Factor Friday - September 11, 2026

Size Snaps Back as The Market Retraces

Factor leadership shifted again this week. Size rose 2.32%, reversing last week's 0.90% decline to retake second place, while Downside Beta, last week's top performer at 3.20%, cooled to essentially flat at 0.05%. Value led for a second straight week at 2.42%, only slightly softer than last week's 2.83%. Liquidity (-1.52%), Momentum (-1.95%), Growth (-2.74%) and the Market (-4.98%) all finished lower, with the Market posting the week's sharpest reversal after a 22.57% four-week advance. Over four weeks the Market still leads by a wide margin at 22.57%, with Size (3.10%) and Liquidity (1.61%) the only other factors positive across that span. Year to date, Size remains the sole factor in positive territory at 8.13%, while the Market (-22.16%) and Momentum (-12.63%) carry the deepest drawdowns. For investors, the whipsaw between Size and Downside Beta over the past two weeks shows how quickly factor leadership can rotate, and it is happening independent of the Market's direction, which swung from a positive week to a near -5% decline. Size's steady position as the year's only durably positive factor remains the more important signal than either week's rotation at the top.

Rolling Factor Rankings and Rotation

Size, which had fallen to last place seven days ago at -0.90%, rose to second at 2.32%, while Downside Beta, last week's leader at 3.20%, slipped to third at just 0.05%. Value held the top spot for a second straight week, easing only slightly from 2.83% to 2.42%. The Market fell four places, from fourth to last, swinging from 1.22% to -4.98%, the week's largest move in either direction. The month-to-date ranking tells a steadier story: Value (4.06%), Size (3.02%) and Downside Beta (1.81%) hold the top three spots, with Momentum (-3.11%) at the bottom, so this week's swap between Size and Downside Beta looks more like normal volatility than a change in its underlying trend.

Market and Key Factor Betas

Market Beta

Market-beta composition is unchanged from last week, the same ten names anchoring both ends of the list for an eighth straight week. Lido (LDO) edged back ahead of Zero (ZRO) for the top spot, 1.62 to 1.60, with the rest of the high-beta cohort holding its order: meme coins and gaming tokens, cross-chain messaging, modular infrastructure and a naming-service name. The defensive end is just as static, WLFI still lowest at 0.69 and Bitcoin (BTC) next at 0.89, followed by enterprise interoperability and legacy payment chains alongside older smart-contract platforms. An unchanged roster through a sharp Market reversal says positioning in broad beta has not moved with the Market itself. The same book that captured the four-week rally is sitting through this week's pullback unchanged.

Downside Beta

Downside Beta's retreat this week came with real movement beneath the surface. EOS still sits alone atop the beta list at 4.72, more than six times the next name, Stellar (XLM) at 0.70, with the rest of the high-beta cohort, the names most exposed to the Market's worst sessions, clustered in legacy layer-1 and enterprise-adjacent tokens led by Quant (QNT) and Filecoin (FIL), with Algorand (ALGO), Hedera (HBAR) and Polkadot (DOT) filling out the list. Zero (ZRO) dropped out of the top 10 entirely this week, replaced by TRUMP at the bottom of the list, while Polkadot (DOT) climbed three spots. The bottom of the list runs the other way: WIF, WLFI, Jasmy (JASMY) and Ethena (ENA) remain the most negatively exposed names, with Ethena's beta slipping from -1.95 to -2.01, while Sei (SEI) dropped out of the bottom 10 and Hyperliquid (HYPE) took its place. With one name churning at each end of the list and Downside Beta's return collapsing to near zero, this reads as a fading defensive bid rather than a broadening one. A repeat next week of either swap would say more.

Size

Size's reversal from last week's -0.90% to this week's 2.32% shows up entirely in price, not composition. The high size-beta cohort is unchanged for a second straight week, ten names run by WIF at 4.46 and FET at 4.34, alongside Zero (ZRO), Filecoin (FIL), Jasmy (JASMY), PENGU, The Graph (GRT), BONK and Sandbox (SAND). The low end mirrors last week's order, still led by WLFI and TAO, followed by Ethereum (ETH), Solana (SOL), Sui (SUI) and Avalanche (AVAX) among the ten smallest size betas, with no changes in rank. Factor scores across the portfolio barely moved week over week. Zero's beta eased from 4.21 to 3.89, the largest shift in the top 10 and still not enough to change any name's rank. Size remains the only factor positive year to date at 8.13%, so this week's bounce reads as a return to trend rather than a new development. The two-week swap with Downside Beta is the less important story than Size's steady year-to-date lead.

What Factors are Driving Markets

Factor contributions across the CF DACS Sectors universe total 5.4% over the trailing 30 days, against an index return of 24.8%, leaving the index 19.5 percentage points (pp) ahead of the factor sleeve, narrower than the 23.8 pp gap a week earlier even as the index itself cooled from 30.3% to 24.8%. Size, Downside Beta and Momentum are essentially tied as the largest contributors, each adding close to 2%, while Growth (-1.0%) is the lone drag. The narrowing gap says the factor sleeve is doing slightly more of the work than it was a week ago, but with three factors clustered together and none dominant, Sectors performance still looks driven mostly by forces outside this factor set.

In the CF DACS Services universe, factor contributions total 3.2% against an index return of 30.2%, a 27.0 pp gap that has narrowed from 28.7 pp a week earlier even as the index itself held roughly steady. Size is the clear leading contributor at 2.6%, with Downside Beta adding a further 1.1%, while Growth (-0.9%) is again the largest drag. Services remains an index-led category, with the factor sleeve explaining only about a tenth of the 30-day advance.

The Settlement universe again carries the clearest index-level signal of the three. Factor contributions are effectively flat, totaling just -0.1% over the trailing 30 days, while the index returned 24.2%, a 24.4 pp gap that has narrowed modestly from 28.1 pp the week before. No single factor contributed more than a few hundredths of a percentage point in either direction, so the month's advance sits almost entirely outside the systematic style basket, exactly as it did last week. Settlement's return continues to reflect forces the factor set simply does not capture.

Appendix: CF Factor Methodology

Market Factor

The market factor captures the broad, systematic risk that permeates the digital asset ecosystem. It reflects aggregate influences such as macroeconomic conditions, investor sentiment, and overall market volatility. As such, this factor is defined by the daily returns of the CF Broad Cap (Free Float Market Cap Weight) Index, offering a comprehensive and capitalization-weighted representation of the asset class.

Size Factor

The size factor captures the return differential associated with asset scale, reflecting the hypothesis that smaller-cap digital assets tend to outperform their larger-cap counterparts. This effect is understood to compensate for elevated operational and financial risks while exploiting potential market inefficiencies. In this framework, the size factor is defined by each asset’s fully diluted market capitalization. The value is sign-inverted so that higher z-scores are assigned to smaller assets and vice-versa.

Value Factor

The value factor reflects a protocol’s ability to generate economic output relative to its capital base and market valuation, combining measures of both efficiency and user engagement. It is constructed as the average z-score of two key ratios: transaction fees relative to total value locked (Fees/TVL) and daily active users relative to market capitalization (DAU/MCap). This composite metric captures how productively a protocol utilizes its resources while also serving as a proxy for user-driven demand. A higher combined score indicates efficient resource utilization and strong user engagement.

Momentum Factor

The momentum factor captures short-term price persistence by identifying assets that have recently exhibited strong performance. It is computed as the average z-score of two metrics: the 2 weeks cumulative performance and the 2 weeks risk-adjusted cumulative performance. This approach aligns with established findings in traditional financial literature and demonstrates empirical relevance in digital assets, where price trends tend to exhibit momentum over short horizons.

Growth Factor

The growth factor captures the expansion of a protocol’s network activity and user adoption. In the context of digital assets, it reflects metrics such as fee generation and user engagement, which serve as indicators of increased platform utilization and operational scale. The factor is defined as the average z-score of 30-day fee growth and 30-day weekly active user growth, thereby identifying assets exhibiting consistent and measurable increases in underlying network usage.

Downside Beta

The downside beta factor captures an asset’s sensitivity to adverse market conditions by isolating its behavior during periods of negative market returns. Empirical evidence shows that assets with lower downside beta tend to outperform their higher-beta counterparts over the long-term, due to their reduced participation in market drawdowns and more stable return profiles during periods of elevated volatility. As such, it is estimated through a regression of the asset’s daily returns over the most recent four-week period against market returns observed exclusively during negative sessions. The resulting value is sign-inverted to ensure that assets with lower downside exposure are assigned higher z-scores.

Liquidity Factor

The liquidity factor captures the ease with which a digital asset can be traded without significantly impacting its price. Empirical evidence shows that illiquid assets tend to command a higher risk premium than their more liquid counterparts, serving as compensation for trading friction and price volatility. To quantify this, the factor is measured using token turnover, defined as trading volume as a percentage of circulating supply. The value is sign-inverted such that higher z-scores are assigned to less liquid assets.

For further detail, view the CF Factors Methodology Document, the CF Factor Data Suite, and Our paper “A Factor Model for Digital Assets” in Springer Nature’s Mathematical Research for Blockchain Economy.