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Latticework Wealth Management, LLC

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Happy New Year, Beginning Thoughts, and Information for International Viewers

27 Friday Dec 2019

Posted by wmosconi in active investing, asset allocation, Average Returns, behavioral finance, benchmarks, bond market, cnbc, Consumer Finance, economics, Education, finance, financial advice, Financial Advisor, financial goals, financial markets, Financial Media, financial planning, financial services industry, gross returns, historical returns, Individual Investing, individual investors, investing, investing advice, investing information, investing tips, investment advice, investments, market timing, passive investing, personal finance, portfolio, rebalancing, rebalancing investment portfolio, risk, risk tolerance, risks of bonds, risks of stocks, S&P 500, S&P 500 Index, statistics, stock market, Stock Market Returns, Stock Market Valuation, stock prices, stocks, Valuation, volatility

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#assetallocattion, #financialmarkets, 401(k), asset allocation, behavioral finance, economics, education, finance, financial, invest, investing, investments, math, noise, portfolio, portfolio management, stocks, trading, uncertainty

I am looking forward to sharing more information regarding investing, finance, economics, and general knowledge about the financial services industry in 2020.  I am hopeful to increase the pace with which I publish new information.  Additionally, I am happy to announce that I reached viewers in 108 countries in all six continents.  Countries from Japan, France, Germany, and Russia to Ghana, Colombia, and even Nepal.

Since the number of my international viewers has grown to nearly 30% of overall viewers of this blog I wanted to allocate a short potion of this post to the international community.  Some of my comments are most applicable to the US financial markets or the developed markets across the globe.  If you are living in a country that is considered part of the developing markets, I would strongly recommend that you seek out information in your country to see how much of my commentary is applicable to your stock or bond market and situation in general.  It is extremely important to realize that tax structure, transparency of information, and illiquidity of stock and bond can alter the value of what I might say.  During the course of the coming year, I will attempt to add in some comments to clarify the applicability.  However, as the aforementioned statistic regarding the global diversity of viewers of this blog suggests, I would be remiss if I did not acknowledge that I will not hit on all the issues important to all international individual investors.

I encourage you to take a close look at your portfolio early on in 2020.  It is a perfect time in terms of naturally wanting to divide up investing into calendar increments.  As you listen to all the predictions for the New Year, I would encourage you to look at your personal portfolio and financial goals first.  The second step is to always look at that economist’s or analyst’s predictions at the beginning of 2019.  Now I am not implying that incorrect recommendations in the previous year will mean that 2020 investing advice will be incorrect as well.

To help you with a potential way to look at the outlook for positioning your portfolio of investments, I recently published a summary on the topic of rebalancing a portfolio.  You can find the link below:

https://latticeworkwealth.com/2019/12/14/rebalancing-investment-portfolio-asset-allocation/

Now, there will always be unknown items on the horizon that make investing risky.  You hear that we need to get more visibility before investing in one particular asset class or another.  It usually means that the analyst wants to be even more certain how the global economy will unfold prior to investing.  I will remove the anticipation for you.  There will only be a certain level of confidence at any time in the financial markets.

One can always come up with reasons to not invest in stocks, bonds, or other financial assets.  The corollary also is true.  It can be tempting to believe that it is now finally “safe” to invest even more aggressively in risky stocks, bonds, or other assets.  As difficult as it might be, you need to try to take the “emotion” of the investing process.  Try to think of your portfolio as a number rather than a dollar amount.  Yes, this is extremely difficult to do.  But I would argue that it is much easier to look at asset allocation and building a portfolio if you think of the math as applied to a number instead of the dollars you have.  Emotional reaction is what leads to “buying high and selling low” or blindly following the “hot money”; that is when rationality breaks down.

Here is an experiment for you to do if you are able.  There are two shows I would recommend watching once a week.  The first show is Squawk Box on CNBC on Monday which airs from 6:00am-9:00am EST.  The second show is the Closing Bell on CNBC on Friday afternoon which airs from 3:00pm-5:00pm EST.  You only need to watch the last hour though once the stock and bond markets are closed.  Note that these shows do air each day of the week.  Now depending on whether or not you have the ability to tape these shows first and skip through commercials, this exercise will take you roughly 12-16 hours throughout the month of January.  You will be amazed at how different the stock and bond markets are interpreted in this manner.

When you remove the daily bursts of information, I am willing to bet that you will notice two things:

Firstly, Friday’s show should demonstrate that many “experts” got the weekly direction of the market wrong.  It is nearly impossible to predict the direction of the stock market over such a short period.

Secondly, Monday’s show should illustrate what a discussion of all the issues that have relatively more importance are.  However, this is not always a true statement though.  Generally though, financial commentators and guests appearing on the show will have had the entire weekend to reflect on developments in the global financial markets and current events.  Since the stock, bond, and foreign exchange markets are closed on Saturday and Sunday, there is “forced” reflection for most institutional investors, asset managers, research analysts, economists, and traders.  The information provided is usually much more thoughtful and insightful.

I believe that the exercise will encourage you to spend less time attempting to know everything about the markets; rather, it may be more helpful to carefully allocate your time to learning about the financial markets.  After you devote your time to watching CNBC in this experiment, I recommend one other ongoing personal experiment.  Try picking three financial market guests that appear on CNBC during January and see how closely their predictions match reality.  You might want to check in once a month or so.  I think that this exercise will show you how futile it is to try and time and predict the direction/magnitude of the stock market and other financial markets too (e.g. bonds and real estate).

Best of luck to you in 2020!  As always, I would encourage anyone to send in comments or suggestions for future topics to my email address at latticeworkwealth@gmail.com.

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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