Quantitative & Derivatives Strategy
Flows & Liquidity : What would it take for equity allocations to rise to 2007 peaks?
February 15, 2024
Flows & Liquidity : What would it take for equity allocations to rise to 2007 peaks?
Flows & Liquidity : What would it take for equity allocations to rise to 2007 peaks?
This document is being provided for the exclusive use of blake@sandboxfp.com.
15 February 2024

This material is neither intended to be distributed to Mainland China investors nor to provide securities investment consultancy services within the territory of Mainland China. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan.

Flows & Liquidity

What would it take for equity allocations to rise to 2007 peaks?

J.P. Morgan does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. Investors should consider this report as only a single factor in making their investment decision.

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  • Our central case remains that equity upside is limited from here, constrained by the fact that investors’ equity allocation globally is approaching the post-Lehman high seen in early 2015.
  • At the same time, we recognize that a further increase in equity allocations from current levels to the previous 2007 cycle peak is not necessarily an unreasonable assumption given the current backdrop of higher interest rates.
  • Given the relatively lower share of bonds in non-bank investor portfolios, however, much of the adjustment to 2007 highs in equity allocations would need to come from an equity market rally in double digits, which, given already elevated positioning and valuations, appears challenging.
  • The new SEC rules expanding dealer registration requirements are hitting the UST market at a critical juncture.
  • Momentum-based investors’ short-duration positions are not yet extreme, while longs in US and Japanese equities look rather high.
  • Questions and answers on Tether.
  • Despite some pullback following this week's US CPI report, global equities are still up nearly 3% YTD, while the Global Agg bond index is down 3.5%. As a result, the implied equity allocation of investors at an aggregate level, which depends on the share of equities vs. bonds and cash among non-bank investors globally, surpassed the previous end-2021 peak and is approaching the post-Lehman high of 47% seen at the beginning of 2015.
  • While our bias remains that this post-Lehman high in the implied equity allocation will represent a constraint for the equity market, thus limiting the equity upside from here, it is tempting at the same time to look at previous cycles' peaks, i.e. those seen in 2007 or 2000, to make the case for further upside in equity markets from here. The argument being that the higher interest backdrop seen before the Lehman crisis is more relevant to the current juncture than the low interest rate backdrop seen after the crisis. As a result, an increase in the  implied equity allocation of non-bank investors to the 2007 or even 2000 peak is not an unreasonable assumption.
  • What is the upside for equity prices if the implied equity allocation of non-bank investors in our global framework rises to the 2007 or 2000 peaks? Could such an increase in equity allocations take place via other adjustments, e.g. a decline in bond prices, rather than largely equity price rises?

  • To answer this question we conduct a sensitivity analysis of our framework of the implied equity allocation of non-bank investors globally for different cash and bond allocation combinations. As a reminder, this metric is based on the share of the global market cap of equities, total outstanding bonds held by non-banks (i.e. excluding central banks, commercial banks and reserve managers) and the total amount of cash measured as M2 money supply, which, by definition, reflects cash holdings of non-bank investors (see also Charts A51 to A54 in the Appendix).
  • Figure 1 to Figure 3 show the implied allocations of non-bank investors to equities, bonds and cash. Starting with the 2007 comparison, cash allocations are already marginally below their 2007 troughs, so one way to approach this question is to look at how much would equities have to rise, or bonds decline, for their respective weights to reach their 2007 levels. For equities alone, to get from the current equity weight of around 47% to the 2007 peak of just under 50% would take an appreciation of around 12% from current levels, all else equal.

Figure 1: Implied equity allocation by non-bank investors globally

Global equities as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan.

Figure 2: Implied bond allocation by non-bank investors globally

Global bonds as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan.

Figure 3: Implied cash allocation by non-bank investors globally

Global cash held by non-bank investors as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan.

  • What about bonds? The current non-bank investor weight to bonds is around 19%, compared to a 2007 trough of around 15.4%. To get to this weight purely by a bond price adjustment would take around a 23% decline in their value, which, given an average duration of 6.6 years on the Bloomberg Multiverse index (which includes the Global Agg and Global High Yield indices), would imply around a 350bp rise in yields. Taken at face value, this appears to be a rather large and unlikely adjustment, given the 300bp rise in the yield-to-worst on the Multiverse index that has already taken place since its 2020 trough of around 1%. It reflects the fact that the bond holdings of non-bank investors are markedly lower than equities as the banking sector holds nearly half of outstanding bonds.
  • Thus far, we have looked at adjustments in isolation and ignored cash weights. Alternatively, we can look at the adjustment to equities and bonds required to keep the combined total of global non-bank investors’ holdings of financial assets (and, by implication, the cash weight) little changed. The combination of a 6.5% rise in equities and a 16% decline in bond prices, or around a 240bp rise in the average yield, would simultaneously keep cash allocations unchanged and shift the equity allocation to 2007 levels. But, again, a 240bp increase in global bond yields looks rather unlikely. Thus, any increase in equity allocations to the previous 2007 peak would have to come mostly from a large appreciation in equity holdings rather than a reduction in bond or cash holdings.
  • Needless to say, to reach the 1999 peak in implied equity allocations of close to 54% would require even larger adjustments. To get from the current equity weight of around 47% to the 2000 peak of 54% would take an appreciation of around 32% from current levels, all else equal. Alternatively, by keeping cash unchanged, to get to the 2000 peak in equity and trough in bond allocations would require a 16% appreciation in equities and a 31% decline in bond prices, or around a 470bp rise in yields. But, again, a 470bp increase in global bond yields looks rather unlikely. Thus, any increase in equity allocations to the previous 2000 peak would have to come mostly from a very large increase in equity prices, rather than a reduction in bond or cash holdings.
  • In all, our central case remains that equity upside is limited from here, constrained by the fact that investors’ equity allocation globally is approaching the post-Lehman high seen in early 2015. At the same time, we recognize that a further increase in equity allocations to the previous 2007 cycle peak, is neither an unlikely scenario nor an unreasonable assumption, against the current backdrop of higher interest rates. Given the relatively lower share of bonds in non-bank investor portfolios, much of the adjustment to 2007 highs in equity allocations would need to come from a sharp equity market rally in double digits, which, given already elevated positioning and valuations, appears challenging. And with financial conditions already providing a tailwind for growth, against a backdrop of tight labor markets and still-elevated inflation, such a further loosening in financial conditions could eventually force central banks to push back easing expectations even further, or even deliver further hikes.

The new SEC rules expanding dealer registration requirements are hitting the UST market at a critical juncture

  • The SEC last week adopted rules to include certain market participants as “dealers” or “government securities dealers” in an effort to boost market transparency and resilience following episodes of abrupt deteriorations of liquidity in the past, such as that seen in March 2020 for USTs.
  • The SEC argues that technology and high-speed trading have resulted in a greater portion of the UST trading volume being attributed to unregistered firms. The SEC thus believes that unregistered firms, including high frequency traders and certain hedge funds whose activities have the effect of providing liquidity to markets, should be registered as dealers.
  • The impact of the new rules on Treasury market liquidity is unclear. Many argue that the capital and disclosure requirements of the new rules will make it more expensive for certain liquidity providers to operate, inducing them to pull back and thus hurting market liquidity. Others counter argue that, while some firms might pull back due to higher costs, others already registered as dealers will step in by increasing their liquidity-providing activities. And this could have the effect of increasing overall market liquidity as most liquidity provision would be made by registered firms operating with sufficient capital and transparency.
  • We believe that both arguments have some validity. In principle, having some liquidity providers pulling back and, so, reducing the overall number of liquidity providers would have the effect of reducing competition and overall bid-offer spreads could thus be higher on average. At the same time, having more liquidity provided by entities that are better capitalized and more transparent could reduce the risk of the abrupt withdrawals in liquidity seen in periods of high volatility and uncertainty. The higher frequency of abrupt withdrawals in liquidity provision over the past decade has coincided with the proliferation of high-frequency, algorithmic market makers in the UST market.
  • It will take some time to see the full impact of the new rules as they will not come into effect immediately. According to the SEC, there would be a one-year compliance period from the “Effective Date” of the rules , which is 60 days after their publication in the Federal Register, perhaps around April. Therefore, they will come into effect around April 2025.
  • What is perhaps concerning is that the new rules and the uncertainty about their eventual impact are hitting the UST market at a difficult juncture, given the reduction in liquidity over the past years. As shown in the figures below, both market depth and market breadth metrics for USTs declined following the rise in interest rates and rate volatility over the past two years. Figure 4 to Figure 7 show the market depth and market breadth on 5y and 10y USTs. The market depth measures the average size of the three tightest bids and offers, and effectively measures how large a trade can be placed without moving markets. Market breadth effectively measures the price impact of trading volumes. As a reminder to our readers, our metric for market breadth is based on the Hui-Heubel ratio from the academic literature, which effectively captures the price impact of volumes on prices or market breadth. The equation below shows the construction behind this indicator, based on the ratio of intraday prices changes divided by turnover:
    • Hui-Heubel Liquidity ratio = (Pmax – Pmin) / Pmin / (V/OI)
  • where Pmax is the highest daily price over a 5-day rolling period, Pmin is the lowest daily price over the same period, V is the average daily volume for a particular futures contract over a 5-day period, and OI is the average open interest over the same period. Effectively, it measures the max-min range normalized by effectively capturing turnover. The lower this liquidity ratio is, the higher the number of trades behind each percentage price change and, thus, the higher the market breadth of liquidity.
  • Both metrics of liquidity suggest that a significant deterioration in liquidity conditions took place as the sell-off started in 2021 and accelerated in 2022. Moreover, this deterioration in liquidity has only partially unwound since mid-2023. The uncertainty arising from the new SEC rules is likely to prolong the current backdrop of low UST liquidity, even if, eventually, the new rules reduce the frequency of abrupt withdrawals in liquidity provision.

Figure 4: Hui and Heubel liquidity ratio for futures on 10y UST bonds

Y axis in reverse order as a higher ratio implies lower market liquidity. The black line shows a smoothed version of the same series. The smoothing is done using a Hodrick-Prescott filter with a Lambda parameter of 10000.

Source: Bloomberg Finance L.P., J.P. Morgan.

Figure 5: Hui and Heubel liquidity ratio for futures on 5y UST bonds

Y axis in reverse order as a higher ratio implies lower market liquidity. The black line shows a smoothed version of the same series. The smoothing is done using a Hodrick-Prescott filter with a Lambda parameter of 10000.

Source: Bloomberg Finance L.P., J.P. Morgan.

Figure 6: Market Depth on 10y USTs & Futures

5-day moving average of the daily average size of tightest three bids and asks each day, $mn for cash USTs and no. of contracts for futures.

Source: Brokertec, J.P. Morgan.

Figure 7: Market Depth on 5y USTs & Futures

5-day moving average of the daily average size of tightest three bids and asks each day, $mn for cash USTs and no. of contracts for futures.

Source: Brokertec, J.P. Morgan.

Momentum-based investors’ short-duration positions not yet extreme, while longs in US and Japanese equities looks rather high

  • Given the moves in equity and bond markets over the past few weeks, we update our positioning framework for momentum-based investors (see also Table A3 and A4 in the Appendix).
  • For bonds, Figure 8 shows the average of the z-scores for shorter- and longer-term momentum for 10Y USTs and Bunds. It suggests that the z-score for 10y USTs reached -1.2 standard deviations after the stronger-than-expected US CPI release on Feb 13th, before moving back to -1.0 on the following day, but that it remains some way from the -1.5 to -2.0 region where we see a heightened risk of profit-taking or mean-reversion signals being triggered. For 10y Bunds, the signals reached -0.8 standard deviations, again some way from that threshold. This suggests momentum-based investors have been building short duration positions, but that they have not yet reached extreme levels to trigger mean reversion.

Figure 8: Momentum signals for 10Y USTs and 10Y Bunds

Average z-score of Short and Long term momentum signal in our Trend Following Strategy framework shown in Tables A3 and A4 below in the Appendix.

Source: Bloomberg Finance L.P., J.P. Morgan.

  • What about equities? Figure 9 shows the average of the shorter- and longer-term signals for the S&P 500, Eurostoxx 50, Nikkei and MSCI EM. It suggests that, for the S&P and Nikkei, momentum is in the 1.5-2.0 range with a heightened risk of mean reversion or profit-taking signals, though for the Eurostoxx 50, at 1.0, and MSCI EM, at just 0.3, the positions are some way from extreme levels. In other words, long positions by momentum-based investors in US and Japanese equities look rather elevated, while positions in Euro area and EM equities look more modest.

Figure 9: Momentum signals for equities

Average z-score of Short and Long term momentum signal in our Trend Following Strategy framework shown in Tables A3 and A4 below in the Appendix.

Source: Bloomberg Finance L.P., J.P. Morgan.

Questions and answers on Tether

  • We argued in our previous publication that  the increasing concentration in Tether is a negative for the stablecoin universe and the crypto ecosystem more broadly, given Tether’s lack of transparency and regulatory risks. Several questions arose from our client conversations, which we try to answer below.
  • Who uses Tether? The Tether user base looks rather diverse across both centralized and decentralized spaces. This includes individuals and entities operating within the US, although much of the user base resides offshore in less stringent regulation jurisdictions beyond the direct reach of US regulators. A recent report from the UN suggests that USDT has become a prominent payment method for money laundering and scams in Southeast Asia.
  • Can US regulators exert control on Tether's offshore  usage? US regulators can exert some control on Tether's offshore usage via OFAC (Office of Foreign Assets Control), which applies to foreign entities whose activities are related to the US financial system. Tether's association with Tornado Cash, a privacy enhancement platform on the ethereum network, is an example. The use of USDT with this  privacy enhancement  has reportedly enabled users to anonymize their transactions for illicit activity. The US Treasury Department's  Office of Foreign Assets Control (OFAC) blacklisted Tornado Cash in 2022, citing allegations of foreign-based hackers exploiting the protocol for illicit transactions.
  • While direct legal actions against offshore entities and decentralized firms are complex, indirect measures and international cooperation could potentially hinder the usage of Tether. Stablecoin regulations, in particular, are set to be coordinated globally via The Financial Stability Board (FSB) across the G20, further constraining the usage of unregulated stablecoins such as Tether.
  • Will upcoming stablecoin regulations have an impact on Tether? Upcoming stablecoin regulations in the US and Europe this year aim to address concerns around stablecoin issuers, their reserves, their liquidity and their stability as well as provisions for AML and KYC requirements. These upcoming stablecoin regulations would likely put indirect pressure on Tether as its attractiveness would diminish relative to stablecoins with more transparency and greater compliance with new regulatory/KYC/AML standards. This challenge for Tether would also apply to the DeFi space where Tether is widely used as a source of collateral and liquidity.
  • Are Tether's latest disclosures enough to reduce concerns? In Q4'23, Tether disclosed an assurance opinion by BDO, affirming the accuracy of its Consolidated Reserves Report ( Figure 10). Holdings include US Treasury securities, US corporate securities, gold, bitcoin reserves. These assets generated good returns for Tether last year due to elevated interest rates and underlying assets’ price appreciation. However, there are significant price risks associated with assets other than US Tbills. In addition, Tether’s reports are still lacking a full and detailed asset breakdown and independent audits (instead of auditor’s assurances). S&P Global Ratings Stablecoin Stability Assessment, which provides an assessment of the stability of various stablecoins, has set theTether's ability to maintain its peg to the U.S. dollar at 4 (with 5 being weak and 1 being strong). S&P's weak rating for Tether is due to the lack of transparency about its reserves and its reserve management practices, to the lack of asset segregation to protect against issuer's insolvency, to the price risk associated with its reported asset holdings (such as loans, corporate securities, gold and bitcoin) and to the limitations to USDT's primary redeemability. So, overall, we do not think the latest disclosures by Tether are enough to reduce concerns.

Figure 10: Tether’s asset weights excluding cash and cash equivalents

In $bn. US T-bills account for 65% of total reserves and 75% of Cash and Cash Equivalents. Secured loans according to Tether are fully collateralized by liquid assets, are constantly monitored, are measured at amortised cost and are adjusted for expected credit losses. 

Source: Tether reserve reports

Appendix

Table A1:Weekly Flow Monitor

$bn per week. The first two rows include Mutual Fund and ETF flows globally, i.e. flows for funds domiciled both inside and outside the US(source: EPFR). The last four rows only include funds domiciled in the US.International Equity funds are equity funds domiciled in the US that invest outside the US (source: ICI and Bloomberg Finance L.P.)

Source: EPFR, Bloomberg Finance L.P., ICI, J.P. Morgan.

Chart A1: Fund flow indicator

Difference between flows into Equity and Bond funds: $bn per week. Difference between flows into Equity vs. Bond funds in $bn per week. Flows include Mutual Fund and ETF flows globally, i.e. funds domiciled both inside and outside the US (source: EPFR) The thin blue line shows the 4-week average of difference between Equity and Bond fund flows. Dotted lines depict ±1 StDev of the blue line. The thick black line shows a smoothed version of the same series. The smoothing is done using a Hodrick-Prescott filter with a Lambda parameter of 100.

Source: EPFR, J.P. Morgan.

Chart A2: Global equity & bond fund flows

$bn per year of Net Sales, i.e. includes net new sales + reinvested dividends for Mutual Funds and ETFs globally, i.e. for funds domiciled both inside and outside the US. Flows come from ICI (worldwide data up to Q3’23). Data since then are a combination of monthly and weekly data from Lipper, EPFR and ETF flows from Bloomberg Finance L.P.

Source: ICI, EPFR, Lipper, Bloomberg Finance L.P., and J.P. Morgan.

Table A2: Trading turnover monitor

Volumes are monthly and Turnover ratio is annualised (monthly trading volume annualised divided by the amount outstanding). UST Cash is primary dealer transactions in all US government securities. UST futures are from Bloomberg Finance L.P. JGBs are OTC volumes in all Japanese government securities. Bunds, Gold, Oil and Copper are futures. Gold includes Gold ETFs. Min-Max chart is based on Turnover ratio. For Bunds and Commodities, futures trading volumes are used while the outstanding amount is proxied by open interest. The diamond reflects the latest turnover observation. The thin blue line marks the distance between the min and max for the complete time series since Jan-2005 onwards. Y/Y change is change in YTD notional volumes over the same period last year.

Source: Bloomberg Finance L.P., Federal Reserve, Trace, Japan Securities Dealer Association, WFE, J.P. Morgan.

ETF Flow Monitor (as of 14th Feb)

Chart A3: Global Cross Asset ETF Flows

Cumulative flow into ETFs as a % of AUM

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A4: Bond ETF Flows

Cumulative flow into bond ETFs as a % of AUM

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A5: Global Equity ETF Flows

Cumulative flow into global equity ETFs as a % of AUM

Source: Bloomberg Finance L.P., J.P. Morgan. Note: We include ETFs with AUM > $200mn in all the flow monitor charts. Chart A5 exclude China On-shore (A-share) ETFs from EM and in Japan. We subtract the BoJ buying of ETFs.

Chart A6: Equity Sectoral and Regional ETF Flows

Rolling 3-month and 12-month change in cumulative flows as a % of AUM. Both sorted by 12-month change

Source: Bloomberg Finance L.P., J.P. Morgan.

Short Interest Monitor

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

Short Interest as a % share of share outstanding.

Source: S3, J.P. Morgan

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

Short Interest as a % share of share outstanding. Last obs is for 13th Feb 2024.

Source: S3, J.P. Morgan

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

Short Interest as a % share of share outstanding.

Source: S3, J.P. Morgan

Chart A10: S&P500 sector short interest

Short interest as a % of shares outstanding based on z-scores. A strategy which overweights the S&P500 sectors with the highest short interest z-score (as % of shares o/s) vs. those with the lowest, produced an information ratio of 0.7 with a success rate of 56% (see F&L, Jun 28,2013 for more details).

Source: NYSE, Bloomberg Finance L.P., J.P. Morgan

Chart A11a: Cross Asset Volatility Monitor 3m ATM Implied Volatility (1y history) as of 13th Feb-2024

This table shows the richness/cheapness of current three-month implied volatility levels (red dot) against their one-year historical range (thin blue bar) and the ratio to current realised volatility. Assets with implied volatility outside their 25th/75th percentile range (thick blue bar) are highlighted. The implied-to-realised volatility ratio uses 3-month implied volatilities and 1-month (around 21 trading days) realised volatilities for each asset.

Chart A11b: Option skew monitor

Skew is the difference between the implied volatility of out-of-the-money (OTM) call options and put options. A positive skew implies more demand for calls than puts and a negative skew, higher demand for puts than calls. It can therefore be seen as an indicator of risk perception in that a highly negative skew inequities is indicative of a bearish view. The chart shows z-score of the skew, i.e. the skew minus a rolling 2-year avg skew divided by a rolling two-year standard deviation of the skew. A negative skew on iTraxx Main means investors favour buying protection, i.e. a short risk position. A positive skew for the Bund reflects a long duration view, also a short risk position.

Source: J.P. Morgan.

Chart A11c: Equity-Bond metric map

Explanation of Equity - Bond metric map: Each of the five axes corresponds to a key indicator for markets. The position of the blue line on each axis shows how far the current observation is from the extremes at either end of the scale. For example, a reading at the centre for value would mean that risky assets are the most expensive they have ever been while a reading at the other end of the axis would mean they are the cheapest they have ever been. Overall, the larger the blue area within the pentagon, the better for the risky markets. All variables are expressed as the percentile of the distribution that the observation falls into. I.e. a reading in the middle of the axis means that the observation falls exactly at the median of all historical observations. Value: The slope of the risk-return trade-off line calculated across USTs, US HG and HY corporate bonds and US equities(see GMOS p. 6, Loeys et al, Jul 6 2011 for more details). Positions: Difference between net spec positions on US equities and intermediate sector UST. See Chart A13. Flow momentum: The difference between flows into equity funds (incl. ETFs) and flows into bond funds. Chart A1. We then smooth this using a Hodrick-Prescott filter with a lambda parameter of 100. We then take the weekly change in this smoothed series as shown in Chart A1. Economic momentum:The 2-month change in the global manufacturing PMI. (See REVISITING: Using the Global PMI as trading signal, Nikolaos Panigirtzoglou, Jan 2012). Equity price momentum: The 6-month change in the S&P500 equity index. As of 9th Feb 24.

Source: Bloomberg Finance L.P., J.P. Morgan.

Spec position monitor

Chart A12: Weekly Spec Position Monitor

Net spec positions are proxied by the number of long contracts minus the number of short contracts using the speculative category of the Commitments of Traders reports (as reported by CFTC). To proxy for speculative investors for equity and US Treasury bond futures positions we use Asset managers and leveraged funds (see Chart A13), whereas for other assets we use the legacy Non-Commercial category. This net position is then converted to a dollar amount by multiplying by the contract size and then the corresponding futures price. We then scale the net positions by open interest. The chart shows the z-score of these net positions. US rates is a duration-weighted composite of the individual UST futures contracts excluding the Eurodollar contract.

Source: Bloomberg Finance L.P., CFTC, J.P. Morgan

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

Difference between net spec positions on risky & safe currencies. Net spec position is calculated in USD across 5 “risky” and 3 “safe”currencies (safe currencies also include Gold). These positions are then scaled by open interest and we take an average of “risky” and “safe” assets to create two series. The chart is then simply the difference between the“risky” and “safe” series. The final series shown in the chart below is demeaned using data since 2006. The risky currencies are: AUD, NZD,CAD, RUB, MXN and BRL. The safe currencies are: JPY, CHF and Gold.

Source: Bloomberg Finance L.P., CFTC, J.P. Morgan.

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

CFTC positions in US equity futures by Leveraged funds and Asset managers (as a % of open interest). It is an aggregate of the S&P500, DowJones, NASDAQ and their Mini futures contracts.

Source: CFTC, Bloomberg Finance L.P. and J.P. Morgan

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

Difference between net spec positions on US equity futures vs.intermediate sector UST futures. This indicator is derived by the difference between total CFTC positions in US equity futures by Asset managers + Leveraged Funds scaled by open interest minus the Asset managers + Leveraged Funds spec position on intermediate sector UST futures (i.e. all UST futures duration weighted ex ED and ex 2Y UST futures) also scaled by open interest.

Source: CFTC, Bloomberg Finance L.P. and J.P. Morgan

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

The dotted line shows the average beta since 2013.

Source: Bloomberg Finance L.P., J.P. Morgan.

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

The dotted line shows the average beta since 2013.

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A18: Performance of various type of investors

The table depicts the performance of various types of investors in % as of 13th February 2024.

Source: Bloomberg Finance L.P., HFR, Pivotal Path, J.P. Morgan.

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

Average z-score of Short and Long term momentum signal in our Trend Following Strategy framework shown in Tables A3 and A4 below in the Appendix.

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A20: Momentum signals for S&P500

Average z-score of Short and Long term momentum signal in our Trend Following Strategy framework shown in Tables A3 and A4 below in the Appendix.

Source: Bloomberg Finance L.P., J.P. Morgan.

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

Rolling 21-day equity beta based on a bivariate regression of the daily returns of our Balanced Mutual fund and Risk Parity fund return indices to the daily returns of the S&P 500 and BarCap US Agg indices. Given that these funds invest in both equities and bonds we believe that the bivariate regression will be more suitable for these funds. Our risk parity index consists of 25 daily reporting Risk Parity funds. Our Balanced Mutual fund index includes the top 20 US-based active funds by assets and that have existed since 2006. Our Balanced Mutual fund index has a total AUM of$700bn, which is around half of the total AUM of $1.5tr of US based Balanced funds which we believe to be a good proxy of the overall industry It excludes tracker funds and funds with a low tracking error. Dotted lines are average since 2015.

Source: Bloomberg Finance L.P., J.P. Morgan.

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

Proxied by the ratio of the monthly performance of HFRI Equally-Weighted Equity Hedge fund index divided by the monthly performance of MSCI ACWorld Index

Source: Bloomberg Finance L.P., HFR, J.P. Morgan

Chart A23: USD exposure of currency hedge funds

The net spec position in the USD as reported by the CFTC. Spec is the non-commercial category from the CFTC.

Source: CFTC, Barclay, Datastream, Bloomberg Finance L.P., J.P. Morgan.

CTAs – Trend following investors’ momentum indicators

Table A3: Simple return momentum trading rules across various commodities

Optimal lookback period of each momentum strategy combined with a mean reversion indicator that turns signal neutral when momentum z-score more than 1.5 standard deviations above or below mean, and a filter that turns neutral when the z-score is low (below 0.05 and above -0.05) to avoid excessive trading. Lookbacks, current signals and z-scores are shown for shorter-term and longer-term momentum separately, along with performance of a combined signal. Annualized return, volatility and

information ratio of the signal; current signal; and z-score of the current return over the relevant lookback period; data from 1999 onward.

Source: Bloomberg Finance L.P., J.P. Morgan calculations.

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

Optimal lookback period of each momentum strategy combined with a mean reversion indicator that turns signal neutral when momentum z-score more than 1.5 standard deviations above or below mean, and a filter that turns neutral when the z-score is low (below 0.05 and above -0.05) to avoid excessive trading. Lookbacks, current signals and z-scores are shown for shorter-term and longer-term momentum separately, along with performance of a combined signal. Annualized return, volatility and

information ratio of the signal; current signal; and z-score of the current return over the relevant lookback period; data from 1999 onward.

Source: Bloomberg Finance L.P., J.P. Morgan calculations.

Corporate Activity

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

% of GDP, G4 includes the US, the UK, the Euro area and Japan. Last observation as of Q3 2023.

Source: ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.

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

$tr per quarter, G4 includes the US, the UK, the Euro area and Japan. Last observation as of Q3 2023.

Source: ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.

Chart A26: Global M&A and LBO

$tr. M&A and LBOs are announced.

Source: Dealogic, J.P. Morgan.

Chart A27: US and non-US share buyback

$bn, are as of Feb’24. Buybacks are announced.

Source: Bloomberg Finance L.P., Thomson Reuters, J.P. Morgan

Pension fund and insurance company flows

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

Equity and bond buying in $bn per quarter. G4 includes the US, the UK,Euro area and Japan. Last observation is Q3 2023.

Source: ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.

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

Equity and bond as % of total assets per quarter. G4 includes the US, the UK, Euro area and Japan. Last observation is Q3 2023.

Source: ECB, BOJ, BOE, Federal Reserve flow of funds., J.P. Morgan

Chart A30: Pension fund deficits

US$bn. For US, funded status of the 100 largest corporate defined benefit pension plans, from Milliman. For UK, funded status of the defined benefit schemes eligible for entry to the Pension Protection Fund, converted to US$at today’s exchange rates.

Last obs. is Jan’24 for US & UK.

Source: Milliman, UK Pension Protection Fund, J.P. Morgan.

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

Cash and alternative investments as % of total assets per quarter. G4 includes the US, the UK, Euro area and Japan. Last observation is Q3 2023.

Source: ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.

Credit Creation

Chart A32: Credit creation in the US, Japan and Euro area

Rolling sum of 4-quarter credit creation as % of GDP. Credit creation includes both bank loans as well as net debt issuance by non-financial corporations and households. Last obs. is Q3’23 for US and Japan, & Euro Area.

Source: Fed, ECB, BoJ, Bloomberg Finance L.P., and J.P. Morgan calculations.

Chart A33: Credit creation in EM

Rolling sum of 4-quarter credit creation as % of GDP. Credit creation includes both bank loans as well as net debt issuance by non-financial corporations and households. Last obs. is for Q2’23.

Source: G4 Central banks FoF, BIS, ICI, Barcap, Bloomberg Finance L.P., IMF, and J.P.Morgan calculation

Chart A34: Monthly net issuance of US HG bonds

$bn. Jan 2024.

Source: Dealogic, J.P. Morgan

Table A5: Equity and Bond issuance

$bn, Equity supply and corporate announcements are based on announced deals, not completed. M&A is announced deal value and buybacks are announced transactions. Y/Y change is change in YTD announcements over the same period last year.

Source: Bloomberg Finance L.P., Dealogic, Thomson Reuters, J.P. Morgan.

Bitcoin monitor

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

In number of contracts. Last obs. for 14th Feb 2024.

Source: J.P. Morgan

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

Both the y-axis in $bn

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A37: Ratio of Bitcoin market price to production cost

Based on the cost of production approach following Hayes (2018).

Source: J.P. Morgan

Chart A38: Flow pace into publicly-listed Bitcoin funds including Bitcoin ETFs

$mm per week, 4-week rolling average flow

Source: Bloomberg Finance L.P., J.P. Morgan.

Japanese flows and positions

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

In bn of shares. Topix on right axis.

Source: Tokyo Stock Exchange, J.P. Morgan.

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

$bn, Last weekly obs. is for 2nd Feb’ 24.

Source: Japan MoF, Bloomberg Finance L.P., and J.P. Morgan.

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

$bn, Last weekly obs. is for 2nd Feb’ 24.

Source: Japan MoF, Bloomberg Finance L.P., and J.P. Morgan.

Chart A42: Overseas CFTC spec positions

CFTC spec positions are in $bn. For Nikkei we use CFTC positions in Nikkei futures (USD & JPY) by Leveraged funds and Asset managers.

Source: Bloomberg Finance L.P., CFTC, J.P. Morgan calculations.

Commodity flows and positions

Chart A43: Gold spec positions

$bn. CFTC net long minus short position in futures for the Managed Money category.

Source: CFTC, Bloomberg Finance L.P., J.P. Morgan.

Chart A44: Gold ETFs

Mn troy oz. Physical gold held by all gold ETFs globally.

Source: Bloomberg Finance L.P., J.P. Morgan.

Chart A45: Oil spec positions

Net spec positions divided by open interest. CFTC futures positions for WTI and Brent are net long minus short for the Managed Money category.

Source: CFTC, Bloomberg Finance L.P., J.P. Morgan.

Chart A46: Energy ETF flows

Cumulative energy ETFs flow as a % of AUM. MLP refers to the Alerian MLP ETF.

Source: CFTC, Bloomberg Finance L.P., J.P. Morgan.

Corporate FX hedging proxies

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

Rolling 26 weeks average betas based on a bivariate regression of the weekly returns of individual stocks in the Eurostoxx 50 index to the weekly returns of the MSCI AC World and JPM EUR Nominal broad effective exchange rate (NEER).

Source: Bloomberg Finance L.P., J.P. Morgan

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

Rolling 26 weeks average betas based on a bivariate regression of the weekly returns of stocks in the S&P500 index to the weekly returns of the MSCI AC World and JPM USD Nominal broad effective exchange rate(NEER).

Source: Bloomberg Finance L.P., J.P. Morgan

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

Rolling 26 weeks average betas based on a bivariate regression of the weekly returns of individual stocks in the FTSE 100 index to the weekly returns of the MSCI AC World and JPM GBP Nominal broad effective exchange rate (NEER).

Source: Bloomberg Finance L.P., J.P. Morgan

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

Rolling 26 weeks average betas based on a bivariate regression of the weekly returns of individual stocks in the MSCI EM index to the weekly returns of the MSCI AC World and JPM EM Nominal broad effective exchange rate (NEER).

Source: Bloomberg Finance L.P., J.P. Morgan

Non-Bank investors’ implied allocations

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

Global equities as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan

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

Global bonds as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan

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

Global cash held by non-bank investors as % total holdings of equities/bonds/M2 by non-bank investors. Dotted lines are averages.

Source: Bloomberg Finance L.P., J.P. Morgan

Chart A54: Implied commodity allocation by non-bank investors globally

Proxied by the open interest of commodity futures ex gold as % of the stock of equities, bonds and cash held by non-bank investors globally.

Source: Bloomberg Finance L.P., J.P. Morgan

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Completed 15 Feb 2024 12:52 PM GMTDisseminated 15 Feb 2024 12:52 PM GMT