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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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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Variance term premia

October 27, 2018

Variance term premia are surcharges on traded volatility that compensate for bearing volatility risk in respect to underlying asset prices over different forward horizons. The premia tend to increase in financial market distress and decrease in market expansions. Variance term premia have historically helped predicting returns on various equity volatility derivatives. The premia themselves can be estimated based on variance swap forward rates and their decomposition into expected underlying price variance and risk premia. In particular, the variance term premia are obtained as the difference between forward swap rates and realized volatility forecasts, whereby the latter are related to a “volatility state vector”.

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Multiple risk-free interest rates

October 6, 2018

Financial markets produce more than one risk-free interest rate. This is because there are several separate market segments where structured trades replicate such a rate. Differences in remuneration arise for two reasons. First, financial frictions can prevent arbitrage. Second, some risk-free assets pay additional convenient yields, typically by virtue of their liquidity and suitability as collateral. Put simply some “safe assets” have value beyond return. U.S. government bonds, in particular, seem to provide a sizable consistent convenience yield that tends to soar in crisis. This suggests that there are arbitrage opportunities for investors that are flexible, impervious to convenience yields and tolerant towards temporary mark-to-market losses.

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How lazy trading explains FX market puzzles

September 29, 2018

Not all market participants respond to changing conditions instantaneously, not even in the FX market. Private investors in particular can take a long while to adapt to changes in global interest rate conditions and even institutional investors may be constrained by rules and lengthy process. A theoretical paper shows that delayed trading goes a long way in explaining many empirical puzzles in foreign exchange markets, i.e. deviations from the rational market equilibrium, such as the delayed overshooting puzzle or the forward discount puzzle. Understanding these delays and their effects offers profit opportunities for flexible information-efficient traders.

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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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