Markets’ neglect of macro news

June 12, 2021

Empirical evidence suggests that investors pay less attention to macroeconomic news when market sentiment is positive. Market responses to economic data surprises have historically been muted in high sentiment periods. Behavioral research supports the idea that investors prefer heuristic decision-making and neglect fundamental information in bullish markets, but pay more attention in turbulent times. This allows prices to diverge temporarily from fundamentals and undermines the conventional risk-return trade-off when sentiment is high. Low-risk portfolios tend to outperform subsequently. The sentiment bias also means that fundamental predictors of market prices work better in low-sentiment periods than in high-sentiment periods.

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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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The macro forces behind equity-bond price correlation

May 15, 2021

Since the late 1990s, the negative price correlation of equity and high-grade bonds has reduced the volatility of balanced portfolios and boosted Sharpe ratios of leveraged “long-long” equity-bond strategies. However, this correlation is not structurally stable. Over the past 150 years, equity-bond correlation has changed repeatedly. A structural economic model helps to explain and predict these changes. The key factor is the dominant macro policy. In an active monetary policy regime, where central bank rates respond disproportionately to inflation changes, the influence of technology (supply) shocks dominates markets and the correlation turns positive. In a fiscal policy regime, where governments use debt financing to manage the economy, the influence of investment (financial) shocks dominates and the correlation turns negative. In a world with low inflation and real interest rates, the fiscal regime is typically more prevalent.

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Macro information waste and the quantamental solution

May 1, 2021

Financial markets are not macro information efficient. This means that investment decisions miss out on ample relevant macroeconomic data and facts. Information goes to waste due to research costs, trading restrictions, and external effects. Evidence of macro information inefficiency includes sluggishness of position changes, the popularity of simple investment rules, and the prevalence of herding.  A simple and practical enhancement of macro information efficiency is the construction of quantamental indicators. A quantamental indicator is a time series that represents the state of an investment-relevant fundamental feature in real-time. The term ‘fundamental’ means that these data inform directly on economic activity, unlike market prices, which inform only indirectly. The key benefits of quantamental indicators are that [1] they fit machine learning pipelines and algorithmic trading tools, thus making a broad set of macro information tradable, [2] they support the consistent use of macro information, [3] they can be applied across traders (or programs), strategy types and asset classes and are, thus, cost-efficient.

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Statistical arbitrage risk premium

April 17, 2021

Any asset can use a portfolio of similar assets to hedge against its factor exposure. The factor residual risk of the hedged position is called statistical arbitrage risk. Consequently, the statistical arbitrage risk premium is the expected return of such a hedged position. A recent paper shows that both theoretically and empirically this premium rises in the stock’s statistical arbitrage risk. ‘Unique’ stocks have higher excess returns than ‘ubiquitous’ stocks. The estimated premium is therefore a valid basis for investment strategies. Statistical arbitrage risk can be estimated by using ‘elastic net’ estimation and related machine learning. This method selects a relatively small hedge portfolio from a large array of candidate stocks.

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Market dynamics: belief, risk, and ambiguity effects

April 3, 2021

To understand financial market dynamics, it is helpful to distinguish beliefs, attitudes towards risk, and attitudes towards ambiguity. Beliefs are subjective evaluations of future cash flows. Risk refers to uncertainty within a model of the asset’s return. And ambiguity means uncertainty about the model and probability distributions. Accordingly, one can separate price dynamics into three effects: changes in beliefs, changes in risk premia and changes in ambiguity premia. Ambiguity premia seem to be dominant, particularly when investors have little information about the nature of a particular risk. Traditional risk premia seem to be much less significant. Belief effects are negligible when ambiguity is high but increase as information accumulates. Often trading opportunities arise from the mean reversion of ambiguity premia and the “under-adjustment” of beliefs.

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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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The financial stability interest rate

March 6, 2021

The financial stability interest rate is a threshold above which the real interest rate in an economy triggers financial constraints and systemic instability. It is different from the natural rate of interest, which balances growth and inflation. Indeed, the relationship between the financial stability interest rate and the natural interest rate may be one of the most important predictors of medium-term market direction and future crisis risk. A low financial stability rate versus the natural rate will create a tendency for real interest rates to rise to levels that disrupt financial relations. Factors that lower the financial stability rate include leverage and asset quality in the financial system. It is possible to build time series of financial conditions and stability rates.

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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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Contagion and self-fulfilling dynamics

February 6, 2021

Contagion and self-fulfilling feedback loops are propagation mechanisms at the heart of systemic financial crises. Contagion refers to the deterioration of fundamentals through the financial network, often through a cascade of insolvencies. A critical factor is the similarity of assets held by financial institutions. The commonality of assets erases some of the benefits of diversification because it facilitates contagion. The potential role of investment funds in aggravating contagion through fire sales has much increased over the past 20 years. Self-fulfilling feedback loops denote the shift from one equilibrium to another, possibly without a change in ‘fundamentals’. They arise from multiple equilibria and strong interdependencies in a financial network. Bank runs are a classic example. Simple metrics that track both types of systemic risk are principal components and cross-correlation coefficients of different types of financial assets.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry 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 two decades. 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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