Wednesday, July 17, 2019

Combining momentum and value into a simple strategy to achieve higher returns


In this post I'll introduce a simple investing strategy that is well diversified and has been shown to work across different markets. In short, buying cheap and uptrending stocks has historically led to notably higher returns. The strategy is a combination of these two different investment styles, value and momentum. In a previous post I explained how the range of possible outcomes in investing into a single market is excessively high. Therefore, global diversification is the key to assure that you achieve your investment objective. This strategy is diversified across strategies, markets and different stocks. The benefits of this strategy are the low implementation costs, a high diversification level, higher expected returns and lower drawdowns.

We'll use data from Barclays for the CAPEs which represent valuations, and Yahoo Finance using quantmod for the returns that do not include dividends, which we'll use as absolute momentum. Let's take a look at the paths of valuation and momentum for the U.S. stock market for the last seven years:


The two corrections are easy to spot, because momentum was low, and valuations decreased. The U.S. stock market currently has a strong momentum as measured by six-month absolute return, but the valuation level is really high. Therefore the U.S. is not the optimal country to invest in. So, which market is the optimal place to be? Let's look at just the current values of different markets:


There is only one market that is just in the right spot: Russia. It has the highest momentum and second lowest valuation of all the countries in this sample. In emerging markets things happen faster and more intensively, which leads to more opportunities and makes investing in them more interesting. Different markets also tend to be in different cycles, which makes this combination strategy even more attractive. Let's discuss more about these strategies and why they work well together.

Research on the topic

Value and momentum factors are negatively correlated, which means that when the other one has low returns, the other one's returns tend to be higher. Both have been found to lead to excess returns and are two of the most researched so-called anomalies. Both strategies have been tried to be explained using risk-based and behavioral factors, but no single explanation has been agreed on for either of the strategies. The fact that there are multiple explanations for the superior performance can rather be viewed as a good thing for the strategies.

In their book "Your Complete Guide to Factor-Based Investing", Berkin and Swedroe found out that the yearly returns of the two anomalies using a long-short strategy was 4.8 percent for value and 9.6 percent for the momentum anomaly. This corresponds to the return of the factor itself and can directly be compared to the market beta factor, which has had a historical annual return of 8.3 percent during the same period. This means that investing just in the momentum factor and therefore hedging against the market would have led to a higher return than just investing in the market. It is important to notice that investing normally just using a momentum strategy without shorting gives exposure to both of the market beta and momentum factors, which leads to a higher return than investing just into either of these factors.

Andreu et al. examined momentum on the country level and found out that the return of the momentum factor has been about 6 percent per annum for a holding period of six months. For a holding period of twelve months, the return was cut in half (source). It seems that a short holding period seems to work best for this momentum strategy. They researched investing in a single country and three countries at a time and shorting the same amount of countries at a time. The smaller amount of countries led to higher returns, but no risk measures were presented in the study. As a short-term strategy I'd suggest equal weighting some of the countries with high momentum and low valuation. I've also tested the combination of value and momentum in the U.S. stock market, and it seems that momentum does not affect the returns at all on longer periods of time.

Value on the other hand tends to correlate strongly with future returns only on much longer periods, and on shorter periods the correlation is close to zero as I demonstrated in a previous post. However, the short-term CAGR of the value strategy on the country level in the U.S. has still been rather impressive at 14.5 percent for a CAPE ratio of 5 to 10, as shown by Faber (source, figure 3A). I chose to show this specific valuation level, since currently countries such as Turkey and Russia are trading at these valuation levels (source).

The 10-year cyclically adjusted price to earnings ratio that was discussed in the previous chapter, also known as CAPE, has been shown to be among the best variables for explaining the future returns of the stock market. It has a logarithmic relationship with future 10-15 year returns, and an r-squared as high as 0.49 across 17 country-level indices (source, page 11). A lower CAPE has also lead to smaller maximum and average drawdowns (source).

Faber has shown that investing in countries with a low CAPE has returned 14 percent annually since 1993, and the risk-adjusted return has also been really good (source). The strategy, and value investing as a whole, has however underperformed for the last ten years or so (source). This is good news if you believe in mean reversion in the stock market.

The two strategies work well together on the stock level, as shown by Keimling (source). According to the study, the quintile with highest momentum has led to a yearly excess return of 2.7 percent, and the one with the lowest valuation has led to a yearly excess return of 3 percent globally. Choosing stocks with highest momentum and lowest valuations has over doubled the excess return to 7.6 percent. O'Shaughnessy has shown that the absolute return for a quintile with the highest momentum was 11.6 percent, and 11.8 percent for value. Combining the two lead to a return of 18.5 percent (source).

Lastly, let's take a closer look at some selected countries and their paths:


As expected, the returns of the emerging markets vary a lot compared to U.S. market. U.S. has performed extremely well, but the historical earnings haven't kept up with the prices. Israel on the other hand has gotten cheaper while the momentum has been good. Even though the momentum of U.S. is higher than any other point in time in this sample, Russia's momentum currently is, and Turkey's momentum has been way higher. Both Russia's and Turkey's valuations are less than a third of U.S. valuations, which makes these markets very interesting.

In conclusion, combining value and momentum investing into a medium-term strategy is likely to lead to excess returns as shown by previous research. The strategy can be easily implemented using country-specific exchange traded funds, and the data is easily available. Currently only Russia is in the sweet spot for this strategy, and Turkey might be once it gains some momentum. Investing to just one country is however risky, and I suggest diversifying between the markets with high momentum and low valuations.


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The R code used in the analysis can be found here.



Saturday, June 8, 2019

The most important chart for long-term investors


Time is the investor's best friend. The longer the investment horizon, the less the investment returns depend on factors such as crashes and current valuation levels. It is known that the chance for losing in the stock market on a 20-year period has historically been about zero. This post attempts to expand on this fact and take a look at how risky the U.S. stock market has actually been for long-term investors.

As usual, we'll use data from Robert Shiller to answer the questions. The data begins from the year 1871, long before the actual S&P 500 index was created. We'll only consider lump-sum investing, since dollar cost averaging is another story.

Let's first look at the inflation-adjusted returns for an U.S. investor, including reinvested dividends.  Keep in mind, that the U.S. stock market has been one of the best performing in the world, and future returns are likely to be lower because of high valuations and lower productivity and population growth. The upper and lower bands are the 95 percent prediction intervals, i.e. 95 percent of the time the investment return has been between these bands. The y-axis tells how many times your investment would have been multiplied. Notice that the axis is logarithmic.

This chart demonstrates how uncertain investing is. The range of outcomes is very large, but it doesn't necessarily tell the full truth. France once had a 66-year period where stocks didn't beat inflation, for Italy the longest streak was 73 years and for Austria a painful period of 97 years. This is why global diversification is important. There has however been a 5 percent chance that the investment would have increased 64-fold in the U.S. for the same period. The risk works both ways.

Let's also look at the nominal, non-inflation-adjusted returns to see how inflation eats returns:

The inflation in the U.S. has been quite high, over three percent annually. Inflation of course affects different companies in a different way, but the net effect is that lower inflation does not necessarily lead to higher inflation-adjusted returns.


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The R code used in the analysis can be found here.

The original post had a problem with the calculation of dividends. The charts and code have now been updated, and the true returns were higher than in the original post. Sorry for the inconvenience.


Tuesday, January 29, 2019

Correlation analysis of cyclically adjusted valuation measures and subsequent returns


In this post we'll test three different cyclically-adjusted valuation measures: CAPE (earnings), CAPD (dividends) and CAPB (book value). CAPE is calculated like the P/E ratio, but by dividing the current real price with the last ten year's average inflation-adjusted earnings. CAPD uses dividends instead of earnings, and CAPB uses book value. We'll test the optimal measurement (i.e. forward-looking return) period and formation period for all three valuation measures by calculating correlations with the future returns.

Typically CAPE, also known as P/E10, is calculated by using a 10-year formation period. The maximum time period for both the formation period and measurement period we'll use is 30, which means that  the performance of for example P/E1-P/E30 will be tested by looking forward 1-30 years.

We'll use Shiller and Goyal data from the US, which both begin from the year 1871. We'll plot the measurement period (in years) on the x-axis and r-squared on the y axis, and we'll make a distinct line for each of the formation periods.


As you can see from the plot above, a measurement period of about ten years, or maybe a little more, has worked the best for CAPE. As expected, valuation measures don't do a good job explaining short-term returns. However, this also applies to long-term returns, which gives the lines a bell-curved shape. The lines with shorter formation periods are lower than the rest, which means that short-term valuation measures such as normal trailing twelve-month P/E also don't work as well as the long-term valuation measures.


For CAPD, the correlation turns positive at long measurement periods, which is rather unwanted. The better performance of the long-term and the worse performance of the short-term valuation measures are more apparent with CAPD.


For CAPB, longer measurement periods of about twenty years seem to work the best. The r-squared is much larger than with CAPE or CAPD. Even the worst formation periods seem to work better in explaining future returns than the CAPE with the best formation period. This is consistent with Keimling's research (pdf, page 16), which suggests that normal P/B is almost as strong in predicting future returns as CAPE. The plot above shows that the cyclically-adjusted P/B is even stronger than CAPE in predicting future returns.

The reason why the r-squared of the CAPE is lower than what is often quoted is because of the long time period of the data. As you can see from the plot below, the rolling 10-year correlation of CAPE and subsequent returns has been rising over time. 

Another way of viewing these correlations is bringing them into Excel and color coding them. Notice that we are now using simple correlations instead of the r-squared. The x-axis tells the formation period of the valuation measure, and y-axis tells the measurement period i.e. how long into the future the valuation measure is used to predict.


The 10-year CAPE has surprisingly high explanatory power even for forecasting 1-year periods. The explanatory power starts declining noticeably from P/E8 to the left and P/E14 to the right.


For CAPD, the correlations are weaker, but the shape is about the same.


CAPB has the strongest correlations with future returns, but the shape is way different. Interestingly the strongest explanatory power regarding future returns comes from 22-24-year P/B and for a measurement period of 5-15 years.

This post was partly inspired by the O’Shaughnessy Quarterly Investor Letter Q4 2018.

Be sure to follow me on Twitter for updates about new blog posts!

The R code used in the analysis can be found here.


Sunday, October 28, 2018

How quickly do stock market valuations revert back to their means?


Mean reversion is the assumption that things tend to revert back to their means in the long run. This is especially true for valuations and certain macroeconomic variables, but not so much for stock prices themselves. In this post we'll look at the mean reversion of different valuation measures by forming equal sized baskets from each valuation decile and letting the valuations change as time goes on.

This study (pdf) shows an interesting graph on page 23 about the mean reversion of the 10-year price-to-earnings ratio also known as CAPE. In this post the study will be replicated using also international CAPE, P/E and P/B. I'll replicate the results using a longer time frame of twenty years. Let's start with CAPE using Shiller data of the US stock market from years 1926 to 2008:

Click to enlarge images

Using a longer time frame over reversion becomes visible, i.e. high valuations tend to eventually lead to low valuations and vice versa. The only exception is the decile with the highest valuation, which is explained by the housing bubble after the tech bubble. The valuations seem to revert back to their means in 11-12 years.

Let's look at the mean reversion of the same metric using Barclays data from years 1982 to 2008 from 26 different countries or continents:


The mean reversion happens again in about 12 years, but the over reversion seems to disappear. This might be caused by US having different kind of bubbles and busts than the rest of the world, or because of the shorter time period. The dataset is many times larger and should give a clearer picture of the mean reversion than using only US data.

Next, we'll look at price-to-book:


It seems to take longer for the P/B to revert back to its mean, which is logical since CAPE uses historical 10-year earnings. There is however still some noticeable over reversion.

Let's look at price-to-earnings ratio next:


The P/E ratio seems to revert back to its mean a little bit quicker than the rest, in about 9-10 years. There is still some over reversion.

In summary, different valuation measures tend to revert back to their means in about ten years, and over revert after that.


Hope you enjoyed this short post. Be sure to follow me on Twitter for updates about new blog posts!

The R code used in the analysis can be found here.


Monday, August 6, 2018

Mapping the stock market using self-organizing maps


Self-organizing maps are an unsupervised learning approach for visualizing multi-dimensional data in a two-dimensional plane. They are great for clustering and finding out correlations in the data. In this post we apply self-organizing maps on historical US stock market data to find out interesting correlations and clusters. We'll use data from ShillerGoyal and BLS to calculate the historical valuations levels, interest rates, inflation rates, unemployment rates and future ten-year total real returns from years 1948 to 2008.

Click to enlarge images

You can see a clear correlation between the different valuation measures, and that low valuations have led to high returns. There's a slight negative correlation between the valuation measures and unemployment, i.e. valuations have been higher when unemployment has been lower. Charlie Bilello has a great article on the subject. There's also a positive correlation between unemployment and rates, which means that rates have typically been higher when unemployment has been higher.

Next, let's look at clusters formed using hierarchical clustering. We'll form four clusters on the same plane as used in the above analysis. Let's look at the results:


The balls inside each hexagon correspond to each month. We are currently in the green cluster, which has typically lead to low returns. Why has low unemployment, low rates and low inflation led to low returns, aren't these things good for the stock market? I see two possible causes: these conditions tend to revert back to their mean (which means worsening macroeconomical conditions), and investors tend to extrapolate past returns into the future (a great tweet on the subject by Michael Batnick). The second part causes high valuations, which is present in the green cluster.

Which cluster is the best place to be in? I'd say the gray one, but the data seems to support the blue one as well. The good thing is that there are other countries that are in both of these clusters. Even though I recommend looking at valuations alone rather than macroeconomic indicators, a good place worth checking for all that macro stuff is tradingeconomics.com.

The R code used in the analysis is available here.


Sunday, July 22, 2018

How likely is a stock market crash?


In this post we'll look at the odds of a stock market crash from the view point of valuation. We'll use my favorite valuation measure Shiller P/E or CAPE ratio, which is just like regular P/E except it's calculated by using earnings of the last ten years instead of just one year.

According to multpl.com, the CAPE ratio is currently at 32.57, which is in the 97th percentile when compared to history. We'll perform logistic regressions to calculate the probability of a correction (which is defined to be a decline of over ten percent from all-time highs) and the probability of a crash (a decline of over twenty percent). We'll use data from Robert Shiller to do the analysis. The data is from years 1881 to 2005.


The probability of a correction during the next year is a little bit higher than usual at 25 percent, as you can see at the point where the two lines intersect. Let's look at the probability of a crash next:


The probability of a crash seems to rise exponentially as the valuations rise. However the probability is less than I expected at fifteen percent.

The R code used in the analysis is available here.