Market Timing

And I’m back.

I know. You have not had any material to read when you wake up at 3:02 am and cannot go back to sleep. Well this topic today should put you to sleep in fifteen minutes.

Ready. Go.

Everyone in the financial industry talks about market timing. Is it possible? Are there professionals that can time the market consistently? Are those that have timed the market just lucky?

Let’s start with defining market timing. The definition itself is rather ambiguous. Generally, market timing is buying (or selling) the market (say S&P 500) right before the market (S&P 500) is about to go up (down). Personally, when I hear market timing, I think VERY short term…like within days and weeks. Once you start hitting months, I see it as tactical/strategic asset allocation as opposed to market timing. Therefore, based on my definition, I don’t see any professional being able to “time the market” on a consistent basis. Otherwise, all investors would invest with that individual/company. [Sidenote: going forward in this article the word market is synonymous with the S&P 500. ]

I implicitly left open that perhaps one could be able to look out months, or years, and have a general feel of where the market is headed. What do I mean by this?

I think there are several indicators that can be used to signal that the market is under/overvalued. I won’t delve into those indicators now (but there are a lot of good ones). However, these indicators cannot predict (in my opinion) with consistency very short term – daily or weekly – movements of the market. To use a temperature analogy, the indicators can gauge how cold, lukewarm, or hot the market is…but they cannot tell you the specific Fahrenheit degree. Sticking with the analogy, the inability of indicators to tell the specific degree is in direct agreement with the famous Keynes statement that, “The market can stay irrational longer than you can stay solvent.”

One of the best pieces on that topic is from the people at Gestaltu. They found that over a long period of time, it’s better to hold onto the market even when the market is in the 80th percentile (meaning at that point in time, the market has only been more expensive 20% of the time so one would think it’s pretty overvalued) than to sell the market when it crosses the 50th percentile.

The market goes up longer than one would expect, and the market goes down further than one would expect.

That was a short tangent.

So what am I trying to say?

To repeat, indicators can give warning that the market is going to drop (let’s be directional here and focus on drawdowns) in the upcoming months or years.

“How is that any good?” you may ask. “I’m supposed to ‘sit in cash’ for months, or years, and just wait? Wait on the sidelines?”

Yes, exactly. You are supposed to wait, and be patient. Sit in cash? No, do not sit in cash. Earn a conservative 2% by investing in treasuries. Then, when the market reverses and tanks (say 20%) then you earn 2% from income + say another 5-6% in the treasuries appreciating + you miss a 20% correction. That’s a total return of nearly 30%!

You may not like the idea of “sitting on your hands,” but look at the below chart. I looked at the largest pullbacks in the S&P 500 since 1950. I used the time periods based on Wesley Gray’s numbers here.

drawdown final

The goal of the chart? Look how far back those losses took the market. Obviously, the largest one is the 2007-2009 recession. It destroyed your gains as far back as April of 1997! That means you could have placed your money under your mattress from April 1997 until February 2009 and you would be no different (monetarily, not emotionally) than someone who invested in the market that entire time. There was another crash that was almost equally as devastating to your gains back in the early part of the ’70s. And the dot-com crash wiped out more than five years of gains.

My point is NOT to convince you that I’m a bear investor (I am definitely not), but to emphasize patience when the market is pretty overheated. Going back to my temperature analogy, the water is pretty hot right now. My opinion? There is a much higher chance that the market will drop 20% than will go up 20%. I don’t like the current risk/return probability. Where can I obtain a 30% return (since that’s  what everyone wants!)? Sit in treasuries and wait for the correction/pullback/whatever you want to call it that will (probably) come in the next 12 to 24 months. It’s a similar concept of paying down debt that is charging you 5%, or sit in cash. You are “earning” 5% by paying down that debt…though it does not feel like it.

And if it doesn’t come? You earned 2%. You learned some discipline. And you learned to never trust me. Jk.


Just so people don’t think I’m a bear investor, I’m tempted to have my next article focus on the speed of the recovery AFTER those losses from the above chart occurred, as I think they are occur much faster than you may believe.


Adam McCurdy, CFP®, EA

As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyMarket Timing

How much do losses really hurt?

A lot.

Let’s find out why.

From a previous article I wrote – Drawdowns – Can they help you? – a reader (I must have close to 10 readers by now!) would know that the S&P 500 lost almost 60% of its value at the lowest points in both the dot-com crash in 2000–2002, and the financial crisis in 2008 –2009. How great does your return need to be following a drawdown of such magnitude in order to simply break even? Below is a chart that displays those returns.

numbers - drawdowns


The alarming thing is not that diversified indexes like the S&P 500 could drop 60% over a period of time (perhaps I’m just immune now to that type of pain). It’s that investors, with the click of a button, can invest in products that have a chance of dropping 100%. Most investors understand default risk when it comes to buying a single stock or a single bond from a company. However, oftentimes, when investors purchase mutual funds and ETFs, they believe that product is diversified enough to protect them from the large losses displayed in the above chart.

Let’s use oil as an example. If an investor back in 2007 said, “Hey, I’m pretty bullish about oil. I want to invest.” (Before you laugh, remember that most everyone was bullish about most everything back in 2007.) Watch, or read, The Big Short to find the dozen people in the world who were NOT bullish. Anyway, this investor finds a highly traded ETN (exchange-traded note) called iPath S&P GSCI Crude TR ETN. The price of this ETN peaked around 83.16 on June 30, 2008. Today, it’s trading at $4.80. That is a 94.23% loss. One may argue that there is no way the investor has stayed invested this entire time – I will concede that point. However, on February 27, 2009,  the ETN traded at 17.69. That is only around 242 days later and the investor already lost 78.73%.

Let’s go further. Say after falling to $17.69, a second investor thought, “Okay, we are through the worst. I think oil has been hit too hard. I want to invest.” This investor buys the ETN on March 31, 2009 at $19.09. I use the end of March 2009 specifically because  it is the month people say was the trough of the recession. Fast-forward to today and this second investor has still lost 75%! Even though this individual missed the worst part of the fall!

Another (just as realistic) scenario would be the NASDAQ Composite. Back in March 2000 it reached a high price of 5,132. By October 2002 it reached a low of 1,108. A drop of 78%. Having a large holding of stocks did not stop this huge drop.

Some emerging markets index ETFs dropped more than  70% during the financial crisis.

A 60% loss is almost the “realistic” worst case norm for “investment-grade” stocks such as Dow Jones Industrial Average and the S&P 500. However, once an investor leaves that arena, any loss is fair game.

I stress this is for two reasons. The first is that I myself was tempted to “play with oil” a couple years ago and I looked at this specific ETN. On June 30, 2014  it traded at $25.54. Technically, I earned almost 81% by NOT investing since that’s how much I would have lost if I bought the ETN at that level.

The second (and more painful) reason is that even sophisticated investors are not immune to flaws. Since we are in a “circle of trust” (right??) I will tell you about my worst investment. Back in 2014, I invested in a stock I believed was fundamentally sound. The financial statements were strong. Growth (both revenue and net income) was in the high double digit range. Return on invested capital was amazing. Debt was non-existent. Consumer confidence was high, and improving. The valuation of the stock was trading around, or slightly below, industry average. What was not to like?

I was so confident that I bought the stock outright, wrote a call option for a strike price 10% above the current price, and wrote a put option on it so that if the price dropped 10%, then I would buy more. After a month and a half went by and the stock had dropped, being the brash investor I was, I wrote ANOTHER put option on it. A month later, I had the first put “put” to me at $85. The next year and a half went in similar fashion. I continued to write put options, and have them put to me, and I watched as the stock bottomed around $35 last Friday. That is about a 63% loss. The recent drop of the S&P 500 saved my sanity as before the index had appreciated, but now it was in the red (accounting for dividends it was down around 1%…). What’s the saying – ‘Misery loves company’?

I think the most ironic thing was that I held on to about half of my original position (Yes, somehow I was able to muster the strength to liquidate half of the position over time) to avoid “getting out” at the bottom. However, on Friday I simply needed the funds for another investment and I was forced to liquidate….at the bottom. It has now gone up around 10%.

Simply said, it hurts.

I’ve made a number of great investments with real estate. Almost all the individual stocks I bought have gone up during their respective holding period. The great majority of the put options that I wrote have expired worthless. However, this one investment really sticks with me.

Interestingly, I’m still very confident about the company. The growth is still there, as well as the return on invested capital. The valuation is now half of the industry average as the company’s earnings have continued to increase – it’s just that other investors are now only willing to pay 8x its earnings, as opposed to 19x when I originally invested.

I would not classify the original investment as a mistake. The mistake, and flaw, was to not limit my investment to a specific percentage of my assets. The mistake was to continually invest more money into a single stock even after it had dropped 25%.  My conviction about the company impaired my decision-making.

Sophisticated investors, financial advisors, and portfolio managers are not immune to mistakes and flaws. The difference between the successful ones, and the “others,” is that successful ones learn from those mistakes.

As I said at the beginning, losses hurt a lot.


Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyHow much do losses really hurt?

The January Effect

First, what do people mean when they use the term “January Effect” regarding a stock investment? As defined by a legitimate source (Wikipedia…) “The January effect is a hypothesis that there is a seasonal anomaly in the financial market where securities’ prices increase in the month of January more than in any other month.” Believers support this hypothesis because they say investors sell in December for tax harvesting reasons, and then buy back the stocks in January. I don’t believe it, and neither should you. Actually, at one point, it may have been true. However, once it became public information, any “effect” would have been eroded.

I performed my own January experiment, though I’m sure somewhere out there in the myriad of financial articles someone already beat me to it. This is a behavior finance study. If stocks, and by stocks I mean the S&P 500, are down for the month of January, do investors “flee” from stocks, and therefore stocks have a bad eleven months?

Let’s start with a simple linear regression. We will have two variables. The dependent variable (Y) will be the return of the accumulated eleven months after January. The explanatory variable (X), will be the return for January. We will use data from Yahoo finance and analyze only the price movement, which means we ignore dividends. The time period will be from 1980 to 2015. Below are the results.



    Min    1Q   Median       3Q   Max

-0.43070 -0.06902  0.01152  0.09488  0.28261


              Estimate Std. Error   t value      Pr(>|t|)  

(Intercept)  0.08493 0.02558    3.320        0.00216 **

janreturn 0.14626    0.51329    0.285       0.77741  

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1518 on 34 degrees of freedom

Multiple R-squared:  0.002383,  Adjusted R-squared:  -0.02696

F-statistic: 0.0812 on 1 and 34 DF,  p-value: 0.7774

january return vs rest of year

What does the statistics output and graph tell us? First let’s review the assumptions that must hold true when conducting a regression test. There are four of them:

  1. Linearity between variables – For example, if X increases by 1 percentage point, then Y increases by 2 percentage points. A graph is a good way to quickly tell if there is a linear relationship.
  2. Normal distribution of errors – The errors are random and stay within a certain range.
  3. Errors are independent – The errors do not exhibit a pattern. This can be shown through a graph.
  4. Equal variance – The errors must have the same variance.

From the above graph, we see there is minimum linear relationship between the X and Y variables. If you were to draw a “least-squares” line, it would probably be very flat and go through the zero on the Y-axis. Therefore, one assumption is already violated. The other three assumptions involve the error of the regression. There are actually multiple ways to display the error. Below are the graphs.

january residuals

There is the residual (aka error; this is calculated as the actual observation of Y subtracted by the predicted value of Y). This value is on the Y axis of three of the four graphs. The other value is called the “studentized” residual. Simply stated, this helps show any outlier errors (observations). If a point lies outside of the + / – of 3 then we may be concerned. Especially since we have a relatively same sample size of 35. Now, what we are looking for is a “cloud” of points. In three of the four graphs, that is exactly what we see. However, the first graph is actually a very straight line. This shows that when the residuals/errors are plotted again the Y variable, a pattern is corrected. This violates another assumption – equal variance. You can tell that as the Y value increases, so does the residual/error.

We now have at least two violations of the four assumptions. This means that the variables are not accurate for a regression. In other words, the return of January does not predict the rest of the year’s return. We can see this statistically in the output. For the janreturn (X) coefficient (the row is bolded), the t stat is below the absolute value of 2. This means that we are not 95% confident that January’s return predicts the cumulative returns for the other months. In other words, this regression is not statistically significant – it does not explain anything!

Let’s not stop there. We tried a simple regression. Now let’s try a logistic regression. This is used when we want our Y variable to be binary – 0 or 1. We will classify 0 to mean that the cumulative returns between February and December are negative or 0. We will classify 1 to mean that the cumulative returns are above 0, or positive. Our X variable will remain the same. Below is the output.


Deviance Residuals:

  Min      1Q  Median   3Q     Max

-1.333   1.068   1.144   1.187   1.335


            Estimate   Std. Error   z value    Pr(>|z|)  

(Intercept)  1.254262   0.405750   3.091 0.00199 **

janconvert  -0.001984   0.081380  -0.024 0.98055  

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 38.139  on 35  degrees of freedom

Residual deviance: 38.138  on 34  degrees of freedom

AIC: 42.138

Number of Fisher Scoring iterations: 4


The result is the same as before. The coefficient of our X variable is not statistically significant. For this study, we can use the z stat and the t stat interchangeably.

Okay, one last shot, still using logistic regression. Let’s convert our X variable into a binary variable, similar to our Y variable from our second regression. Instead of caring about what the actual return is in January, we just care if it’s positive or negative. Any luck?


Deviance Residuals:

Min       1Q   Median    3Q      Max

-1.8465   0.6335   0.6335   0.8203   0.8203


           Estimate Std. Error z value Pr(>|z|)

(Intercept)   0.9163     0.5916   1.549 0.121

januaryyesorno   0.5878     0.8097   0.726 0.468

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 38.139  on 35  degrees of freedom

Residual deviance: 37.614  on 34  degrees of freedom

AIC: 41.614

Number of Fisher Scoring iterations: 4


Unfortunately, again there is no statistical evidence that the return of January accurately predicts the return for the rest of the year.

I know I said that was our last try, but I want to do one last trick to see if we can find some significance. I will assume no intercept for our last regression. Below is the output.


Deviance Residuals:

Min       1Q   Median    3Q      Max

-1.8465   0.6335   0.6335   1.1774   1.1774


           Estimate Std. Error z value Pr(>|z|)  

januaryyesorno   1.5041     0.5528   2.721  0.00651 **

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 49.907  on 36  degrees of freedom

Residual deviance: 40.270  on 35  degrees of freedom

AIC: 42.27

Number of Fisher Scoring iterations: 4


Look at that!! We discovered the key to the market! This equation is technically statistically significant, which means the January return explains the rest of the year. We will make millions together!!!

But wait a minute.

Actually, all this proves is that you can tinker with data until you find something you like, aka data mining. Would you trust your hundreds of thousands or millions of dollars on the above equation? Neither would I.

This leads into my next article that will talk about how 2+2 may not always equal 4, meaning numbers can lie. Or rather – numbers can be deceitful.


Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyThe January Effect

Random walk – what does that even mean?!?

Autocorrelation. I guess I should throw in autoregression as well (special thanks to Abbie).

That is all you need to know….

But if you do want to know more, then I will proceed.

When academia say that the market, be it the Dow Jones Industrial Average or the S&P 500, is simply a “random walk”, what they mean is that the price of those indexes just wanders around seemingly aimlessly. That is, the price yesterday is highly correlated with the price today. Going further, the price yesterday has a great influence on today’s price. Hence the word autocorrelation – when what happens at time t is correlated with time t -1.

What does this look like? Below is a plot of the price of the S&P 500 since January 3, 1950 until December 16, 2015. That is 16,597 trading days! I believe any professor would say that is a large enough sample size…

SP500 16597 days

What does this chart say? It may be hard to see, but it shows how close the price today is from yesterday’s price. However, let’s plot Y versus Y t -1 (Price today versus Price yesterday). This may be more helpful to visualize the correlation.

price yesterday vs price today

Wow…I do not even need to plot a regression line. The dots create one themselves!

Now, what does “autocorrelation” look like? Below is a plot:

ACF for sp500

If you look carefully there are two parallel blue dotted lines. When the black mass is above or below the dotted lines then there is statistical significance for the acf (autocorrelation) values. This means that Y (Price today) and the lag of Y (be it, yesterday’s price, last week’s price, last month’s price, etc.) are pretty correlated. You can see that the black is above the blue dotted line almost until Day 5000! Then it instantly drops below the bottom dotted blue line. We care about the absolute value so positive or negative makes no difference, and it’s a little beyond the scope of this article.

But let’s delve into it anyway! Day 5000 is about 20 years of trading, as there are around 250 trading days in a year (so 5000 / 250 = 20). Therefore, the price now is so large relative to the price 5000 (20 years) trading days ago that it would exhibit be negative correlation. Or perhaps another way to think about it is. You have two prices, say price 100 and price 6000. If you move forward to price 99 and backward to price 6001 then you would expect these prices to move inversely, meaning you would think price 99 would increase relative to price 100 (and price 6000 and price 6001 for that matter) and you would think price 6001 would decrease relative to the aforementioned prices. That is my best shot. Someone who is smarter than I am can confirm or deny this last paragraph.

Moving along.

If we create an autoregressive model, we can now intuitively estimate that the coefficient of Y t-1 (price of yesterday) will be extremely close to 1. This is because in a “random walk” acf values stay above/below the dotted lines for a long time. If the coefficient is below 1, then Y (price) will be pulled back to its mean and the autocorrelation will drop off rather quickly (fall within the two dotted lines). And if the coefficient is above 1, then the apocalypse occurs. So what is the coefficient (autoregressive term) of yesterday’s price??



Estimate            Std. Error           t value         Pr(>|t|)

(Intercept)                 0.0655848       0.0827029         0.793             0.428

yesterdaysprice   1.0001207     0.0001123      8905.497    <2e-16 ***

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.039 on 16594 degrees of freedom

Multiple R-squared:  0.9998,    Adjusted R-squared:  0.9998

F-statistic: 7.931e+07 on 1 and 16594 DF,  p-value: < 2.2e-16


And there you have it. The coefficient for yesterday’s price (the X variable in this equation) is exactly 1. All of the “statistical” measures hold up (i.e. the t value is greater than 2, p value is less than .05). Now you may have heard the phrase, random walk…with a drift. This means that the intercept, B0, is not equal to 0. However, from this data we cannot be sure.

Now when you are at your next cocktail party and your neighbor uses the phrase random walk when speaking about investments, you can keep him or her honest! Your welcome.



Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyRandom walk – what does that even mean?!?

Drawdowns – Can they help you?

I always assumed that it was generally “good” for a client to reallocate their funds when there were big moves in the market. I say generally because sometimes big moves are very short, or technical, occurrences that don’t hold any weight (think flash crash). It is the “buy low” adage.

I wanted to back this adage up with some numbers, so I decided to do my own experiment. I must say, I was just as surprised at the results I found as I am every time I flip on the TV and see that Donald Trump is still a presidential candidate (seriously?? How many bankruptcies has that “successful businessman” declared?).

Before I lay out the experiment and results, however, I want to reiterate what every one of my posts ends with. This is just for informational use only. Take it for what you will, but don’t expect the next twenty years (and future recessions) to behave exactly like the last twenty years. Wasn’t it Mark Twain that said, “History doesn’t repeat, it rhymes”?


Here are the four investments within the experiment


  •         S&P 500 Total Return
  •         US Barclays Aggregate Bond Total Return
  •         Balanced Portfolio
  •         Tactical Portfolio


The balanced portfolio consisted of the S&P 500 and the US Barclays Aggregate Bond. Every year it starts off invested in 50% of each of those two investments. At the end of the year, it reallocates back to a 50/50 split.

The tactical portfolio is a little trickier. It also only invests in the S&P 500 and the US Barclays Aggregate Bond. It starts the same way as well, invested in 50% of each fund, and rebalances back to a 50/50 split at the end of every year. However, there is a “trigger” that causes a difference in the allocation. That trigger is a 10% drop in the S&P 500. When there is a 10% drop from a high in the S&P 500 price, 5% of the portfolio is moved from the US Barclays Aggregate Bond into the S&P 500. If there is another 10% drop (so a total of 20% from the high of the S&P 500), there is another 5% reallocation. This continues with every 10% drop. In order for the equity exposure to be lowered, the S&P 500 must reach its previous high. I will give a brief example:

  1.       S&P 500 price is 100.
  2.       Price drops to 90.
  3.       Move 5% of the portfolio from US Barclays Aggregate Bond to S&P 500
  4.       Price drops to 75.
  5.       Move another 5% of the portfolio from US Barclays Aggregate Bond to S&P 500
  6.       Price rises to 95
  7.       Do nothing
  8.       Price rises to 100.
  9.       Move 10% of the portfolio from S&P 500 to US Barclays Aggregate Bond.

I ran this experiment starting 12/31/95 until the end of October of this year. Below are the results.


As you can see, the S&P 500 had the highest total return. However, if you factor in the volatility that came with that return, the S&P 500 actually performed the worst. It has the lowest Sharpe ratio. The best “risk-adjusted” return goes to the US Barclays Aggregate Bond. However, you can see it actually had the lowest overall return of the four investments. I don’t want to focus too much on this risk-return tradeoff as that is a separate discussion in itself.

What I found most surprising was the lack of return of the Tactical portfolio versus the Balanced portfolio. Sure, it returned a higher total return (4%!!!!), but it took on more volatility than the Balanced portfolio. You can see that in a down year, the Tactical portfolio under-performed the Balanced portfolio, but it recovered faster in the recovery phases.

To give you some detail behind the charts, in 2000 there was a 10% drop from the S&P 500 high price with no recovery. In 2001, the price continued to fall another 10%, then another 10% (so over 30% drop from its high in 2000). By mid-2002, two more 10% drops occurred. Therefore, from about March of 2000 to July 2002, the S&P 500 dropped more than  50% (not quite reaching 60%).

Based on my rules that I established for the Tactical portfolio, we would have reallocated 5% of the portfolio to the S&P 500 five times, and all five times the S&P 500 price keeps on falling.

It took until around mid-2007 to reach the high price of the S&P 500 from 2000. Therefore, we moved 25% of our portfolio from the S&P 500 to US Barclays Aggregate Bond. Now this is ironic…within a couple months of obtaining that high price, the S&P 500 once again started dropping, and did not stop dropping until March of 2009. This was the Great Recession.

Again, the S&P 500 lost over 50% of its value at one point, but did not quite reach a 60% drop. It took until around April 2013 for the S&P 500 to recover to its previous high. This time it continued its rally without a 10% drop until this year in May, when it set a new high. By August, it had dropped by over 10%, setting off a trigger for the Tactical portfolio. However, most of this drop in price was recovered in October.

The biggest issue with the Tactical portfolio over the last twenty years is that when the S&P 500 has fallen 10%, more times than not it has continued to fall. Perhaps a 10% trigger is too small. What happens if we increase the trigger to 20%? Therefore, we only reallocate every 20% drop in the S&P 500 price. Below are the results.

Drawdown 20

The results are actually worse!

Okay, last try. Originally, when the S&P 500 reached its previous high after those large drops, we simply moved the percentages that had been moved to S&P 500 back to the US Barclays Aggregate Bond. For example, after a 50% total drop in the S&P 500 we would have allocated an extra (5% x (50/10)) 25% toward equities. Now that the price had recovered, we would move 25% of the total portfolio into US Barclays Aggregate Bond. Instead of doing that, what if we rebalanced to a 50/50 split between the S&P 500 and the US Barclays Aggregate Bond? Below are the results.
Drawdown 5050

Ahh!!! The results improved! However, the Tactical portfolio earned a better total return of only 12%…over twenty years. Nothing to brag about. And its Sharpe ratio was still worse. Another issue that we are facing is a form of data mining. We cannot simply back-test our portfolio and adjust until we receive superior results. Why? Because the next twenty years will not behave exactly as the last twenty years.

What does this research tell us?

Right away, I would say that simply switching your allocation due to price drops is wasted effort. You should use valuation or momentum techniques to know when to reallocate. However, that is a separate discussion.

This data should reiterate how much volatility occurs in the market. Investors tend to have short-term memory. More than six years have lapsed since the over 50% pullback from the last recession, and I’m sure people will point to that recession and say it was an outlier. However, all you have to do is go back another five years and there was ANOTHER 50% pullback from the dot-com explosion.

Another hindrance is that the returns that investors observe do not shed light on the depths of those two pullbacks. Why? Almost all returns presented to investors will be calendar year returns, or rolling year returns. This disguises the true nature of the beast, so to speak. Look up at my first chart. Under the S&P 500 column, you do not see any negative returns around 50%. Even if you sum the 2000-2002 returns, you only get around 43%. BOTH of the last two recessions barely missed hitting a 60% drop.

This leads into something else the numbers do not show: The emotions that investors were (will be) flooded with at the depths of those (future) pullbacks. When the S&P 500 was down, say, 55%, do you believe any investor was thinking, “Okay, now the market will recover.” Sure, there were astute investors who looked long term and thought the market would recover. However, I doubt even they would be comfortable increasing their equity allocation. And I would say a high percentage of investors bailed then, if not sooner, and sat in cash. They then missed the inevitable recovery that occurred.

It’s not during the good times that an investor needs a financial advisor, it’s during the bad times. And hopefully that advisor is competent.

Just a prelude to my next article. In the second chart I presented, the total returns for the Balanced and Tactical portfolios were 313% and 311%, respectively. However, if you look at the side chart you will notice the “average” return for the same portfolios were 7.71% and 7.79%, respectively. How does a portfolio have a higher total return, yet a lower average return?!?! More to follow…


Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyDrawdowns – Can they help you?

One Year Returns

Ben Carlson recently wrote a great article on how one year returns do not say much. They are not good measurements to judge how an investment or asset class (or even portfolio manager) has performed. He wrote his article in response to someone in the financial industry who posted a chart that showed the trailing one year return for all (or almost all) asset classes was negative. As he pointed out, charts like these always raise questions/comments like:

Does diversification even work?

Is everything broken?

Why hire an advisor? (This one hurts…)

This is why “cash is king”.

And much more.

I will quickly point out that anyone who questions diversification after seeing that chart needs to review their primary education. The chart shows the worst trailing return is -25.4%, while the “best” performer was -0.3%. That is a 25.1% difference in return! Pretty sure that proves diversification “still” works.

Moving on.

I want to piggyback on what Ben Carlson says about one year returns mean nothing by showing the calendar one year returns of the S&P 500 Total Return going back to 1996. I also want to use a different starting point…09/30/95. Instead of “calendar” annual returns, it will be October to October returns. And just to throw in more numbers, I added June to June returns. Let us juxtapose these one year returns.

One year returns

The time periods overlap, but look how different each one is from its companions. I bet my next month’s salary that , if I did not tell you, you would think these were different asset classes, and not the S&P 500 on different starting points.

Some periods are bold that I think show the widest disparities.

Which one has the greater accumulated return? Can you guess? I’ll show you.

One year returns II

When all is said and done, their accumulated returns are pretty similar when you factor in this is over twenty years’ worth of data. The one with the best return is simply the one that started investing earlier. The worst one is the one that has the latest starting point…or is it because we started investing in May. Oh no…I did not follow the “sell in May and go away” motto!

However, I will prove that it is simply because the May starting point (meaning 05/31/96) is later than the other two. Below is a May starting point of 05/31/95:

one year returns III

See! A higher accumulated return.

What is my point? Even within the same asset class, trailing one year returns are very deceptive and you should not waste your time analyzing them.


Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.


Adam McCurdyOne Year Returns

Long Term Care Insurance versus Everything Else

Within the last two weeks, I had a client inquire about a long term care (LTC) policy and a reporter call regarding pros and cons of LTC policies. I took it as a sign of what to discuss this week, and it’s also a good change of pace from the investment topics that I focused on over the past month.

First, let me describe the essence of an LTC policy. This is a policy written on an individual (the insured) to provide funding to the insured if/when the insured needs to recover in a nursing home setting due to chronic illnesses (alzheimer’s, dementia, arthritis, etc.). In order to begin receiving benefits, one of two things needs to occur:

  1. The individual cannot perform two of the six activities of daily living (i.e. cannot dress, eat, bathe, use toilet, maintain continence, transfer in/out of bed or chair) OR
  2. The individual has become severely cognitively impaired.

That is the core of the LTC policy. Now for the nuances of the policy.

Similar to auto insurance, homeowners insurance, and disability policies, there is a “deductible” in the form of a wait time from when you enter a nursing home and when you begin receiving benefits. It is called the “elimination period.” Typical policies have thirty- to ninety-day elimination periods. The shorter the elimination period, the more expensive the policy. Medicare covers the first twenty days and then there is a co-payment for the next eighty days. Therefore, a sixty to ninety day elimination period makes sense.

The benefit structure is now, for most policies, a pool of money scheme. This means that you pay for a monthly (or daily) benefit, say $4,700 a month, which will last a certain time frame, usually between two to four years. If it is four years, then your pool of money is $225,600. There are two ways to receive these benefits. Either you will be reimbursed up to your daily or monthly limit, or the insurance company will pay you on a per diem basis regardless of the cost of the nursing home. Policies are generally set up for reimbursement.

So far, I have used the term “nursing home.” However, you can also receive benefits for these other types of care:

  • Assisted living care – For the frail elderly, but not at the high level needed in a nursing home.
  • Hospice care– To help ease the physical and psychological pain associated with dying.
  • Home Health Care – A nurse practitioner will come to the insured’s residence.
  • Adult Day Care Centers – If insured lives with a caregiver who works, the insured can be transported to the facility while the caregiver works during the day.

Now let us delve deeper. Who needs an LTC policy and how much does it cost? Statistics always change, but should be relatively accurate. According to data from 2009, about 1% of people between the ages of 65 and 74 resided in a nursing home. The percentage jumped to 6% for those between 75 and 84. Then it quadrupled to 25% for those aged 85 and older. There is a 40% chance of a person aged 65 and older of entering a nursing home at some point. And for those that enter a nursing home, 50% of the people stay longer than 1 year. I interpret these stats to say that around 20% of people could end up needing an LTC policy.

What about cost?

I reviewed four policies for my client. The average cost was $2,928 per year for a pool of money totaling $228,800 of benefits. Her age is 40 and we asked for a standard rating. The policies have elimination periods of ninety days and a 3% inflation protection. Therefore, at age 75, if my client entered into a nursing home she would have $643,812 of benefits and she would have paid a total of $102,485 in premiums.

Now, is this good? Let’s try to quantify a few different scenarios.


The chart above shows our first scenario. Instead of buying an LTC policy at age 40, a client could put money aside and invest it in her brokerage account. (I will not jump into the tax advantages of investing in a Roth or Traditional IRA, or 401(k) in order to keep it simple). Let’s focus on the “Average” column. For the client to have access to the same amount of benefits (in today’s dollars – $228,800, inflation adjusted – $643,812), and to “invest” the same amount of money per year ($2,928), the client would need to earn 8.72% per year for 35 years. That type of return is not out of the realm of possibilities, but it is definitely an optimistic assumption. Strictly looking at the numbers, it would appear an LTC policy is the safer choice.

Now check out the chart below.


The scenario above shows that the client waits until age 65 to purchase an LTC policy. However, she does start saving at age 40 and targets those funds for medical expenses. The goal is to obtain the same amount of benefits ($643,812) as seen in the first scenario. Let us keep the rate of return (8.72%) equal to the first scenario. You can see that the LTC policy will provide a little less than half of the benefits while the investments in the brokerage account fill the gap. However, you should notice that the total cost and investment is about $8,000 higher than the first scenario. You could call this the “penalty” of waiting to purchase the LTC policy. The difference is even smaller when you shrink the $8,000 to today’s dollars ($2,950) and spread that over 35 years.

Now for a third scenario:


This last scenario does not utilize an LTC policy at all. Instead, you utilize another insurance product – whole life insurance – and team it with a brokerage account. The death benefit of the life insurance grows at an annual rate of 6.35%, while the investments in the brokerage account earn 7.5%. The end result is that less benefits will be available for nursing home expenses, and you will have invested more than the other two scenarios ($130,865).

When you “run the numbers” (something a past co-worker would always tell clients when they asked a question), it would appear that an LTC policy would be less costly and less risky than the other options.

Okay, let’s get one!

But wait. Hold on. If I knew with 100% certainty that my client would not only incur nursing care expenses, but knew the exact amount, then absolutely – an LTC policy would be the best route. However, 1. You do not know if a client will incur those expenses and 2. You do not how much those expenses will be.

I believe there are two major risks with an LTC policy.

The first is cost of the policy. Insurance companies are allowed to raise premiums on a class basis even if you are locked in a contract that said the premium will remain the same. This could be a very real scenario considering the age of the LTC product. It originated in the 80s, was modified throughout the 90s, and is still slowly gaining traction. How accurate are the insurance companies in projecting these future medical expenses? I am not sure, but if homeowner’s insurance is any predictor, then we are in trouble. I am the treasurer of my condo association and our insurance premium rose from $1,700 to $2,600….that is not a typo. It rose 53% in one year. No claims arose last year. No accidents. Nothing. Just a “run of the mill” increase.

The second major risk, which I briefly touch on, is the uncertainty of your client’s future expenses. You can only estimate what medical expenses will arise thirty five years from now for your client. You can use family history, sure, such as if there is history of chronic illness. However, how those illnesses are treated today could be vastly different thirty-five years from now.

Due to those two risks, I oftentimes find myself steering clients away from LTC. Insurance is supposed to offer certainty, but these two risks destroy that peace of mind. As mentioned before, only one out of five people aged 65 or higher stay in nursing homes for more than 1 year. The average annual cost is around $90,000. Therefore, even the individuals who do incur those expenses should have enough retirement assets to protect them. If they do not, their problem is not medical expenses. Unfortunately, it is that they did not properly plan for retirement in general.

Look at the opportunity cost of LTC. If a client does not incur significant nursing care expenses, they sacrificed around half a million dollars due to lost opportunity cost. I am even being fair and assuming a reasonable 7.5% return. Remember, most policies have an elimination period of three months and Medicare provides some benefits. Therefore, you may incur nursing care expenses and not even receive any benefits from the policy you purchased.

If a client is very risk averse and wants certainty, I would be more inclined to utilize whole life insurance and pair that with savings in a brokerage account. That way, whether it is that client or her beneficiaries, those assets will be used. (Side note, I like the alternate options that whole life brings to a client’s financial life aside from LTC. And for those wondering, I do not receive any commissions as I do not write any insurance policies.)

If a client had prior positive experience with nursing care, perhaps her parents or grandparents stayed in a nursing home, I would be hesitant to have the client purchase a policy at age 40. Rather, I recommend that the client wait until age 65 (taking on the insurability risk) and have the client put away money specifically for future medical expenses. This is assuming no medical history was present. This way, you are closer to the 75 age mark and can make a more informed decision.

If a client insisted that she wanted an LTC policy right now, I would limit the amount of benefits, which would lower the premium. I would use a rule of thumb on how much extra money she could save per month, perhaps $100. Something nominal. That would amount to an annual premium of $1,200 and we would purchase a policy for that amount.

These last two paragraphs are ways to “hedge” your bets with/against LTC. Point is, I would not dive head first into the LTC pool. Every client’s financial situation is different; therefore very little in financial planning is black and white. Keep an open mind.

Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.


Adam McCurdyLong Term Care Insurance versus Everything Else

Value investor struggling with momentum?

This is a question that has frustrated my mind for at least five years. I will give a brief description of value investing before giving a more detailed discussion of momentum.

Value Investing: Invest in a company that has strong fundamentals (strong balance sheet, very manageable debt, positive earning, etc.) but has had its market valuation hit (i.e. trading at an EBIT/TEV at half of the industry average, P/E ratio has been cut in half, etc.). Even though it is a simple concept, it is easy to land in pitfalls. One pitfall is to invest in a company, or asset class, just because it has dropped significantly in value. The saying, “Invest once something goes down” is abused. You still need to put forth some analytical work and understand WHY there is a drop in the valuation or price. Or else you are investing blindly. Look at commodities. Over the last ten years, and last five years, most commodity funds have negative performance, or best case are flat.

Now for momentum investing. The premise for momentum is that a stock, or an asset class, tends to stay overvalued or undervalued longer than one would believe is rational. This raises the question, how much (if at all) does the behavior of investors factor into performance. This leads to the bigger question, are markets efficient? Hence, my struggle over the years with momentum. My short answer to that question is – Yes, markets are efficient…over the long term. However, in any given day, week, month, or even year, there can (and have been) inefficiencies.

Back to momentum.

There are several ways to calculate, or display, the “momentum” factor.

  1. Fama/French utilize the returns over the prior two to twelve months to find the “momentum” stocks. What is up versus what is down.
  2. Ben Carlson describes relative momentum and absolute momentum. The former compares an investment against another investment. Whichever one has the stronger performance over a specific time, usually twelve months or less, is the investment to choose. The latter compares an investment against itself, also over a specific time interval, to see if it is outperforming cash. If not, then invest in cash.
  3. Perhaps the more well-known rule is trending performance. Wesley Gray does a good job researching this rule. Here, you compare the current price of an investment versus the average price over a specific time-frame of that investment. If the current price is greater, then invest. If not, go to an alternative investment. Instead of using a current price, people use the 50 day moving average versus the 200 day moving average.

You can spice up your strategy (though try to keep it simple since simplicity trumps complexity) by combining different elements of the momentum rules. For example, use a trending performance rule together with the relative momentum rule. Steve Blumenthal displays the use of three asset classes to outperform the S&P 500.

You may think, “Okay, this is great!” However, just like any rules-based strategy, the most difficult part is following the rule 100% of the time. It can be a psychological strain for an investor to move all of his/her assets from stocks to bonds to cash. And just like all strategies, no strategy outperforms its rivals 100% of the time. There can be lagging performance for years. YEARS! And what has worked in the past may not work in the future.

Another caveat is that the momentum strategy will be applied incorrectly. If you notice, the above links show research/data on the momentum strategy either using thousands of stocks (i.e. Fama/French) or entire asset classes. In my opinion, it would be very dangerous for an investor to read about momentum, and then try to implement on his/her own. Below is my fear.

  1. Investor reads about momentum.
  2. Analyzes the presented data.
  3. Likes the out-performance.
  4. Applies momentum rule to portfolio.
  5. Portfolio consists of 20 stocks.
  6. Portfolio drastically under-performs S&P 500.
  7. Investor gives up and sits on cash for five years.
  8. Investor gets back into market.
  9. Five months later market turns into a bear market.
  10. Investor gives up.

A bleak picture for sure. Unfortunately, the above scenario is far too common in the investment world. To prove my point, I chose five stocks and analyzed the data for the last ten years. I applied a simple trending momentum, moving average strategy on those five stocks. It went like this:

  1. Pulled the last ten years’ worth of data from Yahoofinance.
  2. Set up a simple 50 Day Moving Average versus 200 Day Moving Average.
    1. If 50 Day MA > 200 Day MA then invest in stock.
    2. If not, go to cash.
  3. Compare the trending momentum performance versus a buy and hold strategy.
    1. Buy and Hold – Buy 100 shares of stock and do not trade.

The study seems simple enough. In order to keep it simpler, I took out the effect of dividends and transaction costs. The results are alarming for those who want to use momentum rules for their individual stock portfolio.


As you can see for four of the five stocks, a trending momentum strategy drastically UNDER-performed a basic buy and hold strategy. Now, there could be reasons for this under-performance.

  1. I chose large cap stocks.
  2. I did not choose a stock from all sectors.
  3. All stocks had an overall positive return from year one until now.
  4. Sample size is extremely small relative to the thousands of stocks traded on the exchanges.

My list is by no means exhaustive.

Why the drastic under-performance? I believe it is due to the sudden price drops, or rises, that occur when one invests in individual stocks. If a stock drops by ten to twenty percent over a few days, there is not enough time for the trending performance to adjust and trigger a sale. Also, the inverse occurs. If a stock rises by ten to twenty percent in a few days, the investor will miss the recovery. One ends up “leap-frogging” back and forth to no avail. Perhaps this shows that too much volatility will break the rule.

I believe momentum strategies can work if applied correctly. My point is for individual investors to heed warning when trying to emulate the performance of these momentum strategies.


Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyValue investor struggling with momentum?

Let’s talk about the little things, like fees.

As an advisor, I am constantly surprised when I review a prospect’s investment portfolio and discover the different funds, be it ETFs or mutual funds. There are three things that jump out at me:

  • Over/under diversification
  • Investment performance
  • Fee Cost

Each one of those items deserves a separate discussion. This article (as my title so subtly hints) will discuss the latter item.

Before I talk about the varying fee structure of ETFs and mutual funds, I would like to take a slight detour and talk about transaction costs. This is the cost that the brokerage firm, or custodian, charges you to place a trade. There is a huge disparity between the different custodians (that’s what I will call them going forward) in this industry. I can give you a few examples. At my previous employer, if a client traded a mutual fund the cost would be $20. However, if an investor was not a client but was at the same custodian (known as a retail investor) the cost would be $76. My previous employer utilized two custodians so if a client were to trade a mutual fund at the other custodian the cost would be $75. I spoke with a tax client of mine and he explained that he receives one hundred free trades a year from his custodian. I also know that there may be no transaction costs at a custodian if you trade their funds. This is the case with places like Vanguard or Charles Schwab.

As you can see in the five(ish) examples, transaction costs differ depending on the custodian, if you are working with an advisor, are you using the custodian’s funds, etc. My point is that transaction costs really do not matter if you have one million dollars, or even five hundred thousand depending on how many funds are comprised in your portfolio. However, it DOES matter when you are investing one hundred thousand, or fifty thousand, or lower amounts for beginning investors. In my opinion, this is when the custodian really does matter as the investor needs to be very cognizant of every trade that is placed. For example, if the investor has ten thousand dollars, five different funds, and he/she would like to rebalance twice a year then it would cost (using a $75 transaction cost since that’s most realistic for a retail investor) $750! Look at the percentage of those costs to the portfolio

$750 / $10,000 = 7.5%!!!!!

You may think, “Well I would never trade that much?” However, placing ten different trades throughout the year is by no means excessive trading. Even the most conservative investors could easily place that many trades throughout the year. This just shows you that you have to be VERY CAREFUL when dealing with smaller size portfolios. My opinion would be to stick with index funds of the custodian you are utilizing as they may (should) be free of transaction costs. It will save you so much money! In other words, it is like EARNING a 7.5% return for being diligent.

Sorry for the tangent. Let’s move on to the main discussion, the underlying fees of the funds you (or your advisor) utilize. Some people are not even aware of these fees since they never see the fees “pulled” from their account. It is because the fees are not visible that oftentimes they are never discussed. And just like the transaction costs, there is a wide disparity between different funds. The demand for ETFs was generated by having lower management fees than mutual funds since earlier ETFs were only index funds and needed little management. For example iShares core S&P 500 has an annual fee of 0.007%, in other words 7 basis points. There are 100 basis points (bps) in 1%. However, as active managers are sliding into ETFs the management fees are beginning to rise to the lower end of mutual funds, which are in the 45 bps to 75 bps range. For mutual funds, the average fee ranges between 50 bps and 125 bps.

You must be thinking, “Why would anyone purchase a fund that has a management fee of 100 bps when they could purchase a fund that charges 50 bps?” I can give you two quick answers.

  1. They simply are not aware of the annual management fee.
  2. They are sold the mutual fund that has a higher fee.

You may now be asking, “Okay 50bps difference, that’s HALF of 1%! That’s minuscule.” In order to answer that question I created the below chart.

Blog - fees

I used a real life example. A client asked that I review her portfolio. The first thing I noticed was her high accumulative fee in the fifteen different funds (over-diversification!) she held. It was 94 bps. For comparison purposes, my core investment model is 43 bps. As you can see, there is a clear difference in fees but the amazing thing is the opportunity cost to those fees! Over a twenty year time-frame, an investor starting with three million dollars would lose over a million dollars! That is just with a 51 bps difference per year…..Not so minuscule now.

The above chart focuses more on the accumulative damage and opportunity cost of the fees. The below chart displays just the fee difference on a yearly basis.

Blog - fees II

Every year, investors lose tens of thousands of dollars to fees that could be avoided. Do not run into the same pitfall.

Adam McCurdy, CFP®, EA


As always, this if for informational and educational purposes. Nothing contained herein constitutes tax, legal, insurance or investment advice, or the recommendation of or an offer to sell, or the solicitation of an offer to buy or invest in any investment product, vehicle, service or instrument. What has worked in the past may not work in the future. Past returns do not guarantee future results.

Adam McCurdyLet’s talk about the little things, like fees.