Academic research suggests that high and rising consumer price inflation puts upward pressure on real discount rates and is a headwind for equity market performance. A fresh analysis of 17 international markets since 2000 confirms an ongoing pervasive negative relation between published CPI dynamics and subsequent equity returns. Global equity index portfolios that have respected the inflation dynamics of major currency areas significantly outperformed equally weighted portfolios. Even the simplest metrics have served well as warning signals at the outset of large market drawdowns and as heads-ups for opportunities before recoveries. The evident predictive power of inflation for country equity indices has broad implications for the use of real-time CPI metrics in equity portfolio management.

The below post is based on proprietary research of Macrosynergy and is related to several past articles.
This post ties in with this site’s summary on macro information efficiency.

Why inflation matters for equity returns

In most modern economies consumer price index (CPI) inflation is positively related to real interest rates and, therefore, negatively to the trade-off between companies’ dividend dynamics and their discount factors. The link between inflation and expected real interest rates reflects that – all other things equal – high inflation calls for tighter monetary policies and higher risk premia for holding long-maturity nominal debt.

Academic research has provided evidence that equity markets often fail to adjust to persistent cross-country shifts in inflation in a timely and efficient manner. While equity investors focus on tracking firm-specific price effects and cash flows, they seem to pay less attention to aggregate local inflation and appear sluggish in adjusting long-term discount factors across countries. Since equity is a long-duration asset even small calibration errors in discount factors can have a large impact. Empirically, real equity returns in lower-inflation markets tend to outperform those in higher-inflation markets. No such effect can be found in fixed income markets. View related post here.

In past decades inflation risk premia in the U.S. and the euro area diminished and even turned negative. The compression of premia reduces real long-term interest rates. Papers suggest that this happened not because uncertainty declined but because the balance of risk shifted from high inflation problems to deflationary recessions. Put simply, in deflation or “lowflation” environments markets pay a premium for bonds and interest rate swap receivers as hedge against deflation risk rather than demanding a discount for exposure to high inflation risk. View related post here.

Downward shifts in expected inflation also seem to increase equity valuations due to “nominal stickiness”. A slowdown in consumer prices reduces short-term interest rates but does not immediately reduce earnings growth by the same rate, thus increasing the discounted present value of future earnings. View related post here.

The nominal risk-free rate is more sensitive than the cash flows to changes in expected inflation. This ‘discounting effect’ arises from the stickiness of cash flows… Price stickiness implies nominal cash flow growth is sticky in the short run [due maybe to longer-term contracts], and so expected nominal cash-flow growth changes less than one-for-one with changes in expected inflation…. The nominal risk-free rate falls with expected inflation…This effect is not perfectly offset by a fall in nominal expected cash flows, because of their stickiness. [A fall in expected inflation] leads to an increase in the value of unlevered equity, but also in the present value of coupons paid to debtholders. The former effect dominates the latter, so a decrease in expected inflation increases the value of levered equity” [Katz, Lustig and Nielsen]

A simple empirical check of the inflation changes as equity trading signal

If meaningfully measured changes in inflation contain some persistent component and if equity markets do not instantaneously and fully price in their consequences, they should have a systematic negative relation with broad equity market performance subsequent to the release of the data. Rising inflation should, on average, entail sub-par or negative performance and declining inflation should entail outperformance.

Inflation is a macro factor. Hence it should have a broad market impact but be specific to the currency area in which it is measured. For an empirical check-up we use the following dataset:

  • We take reported CPI inflation changes over the latest three months from 2000 to 2022 (April) for 17 larger currency areas from the J.P. Morgan Macrosynergy Quantamental System (“JPMaQS”). The quantamental format matches measurements with the time at which they are the latest instance published to financial markets. JPMaQS uses business daily timestamps that conceptually refer to the end of the New York trading day. These are called real-time dates. For any given real-time date an indicator is calculated based on the full information state, typically a time series. This allows realistic backtesting. Information state-contingent time series are called vintages. This implies that a transformation (such as % change) of a quantamental indicator is not the same as a quantamental indicator of a transformation. The former operates on the first dimension (real-time dates) while the latter operates on the second dimension (observation dates). CPI inflation changes here refer to the latest reported changes in inflation, i.e. the latest instance of the market perception of changes.
  • Futures returns have also been taken from “JPMaQS” for the main equity indices in each country. Note that they measure performance in local currency terms and as locally-funded positions they do not entail any direct exposure to exchange rate risk. See Annex 2 below.

To make the effects of inflation changes comparable across the range of 17 developed and emerging countries, they have been scaled based on the currency areas’ effective inflation target. This effective inflation target is a weighted average of the official inflation reference rate and actual past performance. Reported changes in inflation have simply been divided by the higher of this effective target or 2 (lower barrier). Otherwise, the metric has the most simple standard format. No optimization was applied for representing inflation changes.

For the overall data panel, there has been a highly significant negative correlation between recorded inflation changes and subsequent returns on both a monthly and quarterly basis. Moreover, negative correlation has been pervasive. It prevailed both in the 2000s and 2010s and in two-thirds of all years since 2000. Moreover, negative correlation prevailed all 17 analysed countries on a quarterly basis and in 16 out of 17 countries on a monthly basis (with the UK being the only exception)

To assess the usefulness of inflation changes as a trading signal one can compare a long-only cross-country portfolio with a similar portfolio that uses a country-specific inflation overlay.

  • In particular, we define the long-only portfolio as an equally-weighted average of positions in all 17 country index futures in the sample with constant risk capital in the derivative and a long-term volatility scale of 10% annualized.
  • We define an inflation overlay simply as the subtraction of a winsorized z-score (inflation changes divided by their panel standard deviations and contained at a maximum value of 2) from a notional unit value. Positions are being rebalanced once per month at the beginning of the month.

This is a most simple rule but easy to understand and intuitive. It means that if there has been no inflation change recorded in a country the position is equal to the long-only portfolio. If there an increase in inflation has been recorded it reduces the position in that country. And if the inflation increase has been more than one standard deviation the portfolio even takes a short position. Conversely, if inflation has decreased the long position is increased up to a maximum of 2 (i.e. double the long-only exposure).

The balanced accuracy of the inflation change score alone has been 52.9% on a monthly basis for the overall panel since 2000. This means that the inflation change has correctly predicted the direction of equity returns in 52.9% of all months and countries on average for positive and negative predictions. As a result, the signal overlay benefited the performance of the long equity portfolio significantly. The portfolio’s Sharpe ratio since 2000 would have increased from 0.55 to 0.92 with considerably smaller maximum drawdowns. The Sortino ratio would have increased from 0.76 to 1.35. None of this performance enhancement required any form of signal or trading optimization. Depending on position size, transaction costs would have reduced the benefit, but typically not more than by some 0.1 of these ratios.

Qualitatively, using inflation differentials to manage directional exposure to the equity markets has delivered three benefits:

  • First, the inflation-based strategy reduced or fully avoided drawdowns in some of the most turbulent periods for equity. Naturally, this would not always be the case but would happen in periods where rising inflation either triggers a shift to monetary tightening or compromises central banks’ ability to respond to financial or economic headwinds.
  • Second, the inflation-based signal has bolstered performance in post-crisis recoveries, by consistently tracking disinflationary impetus to central bank support actions.
  • Third, while inflation-based equity management has added great value in crisis times it has not significantly subtracted performance in normal times. This sets it apart from other tail-risk strategies, such as options and trend-following strategies. While the inflation overlay signal has a small aggregate short bias on its own, its ability to calibrate exposure across countries and minor inflation cycles has apparently offset the related shortfall of risk premia.

The benefit of inflation signals for market timing has materialized conspicuously during the dotcom crisis of the early 2000s. the great financial crisis at the end of the 2000s and the COVID pandemic of the early 2020s. (And the inflation-based signal also prepared well for the market drawdown of 2022 to date). The below position heatmaps (red shorts, blue longs) for crisis periods illustrate the typical patterns. Prior to crisis escalation at least some country positions sent warning signals and a few months after the crisis escalation, when resulting disinflationary effects showed in CPI dynamics, the signals turned strongly positive.

A simple empirical check of excess inflation as equity trading signal

An alternative inflation-based signal is “excess inflation”, meaning the latest annual CPI inflation rate less the effective inflation rate, both in quantamental real-time format. Again, for comparability across countries the metric has been scaled by the effective inflation target. Naturally, excess inflation is a little more sluggish in measuring inflation problems than inflation changes, but may better delineate periods in which the central bank is supportive from those where it is compromised in its capacity to lean against market trends.

Indeed, the correlation of excess inflation with subsequent equity index returns has been even stronger than for inflation changes. Also, it has been consistently positive across decades and countries.

Using excess inflation as an overlay for the equally-weighted long-only portfolio produces similar benefits. The inflation-based management increases the 22-year Sharpe ratio from 0.55 to 0.96 and the Sortino ratio from 0.76 to 1.37. The balanced accuracy of the overlay signal alone has been higher than for inflation change at 53.3%.

However, the excess inflation-based management also posted qualitative performance differences. While it prevented meaningful drawdowns during the dotcom crisis, the great financial crisis, and the European sovereign crisis, it took the full hit on the Pandemic crisis. In exchange, it performed even better during the recovery after both the great financial crisis and after the COVID drawdown. In general, excess inflation is a slow-moving indicator that is probably best balanced with metrics of short-term inflation changes.

Looking at the PnL impact of the excess inflation overlay on its own one can see that it has been highly seasonal. This is not surprising, since the indicator requires significant target deviations of inflation to have a chance of creating PnL in a country. However, during such deviations, 2000-03, 2007-09 and 2020-22 excess inflation provided good guidance for market timing.

Broader applications of CPI dynamics as equity trading signal

The plausible theoretical and stable empirical relation between CPI dynamics and equity performance in modern economies has broader implications for equity portfolio management:

  • Plausibly, inflation signals should not only be relevant for cross-country allocations but also for relative value or allocations between stocks and sectors that are more less susceptible to changes in discount factors. A criterion could be “equity duration”, which measures the sensitivity of an equity price to changes in the discount rate.
  • Inflation signals likewise lend themselves as warning signals for factor and trend portfolios. For example, positions in trends that imply inflationary dynamics are more likely to be attacked by central banks if actual CPI inflation has been high or rising. Generally, popular factor strategies are more likely to be derailed by tighter monetary policy if reported CPI inflation is on the high side.
  • Past recorded inflation changes are hardly the optimal predictor of future inflation risks. The below analysis is a proof of concept only, using the simplest design. Exchange rates, core inflation dynamics, and commodity prices all have bearing on the outlook as well. Broader inflation pressure indicators are likely to be more accurate in predicting related headwinds for equity markets.

 

Annex 1: J.P. Morgan Macrosynergy Quantamental System (“JPMaQS”)

Data for the above analysis come from the J.P. Morgan Macrosynergy Quantamental System (“JPMaQS”). JPMaQS is a service that makes it easy to use quantitative-fundamental (quantamental) information for algorithmic trading. Historically, quantamental information has come in formats that make it hard to trade on it: publication timestamps have been disregarded and forgotten, history has been compromised by revisions, models are applied with hindsight, and data records suffer from errors and missing information. JPMaQS strives to clean up this mess for the benefit of all market participants.

Information on JPMaQS and its contents can be viewed here, albeit access requires password and username for J.P. Morgan Markets for now.

Annex 2: Equity index future returns

The return is simply the % change of the futures price. The return calculation assumes rolling futures (from front to second) on IMM (international monetary markets) days. The following country equity indices have been used for futures return calculations:

AUD: Standard and Poor’s / Australian Stock Exchange 200
BRL: Brazil Bovespa
CAD: Standard and Poor’s / Toronto Stock Exchange 60 Index
CHF: Swiss Market (SMI)
GBP: FTSE 100
INR: CNX Nifty (50)
JPY: Nikkei 225 Stock Average
KRW: Korea Stock Exchange KOSPI 20
MXN: Mexico IPC (Bolsa)
MYR: FTSE Bursa Malaysia KLCI
PLN: Warsaw General Index 20
SEK: OMX Stockholm 30 (OMXS30)
SGD: MSCI Singapore (Free)
THB: Bangkok S.E.T. 50
TWD: Taiwan Stock Exchange Weighed TAIEX
USD: Standard and Poor’s 500 Composite
ZAR: FTSE / JSE Top 40

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Ralph Sueppel is founder and director of SRSV, a project dedicated to socially responsible macro trading strategies. He has worked in economics and finance for over 25 years for investment banks, the European Central Bank and leading hedge funds. At present, he is head of research and quantitative strategies at Macrosynergy Partners.