• Purpose of This Blog and Information about the Author

Latticework Wealth Management, LLC

~ Information for Individual Investors

Latticework Wealth Management, LLC

Tag Archives: Financial Media

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

≈ 3 Comments

Tags

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

≈ 4 Comments

Tags

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.

Are Stocks Currently Overvalued, Undervalued, or Fairly Valued? Answer: Yes.

10 Tuesday May 2016

Posted by wmosconi in academia, academics, asset allocation, Average Returns, business, CAPE, CAPE P/E Ratio, Consumer Finance, Cyclically Adjusted Price Earnings Ratio, Education, finance, finance theory, financial advice, financial goals, financial markets, Financial Media, Financial News, financial planning, financial services industry, Forward P/E Ratio, Individual Investing, individual investors, interest rates, investing, investing advice, investing information, investing tips, investment advice, investments, Nobel Prize, Nobel Prize in Economics, P/E Ratio, passive investing, personal finance, portfolio, risk, Robert Shiller, Schiller, Shiller P/E Ratio, statistics, stock market, Stock Market Returns, Stock Market Valuation, stock prices, stocks, Valuation, volatility

≈ 1 Comment

Tags

business, CAPE P/E Ratio, Cyclically Adjusted Price Earnings Ratio, economics, education, finance, financial advice, financial markets, Financial Media, Financial News, financial planning, financial services, financial services industry, individual investing, interest rates, investing, investment advice, investments, P/E Ratio, personal finance, portfolio, portfolio allocation, portfolio management, Robert Shiller, Shiller, statistics, stock market, Stock Market Valuation, stock valuation, stocks, Valuation, volatility

Confusing and frustrating as it may be, the answer about the current valuation of stocks will always be different depending on who you ask. Various economists, mutual fund portfolio managers, research analysts, financial news print and TV personalities, and other parties seem to disagree on this very important question.  Financial professionals will offer a wide range of financial and economic statistics in support of these opinions on the current valuation of stocks.  One of the most often cited statistics in support of a person’s opinion is the P/E ratio of the stock market at any given point in time.   Many financial professionals use it as one of the easiest numbers to be able to formulate a viewpoint on stock valuation.  However, when it comes to any statistic, one must always be skeptical in terms of both the way the number is calculated and its predictive value.  Any time one number is used to describe the financial markets one must always be leery.  A closer examination of the P/E ratio is necessary to show why its usage alone is a poor way to make a judgement in regard to the proper valuation of stocks.

The P/E ratio is short for Price/Earnings ratio. The value is calculated by taking the current stock price divided by the annual earnings of the company.  When it is applied to an entire stock market index like the S&P 500 index, the value is calculated by taking the current value of the index divided by the sum of the annual earnings of the 500 companies included in the index.  One of the very important things to be aware of is that the denominator of the equation may actually be different depending on who is using the P/E ratio.  Some people will refer to the P/E ratio in terms of the last reported annual earnings for the company (index).  Other people will refer to the P/E ratio in terms of the expected earnings for the company (index) over the next year.  In this particular case, the P/E ratio is referred to as the Forward P/E ratio.  Both ratios have a purpose.  The traditional P/E ratio measures the reported accounting earnings of the firm (index).  It is a known value.  The Forward P/E ratio measures the profits that the firm (index) will create in the future.  However, the future profits are only a forecast.  Many analysts prefer to use the Forward P/E ratio because the value of any firm (or index of companies) is determined by its future ability to generate profits for its owners.  The historical earnings are of lesser significance.

The P/E ratio is essentially a measure of how much investors value $1 worth of earnings and what they are willing to pay for it. For example, a firm might have a P/E ratio of 10, 20, 45, or even 100.  In the case of a firm that is losing money, the P/E ratio does not apply.  In general, investors are willing to pay more per each $1 in earnings if the company has the potential to grow a great deal in the future.  Examples of this would be companies like Amazon (Ticker Symbol:  AMZN) or Netflix (Ticker Symbol:  NFLX) that have P/E ratios well over 100.  Some companies are further along in their life cycle and offer less growth opportunities and tend to have lower P/E ratios.  Examples of this would be General Motors or IBM that have P/E ratios in the single digits or low teens, respectively.  Investors tend to pay more for companies that offer the promise of future growth than for companies that are in mature or declining industries.

When it comes to the entire stock market, the P/E ratio applied to a stock market index (such as the S&P 500 index) measures how much investors are willing to pay for the earnings of all the companies in that particular index. For purposes of discussion and illustration, I will refer to the S&P 500 index while discussing the P/E ratio.  The average P/E ratio for the S&P 500 index over the last 40 years (1966-2015) was 18.77.  When delivering an opinion on the valuation of the S&P 500 index, many financial professionals will cite this number and state that stocks are overvalued (undervalued) if the current P/E ratio of the S&P 500 index is above (below) that historical average.  If the current P/E ratio of the S&P 500 index is roughly in line with that historical average, the term fairly valued will usually be used in relation to stocks.  The rationale is that stocks are only worth what their earnings/profits are over time.  There is evidence that the stock market can become far too highly priced (as in March 2000 or December 2007) or far too lowly priced (as in 1982) based upon the P/E ratio observed at that time.  Unfortunately, the relative correlation between looking at the difference between the current P/E ratio of the stock market and the historical P/E ratio does not work perfectly.  In fact, it is only under very extreme circumstances and with perfect hindsight that investors can see that stocks were overvalued or undervalued in relation to the P/E ratio at that time.

Here are the historical P/E ratios for the S&P 500 index from 1966-2015 as measured by the P/E ratio at the end of the year. Additionally, the annual return of the S&P index for that year is also shown.

Year P/E Ratio Annual Return
2015 22.17 1.30%
2014 20.02 13.81%
2013 18.15 32.43%
2012 17.03 15.88%
2011 14.87 2.07%
2010 16.30 14.87%
2009 20.70 27.11%
2008 70.91 -37.22%
2007 21.46 5.46%
2006 17.36 15.74%
2005 18.07 4.79%
2004 19.99 10.82%
2003 22.73 28.72%
2002 31.43 -22.27%
2001 46.17 -11.98%
2000 27.55 -9.11%
1999 29.04 21.11%
1998 32.92 28.73%
1997 24.29 33.67%
1996 19.53 23.06%
1995 18.08 38.02%
1994 14.89 1.19%
1993 21.34 10.17%
1992 22.50 7.60%
1991 25.93 30.95%
1990 15.35 -3.42%
1989 15.13 32.00%
1988 11.82 16.64%
1987 14.03 5.69%
1986 18.01 19.06%
1985 14.28 32.24%
1984 10.36 5.96%
1983 11.52 23.13%
1982 11.48 21.22%
1981 7.73 -5.33%
1980 9.02 32.76%
1979 7.39 18.69%
1978 7.88 6.41%
1977 8.28 -7.78%
1976 10.41 24.20%
1975 11.83 38.46%
1974 8.30 -26.95%
1973 11.68 -15.03%
1972 18.08 19.15%
1971 18.00 14.54%
1970 18.12 3.60%
1969 15.76 -8.63%
1968 17.65 11.03%
1967 17.70 24.45%
1966 15.30 -10.36%

Average             18.77

The P/E ratio for the S&P 500 index has varied widely from the single digits to values of 40 or above. The important thing to observe is that very high P/E ratios are not always followed by low or negative returns, nor are very low P/E ratios followed by very high returns.  In terms of a baseline, the S&P 500 index returned approximately 9.5% over this 40-year period.  As is immediately evident, the returns of stocks are quite varied which is what one would expect given the fact that stocks are known as assets that exhibit volatility (meaning that they fluctuate a lot because the future is never known with certainty).  Thus, whenever a financial professional says that stocks are overvalued, undervalued, or fairly valued at any given point in time, that statement has very little significance.  Whenever only one data point is utilized to give a forecast about the future direction of stocks, an individual investor needs to be extremely skeptical of that statement.  The P/E ratio does hold a very important key for the future returns of stocks but only over long periods of time and certainly not over a short timeframe like a month, quarter, or even a year.

An improvement on the P/E ratio was developed by Dr. Robert J. Shiller, the Nobel Prize winner in Economics and current professor of Economics at Yale University. The P/E ratio that Dr. Shiller developed is referred to as the Shiller P/E ratio or the CAPE (Cyclically Adjusted Price Earnings) P/E ratio.  This P/E ratio takes the current value of a stock or stock index and divides it by the average earnings of a firm or index components for a period of 10 years and also takes into account the level of inflation over that period.  The general idea is that the long-term earnings of a firm or index determine its relative valuation.  Thus, it does a far better job of measuring whether or not the stock market is fairly valued or not at any given point in time.  However, another very important piece of the puzzle has to do with interest rates.  Investors are generally willing to pay more for stocks when interest rates are low than when interest rates are high.  Why?  If it is assumed that the future earnings stream of the company remains the same, an investor would be willing to take more risk and invest in stocks over the safety of bonds.  A quick example from everyday life is instructive.  Imagine that your friend wants to borrow $500 for one year.  How much interest will you charge your friend on the loan?  Let’s say you want to earn 5% more than what you could earn by simply buying US Treasury Bills for one year.  A one-year US Treasury Bill is risk free and, as of May 10, 2016 yields interest of 0.50%.  Therefore, you might charge your friend 5.5% on the loan.  Now back in the early 1980’s, one-year US Treasury Bills (and even savings accounts at banks) were 10% or higher.  If you were to have provided the loan to your friend then, you would not charge 5.5% because you could simply deposit the $500 in the bank.  You might charge your friend 15.5% on the loan assuming that the relative risk of your friend not paying you back is the same in both time periods.  It is very similar when it comes to investing in stocks.  Due to the fact that stocks are volatile and future profits are unknown, investors tend to prefer bonds over stocks as interest rates rise.  This phenomenon causes the value of stocks to fall.  Conversely, as interest rates fall, the preference for bonds decreases and investors will choose stocks more and prices go up.  Now this assumes that the future earnings of the company or index constituents stay the same in either scenario.

With that information in mind, a better way to gauge the relative valuation of stocks in terms of being overvalued, undervalued, or fairly valued, would be to look at the Shiller P/E ratio in combination with interest rates. It is most common for investors to utilize the 10-year US Treasury note as a proxy for interest rates.  Here are the historical values for the Shiller P/E ratio and the 10-year US Treasury note over the same 40-year period (1966-2015) as before:

Year CAPE Ratio 10-Year Yield
2015 24.21 2.27%
2014 26.49 2.17%
2013 24.86 3.04%
2012 21.90 1.78%
2011 21.21 1.89%
2010 22.98 3.30%
2009 20.53 3.85%
2008 15.17 2.25%
2007 24.02 4.04%
2006 27.21 4.71%
2005 26.47 4.39%
2004 26.59 4.24%
2003 27.66 4.27%
2002 22.90 3.83%
2001 30.28 5.07%
2000 36.98 5.12%
1999 43.77 6.45%
1998 40.57 4.65%
1997 32.86 5.75%
1996 28.33 6.43%
1995 24.76 5.58%
1994 20.22 7.84%
1993 21.41 5.83%
1992 20.32 6.70%
1991 19.77 6.71%
1990 15.61 8.08%
1989 17.05 7.93%
1988 15.09 9.14%
1987 13.90 8.83%
1986 14.92 7.23%
1985 11.72 9.00%
1984 10.00 11.55%
1983 9.89 11.82%
1982 8.76 10.36%
1981 7.39 13.98%
1980 9.26 12.43%
1979 8.85 10.33%
1978 9.26 9.15%
1977 9.24 7.78%
1976 11.44 6.81%
1975 11.19 7.76%
1974 8.92 7.40%
1973 13.53 6.90%
1972 18.71 6.41%
1971 17.26 5.89%
1970 16.46 6.50%
1969 17.09 7.88%
1968 21.19 6.16%
1967 21.51 5.70%
1966 20.43 4.64%

Average                19.80                          6.44%

These two data points provide a much better gauge of whether or not stocks are currently overvalued or undervalued. For example, take a look at the Shiller P/E ratio in the late 1970’s and early 1980’s.  The value of the Shiller ratio is in the single digits during this time period because interest rates were higher than 10%.  Lately interest rates have been right around 2.0%-2.5% for the past several years.  Therefore, one would expect that the Shiller P/E ratio would be higher.  Now the historical average for the Shiller P/E ratio was 19.80 over this period.  The Shiller P/E ratio was in the neighborhood of 40 during 1998-2000 which preceded the bursting of the Internet Bubble in March 2000.  The Shiller P/E ratio was at its two lowest levels of 7 and 8 in 1981 and 1982, respectively which is when the great bull market began.  However, while this Shiller P/E and interest rates are better than simply the traditional P/E ratio, there are flaws.  The Shiller P/E in 2007 was 24.02 right (and interest rates were around 4.0% which is on the low side historically) before the huge market drop of the Great Recession between September 2008 and March 2009.  In fact, the S&P 500 index was down over 37% in 2008, and the Shiller P/E did not provide an imminent warning of any such severe downturn.  Therefore, even looking at these two measures is imperfect but better than the normal P/E ratio in isolation.

To summarize the discussion, individual investors will always be told on a daily basis by various sources that the stock market is currently overvalued, undervalued, and fairly valued at the same time. One of the most commonly used rationales is a reference to the current P/E ratio in relation to the historical P/E ratio.  As we have seen, this one data point is a very poor indicator of the future direction and relative value of stocks at any given period of time, especially for short periods of time (one year or less).  The commentary and opinions provided by financial “experts” to individual investors when the P/E ratio is mentioned normally relates to the short term.  By looking back at the historical data, it is clear that this one data point is really only relevant over very long periods of time.  The Shiller P/E ratio in combination with current interest rates is a great improvement over the traditional P/E ratio, but it is even imperfect when it comes to forecasting the future returns of the stock market.  There are two general rules for individual investors to take away from this discussion.  Whenever a comment is made about the current value of stocks and only one statistic is provided, the opinion should be taken with a “grain of salt” and weighed only as one piece of information in determining investment decisions that individual investors may or may not make.  Additionally, and equally as important, if a financial professional cites a statistic about stock valuation that you do not understand (even after doing some research of your own), you should always discard that opinion in most every case.  Individual investors should not make major investment decisions in terms of altering large portions of their investment portfolios of stocks, bonds, and other financial assets utilizing information that they do not understand.  It sounds like common sense, but, in the sometimes irrational world of investing, this occurrence is far more common than you imagine.

Subscribe

  • Entries (RSS)
  • Comments (RSS)

Archives

  • March 2021
  • February 2021
  • January 2021
  • December 2020
  • November 2020
  • October 2020
  • January 2020
  • December 2019
  • November 2019
  • October 2019
  • September 2019
  • April 2017
  • July 2016
  • May 2016
  • March 2016
  • December 2015
  • November 2015
  • July 2015
  • June 2015
  • May 2015
  • August 2014
  • March 2014
  • February 2014
  • January 2014
  • December 2013
  • November 2013
  • October 2013
  • September 2013
  • August 2013
  • July 2013

Categories

  • academia
  • academics
  • active investing
  • active versus passive debate
  • after tax returns
  • Alan Greenspan
  • alpha
  • asset allocation
  • Average Returns
  • bank loans
  • behavioral finance
  • benchmarks
  • Bernanke
  • beta
  • Black Swan
  • blended benchmark
  • bond basics
  • bond market
  • Bond Mathematics
  • Bond Risks
  • bond yields
  • bonds
  • book deals
  • books
  • Brexit
  • Brexit Vote
  • bubbles
  • business
  • business books
  • CAPE
  • CAPE P/E Ratio
  • Charity
  • Charlie Munger
  • cnbc
  • college finance
  • confirmation bias
  • Consumer Finance
  • correlation
  • correlation coefficient
  • currency
  • Cyclically Adjusted Price Earnings Ratio
  • Dot Com Bubble
  • economics
  • Education
  • EM
  • emerging markets
  • Emotional Intelligence
  • enhanced indexing
  • EQ
  • EU
  • European Union
  • Fabozzi
  • Fama
  • Fed
  • Fed Taper
  • Fed Tapering
  • Federal Income Taxes
  • Federal Reserve
  • Fiduciary
  • finance
  • finance books
  • finance theory
  • financial advice
  • Financial Advisor
  • financial advisor fees
  • financial advisory fees
  • financial goals
  • financial markets
  • Financial Media
  • Financial News
  • financial planning
  • financial planning books
  • financial services industry
  • Fixed Income Mathematics
  • foreign currency
  • forex
  • Forward P/E Ratio
  • Frank Fabozzi
  • Free Book Promotion
  • fx
  • Geometric Returns
  • GIPS
  • GIPS2013
  • Greenspan
  • gross returns
  • historical returns
  • Income Taxes
  • Individual Investing
  • individual investors
  • interest rates
  • Internet Bubble
  • investing
  • investing advice
  • investing books
  • investing information
  • investing tips
  • investment advice
  • investment advisory fees
  • investment books
  • investments
  • Irrational Exuberance
  • LIBOR
  • market timing
  • Markowitz
  • math
  • MBS
  • Modern Portfolio Theory
  • MPT
  • NailedIt
  • NASDAQ
  • Nassim Taleb
  • Nobel Prize
  • Nobel Prize in Economics
  • P/E Ratio
  • passive investing
  • personal finance
  • portfolio
  • Post Brexit
  • PostBrexit
  • probit
  • probit model
  • reasonable fees
  • reasonable fees for financial advisor
  • reasonable fees for investment advice
  • reasonable financial advisor fees
  • rebalancing
  • rebalancing investment portfolio
  • rising interest rate environment
  • rising interest rates
  • risk
  • risk tolerance
  • risks of bonds
  • risks of stocks
  • Robert Shiller
  • S&P 500
  • S&P 500 historical returns
  • S&P 500 Index
  • Schiller
  • Search for Yield
  • Sharpe
  • Shiller P/E Ratio
  • sigma
  • speculation
  • standard deviation
  • State Income Taxes
  • statistics
  • stock market
  • Stock Market Returns
  • Stock Market Valuation
  • stock prices
  • stocks
  • Suitability
  • Taleb
  • time series
  • time series data
  • types of bonds
  • Uncategorized
    • investing, investments, stocks, bonds, asset allocation, portfolio
  • Valuation
  • volatility
  • Warren Buffett
  • Yellen
  • yield
  • yield curve
  • yield curve inversion

Meta

  • Register
  • Log in

Blog at WordPress.com.

Cancel

 
Loading Comments...
Comment
    ×