J.P. Morgan14 October 2026

Flows & Liquidity

Differences to last July’s correction

Nikolaos Panigirtzoglou · Mika Inkinen · Mayur Yeole · Krutik P Mehta下载原始 PDF

We find the current backdrop for AI infrastructure stocks less favorable relative to the previous correction at the end of last July due to a lack of “buy the dip” behavior by retail investors thus far despite ongoing inflows into equity funds overall, a buyback flow cliff, the lack of a large short base and the more elevated AUM of leveraged equity ETFs.
Despite the recent drawdown, cyclical stocks do not appear oversold from a positioning or flow perspective.

Cross Asset Positioning Monitor aggregates across the various position indicators of Appendix ranging from positioning proxies across various futures contracts, momentum signals as proxies of how trend-following funds/ CTAs are positioned, mutual fund betas as proxies of how mutual fund managers are positioned, risk parity fund positioning and leverage proxies, hedge fund betas as proxies of how hedge fund managers are positioned, client surveys, asset allocation estimates of private non-bank investors at global level, short interest indicators, etc.

Led by Korea’s memory names, the recent correction in AI infrastructure stocks echoes last July’s correction. At the time, fears about competition from China were also weighing on AI infrastructure stocks. How different is the current backdrop for AI infrastructure stocks from that seen last July?
We see several differences:
1. ETF investors had bought the Korean equity dip aggressively during July using both levered and unlevered ETFs as shown in Figure 1. In the current juncture, so far in October there appears to have been rather little buying, of mostly levered ETFs by offshore investors and unlevered ETFs by onshore investors. In other words, there is little sign of “buy the dip” behavior by retail investors overall in the current juncture.

Figure 1: Flows stemming from equity funds that invest in Korea, both onshore and offshore

2. Admittedly, the unlevered Korea equity ETF buying of Figure 1 in July was not entirely “real” as some of that reflected the increase in the short interest. Indeed, Figure 2 shows that the outstanding shares of EWY US Equity, the biggest offshore Korea equity ETF listed in the US, rose by 30% during July, to mostly accommodate the increase in the short interest. As shown in Figure 3, the short interest on EWY rose sharply last July. In the current juncture, so far in October, there has been little change in the short interest as shown in Figure 3. In other words, different to last July, there is currently no large short base to be covered to propagate Korean stocks the same way short covering propagated them last August.

Figure 2: Outstanding shares of EWY US Equity ETF (iShares MSCI Korea)

Figure 3: Short interest of EWY US Equity ETF

3. There is similarly a rather low short base in US memory names (Figure 4) or in major memory and semiconductor ETFs such as SMH and DRAM US equity ETFs (Figure 5). In fact, as we argued in our publication two weeks ago (F&L Sep 30th “Equity vulnerabilities”), the lack of a short base has been an issue for broader equity markets with major ETFs such as SPY US Equity ETF also carrying low short interest (Figure 6).

Figure 4: Short interest on US memory stocks (Micron, Sandisk, Western Digital, Seagate)

Figure 5: Short interest on SMH and DRAM US equity ETFs

Figure 6: Short interest in SPY US Equity ETF

4. An additional vulnerability we highlighted two weeks ago stems from the AUM of leveraged equity ETFs relative to the market cap of underlying stocks. After bottoming out at the end of July, the AUM ratio of leveraged equity ETFs to the market cap of underlying stocks had increased during August and September (including both equity index and individual stock leveraged ETFs), standing at around 70% between its low in April and the high in June (Figure 7). In other words, different to the end of July, the size of leveraged equity ETFs stands at higher level currently.

Figure 7: AUM of leveraged equity ETFs as % of the market cap of underlying stocks

5. There is a buyback flow cliff in the current juncture. The two big Korean memory companies had announced a combined buyback of $40bn starting on August 20th 2026. And although originally scheduled through November 2026, an accelerated completion pushed the wrap-up to mid-October. There was no such buyback flow cliff at the end of last July.
In all, the lack of “buy the dip” behavior by retail investors thus far despite ongoing inflows into equity funds overall, a buyback flow cliff, the lack of a large short base and the more elevated AUM of leveraged equity ETFs, make the current backdrop for AI infrastructure stocks less favorable relative to the end of last July.
Despite these near-term technical vulnerabilities, the fundamental backdrop should create a more constructive backdrop for the tech/AI complex ocer the medium- to longer-term as we argued a few weeks ago (F&L, Sep 30t and JPM View, Oct 2nd).

Despite the recent drawdown, cyclical stocks do not appear oversold from a positioning or flow perspective

The recent underperformance of small caps in the Russell 2000 index and cyclical sectors over the past month or two have raised questions in our conversations about growth concerns and how much recession risk markets are pricing in. Indeed, by our estimates, the implied probability of US recession embedded in the Russell 2000 index after the peak in mid-August. Figure 8 shows the implied recession risks across major asset classes as well as for the Russell 2000 index and traditional cyclical sectors excluding tech (industrials, consumer discretionary, financials, and materials) vs. defensive sectors (healthcare, consumer staples and utilities). Our approach has been to compare the current cycle peak to trough moves in asset prices to those seen during previous recessions. However, while the implied recession risk embedded in the Russell 2000 and cyclical vs. defensive sectors has increased to around 30%, there is relatively little recession risk priced in large cap equities, HG or HY credit, rates or overall base metals.

Figure 8: Implied probability of US recession across asset classes as of Oct 12th

Is this pricing in of recession risks in more cyclical stocks also reflected in positioning and flow indicators? Figure 9 shows the average of our positioning indicators for US equity sectors in the Cross Asset Positioning Monitor above for cyclical sectors minus the average of the positioning indicators for defensive sectors. It suggests that after a gradual increase in cyclical sector positioning over the past few years, there has been some relative pullback in cyclical vs defensive sectors, particularly since mid-August. That said, while the relative positioning has shifted lower, it has not turned short suggesting that we are still some ways from cyclicals being in oversold territory from a positioning perspective despite the recent drawdowns.

Figure 9: Cyclicals vs Defensives sector percentiles from the cross asset positioning monitor

Turning to flows, Figure 10 shows the relative equity ETF flow into cyclical and defensive sector ETFs. We use the cumulative net inflows as a % of AUM into sector ETFs shown in Chart A6a in the Appendix, average across cyclical and defensive sectors and take the difference between the two. Figure 10 suggests that after a broader trend, looking through the volatility, of relatively stronger net inflows into cyclical sectors since early 2023, there appears to have been a shift to relative outflows from cyclical sectors that has started in mid-2026 and shows some signs of having accelerated in recent weeks. Similar to the picture from the positioning chart in Figure 9, however, the picture in Figure 10 does not suggest that cyclicals are in oversold territory from a flow perspective either.

Figure 10: Net inflows as a % of AUM into cyclical sector ETFs – defensive sector ETFs

Given that the sector groupings are relatively broad and that the cyclical exposure of companies within the same sector is not necessarily constant, one way to cross-check the relative performance of cyclicals is to look at company-level sensitivity instead. We do this by looking at companies in the S&P 500 index and estimating their sensitivity to surprises, i.e. the difference between actual releases and survey expectations, in the flash US manufacturing PMI. Given tech has been more of a secular growth story, particularly given the current AI theme, we exclude information technology and communication services as well as Amazon and Tesla from the analysis. For robustness in estimating betas, given PMI releases are monthly, we use a longer sample since 2012 to estimate company-level betas, and take the top quartile of companies ranked by the beta to surprises in the manufacturing PMI as a cyclical sample and bottom quartile as defensives. That said, we acknowledge that as AI adoption proceeds these betas, the relative rankings may change over time. While this exercise is agnostic on sector labels, the average betas of companies classified in cyclical sectors such as consumer discretionary or financials are higher than for companies in defensive sectors such as consumer staples and healthcare.
The relative performance of the top quartile and bottom quartile of companies is shown in Figure 11. It is broadly consistent with the picture in the positioning and flow pictures above, including with the temporary reversals of the inflow trend in 2024 and 2025. And while there has also been a notable drawdown since mid-August, from a performance perspective it also does not look like cyclicals are in oversold territory.

Figure 11: Performance of cyclical vs. defensive companies in the S&P 500

This means that cyclicals remain in a more difficult juncture. Despite the recent drawdown, cyclicals do not appear oversold from a positioning or flow perspective. A recovery in cyclicals and broadening in the equity trade would likely require a combination of lower bond yields, strong earnings and signs that recent weakness in labor markets is fading, but without adding to inflationary pressures in a way that could prompt a more aggressive hiking cycle from the Fed. At the same time, we are some way from oversold territory, downside risks remain considerable.

Appendix

Chart A1a: Global equity & bond fund flows

Chart A1b: Quarterly Balanced/Mixed funds & MMFs flows worldwide

ETF Flow Monitor (as of 14th October)

Chart A6a: Equity Sectoral and Regional ETF Flows

Chart A6b: Regional Equity Allocation Monitor Implied by ETFs

Short Interest Monitor

Chart A7: Short interest on the EEM and EMB US ETF

Chart A8: Short interest on the LQD and HYG US ETF

Chart A9: Short interest on the SPY and QQQ US ETF

Chart A11a: Cross Asset Volatility Monitor 3m ATM Implied Volatility (1y history), as of 12th Oct-2026

Definitions:

Current:: Latest available closing level (12-Oct-26)
Low:: Lowest closing level in the last 1y
Low date:: Date the lowest closing level was reached (or the first time it was reached in the case of several identical low closing levels)
High:: Highest closing level in the last 1y
High date:: Date the highest closing level was reached (or the first time it was reached in the case of several identical high closing levels)
Graph:: Shows the current level and the 25th/75th percentile relative to the 1y high/low
Upside:: Implied return/volatility percentage points from current level up to the High (note: return is calculated as simple difference for spread products)
Upside (σ):: Upside in terms of standard deviations (Upside / Current 1y realized volatility)
Downside:: Implied return/volatility percentage points from current level down to the Low (note: return calculated as simple difference for spread products)
Downside (σ):: Downside in terms of standard deviations (Downside / Current 1y realized volatility)
Implied/realized volatility:: Current 3m implied volatility / current realized 3m volatility

Chart A11d: Implied probability of the S&P500 rising over the next six months with a cutoff set at 75%

Chart A11e: Logistic regression model for direction of the S&P 500 over a 6 month horizon & Ex-ante probability of an up-move from the logistic regression algorithm vs. the ex-post realised 6-month return in the S&P 500

Spec position monitor

Chart A13: Positions in US equity futures by Asset managers and Leveraged funds

Chart A14: Spec position indicator on Risky vs. Safe currencies

Chart A15: Spec position indicator on US equity futures vs. intermediate sector UST futures

Mutual fund and hedge fund betas

Chart A16: 21-day rolling beta of 20 biggest active US bond mutual fund managers with respect to the US Agg Bond Index

Chart A18: Performance of various types of investors

Chart A17: 21-day rolling beta of 20 biggest active Euro bond mutual fund managers with respect to the Euro Agg Bond Index

Chart A19: Momentum signals for 10Y UST and 10Y Bunds

Chart A22: Equity beta of monthly reporting Equity Long/Short hedge funds

Chart A21: Equity beta of US Balanced Mutual funds and Risk Parity funds

Chart A23: USD exposure of currency hedge funds

CTAs – Trend following investors’ momentum indicators

Table A3: Simple return momentum trading rules across various commodities

Table A4: Simple return momentum trading rules across international equity indices, bond futures and FX

Corporate Activity

Chart A24: G4 non-financial corporate capex and cash flow as % of GDP

Chart A25: G4 non-financial corporate sector net debt and equity issuance

Pension fund and insurance company flows

Chart A28: G4 pension funds and insurance companies equity and bond flows

Chart A29: G4 pension funds and insurance companies equity and bond levels

Chart A31: G4 pension funds and insurance companies cash and alternatives levels

Credit Creation

Chart A34: Monthly net issuance of US HG bonds

Bitcoin monitor

Chart A35: Our Bitcoin position proxy, based on open interest in CME Bitcoin futures contracts

Chart A37: Ratio of bitcoin market price to production cost

Chart A36: Cumulative Flows in all Bitcoin funds and Gold ETF holdings

Chart A38: Monthly flows into spot bitcoin ETFs, spot ethereum ETFs and leveraged MicroStrategy ETF

Japanese flows and positions

Chart A39: Tokyo Stock Exchange margin trading: total buys minus total sells

Chart A41: Monthly net purchases of foreign bonds and foreign equities by Japanese residents

Chart A40: Monthly net purchases of Japanese bonds and Japanese equities by foreign residents

Commodity flows and positions

Corporate FX hedging proxies

Chart A47: Average beta of Eurostoxx 50 companies and Eurostoxx Mid-Cap to trade-weighted EUR

Chart A49: Average beta of FTSE 100 companies to trade-weighted GBP

Chart A48: Average beta of S&P500 companies to trade-weighted US dollar

Chart A50: Average beta of MSCI EM companies to trade-weighted EM Currency Index

Non-Bank investors’ implied allocations

Chart A51: Implied equity allocation by non-bank investors globally

Chart A53: Implied cash allocation by non-bank investors globally

Chart A52: Implied bond allocation by non-bank investors globally

Chart A54: Implied commodity allocation (including privately held gold) by non-bank investors globally

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