The U.S. fairness market has periodically been dominated by just a few technology-driven shares, most lately the so-called “Magnificent Seven.” Traditionally, comparable dominance occurred through the Nifty Fifty period within the Nineteen Sixties–Seventies and the dot-com growth within the Nineteen Nineties. These durations of concentrated management typically led to momentary outperformance, however systematically capturing such positive factors has confirmed difficult. Our examine investigates the potential to take advantage of know-how sector dominance utilizing momentum-based methods throughout Fama–French 12 business portfolios, analyzing whether or not long-only, long-short, and rolling-basis approaches can generate persistent alpha, and assessing the restrictions of easy timing strategies.
We’re as soon as once more in a interval when the U.S. fairness market is dominated by a small group of shares — the so-called Magnificent Seven. This group consists of Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta Platforms, and Tesla, seven technology-oriented giants that collectively account for greater than 1 / 4 of the S&P 500’s complete market capitalization. Their affect on total market efficiency has grow to be extraordinary, pushed by innovation in synthetic intelligence, cloud computing, and digital platforms.
This stage of focus is just not unprecedented. Within the late Nineteen Nineties, know-how and telecom shares led the market to excessive valuations earlier than the dot-com correction. Within the Nineteen Sixties and Seventies, the Nifty Fifty represented the same period of dominance by a handful of large-cap progress shares. Even earlier, vitality and industrial firms performed a comparable function throughout postwar expansions.
Because the present management is once more pushed primarily by the know-how sector, our foremost curiosity lies in understanding how dominant this sector actually is in contrast with the remainder of the market and whether or not such dominance will be systematically exploited.
For this evaluation, we utilized the dataset of 12 business portfolios: Nondurable Items (NoDur), Sturdy Items (Durbl), Manufacturing (Manuf), Vitality (Enrgy), Chemical substances (Chems), Enterprise Tools (BusEq), Telecommunications (Telcm), Utilities (Utils), Retailers (Retailers), Well being (Hlth), Finance (Cash), and Different (Different), sourced from Fama-French. The dataset incorporates month-to-month observations for every portfolio from July 1926 to June 2025. Our proxy for the IT sector for the entire 100year interval is the Enterprise Tools (BusEq) sector/business.
To seize the potential of the technological sector’s rising development, we deal with investing on this sphere individually, utilizing a number of totally different approaches, earlier than continuing to an examination of all 12 industries.
As a primary step, we outline the benchmarks, towards which our buying and selling methods can be in contrast:
the common of all 12 business portfolios,
the common of 11 business portfolios excluding the know-how sector – Enterprise Tools (BusEq),
the know-how sector (Enterprise Tools – BusEq) alone.
Fundamental efficiency traits in tables are introduced as follows: the notation perf represents the annual return of the technique, st dev stands for the annual normal deviation, max dd is the utmost drawdown, adjusted Sharpe r is calculated because the ratio of perf to st dev and adjusted Calmar r because the ratio of perf to max dd.
What we will be taught in regards to the efficiency of the technological sector if we examine its efficiency towards friends? Traditionally, we will establish 3 main durations of the technological revolutions. The primary occurred within the Seventies, through the period of the Nifty Fifty and the rise of early computing giants similar to IBM, pushed by improvements like the primary microprocessors. Enterprise Tools sector primarily included producers of workplace and industrial equipment somewhat than trendy IT companies. Regardless of improvements just like the microprocessor, progress was restricted by stagflation and excessive rates of interest, leaving the sector’s efficiency in step with the broader market.
The second wave got here within the Nineteen Nineties, marked by the web growth and the explosive progress of software program and telecom firms main as much as the dot-com bubble. Throughout the Nineteen Nineties, fairness markets have grown quickly as inflation fell, financial coverage stabilized, and monetary deregulation took maintain. The enlargement of index funds, derivatives, and computing know-how improved market effectivity and fueled a protracted structural bull market by means of the Nineteen Nineties, significantly within the know-how sector. Nonetheless, this era was adopted by a noticeable decline in returns thereafter.
After the early-2000s downturn, the know-how sector stayed subdued till round 2010–2012, when cloud computing, smartphones, and platform-based fashions reignited progress. Firms similar to Apple, Amazon, Google, Microsoft, and Fb drove a brand new wave of enlargement by means of the 2010s. In recent times, synthetic intelligence has added additional momentum, reinforcing the dominance of enormous know-how companies in international fairness markets.
Nonetheless, historical past exhibits that after every of those revolutionary durations, markets ultimately returned to actuality. The know-how sector, following phases of exuberant progress, tended to expertise substantial underperformance as valuations normalized and management rotated to different industries. Will the present bull market in AI shares finish the identical?
Our exploration of business portfolios encompass a number of funding approaches with the purpose of beating the benchmarks. We are going to current them one after the other.
Shifting common technique
As a primary step, we determined to design buying and selling methods based mostly on a transferring common. Firstly, we targeted on the a part of the market fashioned by the know-how sector, or extra exactly, on how a lot of its efficiency will be defined by the remaining business portfolios.
For every month, we subtracted the common return of the opposite eleven portfolios from the return of the know-how sector. In different phrases, we labored with the technology-vs.-rest unfold.
We then constructed an fairness curve from this unfold and calculated its transferring common. If the present worth of the unfold exceeded the common worth of the fairness curve over the desired interval, the technique took a protracted place in know-how shares and a brief place within the 11 different sectors/industries for the next month. In any other case, the positions have been reversed, lengthy within the 11 different sectors/industries and quick in know-how shares. When the present worth of the unfold is above its transferring common, it signifies that the know-how sector’s latest efficiency exceeds its long-term development, suggesting strengthening relative demand. In such conditions, buyers could also be reallocating towards know-how, which might sign the continuation of its outperformance within the close to time period. We tried this method on totally different home windows for the transferring common, particularly from 3 months to 12 months. This process was repeated month-to-month. For higher visualisation is graphical interpretation in logarithmic scale.
From each the graph in Determine 4 and Desk 2, we will see that this method is reasonably worthwhile however extraordinarily risky. The durations and not using a clear development are lengthy, and the technique tends to earn cash solely when a know-how bubble is inflating or bursting. In any other case, its efficiency is somewhat weak and falls properly wanting our expectations, which is why we moved on to a unique methods.
Lengthy-only momentum technique
Subsequent, we targeted on a momentum-based method. This time, we in contrast the momentum of the know-how portfolio with the momentum of the common of the remaining 11 business portfolios. If the momentum of know-how was increased, we took a protracted place in know-how for the next month. In any other case, we went lengthy within the common of the opposite industries. This process was repeated on month-to-month bases. As soon as once more, we examined numerous momentum lengths, starting from 3 to 12 months . For higher visualisation, we use a logarithmic scale.
We will see that though the long-only methods obtain increased efficiency, they principally fail to outperform the benchmarks. The return-to-risk values are very comparable throughout totally different settings, and apart from the 12-month momentum, the place we barely outperform the benchmark, different MA lengths are unable to exceed benchmarks in each return-to-risk ratios concurrently. Due to this fact, we determined to experiment additional with momentum and developed a protracted–quick technique.
Lengthy-short momentum technique
On this case, we once more in contrast every month the momentum of the know-how portfolio with the momentum of the common of the remaining 11 portfolios. Nonetheless, this time, if the momentum of know-how was increased, we took a protracted place in know-how and a brief place within the common of the 11 portfolios. In any other case, we reversed the positions, lengthy within the common and quick in know-how. Graphical outcomes are introduced in logarithmic scale.
In comparison with the transferring common methods proven in Determine 4 and Desk 2, the outcomes have improved barely, however relative to the earlier method and particularly to the long-only benchmarks, the efficiency is nothing spectacular. Due to this fact, we determined to take a unique perspective, because it turned out that fixing the best-performing business upfront is just not an optimum method. Know-how is just not at all times the strongest sector, and the durations when it performs properly are comparatively few. Furthermore, this technique additionally introduces a look-ahead bias. Due to this fact, it’s higher to design an algorithmic method that selects the best-performing business relative to the others every interval, somewhat than assuming it would at all times be the know-how sector.
Lengthy-only rolling-basis technique
This rolling-basis technique can be momentum-based, however this time we examine the momentum of all 12 sectors/industries towards one another. For the next month, we make investments solely within the sector with the best momentum, successfully letting the info decide which sector is the perfect performer. As earlier than, we set the momentum calculation window to vary from 3 to 12 months and the method is repeated every month. For higher visualization, we once more use a logarithmic scale.
The outcomes of this technique recommend that this method is extra significant and that it certainly is sensible to “put money into the bubble.” Nonetheless, the efficiency and ratios figures are solely barely higher than the benchmarks. Due to this fact, we moved on to a unique method.
Lengthy-short Rolling-Foundation technique
On this case, we additionally examined a protracted–quick method. Every month, we once more in contrast the momentum of all portfolios, however this time we went lengthy within the best-performing business and quick within the remaining ones with equal weights for the next month. We apply this method primarily to watch the alpha, that’s, the portion of efficiency attributable to technique, somewhat than market actions. Once more, we repeated this course of every month, however this time we prolonged the momentum window, analyzing durations starting from 1 month to 12 months. Once more, graphical interpretation is in logarithmic scale.
With this technique, we will already observe a transparent alpha, displaying that longer horizons (8–12 months) are simpler than shorter ones in figuring out the outperforming sector. When it comes to efficiency and ratio metrics, the outcomes don’t method the benchmarks; nevertheless, we didn’t count on that, as all methods are based mostly on long-short and due to this fact all the metrics are normally decrease than long-only variants.
We got here up with one final concept — to look at when this outperformance really happens, that’s, when the best-performing business begins to outpace the others. To discover this, we performed a easy evaluation by creating common fairness curves of all lengthy–quick momentum methods after which constructing a technique, that’s timing this mixture efficiency fairness curve.
Modified Lengthy-Quick Rolling-Foundation methods
Within the remaining half, we determined to additional refine the lengthy–quick rolling-basis method and developed 3 new methods. For every month, we recognized the portfolio with the best momentum throughout all time home windows, from 1-month to 12-month momentum. Within the following month, for every momentum horizon, we went lengthy within the best-performing sector and quick within the remaining ones, simply as within the earlier part. This time, nevertheless, we mixed all these approaches concurrently, making use of them equally-weighted and aggregating them right into a single composite technique. Let’s label this technique as “common mother“.
The second and third methods are derived from the primary one. After creating the fairness curve of the “common mother” technique, we first deal with its 12-month most. If, in a given month, the “common mother” fairness curve reaches its 12-month excessive, we put money into the “common mother” technique for the next month. In any other case, we keep out of the market. As common, this course of is repeated every month. On this manner, we created the “12M Mother Max” technique.
The final examined technique could be very comparable, however as an alternative of specializing in the utmost, we take a look at the 12-month transferring common. If, within the present month, the worth of the “common mother” fairness curve is above its 12-month transferring common, we put money into the “common mother” technique for the next month. In any other case, we keep out of the market, repeating this course of every month. We label this technique “12M Mother MA“.
All 3 approaches are then plotted collectively alongside the benchmarks for comparability in logarithmic scale.
Reviewing the “common mother” technique, we will observe that there was alpha as much as the Fifties, however that is based mostly on Fama–French information, and particular person sectors at the moment couldn’t be simply traded, so this represents a theoretical alpha. From the Fifties onward, many of the alpha efficiency happens in three distinct durations: the Seventies, Nineteen Nineties, and 2020s, equivalent to new technological eras. Exterior of those durations, the efficiency of the “common mother” is roughly flat. Sadly, making an attempt to enhance the “common mother” through the use of the brand new excessive or transferring common filter method doesn’t enhance the efficiency of return-to-risk ratios. So, on the finish, we will conclude that the easy “purchase the perfect performing sector over the past 12-month interval, quick the remainder, maintain for 1 month” technique is the perfect method to take advantage of the cross-sectional sector efficiency dispersion.
Conclusion
Our evaluation exhibits that technology-driven durations have dominated fairness markets, however systematically exploiting them stays difficult. Easy moving-average and momentum methods targeted solely on know-how can sometimes generate outperformance, but they’re extremely risky and infrequently fail to surpass benchmarks. Rolling-basis approaches that choose the best-performing business every month present clearer alpha, significantly over longer momentum horizons, however efficiency stays modest exterior the main technological revolutions of the Seventies, Nineteen Nineties, and 2020s.
Makes an attempt to time these durations utilizing easy heuristics, similar to new highs or transferring averages, proved troublesome, underscoring the episodic and unpredictable nature of sector-driven outperformance. Whereas momentum methods can seize tendencies throughout technological waves, they need to be carried out with warning as they have an inclination to ship alpha solely over the longer horizons.
Creator: Sona Beluska, Junior Quant Analyst, Quantpedia
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