Individual investors have consistently been coming back to that one article.

1. Before You Take Any Investment, Advice Consider the Source – Version 2.0

Here is a link to the article:

https://latticeworkwealth.com/2019/09/18/investment-advice-cognitive-bias/

This article discusses the fact that even financial professionals have cognitive biases, not just individual investors.  I include myself in the discussion, talk about Warren Buffett, and also give some context around financial market history to understand how and why financial professionals fall victim to these cognitive biases.

2.  How to Become a Successful Long-Term Investor – Understanding Stock Market Returns – 1 of 3

Here is a link to the article:

https://latticeworkwealth.com/2019/09/23/successful-long-term-investing/

It is paramount to remember that you need to understand at least some of the history of stock market returns prior to investing one dollar in stocks.  Without that understanding, you unknowingly set yourself up for constant failure throughout your investing career.

3.  How to Become a Successful Long-Term Investor – Understanding Risk – 2 of 3

Here is a link to the article:

https://latticeworkwealth.com/2019/09/25/successful-long-term-investor-risk/

This second article in the series talks about how to assess your risk for stocks by incorporating what the past history of stock market returns has been.  If you know about the past, you can better prepare yourself for the future and develop a more accurate risk tolerance that will guide you to investing in the proper portfolios of stocks, bonds, cash, and other assets.

4.  Breakthrough Drugs, Anecdotes, and Statistics – Statistics and Time Series Data – 2 of 3

Here is a link to the article:

https://latticeworkwealth.com/2019/11/20/breakthrough-drugs-statistics-and-anecdotes-time-series-statistics/

I go into detail, without getting too granular and focusing on math, about why statistics and time series data can be misused by even financial market professionals.  Additionally, you need to be aware of some of the presentations, articles, and comments that financial professionals use.  If they make these errors, you will be able to take their comments “with a grain of salt”.

5.  Breakthrough Drugs, Anecdotes, and Statistics – Introduction – 1 of 3

Here is a link to the article:

https://latticeworkwealth.com/2019/11/11/breakthrough-drugs-statistics-and-anecdotes-investing/

I kick off this important discussion about the misleading and/or misuse of statistics by the financial media sometimes with an example of the testing done on new drugs.  Once you understand why the FDA includes so many people in its drug trials, you can utilize that thought process when you are bombarded with information from the print and television financial media.  Oftentimes, the statistics cited are truly just anecdotal and offer you absolutely no guidance on how to invest.

                                       Top of All Time

Are Your Financial Advisor’s Fees Reasonable?  Here is a Unique Way to Look at What Clients Pay For

Here is a link to the article:

https://latticeworkwealth.com/2013/08/07/are-your-financial-advisors-fees-reasonable-here-is-a-unique-way-to-look-at-what-clients-pay-for/

This article gets the most views and is quite possibly the most controversial.  Individual investors compliment me on its contents while Financial Advisors have lots of complaints.  Keep in mind that my overall goal with this investing blog is to provide individual investors with information that can be used.  Many times though, the information is something that some in the financial industry would rather not talk about.

The basic premise is to remember that, when it comes to investing fees, you need to start with the realization that you have the money going into your investment portfolio to begin with.  Your first option would be to simply keep it in a checking or savings account.  It is very common to be charged a financial advisory fee based upon the total amount in your brokerage account and the most common is 1%.  For example, if you have $250,000 in all, your annual fee would be $2,500 ($250,000 * 1%).

But at the end of the day, the value provided by your investment advisory is how much your brokerage account will grow in the absence of what you can already do yourself.  Essentially you divide your fee by the increase in your brokerage account that year.  Going back to the same example, if your account increases by $20,000 during the year, your actual annual fee based upon the value of the advice you receive is 12.5% ($2,500 divided by $20,000).  And yes, this way of looking at investing fees is unique and doesn’t always sit well with some financial professionals.

In summary and in reference to the entire list, I hope you enjoy this list of articles from the past year.  If you have any investing topics that would be beneficial to cover in 2020, please feel free to leave the suggestions in the comments.

Breakthrough Drugs, Statistics, and Anecdotes: Three Things Every Individual Investor Needs to Know – Statistics and Time Series Data – Part 2 of 3

20 Wednesday Nov 2019

Posted by wmosconi in asset allocation, correlation, correlation coefficient, finance theory, financial advice, Financial Media, Financial News, financial services industry, historical returns, Individual Investing, individual investors, investing, investing advice, investing tips, risks of stocks, standard deviation, stock market, Stock Market Returns, time series, time series data

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correlation, correlation coefficient, Financial Market History, financial markets, Financial Media, Financial News, invest, investing, investing tips, math, mathematics, noise, statistics, time series, time series data

The first article of this three-part series covered the broad strokes of this issues to be aware of in terms of all the “data” and “relationships” that get thrown around by the financial media (print and television).  Most of the discussion uses data points that are not statistically significant to draw any sort of conclusion.  In fact, time series data is notoriously hard to model and predict the future.  Additionally, the specific time series data of stock market returns is even more difficult.

You can refer to the link below to examine the content of the first article:

https://latticeworkwealth.com/2019/11/11/breakthrough-drugs-statistics-and-anecdotes-investing/

The task at hand for the second article is to put some “meat on the bones” of the discussion.  I realize that anything to do with math and statistics is not easy for everyone (or of interest either).  Therefore, I will be writing a supplemental article that covers the mathematics and statistics in more detail.  The goal here is to be able to identify some of the more common errors that you will encounter.

The first item to talk about is any sort of data that has a substantial trend component.  In layman’s terms, there is a data series where the line graph goes up or down in more of a straight-line manner.  You can think of the Gross Domestic Product (GDP) of the United States here.  Every year the GDP figures will generally go up unless there is a recession.  But, even after the recession passes, the trend for GDP will resume upward.  So, where does the problem come in?

I am going to give a contrived example to illustrate why it is dangerous to compare two series that are trending.  The example will consist of two different equations which are trends.  Both have the same trend component and an error term (we will call that eta).  The variables will be exactly the opposite.  More specifically, the two equations we will use are the following:

Trend_1 = Time + 100 + 0.9 * x + eta

Trend_2 = Time +100 – 0.9 * x + eta

Now the x values and eta values were simply generated by selected variables at random between 0 and 1.  The eta values were also selected at random between 0 and 1.  You can think of eta as representing the general “noise” that occurs on a daily basis when observing stock prices in the financial markets.  So, let’s graph the first 100 observations for these two equations:

Trend_Graph_Statistics_Revised

You will notice that the trend component dominates the line graphs.  However, we know by construction that the two equations which produce trend_1 and trend_2 are fundamentally different.  Now the correlation coefficient between those two equations is 0.9984.  A correlation coefficient of 1 means that the two lines move in lockstep.  Why is this important?  Why is it very dangerous?

Well, financial pundits will talk about these types of graphs all the time.  It looks like there is some relationship, but we know there is very little relationship between the two trends.  In fact, we can look at these equations by subtracting the current value from the previous value to see what changes.   Formally, this topic is called first differencing.  It will allow us to see more clearly what we already know.  Here is the graph:

First_Difference_Graph_Statistics_Revised

Now we have a totally different picture.  We can see that at many times the two trend equations are moving in exactly the opposite direction.  In fact, the correlation coefficient for the first-differenced equations is 0.2675.  There is only a slight positive relationship between the two trends.

In the example above, we can see that looking at the two trends is very deceiving.  Remember that I added the eta term to represent “noise” that is always present in financial market data.  So, anytime someone talks to you about the comparison of two trends, you should be very skeptical.  You always want to see first-differenced data or at least a comparison of changes in some manner.  Otherwise, you will mistakenly assume that there is a strong positive or negative relationship between two time series.

The second example that I am going to use is stock market returns for the S&P 500 Index from 1966 through 2018.  Why start at 1966?  Well, the S&P 500 Index started with its current number of component stocks back in 1957, and I would like to show annual stock returns and also ten-year annualized returns.  This particular topic can get messy quite quickly, so I am not going to cover it in a lot of depth with statistical and mathematical jargon.  For those of you who are interested, I had mentioned that it will be contained in a forthcoming supplemental article.

A great many individuals in the financial markets talk about stock market returns in the same breath as the normal distribution.  What is the normal distribution?  It is the old bell curve that you are familiar with.  The normal distribution is symmetrical and tails off at the end as more and more data points are gathered.  Well, stock market returns are anything but strongly normal.

Let’s first take a look at one-year stock market returns for the S&P 500 Index.

One Year Returns - Histogram - Non Normal

A useful test to see if a particular distribution is normal is the Jarque-Bera test.  Now it is not necessary to know exactly what is being calculated.  However, you should refer to the bottom of the box that reads “Probability”.  The value of 0.179 is called a p-value.  A p-value less than or equal to 0.10 means that we can reject the hypothesis that the one-year distribution of stock returns is normal.  At a value of 0.179, we would not reject the hypothesis of normality for this distribution.  However, the p-value in our case is not large enough to be totally sure and confident. But what about looking at annualized stock market returns over ten-year periods?

We can look at a similar graph to check to see if stock market returns over longer timeframes are indeed akin to the normal distribution (i.e. the bell curve).  Here is the graph:

Ten Year Returns - Histogram - Non Normal

Looking at the same “Probability” value, we have 0.489.  Therefore, we cannot reject the hypothesis that these stock market returns follow a normal distribution. Looking at ten-year annualized stock market data tells us that we can use the normal distribution as an assumption for calculating statistics.

Now why does this matter?  Well, you will here over and over again statistics that apply only to the normal distribution in relationship to actual, observed stock market returns.  We have just seen that stock market returns over the short-term stock returns weakly follow the normal distribution. On the other hand, long-term stock returns are definitely normal. Now I will not get into the technicalities, but time series data is indeed asymptotically normal.  What?  Say again?

This is just a fancy way of saying that, as the number of data points (sample size) approaches infinitely, the time series will look like the normal distribution.  Pretty much all financial market and economic data have very few data points.  In fact, you usually need several hundred data points prior to making any assumptions and using the statistics related to the normal distribution (think standard deviation or correlation coefficients).

Thus, most of the banter in the financial media is just subjective notions of what is going on in the stock market and the economy. More often than not, an assertion by someone in the financial print or television media is more of an educated guess than based on a solid mathematical foundation. That fact explains why financial pundits hedge their statements. Like I say half-jokingly, “I see the stock market going up in the next several months, but of course it might not resume its uptrend or could even take a leg downward”.

Well yes, I guarantee you that every day stocks will go up, down, or remain unchanged. This type of daily commentary in the financial press about the short-term performance of stocks (or other financial assets) is just not helpful and can be downright distracting you from investing for your long-term financial goals.

I apologize for getting too detailed in certain parts of this article.  What are the key takeaways?  First, you should be extremely leery of drawing any conclusions from the comparison of two or more data series that are trending upward or downward.  Second, you need to have several hundred observations prior to invoking any reference to the normal distribution.  So, what is left after that?  As you might imagine, there are not too many comparisons or studies that pass the muster to give you insights on investing or actionable information to make changes to your investment portfolio.

Don’t focus on the mathematics or statistics.  All you need to remember are the two takeaways above.  And, first and foremost, you should always be skeptical whenever you are presented with comparisons and statistics related to the financial market or the economy as a whole.

Breakthrough Drugs, Statistics, and Anecdotes: Three Things Every Individual Investor Needs to Know – Part 1 of 3

11 Monday Nov 2019

Posted by wmosconi in asset allocation, Education, financial advice, financial goals, financial markets, Financial Media, Financial News, financial planning, financial services industry, investing, investing advice, investing information, investing tips, investment advice, investments, math, personal finance, portfolio, S&P 500, S&P 500 Index, statistics, time series, time series data

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asset allocation, Financial Media, Financial News, financial planning, investing, investing advice, investing information, investing tips, noise, statistics, time series, time series data

Although the title might appear to be random at first glance, I promise that there is an underlying theme.  This article is the first in a three-part series that will discuss how individual investors are bombarded with information about what happens in the financial market.  Most of the time you might hear that, 5 out of the last 7 times “x” happened, the S&P 500 index went up by 10% or more.  I will argue that most of these types of comments might be useful trivia for the television show, Jeopardy; however, they should not impact your long-term investment plan.

So, why did I use breakthrough drugs?  Prior to any drug coming to the marketplace, the FDA does a very thorough review of the test results to ensure that the drug is safe and also its efficacy is not overstated.  What if I told you that a pharmaceutical firm came up with a possible cure for lung cancer, and there were successful trials of 10 individuals.  Does that sound like a group too small to draw any conclusions?  Would you take a drug that the testing was only done on a handful of people?  Now the FDA would never allow such a thing, and there are tons of protocols and blind or double-blind randomized testing of many individuals.  It just sounds weird if only 10 people were tested, and there was also no control group (i.e. a separate group given a placebo).

While the drug example seems a bit outrageous and contrived, I bet you can think of similar examples in the daily financial press (e.g. financial television or print media).  Whenever you hear a small number of events happening that “tend to” lead to certain financial market outcomes, you should be extremely wary.  For instance, I just heard today that, after the Singles Day huge ecommerce sale by Alibaba, the stock (Ticker Symbol:  BABA) is up 80% of the time over the course of the next two weeks.  Well, when did Alibaba start Singles Day?  The first Singles Day sale was in 2009.  Therefore, we have 10 data points to work with (2009 to 2018).  Given the information I referred to above, the comment made today simply says that the stock has been up after two weeks 8 out of the last 10 years.  Now I will try to hold in my red flags and bit of ludicrous thoughts, this type of information is not informative at all.  There are just too few observations to draw any sort of valid conclusion.

Here is the plan of attack for the next two articles.  The second part of this discussion will focus on statistics.  Yes, I know this topic is not too much fun and can get complicated very quickly.  However, individual investors need to know a bit about statistics to recognize when a quantitative quote is totally useless.  We will not get too granular though, I promise.  Essentially most financial market data is time series data.  Different rules apply in that case, and these rules are broken all the time by even the most sophisticated professional investors and commentators.  The third part of this discussion will be an in-depth examination of an actual event that grounds my argument in recent events.  I will examine what is called the inversion of the yield curve and how it normally portends a recession for the U.S. economy.  Don’t worry; I am going to explain those terms when the third part of this series rolls around.

Please join me in a critical review of all the financial market and economic data you get bombarded with.  So much of it is just “noise” or simply interesting trivia at best.  Note that the interesting trivia cannot guide or inform your particular asset allocation of investments.  As always, if you have questions along the way, please feel free to comment on this or any other article.

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