Flows & Liquidity
Equity demand and supply for 2024
- Assuming mean reversion in equity betas of institutional investors and an increase in equity supply next year, we come up with an equity demand/supply deterioration of around $1.6tr in 2024 vs. 2023 after an improvement of around $2.5tr this year relative to 2022.
- This deterioration is somewhat at odds with the bullish consensus outlook for equities in 2024, although it is consistent with our equity strategists more bearish outlook.
- Only tentative signs of revival in DeFi and NFT spaces.
- As we approach year-end, the attention is inevitably drawn towards the outlook for next year. Last week we presented our projection for the balance between bond demand and supply for 2024. In this week’s publication we present our outlook for the balance between equity demand and supply.
- A consensus appears to be emerging from year-ahead outlooks of a bullish backdrop for equities on the basis of continued resilience. How consistent is this with our global equity demand and supply estimates? Earlier this year (F&L, Jul 6th), we argued that much of the improvement in the balance between equity demand and supply for 2023 we had projected a year ago had effectively been exhausted in the first half of the year. We update below our previous analysis for global equity supply and demand for the rest of 2023 as well as 2024.
- Starting with institutional investors, in our equity demand-supply analysis from a year ago for 2023 we had argued that the severe de-risking by institutional investors had created ample space for them to propagate equity markets in 2023, assuming simply a reversion to mean from levels in Nov 2022. And in our mid-year update, we in turn argued that much of this support had already taken place. The equity positions of institutional investors are based on a combination of their exposure in cash and other derivative markets. Therefore, looking at our positioning metrics based on equity futures provides one way of gauging the change in positioning by institutional investors. An alternative way of estimating the change in equity demand by institutional investors is to look at their equity betas.
- The equity betas of CTAs, Equity Long/Short hedge funds and Balanced Mutual funds are shown in Figure 1 to Figure 4. Figure 1 shows the average z-score of the short and long lookback period momentum signals for the S&P, Nikkei, Eurostoxx 50, FTSE 100 and MSCI EM indices, which was at relatively low levels of 0.2 at end-2022, rose to rather more elevated levels of just over 1 in mid-2023 and has since declined to around 0.7. This suggests they had bought around $100bn of equities in 2023 overall, with some of the net purchases we had seen into mid-2023 having been unwound in the second half. For 2024, we project some mean reversion to its average in recent years of 0.5, implying net selling of around $40bn, or a deterioration in demand of around $140bn in 2024 vs. 2023.
Figure 1: Weighted average of the z scores of equity index momentum signals
z-score of the momentum signals in our Trend Following Strategy framework shown in Tables A3 and A4 in the Appendix. The line shows the average z-score of the short and long lookback period momentum signals for the S&P 500, Nikkei, EuroStoxx50, FTSE100 and MSCI EM indices. We attach 50% weight on the S&P500 momentum signals and 50% weight on the momentum signals of the remaining four indices combined.
Source : CFTC, Bloomberg Finance L.P., J.P. Morgan.
- What about Equity Long/Short hedge funds? This is the biggest equity hedge fund sector with an AUM of $1tr and a typical leverage of around 2x. We proxy the equity beta of Equity L/S by looking at futures positions of asset managers and leveraged funds as Equity L/S uses futures as an overlay to achieve their desired beta. These futures positions of asset managers and leveraged funds are in turn proxied by the z-score of net CFTC positions as a % of open interest shown in Figure 2 and Chart A13 in the Appendix. A neutral z-score of zero is assumed to correspond to a historical average equity beta of 0.5 for Equity L/S hedge funds. We also assume that a very extreme 3 stdevs move in the z-score of CFTC futures positions corresponds to a very extreme 0.5 change in the equity beta. Using these assumptions, last year’s decline in the beta of Equity L/S funds from close to 0.7 in end-2021 to 0.2 by late 2022 implied a $1.5tr deterioration in equity demand in 2022 vs. 2021. The sharp rise in net spec positions implied a shift in the beta from the 0.2 level in late 2022 to nearly 0.6 by end-June, implying an improvement in demand of around $2tr, with a modest reversal in 2H23. For 2024, assuming mean reversion back to its long-term average around 0.5 would suggest a deterioration in equity demand of around $0.9tr in 2024 vs. 2023.
Figure 2: 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, Dow Jones, NASDAQ and their Mini futures contracts.
Source : CFTC, Bloomberg Finance L.P., J.P. Morgan.
- What about the $150bn universe of risk parity funds? Our calculations (Figure 3) suggest that the risk parity beta to equities effectively doubled from 0.16 in late 2022 to 0.32 in end-Jun23, which is broadly consistent with the level where it stands currently. This implies a demand improvement of $70bn in 2023 vs. 2022, all of which effectively occurred in 1H23. Assuming a reversion to its long-term average of around 0.25 implies a flow deterioration in 2024 vs. 2023 of around $70bn.
Figure 3: 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.
- We do a similar calculation for the much larger $7tr universe of Hybrid Mutual funds including Balanced Mutual funds. Their average beta to equities has increased from 0.54 in late 2022 to around 0.61 currently, implying a net improvement in demand of around $780bn in 2023 vs. 2022, around two-thirds of which took place in 1H23. For next year, we assume a modest reversion to its long-term average of 0.6, which implies a deterioration in demand of around $460bn in 2024 vs. 2023.
- What about pension funds and insurance companies? G4 insurance companies and pension funds, including both defined benefit and defined contribution plans, have typically been steady sellers of equities due to their structural shift away from equities towards fixed income. 2023 has again been no exception, with pension funds and insurance companies selling around $315bn of equities in 1H23, or an annualised pace of $630bn. This is a marked increase in the pace of equity selling from around $350bn in 2022, or a deterioration in equity flow of around $280bn. As we argued in last week’s note, there remains an incentive for private defined benefit pension funds to lock in gains in their funded status and projected net buying of bonds by G4 pension funds and insurance companies to continue at halfway between the 1H23 annualised pace and the average over the past decade. We make a similar projection for equity sales, assuming a pace roughly halfway between the 1H23 annualised pace of $630bn and the decade average of around $330bn. This implies net sales of $480bn, or an improvement in equity demand of around $150bn in 2024 vs. 2023.
- Central banks and SWFs have continued to buy equities in 2023, albeit at a somewhat more modest pace than in 2022. Based on some moderation in the current account balances of oil-producing countries in 2023 compared to very high levels in 2022, and a moderation in oil prices to a YTD average of just under $83/bbl from nearly $100/bbl in 2022, we estimate a decline in equity demand by SWFs of around $280bn in 2023 vs. 2022. For 2024, we project largely unchanged equity demand vs. 2023 given our oil strategists expect an average oil price in 2024 of $83 and IMF forecasts suggest largely unchanged current accounts for oil-producing countries.
- What about retail investor demand for equity funds? Last year had seen a sharp reversal in equity fund flows, including both mutual funds and ETFs, from a record high of close to $1.1tr in 2021 to just $5bn in 2022 (Figure 4). This year, we have seen a second consecutive year of muted demand for equity funds of around $24bn. For 2024, similar to last week’s global bond supply-demand analysis, we project retail demand using a model, described in greater detail in the section below. This framework suggests a net inflow into equity funds in 2024 of around $138bn, or around a $110bn improvement relative to 2023.
Figure 4: 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 Q2’23). 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.
- What about equity supply? One simple way to gauge net equity supply is to look at the change in the free float of the global equity universe as captured by tradeable indices such as the MSCI AC World index. Adjusted for price and FX changes, this change in the free float should capture the increase or decrease in the quantity of shares available to market participants in each period.
- This proxy of net equity supply, shown in Figure 5, suggests that global equity supply in 2022 was close to zero due to weak IPO activity and strong buyback activity and there has been little change in 2023. In coming years, however, we do see some structural increase in net equity supply relative to the 2011-2018 period relatively depressed net supply. One reason is the higher interest rate environment, which should reduce the attractiveness of debt-funded equity buybacks. And a second reason is related to post-pandemic structural changes such as technological advancements, AI, climate change and changes in supply chains that should over time necessitate more investment. Both of these factors imply higher net equity issuance, and we project net supply of around $360bn, halfway between higher issuance in 2020/21 and the close to zero net issuance in 2022/23. This is also roughly double the average annual pace of equity supply from 2011 to 2018. Relative to 2023, this implies an increase in supply of around $350bn.
Figure 5: Net equity supply globally
$bn per year based on the expansion of the MSCI AC World. Adjusted for price and FX changes.
Source : MSCI,J.P. Morgan.
- Where does this leave us for next year in terms of the overall equity demand/supply balance? Assuming mean reversion in equity betas of institutional investors and adding up the demand and supply flow changes above for 2024 vs 2023, we come up with an equity demand/supply deterioration of around $1.6tr (Figure 6) after a net improvement in 2023 of around $2.5tr. This deterioration is somewhat at odds with the bullish consensus outlook for equities in 2024, though it is consistent with our equity strategists more bearish outlook (2024 Global Equity Outlook, Nov 29th). And as we argued in mid-2023, the improvement in the demand/supply balance in 2023 vs 2022 had been exhausted in the first half.
Figure 6: Annual Change in Global Equity Demand/Supply Balance
Change in flows per year in $bn.
Year | 2021 vs 2020 | 2022 vs. 2021 | 2023 vs. 2022 | H1'23 vs 2022 | 2H23 | 2024 vs 2023 |
Demand | ||||||
Retail investors | 991 | -1062 | 18 | 28 | -9 | 114 |
CTAs | -189 | 32 | 184 | 291 | -106 | -140 |
Equity L/S | 474 | -1477 | 1981 | 2002 | -21 | -882 |
Risk Parity Funds |
50 | 0 | 73 | 73 | 0 | -69 |
Balanced MF | 0 | -305 | 789 | 509 | 280 | -456 |
Pension & Insurance Funds |
-136 | 80 | -282 | -141 | -141 | 149 |
SWF/ Central banks |
40 | 355 | -281 | -141 | -141 | -1 |
Total Demand |
1232 | -2378 | 2483 | 2621 | -138 | -1284 |
Supply | -8 | -678 | -7 | -8 | 1 | 346 |
Demand - Supply |
1240 | -1699 | 2490 | 2629 | -139 | -1630 |
Source : J.P. Morgan.
Refining our equity fund flow projection for 2024
- Last week we presented two sets of models for projecting the bond fund flow for 2024. A model tailored for scenario analysis where the bond fund flow depends on contemporaneous bond returns and as a result any bond fund flow forecast becomes conditional on a specific assumption about bond returns next year. And a purely forecasting model where next year’s bond fund flow does not require any assumption about bond returns next year as it is a function of the current year’s bond and equity fund flows and returns (Figure 7).
Figure 7: Forecasting model for the bond fund flow (as % of AUM)
Dependent Variable: BOND_FLOW | |||||
Sample (adjusted): 2007 2023 | |||||
Included observations: 17 after adjustments | |||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. | |
C | 0.05 | 0.02 | 2.67 | 0.02 | |
BOND_FLOW(-1) | 0.29 | 0.35 | 0.84 | 0.42 | |
BOND_RETURN(-1) | 0.36 | 0.28 | 1.30 | 0.22 | |
EQ_RETURN(-1) | -0.23 | 0.09 | -2.46 | 0.03 | |
R-squared | 0.42 |
Source : J.P. Morgan.
- For the equity fund flow, however, we only presented a scenario analysis model, which depends heavily on contemporaneous equity return as a regressor. Different to the bond fund model, we had found that the presence of contemporaneous year’s return is more critical for the equity fund model and if we omit it we get much lower explanatory power, i.e. much lower R-squared. We thus relied on scenario analysis in terms of deriving an estimate for the equity fund flow for 2024 using two different scenarios/equity return assumptions: a bullish equity return assumption under a soft landing scenario with 40% probability and a bearish equity return assumption under a recession scenario with 60% probability.
- The problem with using a scenario-based equity fund flow estimate in order to gauge the equity demand for 2024 is that it becomes circular. This is because the scenario-based model requires an assumption about equity returns for next year which should be a function of equity demand. To overcome the difficulty in creating a purely forecasting model for the equity fund flow, we create instead a model for the difference between equity minus bond fund flows (as a % of their respective AUM). It turns out to be easier to forecast the difference between equity vs. bond fund flows than forecasting the equity fund flow per se.
- This model is shown in Figure 8. It regresses the difference between equity minus bond fund flows as a function of previous year’s fund flow difference and previous year’s return difference. The R-squared is decent at 46%. Using this model and a forecast of a bond fund flow of $466bn for next year (based on the bond fund flow forecasting model of Figure 7 which was presented in last week’s publication) implies an equity fund flow of $138bn for 2024, around $110bn above this year’s level.
Figure 8: Forecasting model for equity minus bond fund flows (as % of respective AUM)
Dependent Variable: EQ_FLOW-BOND_FLOW | ||||
Sample (adjusted): 2007 2023 | ||||
Included observations: 17 after adjustments | ||||
Variable | Coefficient | Std. Error | t-Statistic | Prob. |
C | -0.04 | 0.01 | -3.57 | 0.00 |
EQ_FLOW(-1)-BOND_FLOW(-1) | 0.28 | 0.20 | 1.42 | 0.18 |
EQ_RETURN(-1)-BOND_RETURN(-1) | 0.20 | 0.06 | 3.46 | 0.00 |
R-squared | 0.46 |
Source : J.P. Morgan.
Only tentative signs of revival in DeFi and NFT spaces
- The past months’ hype in crypto markets in anticipation of a spot bitcoin ETF approval in the US has had a positive impact on DeFi and NFT activity. Figure 9 shows a spike in NFT volumes in recent months while Figure 10 reveals a more gentle recovery in DeFi Total Value Locked. These increases come after almost two years of downshifting, thus creating some optimism that the worst might be behind us in terms of the medium-term trajectory for DeFi/NFT activity.
- While we do not doubt this recent revival in DeFi/NFT activity is a positive sign, we believe it is too early to be getting excited about it. First, some recovery in DeFi is natural given more elevated trading activity, some of which reverberates via decentralised exchanges (Figure 11). Second, liquid staking led by Lido is partly responsible for the recent improvement in DeFi activity, a trend that had started since the beginning of 2023 before the hype about spot bitcoin ETF approval emerged (Figure 12). Third the price of ethereum has underperformed other cryptocurrencies, which means that measuring TVL in ether terms would mechanically show some revival given the price of several smaller cryptocurrencies has risen by more than ethereum in recent months.
- That said, we do find encouraging the emergence of new chains and DeFi protocols over the past year such as Aptos, SUI, Pulsechain, Tenet, SEI, Celestia and others. And in the NFT space this year saw the emergence of bitcoin ordinals leading to renewed interest on NFTs with new NFT marketplaces having been launched such as Blur, which has gained significant share within NFT trading.
- Finally, by inspecting Figure 9 and Figure 10, ethereum does not appear to have benefitted much from the recent revival in DeFi/NFT activity. As we explained before in our publication, ethereum has been facing issues related to its network scalability, low transaction speeds and higher fees thus facing competition from alternative layer 1 chains. In particular, certain projects related to gaming and social media that require ultra-low fees are uneconomical in the ethereum blockchain and tend to be launched in alternative layer 1 networks. Upcoming ethereum upgrades could help to address the above issues, potentially helping ethereum to maintain its dominance, but the timing and the effectiveness of these coming upgrades are yet to be seen.
Figure 9: NFT sales volume
In ETH thousands
Source : Cryptoslam, J.P. Morgan.
Figure 10: Total value locked across defi space
In ETH mn
Source : Defilama, J.P. Morgan.
Figure 11: DEX volume by chains
In ETH mn
Source : Messari, J.P. Morgan.
Figure 12: Lido TVL
In ETH mn
Source : Defilama, J.P. Morgan.
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 Q2’23). 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 29th Nov)
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 27th Nov 2023.
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 27th Nov-2023
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 tradeoff 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 24th Nov 23.
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 22nd November 2023.
Source : Bloomberg Finance L.P., HFR, SG CTA Index, 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 Equity Long/Short hedge funds
Proxied by the ratio of the monthly performance of HFRI Asset-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 Q2 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 Q2 2023.
Source : ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.
Chart A26: Global M&A and LBO
$tr. YTD 2023 as of 25th Nov 23. M&A and LBOs are announced.
Source : Dealogic, J.P. Morgan.
Chart A27: US and non-US share buyback
$bn, are as of Nov’23. 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 $bn per quarter. G4 includes the US, the UK,Euro area and Japan. Last observation is Q2 2023.
Source : ECB, BOJ, BOE, Federal Reserve flow of funds, J.P. Morgan.
Chart A29: G4 pension funds and insurance companies equity and bond levels
Equity and bond as % of total assets per quarter. G4 includes the US, the UK, Euro area and Japan. Last observation is Q2 2023.
Source : ECB, BOJ, BOE, Federal Reserve flow of funds., J.P. Morgan
Chart A30: Pension fund deficits
US$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 Oct’23 for US and 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 Q2 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 Q2’23 for US, Japan, and 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 Q1’23.
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. November 2023 up to 23rd.
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 29th Nov 2023.
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 17th Nov’23.
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 17th Nov’23.
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