A Trend Following Deep Dive: Cash (Equities) Is King

Introduction

Trend following in equities has historically been concentrated in a finite, but broad set of index futures – the S&P 500, Korean Kospi, MSCI EM, and Nifty, among others. This is largely borne out of the fact that they are among the most ‘trend compatible’ ways of obtaining exposure to the asset class: operationally straightforward to trade, highly liquid and capital efficient.

As we demonstrated in our work on the optimal market mix, equity index futures play a crucial role in a trend-following portfolio. They are both highly diversifying (when combined with other asset classes, including bonds, commodities and currencies) and highly sensitive to large macroeconomic shifts, including monetary policy changes, inflation regimes and global risk appetite. This macro sensitivity underpins trend followers’ ability to capitalise on broad market selloffs or bull runs. Put differently, equity index futures are a key component of the convex return profile trend-following strategies can offer.

Notwithstanding, trend followers have consistently sought to complement this macro risk exposure with alternative markets, driven by more idiosyncratic risk factors, in a bid to bolster diversification. Within equities, drilling down to the single name level offers one way of doing this.

In what follows, we examine the ways in which cash equities can be deployed within a trend-following framework, assessing the diversification each approach may deliver relative to traditional equity index futures trend.

Starting point: directional, sector trend following

The typical starting point for implementing trend following on cash equities is to first group stocks into baskets, rather than applying models directly to single names. The rationale for this stems from empirical evidence presented by Jegadeesh and Titman (1993) and De Bondt and Thaler (1985), showing that individual stocks exhibit reversal effects at both short and long horizons. Compounded by idiosyncratic noise and event risk, these effects can hamper the development of profitable trends. Grouping individual cash equities into baskets, such as those based on sector or industry classifications, helps mitigate these headwinds by smoothing out stock-level noise and isolating more persistent, basket-level price movements. As such, the starting point for our analysis will be sector-level baskets, based on GICS level 3 (GICS3) definitions.

One might expect directional, time-series trend-following models on sector baskets to be able to capture dispersion inaccessible from trading the index alone. During a sector rotation, like the one seen in the first quarter of this year, such a strategy could be long semiconductors and short software and services, capturing intra-index diversification that is masked at the aggregated level. While conceptually appealing, examining the realised correlation between a trend-following strategy applying univariate, time-series models on GICS3 sectors and traditional equity index futures reveals that the diversification benefit is less meaningful than expected.

Figure 1: Rolling 24-month correlation between equity index trend and directional, time series equity sector trend (based on overlapping 10-day returns)

Source: Man Group database. Date range: January 2005 to February 2026.

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As Figure 1 illustrates, the rolling 24-month correlation between equity index trend and directional, time-series sector trend following is persistently high, averaging approximately 0.81 over a 21-year sample. This likely reflects the fact that both approaches are inherently taking directional bets on equity beta or risk premia, differing only in granularity of exposure. Figure 2 examines this further, decomposing the correlation by return quintile of equity index futures trend.

Figure 2: Pairwise correlation between equity index trend and directional, time-series sector trend by quintile of equity index trend (based on overlapping 10-day returns)

Source: Man Group database. Date range: January 2005 to February 2026.

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Correlation is highest in both tails – and particularly so in the left tail – suggesting directional, time-series sector trend may be susceptible to the same catalysts that drive equity beta (and consequently index futures).

While both strategies share positive traits, including positive long-term returns and macro sensitivity to deliver a convex return profile, this also increases the likelihood of contemporaneous reversals around market inflection points, where trend following is already most vulnerable. Clearly, this is an undesirable feature for markets that are intended to be more idiosyncratic. So how can we better monetise trend following on cash equities?

Cross-sectional, sector trend following

One option is to explore different ways of creating equity baskets, which we will return to shortly. Another is to reformulate portfolio construction on the same basket. Rather than taking directional bets on GICS3 sectors, we can adopt a cross-sectional, market-neutral approach. To put it another way, rather than applying univariate time-series models, we can apply a multi-variate framework based on the relative momentum of the sectors. This means being long those sectors exhibiting the strongest past performance and short the weakest, creating a portfolio with ex-ante market neutrality.

As shown by the aqua blue line in Figure 3, this neutralises the implicit beta loading of directional sector trend, resulting in a fundamentally different risk profile and improved diversification properties.

Figure 3: Rolling 24-month beta to MSCI World for equity index trend versus cross-sectional sector trend

Source: Man Group database. Date range: January 2005 to February 2026.

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Cross-sectional, equity style trend following

As hinted at above, we can also slice the equity universe along a different plane, using well-documented investment factors or styles. Using firm-level accounting metrics, we can form portfolios based on styles such as quality, size or value, among others, and therefore widen the opportunity set to capture a fundamentally different axis of cross-sectional dispersion. Additionally, Gupta and Kelly (2019) provide robust evidence for the alpha generated by applying time-series momentum models on equity style factors, showing that 59 of the 65 characteristic-based factors investigated exhibit positive monthly autocorrelation. Their combined factor trend portfolio also outperformed both a traditional stock momentum and industry momentum (similar to our cross-sectional sector approach presented above) strategy on a risk-adjusted basis. Beyond showcasing the efficacy of the strategy, the authors present further evidence of equity style momentum’s pervasiveness across different formation windows and regions, underscoring the robustness of the effect.

In Figure 4, we construct a similar style trend-following strategy, trading approximately 13 equity styles* across each of the US, Europe, Japan and pan-Asia. Comparing this against a long-only benchmark of market-neutral styles demonstrates the additivity of using trend-following models to time returns.

Figure 4: Long-term performance of style trend-following strategy versus a long-only style benchmark

Source: Man Group database, Bloomberg. Date range: April 2011 to December 2025. Equity market neutral (EMN) benchmark constructed as a composite of 35% Dow Jones Thematic Market Neutral Value Index, 15% Dow Jones Thematic Market Neutral Quality Index, 35% Dow Jones Thematic Market Neutral Momentum Index and 15% Dow Jones Thematic Low Beta Index, scaled up by a diversification benefit factor such that the benchmark achieves the same level of annualised ex-post volatility as the Style Trend strategy. The weights to each style benchmark are chosen to map as closely as possible (from the universe of style benchmarks available) to the weights of different styles within the trend strategy.

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* Note, the estimated 13 styles per region quoted is an aggregate figure, where different measures or formulations of a given style are grouped together. The strategy trades around 80-100 unique formulations per region, with the exact quantity varying based on the region.

The efficacy of style trend following arguably stems from a fundamental market inefficiency. Equity styles are subject to persistent capital flows from various market participants (Figure 5), creating exploitable patterns driven by mechanisms such as institutional rebalancing, style rotations and risk premia harvesting. These are distinct from the speculative or fair-value price-based dynamics that drive traditional, non-synthetic markets, such as index futures. Furthermore, the market neutrality of the underlying styles further reinforces their diversifying properties relative to equity index futures trend following.

Figure 5: Assets under management (AUM) growth in equity style/smart beta ETFs

Source: Man Group database, Bloomberg. Date range: April 2011 to December 2025.

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The diversification imperative

Having established the conceptual merits of multiple cross-sectional approaches, we return to the central question: how much diversification do they actually deliver?

Figure 6 replicates the analysis from Figure 1, but compares the correlation between equity index futures to each of the three cash equity strategies. Both cross-sectional sector trend and style trend exhibit an average correlation of approximately 0.05 over the 21-year sample of data, peaking at a maximum of around 0.4 and a trough of approximately -0.2.

Figure 6: Rolling 24-month correlation (using rolling 10-day returns) between equity index trend and each of directional sector trend, cross-sectional sector trend and style trend

Source: Man Group database. Date range: January 2005 to February 2026.

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Another way of visualising the diversification is by analysing the average correlation between the cross-sectional cash equity approaches and equity index futures per each return quintile of the latter, similar to Figure 2. The below highlights that both strategies exhibit effectively zero correlation irrespective of the return regime.

Figure 7: Pairwise correlation between equity index trend and style trend and cross-sectional sector trend by quintile of equity index trend (based on overlapping 10-day returns)

Source: Man Group database. Date range: January 2005 to February 2026.

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Importantly, this diversification is particularly robust in the left tail of equity index futures returns. Figure 8 shows cumulative performance of each strategy during the five worst peak-to-trough drawdowns for equity index futures trend between January 2005 and February 2026. While directional, time-series sector trend has historically suffered contemporaneous drawdowns, as implied by the spiking correlation in the worst quintile from Figure 2, cross-sectional sector trend delivered positive returns across all five drawdown episodes, while style trend was also positive in four out of five instances.

It is important to caveat that the two cross-sectional cash equity strategies are not explicitly complementary to equity index trend, meaning there is no mechanical guarantee they will outperform during equity index trend drawdowns. However, the evidence demonstrates that both cross-sectional approaches provide independent, differentiated performance during periods where traditional trend following struggles, which in itself is highly valuable from a diversification perspective.

Figure 8: Cumulative strategy returns during the five worst independent equity index trend drawdowns

Source: Man Group database. Date range: January 2005 to February 2026.

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Aside from diversification, Figure 9 shows that each of the cash equity strategies has demonstrated robust, positive long-term returns as well.

Figure 9: Cumulative returns (compounded), log scale

Source: Man Group database. Date range: January 2005 to February 2026.

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Parting thoughts

From a portfolio construction perspective, cash equities sit naturally within the alternative markets universe of a diversified trend-following programme. Cross-sectional approaches, in particular, deliver low correlation to traditional equity index futures, underpinned by idiosyncratic risk factors, rather than the broad macroeconomic variables that typically drive equity beta. This supports the case for their inclusion as a complementary allocation alongside traditional markets.

Indeed, this is a trend we have been observing across the industry, as a greater number of managers integrate cash equity approaches into diversified trend-following portfolios. Notably, the bulk of adoption to date has been concentrated in developed markets, such as the US or Europe, predicated on their more mature underlying infrastructure and deeper liquidity. However, emerging markets, such as Latin America, offer a potentially attractive opportunity to apply both the sector and style strategies discussed in this paper. As these markets become increasingly mature and deepen in liquidity, it offers managers new avenues to capture previously inaccessible trends and further bolster diversification.

The authors would like to thank Jeremy Andre for his contribution.

Bibliography

De Bondt, W. F., and R. Thaler. 1985. "Does the Stock Market Overreact?" *The Journal of Finance* 40 (3): 793–805.

Goodall, R. 2023. "What's Trending: Rolling with the Punches" Man Institute.

Gupta, T., and B. Kelly. 2019. "Factor Momentum Everywhere." *The Journal of Portfolio Management*, Quantitative Special Issue.

Jegadeesh, N., and S. Titman. 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." *The Journal of Finance* 48 (1): 65–91.

Moore, H., Goodall, R., and E. Sanchez Martin. 2026. "A Trend Following Deep Dive: The Optimal Market Mix for a Trend Follower." Man Institute.

Moskowitz, T. J., Y. H. Ooi, and L. H. Pedersen. 2012. "Time-Series Momentum." *Journal of Financial Economics* 104 (2): 228–250.