Equity trend following and macro headwinds

July 1, 2023

Jupyter Notebook

Market price trends often foster economic trends that eventually oppose them. Theory and empirical evidence support this phenomenon for equity markets and suggest that macro headwind (or tailwind) indicators are powerful modifiers of trend following strategies. As a simple example, we calculate a macro support factor for equity index futures in the eight largest developed markets based on labor markets, inflation, and equity carry. This factor is used to modify standard trend following signals. The modification increases the predictive power of the trend signal and roughly doubles the risk-adjusted return of a stylized global trend following strategy since 2000.

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FX trend following and macro headwinds

May 20, 2023

Jupyter Notebook

Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is less likely to be sustained if concurrent economic data signal a deterioration in the competitiveness of the local economy. Macro indicators of such setback risk can slip through the net of statistical detection of return predictors because their effects compete with dominant trends and are often non-linear and concentrated. As a simple example, empirical evidence shows that standard global FX trend following would have benefited significantly merely from adjusting for changes in external balances.

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Trend following: combining market and macro information

July 9, 2022

Classic trend following is based on market prices or returns. Market trends are relatively cheap to produce, popular, and plausibly generate value in the presence of behavioral biases and rational herding. Macro trends track relevant states of the economy based on fundamental data. They are more expensive to produce from scratch and generate value due to rational information inattentiveness. While market trends are timelier, macro trends are more specific in information content. Due to this precision, they serve better as building blocks of trading signals without statistical optimization and are easier to predict based on real-time information. Reason and evidence suggest that macro and market trends are complementary. Two combination methods are [1] market information enhancement of macro trends and [2] market influence adjustment of macro trends.

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Factor momentum: a brief introduction

May 29, 2021

Standard equity factors are autocorrelated. Hence, it is not surprising that factor strategies have also displayed momentum: past returns have historically predicted future returns. Indeed, factor momentum seems to explain all return momentum in individual stocks and across industries. Momentum has been concentrated on a subset of factors, most notably those related to “betting against beta”, a leveraged strategy that is long high-beta stocks and short low beta stocks. Also, factor return autocorrelation has been changing over time. Measures of continuation in factor returns can indicate “momentum crashes”. A plausible cause of factor momentum is mispricing, i.e. drifts of prices in accordance with fundamental gravity, if positions that exploit the mispricing bear systematic risk.

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Building a real-time market distress index

March 20, 2021

A new Fed paper explains how to construct a real-time distress index, using the case of the corporate bond market. The index is based on metrics that describe the functioning of primary and secondary markets and, unlike other distress measures, does not rely on prices and volatility alone. Thus, it includes issuance volumes and issuer characteristics on the primary side and trading volumes and liquidity on the secondary market side. Making use of a broad range of data on market functioning reduces the risk of mistaking a decline in asset values for actual market distress. Distress in a market that is critical for funding the economy and the financial system has predictive power for future economic dynamics and can be a valuable trading signal in its own right. It can be used for more advanced trend following and for detecting price distortions.

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Understanding the disposition effect

February 20, 2021

Investors have a tendency to sell assets that have earned them positive returns and are reluctant to let go of those that have brought them losses. This behavioural bias is called “disposition effect” and is attributed to loss aversion and regret avoidance. It has been widely documented by empirical research. The prevalence of the disposition effect is a key motivation behind trend following strategies. Now there is evidence that this effect is also cyclical: it seems to be stronger in market “bust periods” than in “boom periods”. This is consistent with prospect theory and heightened risk aversion in market downturns.

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Estimating the positioning of trend followers

November 21, 2020

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures markets over plausible lookback windows, under consideration of a leverage constraint, and uses estimated assets under management as a scale factor. For optimization and out-of-sample analysis, the approach can be enhanced by sequential estimation of some key parameters, such as the momentum lookback, the normalized momentum cap and the lookback for realized volatility calculation. Trend follower positions are an important factor of endogenous market risk due to the size of assets under management in dedicated funds and the informal use of trend rules across many trading accounts.

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Fundamental trend following

October 31, 2020

Fundamental trend following uses moving averages of past fundamental data, such as valuation metrics or economic indicators, to predict future fundamentals, analogously to the conventions in price or return trend following. A recent paper shows that fundamental trend following can be applied to equity earnings and profitability indicators. One approach is to pool fundamental information across a range of popular indicators and to sequentially choose lookback windows for moving averages in accordance with past predictive power for returns. The fundamental extrapolation measure predicts future stock returns positively and would historically have generated significant profits. Most importantly, fundamental trend following returns seems to have little correlation with price trend following returns, supporting the idea that these trading styles are complementary.

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How loss aversion increases market volatility and predicts returns

March 21, 2020

Loss aversion means that people are more sensitive to losses than to gains. This asymmetry is backed by ample experimental evidence. Loss aversion is not the same as risk aversion, because the aversion is disproportionate towards drawdowns below a threshold. Importantly, loss aversion implies that risk aversion is changing with market prices. This means that the compensation an investor requires for holding a risky asset varies over time, giving rise to excessive price volatility (relative to the volatility of fundamentals), volatility clustering across time, and predictability of returns. All these phenomena are consistent with historical experience and form a useful basis for trading strategies, such as trend following.

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Retail investor beliefs

August 3, 2019

Survey evidence suggests that retail investors adjust positions rather sluggishly to changing beliefs and the beliefs themselves contradict classic rationality. Sluggishness arises from two features. First, the sensitivity of portfolio choices to beliefs is small. Second, the timing of trades does not depend much on belief changes. Contradictions to classic rationality arise because different investors cling stubbornly to different beliefs with little convergence. Also, retail investors associate higher returns with higher economic growth expectations and lower returns with fears of large drawdowns (contradicting the notion of tail risk premia). Overall this suggests that retail investors feel better informed than the market, with no need for updating their beliefs quickly and thoroughly. Opportunities for professional macro trading may arise through front running retail flows and applying more consistent rationality.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation central counterparty clearing China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for over eighteen years. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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