How To Use Momentum Statistics To Improve Trading Results (2024)

I wrote two articles on Seeking Alpha posted June 3rd to help better illustrate the momentum-based research I have been developing the last decade. You can reread the and the Weakest Momentum selections using the same indicator criteria. Basically, I used just three equally-weighted indicators to generate the 10 best-situated and 10 worst blue-chip equities for shorter-term trades. [I use nine total indicators in slightly different ways than discussed in the articles.] This effort will review some of the results after nearly two months of trading, and suggest some insights investors can take away from the public portfolio experiment.

I used three indicators with a 6-month sort period to generate each score: (1) a direct performance comparison to the S&P 100 index (SP100), (2) changes in the daily Accumulation/Distribution Line, and (3) fluctuations in the daily Negative Volume Index.

In terms of momentum, the clearest picture of winning/losing is to review price change over time vs. a peer index. In this instance, the S&P 100 U.S. mega-cap index was my sort group. Relative price strength or weakness is the name of the game for portfolio construction, so that was my first sort criterion over a 6-month span. 3-month and 6-month periods hold some of the best predictive value, from my work.

The second sort measurement for the exercise was the Accumulation/Distribution Line (ADL). ADL is a recording of how high or low the closing price prints inside the daily trading range. If a stock closes near the high of the day, we can say buying happened during the session. If it closes nearer the low, we can extrapolate that selling took place intraday. While not a perfect indicator of future price direction, this historical record of recent patterns in trading activity has proven useful when part of a larger formula.

The final stat for this momentum ingredient list was the Negative Volume Index (NVI). This indicator only counts price change multiplied by trading volume on falling volume days vs. the last session. Definitely, a unique generator of what the professional and institutional buying/selling crowd is doing on quieter news days. The theory is more knowledgeable and engaged traders will give us better clues of buying and selling trends vs. the emotional crowd on high volume, more news-filled sessions. Large price changes on low volume days can pinpoint supply/demand issues. Future stock price gains/losses can, and do happen without advance warning from the NVI. However, the NVI is a great tool for identifying changes in trend and measuring the underlying health of a stock, more often than not. A basket of blue chips usually performs as predicted by abnormally strong or weak NVI signals the next 3-12 months.

The Picks

My list of the strongest 10% in the S&P 100 index, 10 out of 100, eight weeks ago included: Apple (AAPL), Adobe (ADBE), Amazon (AMZN), Danaher (DHR), Alphabet (GOOG), Home Depot (HD), Johnson & Johnson (JNJ), Microsoft (MSFT), Netflix (NFLX), and NVIDIA (NVDA).

Below is a graph example from the original mention of Home Depot, and an updated version of what I am comparing and contrasting against the other stocks in the S&P 100. The buy area at $251 a share (or less) existed for several days after the story was posted, circled in green on the second chart.

Below is a price performance graph explaining the positive returns created the past eight weeks by the Top 10 in this formula creation.

Overall, the mean average price gain was nearly +23% from the Top 10 (median was between +16.67% and 21.36%), against the equivalent S&P 100 rise of +11.55%. Essentially, this strongest momentum group at the time of publication, continued to climb robustly, DOUBLING the return of the peer index over two months.

The weakest 10% of the index, 10 out of 100, included American International Group (AIG), Boeing (BA), Citibank (C), DuPont (DD), Ford (F), General Electric (GE), Occidental Petroleum (OXY), Simon Property (SPG), United Parcel Service (UPS) and Wells Fargo (WFC).

The mean average of the Bottom 10 for price performance was -1.9% (median between -7.57% and -11.47%). That’s correct, this group of carefully selected stocks "underperformed" the +11.55% peer index gain by roughly -13.45% in a relative calculation!

The conclusion I have found, over many years of real-time testing, is the largest capitalization blue-chips in American tend to stay in downtrends (or uptrends as the case may be) for extended periods of time. Especially when measured against mid-cap and smaller companies, playing blue-chip momentum can be an extremely profitable strategy. The reason is material price moves require a large pool of buyers or sellers in the largest U.S. companies, publicly traded. Once the ball gets rolling, the reasons for price change do not turn on a dime, under normal trading circ*mstances anyway.

Of course, all good things come to an end. When extreme valuations are reached, the trends can quickly reverse. And, unexpected news events like the coronavirus situation, a major lawsuit loss, or an industry peak/valley can greatly affect the profitability of momentum trading.

The biggest outlier in the 20 stocks mentioned in early June has been United Parcel Service. UPS has been able to raise shipping prices and volumes dramatically the last several months as the coronavirus deliver to home phenomenon has lasted far longer than initially projected. Remember when we were going to shut down the economy for 2-4 weeks in mid-March, and quickly get back to normal by early summer? The UPS outlier is more apparent when looking at the median drop of greater than -7.57% for this subset of 10 equities.

Long/Short Strategy

The way I like to trade my momentum results is to go long stocks with the best underlying momentum, while shorting the weakest, in a market neutral design (net neutral or zero for long exposure). If the stock list behaves as projected, I can eliminate market direction risk. I don’t care if the U.S. stock market rises 50% or falls 50% the next 12-24 months, as long as the buys perform in a healthier manner than the sells, I earn a profit. Both can rise, or both can fall in price and I still see a rising brokerage account value! Plus, I don’t have to necessarily worry about the Trumpster or Democrats, the Federal Reserve, Chinese economic policy, if a hurricane is about to hit, or the overvaluation level of the stock market! Think about it. Investors can create net gains no matter what the newsflow in the financial and political world with a smartly diversified, hedged long/short portfolio design.

In our theoretical instance over the last eight weeks, A $10,000 investment in the longs, offset by $10,000 equally-weighted in the shorts would have netted about +25% (+23% in long gains plus +1.9% in short profits), before trading expenses and commissions. Since major online brokers have eliminated nearly all commissions on stock trades, and interest rates today are relatively low to borrow shares on the short side of the equation, a simple $1,000 investment in 20 positions would have run circles around every major stock market index in America for performance the last eight weeks.

Another statistical evaluation stress test I like is to take out the biggest gainer and loser from a sort to make sure one lucky pick is not creating most all the returns. I did mention Apple in a negative light based on its overvaluation level in a June article. My official stance on Apple was a Neutral rating, but if we eliminate it from this experiment (and take out the worst long performer Johnson & Johnson), our gains would have been only marginally lower. The other nine returned +19.7%, and the added elimination of JNJ brought a gain from the middle eight selections of +21.8%.

In a similar fashion, removing both the biggest winner and loser from the weakest 10 stocks would have dramatically dropped the average price loss to -5.4%, from -1.9% for all ten. All told, the elimination of the UPS outlier and Wells Fargo’s extra-large -18% loss produced an even better result for short sellers. Using the middle 16 stocks (8 longs and 8 shorts), the net price gain would have been a similar +27% (+21.8% long gain plus +5.4% in short selling profits), before trading expenses.

Takeaway

Trading momentum trends can be a highly profitable design. There are times when shifts in investor tastes and risk appetites reverse existing momentum. However, during a vast majority of the time, the right technical indicators can help you find future winners and avoid large potential losers.

Often in the real world, you will find yourself talking away momentum characteristics simply because a fundamental valuation argument seems more logical. “This stock cannot possibly go any lower, because it is so cheap.” Then it continues lower another 3-6 months, a common trading conundrum we have all faced. In the end, looking at the hard/cold technical trading facts of individual name supply/demand balance may prove of the same, if not greater, importance in your decision making. Why not add it to your research arsenal?

Using the "full range" of technical indicators I follow, Apple, Adobe, Amazon and Google are members of the bullish Top 10 in the S&P 100 sort today. On the bearish side, Citibank, General Electric, Occidental Petroleum and Wells Fargo remain in the bottom tier of selections.

New and experienced investors alike can benefit from further research and experimentation with momentum/technical indicators and weightings of your own design. Relative strength vs. an index or peer group is one of the easiest to compute and most successful ideas to begin your journey. You can sort time periods from a few days to several years, with varying results in terms of predictive value. A desire to document my technical research and conclusions for individual investors was a major reason I began writing on Seeking Alpha seven years ago.

Thanks for reading. Please consider this article as a first step in your research process. Consulting with a registered and experienced investment advisor is suggested before making any trade.

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Paul Franke