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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The macro information inefficiency of financial markets

December 22, 2018

There are reason and evidence for financial markets failing to be efficient with respect to macro trends. The main reason is cost: “tradable” economic research is expensive and investment firms will only invest in such research if their fees on expected incremental portfolio returns exceed their expenses. This requires them to concentrate scarce research budgets on areas where they see apparent inefficiency. Professional macro research and macro information efficiency are therefore mutually exclusive. Macro inefficiency is evident in the simplicity of popular investment rules, such as trend and carry, the conspicuous absence of economic data in most strategies, and the bias of financial economics towards marketing rather than trading. Academic papers present ample evidence of herding and sequential dissemination of information. Hence, the great incremental value of “tradable” macro research is that it turns informed macro traders into trendsetters as opposed to trend followers and enhances the social benefit of the investment industry overall.

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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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Understanding the correlation of equity and bond returns

December 1, 2018

The correlation of equity and high grade sovereign bond returns is a powerful driver of portfolio construction and the term premia of interest rates. This correlation has turned from positive in the 1970s-1990s to negative in the 2000s-2010s, on the back of similar shifts in the correlation between inflation and economic growth and between inflation and real interest rates. The structural correlation flip has given rise to a risk parity investment boom and contributed to the compression in long-term yields. Both theoretical and empirical analysis suggests that negative equity-bond correlation is due largely to pro-cyclical inflation, i.e. higher inflation coinciding with better economic performance, as opposed counter-cyclical inflation or stagflation. Inflation is more likely to be pro-cyclical if it is low or in deflation (view post here) and driven by demand rather than supply shocks.

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CDS term premia and exchange rates

November 24, 2018

The term structure of sovereign credit default swaps (CDS) is indicative of country-specific financial shocks because rising country risk affects short-dated maturities more than longer-dated ones. This feature allows disentangling global and local risk factors in sovereign CDS markets. The latter align with the performance of other local asset markets. In particular, recent empirical research supports the predictive value of CDS term premia for exchange rate changes. The finding is plausible, because both local-currency assets and CDS term premia have common pricing factors, while CDS curves are cleaner representations of country financial risks.

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Realistic volatility risk premia

November 17, 2018

The volatility risk premium compensates investors for taking volatility risk. Conceptually it is based on the difference between options-implied and expected realized volatility. In equity markets this premium should be positive in the long run and fluctuate overtime depending on the market’s willingness to pay for protection against future changes in price volatility. In practice, measuring the premium overtime is challenging, particularly because expected realized volatility is not known. Using recent realized volatility as a proxy can be highly misleading. However, a realistic estimate can be constructed by considering the trade-off between timeliness and noise ratio of recent price changes and the long-term mean reversion of volatility. This “realistic” volatility risk premium has been positively correlated with subsequent daily volatility index future returns.

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How systemic financial risk is measured

November 10, 2018

Public institutions have developed a wide range of methods to track systemic financial risk. What most of them have in common is reliance on financial market data. This implies that systemic risk indicators typically only show what the market has already priced, in form of correlation, volatility or value. They cannot anticipate market crises. Their main use is to predict when and how market turmoil begins to sap the functioning of the financial system. Some methods may be useful for macro trading. For example, Conditional Value-at-Risk can identify sources of systemic risk, such as specific institutions or market segments. Principal Components Analysis can indicate changing concentration of risk across securities and markets.

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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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