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Let’s suppose there are two types of economists. The first type thinks stocks behave in a predictable, orderly way. They treat stock selection as a ‘science’, rather than an ‘art’, and try to identify factors that drive stocks up or down according to a clearly defined formula. The second type thinks price trends are chaotic, even random, while stock picking is a matter of good luck.
Nassim Taleb, author of Fooled By Randomness, arguably falls into the second group of economists. When asked to explain the success of Warren Buffett, Taleb said that if you gave every person in America a coin and awarded them $1 whenever they flipped heads, some people would inevitably become rich, albeit by chance. At the other end of the spectrum are economists like Ray Dalio, Eugene Fama and Kenneth French. Their models strive to identify patterns within the chaos of financial markets. Dalio’s research looks at the relationship between stock returns and interest rates. Fama and French suggest investors can potentially predict price trends by looking at company-specific factors. Have their methods cracked the mystery of the stock market? Let’s take a closer look…
Ray Dalio, founder of Bridgewater Associates, sees stock market complexity as something that can be understood with the right analytical framework. His studies divide history into periods, or paradigms. Within each paradigm, stock prices are driven by a particular set of market forces, for example, low interest rates, stock buybacks, tax cuts and wide profit margins since 2009. Dalio’s approach is elegant, but someone like Nassim Taleb could insist that Dalio is a victim of ‘Platonicity’ - i.e. the naive desire to cut reality into neatly defined shapes that ignore underlying complexities and nuances.
Is Dalio’s approach simplistic? Let’s explore. Dalio says that since 2009, we have been in the paradigm of ‘Reflation’, where central banks drove stock prices up by pushing interest rates down. This makes intuitive sense. When the central bank rate is lower, interest rates fall across the economy. Stocks become more attractive if returns from dividends and price appreciation exceed returns on cash savings. That’s the theory, but does theory reflect reality? Consider the following Financial Times headline:
GLOBAL STOCKS KNOCKED AFTER CENTRAL BANKS PUSH INTEREST RATES HIGHER
There we have it! Dalio was right, or was he? Maybe it’s not that simple. The following chart shows how the FTSE 100 responded each time the Bank of England notched up interest rates since November 2017. The market generally went down just before a rate hike. This is consistent with headlines like this one from Reuters: London Stocks Slip Ahead Of Key Interest Rate Decisions. The FTSE 100 also dropped immediately after a rate hike, but the impact was short lived. Over the longer-run there was no clear trend.
We can also examine the relationship between interest rates and stock prices using correlation analysis:
Since 2009, the correlation between FTSE 100 returns and Bank of England rate hikes has been 0.05. That is a problem for Dalio’s theory, which would imply a negative correlation. So what is going on here? Between 2009 and 2022, the FTSE 100 oscillated within the range of 3568 and 7800. Interest rates were relatively flat during the same period. We would not expect a strong correlation between a stable datapoint and a more volatile datapoint. Interest rates started to move up in November 2017. However, interest rates did not drive down stock returns in the way that Dalio’s framework would imply. Since 2017, the correlation remained weakly positive (at 0.16).
There was a negative correlation between interest rates and market returns in 2018. During other periods, the market and interest rates moved in tandem. In 2017, rates were notched up from 0.25% to 0.50% while the FTSE 100 rocketed from 7193 to 7622. Earnings also shot up. Furthermore, when stocks and the economy crashed during the coronavirus pandemic, interest rates were lowered to get everything moving again. In addition, central banks often committed to raising interest rates only when economic growth was strong enough. This suggests it is not simply the case that markets fall as interest rates rise.
Should investors ignore Ray Dalio’s framework? No, let’s hear him out. Dalio’s point is that after 2009, central banks lowered interest rates in a way that was unsustainable. A rise in interest rates could put downward pressure on the stock market. Earnings and stocks went up despite the rate hike of 2017. But take a look at earnings in 2022. Notice how they drifted downwards as interest rates increased. This suggests that companies endured small interest rate hikes, but not larger, prolonged increases (at least in 2022). Earnings have also been squeezed by other related factors, namely inflation and higher energy prices.
Which variable is more likely to drive market returns: interest rate hikes or earnings growth? Since 2017, there was a 0.23 correlation between earnings growth and FTSE returns, which was stronger than the 0.16 correlation between rate hikes and stock returns. This makes sense. In theory, investors would only care about higher interest payments if they translated into narrower profit margins. Dalio’s framework acknowledges this, but his approach is primarily macroeconomic. If we want to analyse the link between stock returns and company-specific factors, we should explore factor investing…
Fama and French were pioneers of factoring investing. They found that investors could predict price trends by looking at a company’s price-to-book ratio and its market cap. The factors they analysed were value (price-to-book ratio) and size (market cap). Other academics focused on another factor, namely quality. For instance, Robert Novy-Marx found that a more profitable firm was more likely to beat the market. The other factor that Stockopedia focusses on is momentum, the phenomenon whereby stocks with strong price momentum defy gravity and keep going up.
We can use statistical methods like correlation to analyse the effectiveness of factor investing. The StockRank encompasses many of the datapoints used by finance academics, so it can stand in as a proxy for the factors mentioned above, namely quality, value and momentum. Many datapoints feed into the StockRank, but let’s take a look at the price-to-book ratio and the gross profits-to-assets ratio.
We looked at the price performance of UK stocks in 2022. We ranked companies between 0 and 100, so that companies were assigned a higher Price Performance Rank if they had risen more during 2022. Then we looked at the StockRank for each stock at the end of 2021. This is what we found…
Complete chaos, right? Not quite. Look at the bottom-left and top-right corners. You’ll notice clusters. Then look at the top-left and bottom-right corners. You’ll see more white space. What this means is that a company with a higher StockRank in December 2021 was more likely to have stronger price momentum by the end of 2022. The correlation between StockRank and price momentum was 0.22. This is mildly positive, although readers were perhaps hoping to find a stronger correlation. So what’s going on?
A correlation of 1 would imply that a higher StockRank stock would always beat a lower StockRank stock. However, there were instances where lower deciles beat higher deciles. For example, the 60-70 bucket outperformed the 70-80 bucket. The 40-50 bucket beat the 50-60 bucket. The 20-30 bucket beat the 30-40 bucket. This would of course weaken the correlation between StockRank and price performance.
The benefits of factor investing are more apparent when we look at the extremes – i.e. the top decile and the bottom decile. If we only look at shares with a StockRank below 10 and a StockRank above 90, the pattern becomes clearer. Investors were less likely to pick winners by investing in low StockRank stocks.
The following scatter graph plots the actual price change along the vertical axis. In 2022, lower StockRank stocks almost always generated negative returns. High StockRank stocks had a tough time, but they were more likely to generate positive returns.
When we only look at the top and bottom deciles, the correlation between StockRank and Price Performance Rank jumps from 0.22 to 0.39. A correlation of 0.39 is positive, but only mildly so. There is still a lot of chaos going on. High StockRank stocks generated a broad range of returns. I am reminded of Nassim Taleb’s quote: never cross a river that is on average 4 feet deep.
How can investors tame the disorder of the stock market? Diversification is key. If we group stocks into portfolios based on their StockRank, the correlation between starting StockRank and subsequent price performance becomes 0.89 - very positive indeed! Research suggests that investors gain greater exposure to factors by investing in a diversified portfolio of 20 or more companies. It is difficult to beat the randomness of the stock market with any single stock. But it is possible to beat the randomness with a diversified portfolio.
According to Nassim Taleb, the underlying pattern of the universe is chaos, rather than order. Taleb’s philosophy is useful. His concept of antifragility is the idea that something can get stronger in chaotic circumstances. However, Taleb arguably overlooks the extent to which we can create order out of chaos. For Taleb, successful investors are just lucky coin flippers. But what if lucky coin flippers all flipped coins using a particular technique? We would attribute their success to their technique, rather than luck.
In The Super-Investors of Graham and Doddsville, Warren Buffett explained that a disproportionate number of successful coin-flippers came from the same intellectual village, Graham-and-Doddsville. Investors from Graham-and-Doddsville tended to buy cheap stocks, rather than expensive stocks. Buffett also had a preference for profitable, high quality stocks. Buffett didn’t use the term ‘factor investors’, but his approach was consistent with factor investing. The factors Buffett used were quality and value.
It’s worth ending on a philosophical note. The tension between order and chaos is a recurring theme in the history of our civilization. It is unlikely that we will ever create the perfect utopia. Whenever we think we are on the brink of establishing the Kingdom of Heaven on Earth, a left field event like a pandemic reminds us that we are only human. That doesn’t mean we should give up. During the covid pandemic, The Plague (1947) by Albert Camus became a best selling book. In the novel, the Black Death comes back in the modern day. Camus’ point is that life has no inherent meaning, order or pattern. Absurd events pop out of nowhere. However, we can still live a good, enjoyable life. The villains in The Plague are the defeatists who throw in the towel and accept their fate. The heroes persist against all odds.
There are lessons in The Plague for the stock market. In the novel, the plague comes back in the modern day, when people think the Black Death is the stuff of history books. The plague is an analogy for chaotic events that happen just as we think everything is under control. In economics, we often feel everything is under control. During the 1940s, as Camus was writing The Plague, an engineer turned economist called Bill Phillips was building his MONIAC machine. The MONIAC was worked by water. Phillips would turn valves and coloured liquids would start flowing through, representing an increase in the money supply or other economic variables. The tool represented an idea that was prevalent at the time, the idea that the economy could be modelled and operated like a well oiled machine.
No-one uses MONIACs anymore. The machine was the butt of jokes in Terry Pratchett’s comic novel, Making Money. Wars, recessions, pandemics and other unexpected events remind us that it is impossible to perfectly model something as complex as the economy. Does this mean we should throw in the towel? No! If Bill Phillips can be criticised for anything, he can be criticised for supposing the economy could be modelled perfectly, like a car engine. Investors can nevertheless beat the stock market even if the market can not be modelled perfectly.
Stock picking is arguably a half-way house between complete chaos and perfect order. We, like the citizens of Graham-and-Doddsville, can guard against randomness by adopting particular techniques. The models of economists like Fama and French rebel against financial chaos and provide us with various heuristics and rules of thumb. Research suggests that investors are more likely to pick winning shares by investing in companies with exposure to factors like quality, value and/or momentum. Chaos can be tamed further by building a well diversified portfolio. Even if the stock market is plagued with chaos and randomness, we, like characters in a Camusian novel, have tools, like the StockRank, which can be used to fight against disorder and increase our chances of generating above average returns.
About Alex Naamani
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There is an interesting paradox. Random stock returns implies an extremely well ordered efficient system of share pricing. Price adjustments level the playing field. Bumps in the playing field imply errors in human analysis and execution. In a sense the stock market is the least random system in existence.
you are reiterating some of the reasons that SNAPs generally outperform (as long as the underlying SR outperforms).
All good stuff and worth driving home .
As others have noted, would be good to understand the impact of further constraints, such as
Size
Yield
F Score
and how the number of stocks chosen affects the outcome
I keep my own database and I have been able to explore the relationship between various factors and stock returns. My investment universe is just over 800 shares in size. I took the top and bottom 100 by StockRank (top SR approx>90 bottom <20) 6 weeks ago and sorted the total returns over the following 6 weeks for each 100 into descending order. These were then plotted on the bar chart below. The mean 6 week return was 2.2% for the top 100 and -3.2% for the bottom 100. There are some big winners and losers in both groups. More of the low stockrank shares were losers. Interestingly, the spread of outcomes is much greater for the lower ranked stocks.
Need to look at longer term cycles. Credit Suisse paper of May 22 based on all markets back to 1900 was pretty unequivocal, particularly for that sort of paper:
Historically, the returns on stocks and bonds
have been much lower during hiking than easing
cycles. Indeed, Dimson, Marsh and Staunton
(2016) report that during hiking cycles, it has
historically been hard to identify assets that
perform well. On average, periods of interest
rate rises have been accompanied by inferior
industry returns, smaller rewards from many
factor investing strategies, and lower price
appreciation for a wide variety of real assets.
There's nowhere to hide. I'd say "tin hats on" but there aren't any tin hats. For those of the lucky demographic, just spend the money made from 1982 (interest rates 14%) to 2021 (interest rates 0%).
Hi Charles,
You are right.
Over the longer-run, periods of higher interest rates probably see lower stock market returns. However, over a shorter period, the relationship between rate hikes and stock returns is less clear cut (at least if we look at UK data between 2009-22). So I guess it depends on someone's investment horizon. Someone who has decided, rightly or wrongly, to be in stocks for the next few years may choose to treat rate hikes as noise, rather than signal.
To your point about 'tin hats' and there being nowhere to hide, I guess it's worth considering this: high StockRank stocks did indeed have a tough time in 2022, but lower StockRank stocks had an even tougher time. They generated even lower returns. So again, if someone has decided for whatever reason to be in stocks, then factor investing could be a useful tool that can be used to protect/preserve wealth.
*Past performance is no indicator of future performance. Performance returns are based on hypothetical scenarios and do not represent an actual investment.
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All good stuff. Would the conclusions be the same if you subdivided into Size categories? or even just removing microcaps