In 1838, James Grant published The Great Metroplis, Volume 2. Within, he spoke of David Ricardo, an English political economist that was active in the London markets in the late 1700s and early 1800s. Ricardo amassed a large fortune trading both bonds and stocks. According to Grant, Ricardo’s success was attributed to three golden rules:
“As I have mentioned the name of Mr. Ricardo, I may observe that he amassed his immense fortune by a scrupulous attention to what he called his own three golden rules, the observance of which he used to press on his private friends. These were, “Never refuse an option* when you can get it,”—”Cut short your losses,”—”Let your profits run on.” By cutting short one’s losses, Mr. Ricardo meant that when a member had made a purchase of stock, and prices were falling, he ought to resell immediately. And by letting one’s profits run on he meant, that when a member possessed stock, and prices were raising, he ought not to sell until prices had reached their highest, and were beginning again to fall. These are, indeed, golden rules, and may be applied with advantage to innumerable other transactions than those connected with the Stock Exchange.”
“Cut short your losses” and “let your profits run on” became the core tenets of trendfollowing.
Other prominent early trendfollowers include:

Charles H. Dow, founder and first editor of the Wall Street Journal as well as cofounder of Dow Jones and Company

Jesse Livermore, who is quoted by Edwin Lefèvre as having said, "[t]he big money was not in the individual fluctuations but in the main movements ... sizing up the entire market and its trend."

Richard Wyckoff, whose method involved entering long positions only when the market was trending up and shorting when the market was trending down.
There was even an early academic study of trendfollowing performed by Alfred Cowles III and Herbert Jones in 1933. In the study, titled Some A Posteriori Probabilities in Stock Market Action, they focus on counting the number of sequences – times when positive returns were followed by positive returns, or negative returns were followed by negative returns – to reversals – times when positive returns are followed by negative returns, and vice versa.
Cowles and Jones evaluated the ratio of these sequences and reversals in stock prices over periods ranging 20 minutes to 3 years. Their results:
It was found that, for every series with intervals between observations of from 20 minutes up to and including 3 years, the sequences outnumbered the reversals. For example, in the case of the monthly series from 1835 to 1935, a total of 1200 observations, there were 748 sequences and 450 reversals. That is, the probability appeared to be .625 that, if the market had risen in a given month, it would rise in the succeeding month, or, if it had fallen, that it would continue to decline for another month. The standard deviation for such a long series constructed by random penny tossing would be 17.3; therefore the deviation of 149 from the expected value of 599 is in excess of eight times the standard deviation. The probability of obtaining such a result in a pennytossing series is infinitesimal.
Despite promising empirical and theoretical results for trendfollowing, the next academic studies would not come until nearly a century later.
In 1934, Benjamin Graham and David Dodd published Security Analysis. Later, in 1949, they published The Intelligent Investor.
In these weighty tomes, they outline their methods for successful investing. Graham and Dodd’s method focused on evaluating the financial state of the underlying business. Their objective was to identify a company’s intrinsic value and purchase stock when the market offered a substantial discount to that value.
For Graham and Dodd, anything else was mere speculation.
Graham and Dodd gave fundamental investors – and specifically value investors – their bible.
Anything, then, that was not fundamental investing was technical analysis. And since trendfollowing relied only on evaluating past prices, it was labeled technical analysis.
Unfortunately, academics largely dismissed technical analysis through the 1900s. This is likely due to the fact that it was difficult to study and test. Practitioners follow a large number of different techniques. Sometimes these different techniques can lead to contradictory predictions between technicians.
But in 1993, Narasimhan Jegadeesh and Sheridan Titman published Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. In their paper, they outlined an investment strategy that purchased stocks that had outperformed their peers and sold stocks that had underperformed.
Jegadeesh and Titman called their approach relative strength – a term that had been long used by technicians. Now it is sometimes called crosssectional momentum, relative momentum, or often just momentum.
This simple method outlined by Jegadeesh and Titman created statistically significant positive returns that could not be explained by common risk factors.
This paper ushered in an era of momentum research, with academics exploring how the technique fared across geographies, timeframes, and asset classes. The results were that momentum was surprisingly robust.
Despite the success of relative strength, interest in its close cousin trendfollowing was still nowhere to be found.
Until the financial crisis of 2008.
Technically, one of the most popular research papers about trendfollowing – Mebane Faber’s A Quantitative Approach to Tactical Asset Allocation – was published in 2006. But the majority of interest from academics occurred post2008.
We attribute this interest to trendfollowing’s risk mitigation properties.
The studies typically fall into two camps.
In the first camp was the study of trendfollowing, which tended to follow simple mechanical systems, like moving averages. Faber (2006) fell into this camp, using a 10month moving average crossover.
There are several variations of these systems. For example, one might use the cross of price over the moving average as a signal. Another might use the cross of a shorter moving average over a longer. Finally, some may even use directional changes in the moving average as the signal.
Others tended to focus on what would become known as timeseries momentum. In timeseries momentum, the trading signal is generated when the total return over a given period crosses over the zeroline.
One of the most prominent studies for timeseries momentum was Moskowitz, Ooi, and Pedersen (2011), which demonstrated the anomaly was significant in 58 liquid equity index, currency, commodity, and bond futures.
Trendfollowing moving average rules were still considered to be technical trading rules versus the quantitative approach of timeseries momentum. Perhaps the biggest difference is that the trendfollowing camp tended to focus on techniques using prices while the momentum camp focused on returns.
However, research over the last halfdecade actually shows that they are mathematically related strategies.
Bruder, Dao, Richard, and Roncalli’s 2011 Trend Filtering Methods for Momentum Strategies united movingaverage crossover strategies and timeseries momentum by showing that crossovers were really just an alternative weighting scheme for returns in timeseries momentum. To quote,
The weighting of each return … forms a triangle, and the biggest weighting is given at the horizon of the smallest moving average. Therefore, depending on the horizon n2 of the shortest moving average, the indicator can be focused toward the current trend (if n2 is small) or toward past trends (if n2 is as large as n1/2 for instance).
In Marshall, Nguyen and Visaltanachoti’s TimeSeries Momentum versus Moving Average Trading Rules, published in 2012, timeseries momentum is shown to be related to changes in direction of a moving average. In fact, timeseries momentum signals will not occur until the moving average changes direction.
Therefore, moving average rules which rely on price crossing the moving average are likely to occur before a change in signal from timeseries momentum.
Similar to Bruder, Dao, Richard, and Roncalli, Levine and Pedersen show that timeseries momentum and moving average crossovers are highly related in their 2015 paper Which Trend is Your Friend?. They also find that timeseries momentum and movingaverage crossover strategies perform similarly across 58 liquid futures and forward contracts.
In their 2015 paper Uncovering Trend Rules, Beekhuizen and Hallerbach also link moving averages with returns, but further explore trend rules with skip periods and the popular MACD (moving average convergence divergence) rule. Using the implied link of moving averages and returns, they show that the MACD is as much trend following as it is meanreversion.
These studies are important because they help validate the approach of pricebased systems. Being mathematically linked, technical approaches like moving averages can now be linked to the same theoretical basis as the growing body of work in timeseries momentum.
Market practitioners have long held that the trend is your friend and academic literature has finally begun to agree.
But perhaps, most importantly, we now know that it doesn’t matter whether you take the technical approach using moving averages or the quantitative approach of measuring returns. At the end of the day, they’re more or less the same thing.
In Our Models
We rebalanced our Risk Managed SmallCap Sectors portfolio this week.
Energy and materials remain out of the portfolio. Utilities has now been fully removed as well based on recent momentum weakness.
This means that 6 of the 9 sectors tracked are now exhibiting negative signals.
Of those signals that remain on, several are less than a standard deviation away from turning off. We see continued strength in sectors such as consumer discretionary, health care, and technology – but the remaining sectors are showing weakness.
This signal mix implies that a moderate amount of cash will be built if the smallcap space tumbles another 5% or so. However, a very significant amount of cash will not be built unless the three leading sectors all falter.
This is consistent with our momentum views on the domestic largecap and global space. We expect that if largecap U.S. equities or global equities fall another 5% or so, we will build a small cash position. However, a larger selloff will be required before a more significant position is built.

DISCLOSURE: The views and opinions expressed in this article are those of the authors, and do not represent the views of equities.com. Readers should not consider statements made by the author as formal recommendations and should consult their financial advisor before making any investment decisions. To read our full disclosure, please go to: http://www.equities.com/disclaimer
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