The dollar as barometer for credit market risk

March 9, 2019

The external value of the USD has become a key factor of U.S. and global credit conditions. This reflects the surge in global USD-denominated debt in conjunction with the growing importance of mutual funds as the ultimate source of loan financing. There is empirical evidence that USD strength has been correlated with credit tightening by U.S. banks. There is also evidence that this tightening arises from deteriorating secondary market conditions for U.S. corporate loans, which, in turn, are related to outflows of credit funds after USD appreciation. The outflows are a rational response to the negative balance sheet effect of a strong dollar on EM corporates in particular. One upshot is that the dollar exchange rate has become an important early indicator for credit market conditions.

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How salience theory explains the mispricing of risk

March 2, 2019

Salience theory suggests that decision makers exaggerate the probability of extreme events if they are aware of their possibility. This gives rise to subjective probability distributions and undermines conventional rationality. In particular, salience theory explains skewness preference, i.e. the overpricing of assets with a positive skew and the under-pricing of contracts with a negative skew. There is ample evidence of skewness preference, most obviously the overpayment for insurance contracts and lottery tickets. In financial markets, growth stocks with positively-skewed expected returns have historically been overpriced relative to value stocks. This is important for macro trading. For example, a specific publicly discussed disaster risk should pay an excessive premium, and short-volatility strategies in times of fear of large drawdowns for the underlying should have positive expected value.

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Understanding dollar cross-currency basis

February 9, 2019

Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct funding in USD – if accessible – is cheaper than synthetic funding via swaps. An apparent structural cause of the dollar basis has been regulatory tightening, which has increased balance sheet costs of arbitrage. Moreover, research has found several short-term factors. Thus, a negative dollar basis has been linked to aggregate USD strength, rising market volatility, deteriorating FX market liquidity, monetary tightening in the U.S. relative to other countries, and a decrease of funds in the USD money market. In most of these cases, the dollar basis represents dollar funding conditions not captured by published interest rates and is a valid trading signal.

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Why herding is the death of momentum

February 2, 2019

Momentum trading, buying winning assets and selling losing assets, is a most popular trading strategy. It relies on sluggish market adjustment, allowing the trader to follow best-informed investors before the more inert part of the market does. Herding simply means that market participants imitate each others’ actions. Herding accelerates and potentially exaggerates market adjustments. The more quickly the herd moves, the harder it becomes to follow informed leaders profitably. In a large agile herd, sluggish adjustment gives way to frequent overreaction. Momentum strategies fail. This suggests that popularity and commoditization of momentum strategies (and trend-following) are ultimately self-defying. Conditioning momentum strategies on the estimated degree of herding should produce superior investment returns.

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Drawdown control

January 19, 2019

Containment of drawdowns and optimization of performance ratios for multi-asset portfolios is critical for trading strategies. Alas, short data series or structural changes often render estimates of covariance matrices unreliable. A popular solution is risk-parity with volatility targeting. An alternative is ‘MinMax’ drawdown control, which builds on a broad interpretation of drawdowns as maximum actual or opportunity losses from not adjusting a benchmark portfolio to a specific underlying asset. In the case of one risky and one safe asset, this boils down to managing simultaneously the risks of conventional PnL drawdowns and foregone risk returns. Optimal asset allocation depends only on aversion to different types of drawdowns. Averaging over a plausible range of aversion parameters gives a model portfolio. Empirical evidence for the case of cryptocurrencies suggests that in an environment of uncertain returns MinMax delivers better PnL return-to-drawdown ratios than conventional volatility control.

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Liquidity yields and FX

January 12, 2019

Liquidity yields are convenience yields of financial securities that typically arise from high liquidity, suitability as collateral or preferred regulatory status. New research argues that relative changes in liquidity yields on government bonds across countries have a significant impact on exchange rate dynamics. Theoretically, an unexpected increase in the liquidity yield on government bonds in country A relative to country B triggers an appreciation of the currency of A versus B in very much the same way in which an unexpected rise in the short-term interest rate differential would. Empirically, there is evidence of a significant and consistent impact of relative liquidity yield changes on exchange rate dynamics across the G10.

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The fundamental value trap

January 5, 2019

Fundamental value seems like a straightforward investment approach. One simply looks for assets that are “cheap” or “expensive” relative to their rationally expected risk-adjusted discounted cash flows. In reality, conscientious estimation of fundamental value gaps is one of the most challenging strategies in asset management. It requires advanced financial modeling and often long waiting times for payoff. Few managers have the resources and patience for it. In macro trading, cheapness or dearness is commonly inferred from simple valuation metrics, such as real interest rates, real exchange rates or equity earnings yields. However, by themselves, off-the-shelve metrics cannot create much information advantage. Indeed, they regularly confuse forward-looking expectations with mispricing and lure investors into crowded value traps. Fundamental value should be estimated conscientiously or not at all. The minimum requirement for a valid valuation metric is some reasonable integration with related economic states and trends.

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Equity values and credit spreads: the inflation effect

December 29, 2018

A theoretical paper shows that a downward shift in expected inflation increases equity valuations and credit default risk at the same time. The reason for this is “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. At the same time, a downward shift in expected inflation increases future real debt service and leverage of firms and increases their probability of default. This theory is supported by the trends in U.S. markets since 1970. It would principally argue for strategic relative equity-CDS positions inversely to the broad trend in expected inflation.

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Modern backtesting with integrity

December 15, 2018

Machine learning offers powerful tools for backtesting trading strategies. However, its computational power and convenience can also be corrosive for financial investment due to its tendency to find temporary patterns while data samples for cross validation are limited. Machine learning produces valid backtests only when applied with sound principles. These should include [1] formulating a logical economic theory up front, [2] choosing sample data up front, [3] keeping the model simple and intuitive, [4] limiting try-outs when testing ideas, [5] accepting model decay overtime rather than ‘tweaking’ specifications, and [6] remaining realistic about reliability. The most important principle of all is integrity: aiming to produce good research rather than good backtests and to communicate statistical findings honestly rather than selling them.

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Commodity carry

December 8, 2018

Across assets, carry is defined as return for unchanged prices and is calculated based on the difference between spot and futures prices (view post here). Unlike other markets, commodity futures curves are segmented by obstacles to intertemporal arbitrage. The costlier the storage, the greater is the segmentation and the variability of carry. The segmented commodity curve is shaped prominently by four factors: [1] funding and storage costs, [2] expected supply-demand imbalances, [3] convenience yields and [4] hedging pressure. The latter two factors give rise to premia that can be received by financial investors. In order to focus on premia, one must strip out apparent supply-demand effects, such as seasonal fluctuations and storage costs. After adjustment both direction and size of commodity carry should be valid, if imprecise, indicators of risk premia. Data for 2000-2018 show clear a persistent positive correlation of the carry with future returns.

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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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