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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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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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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The predictability of market-wide earnings revisions

October 13, 2018

Forward earnings yields are a key metric for the valuation of an equity market. Helpfully, I/B/E/S and DataStream publish forward earnings forecasts of analysts on a market-wide index basis. Unfortunately, updates of these data are delayed by multiple lags. This can make them inaccurate and misleading in times of rapidly changing macroeconomic conditions. Indeed, there is strong empirical evidence that equity index price changes predict future forward earnings revisions significantly and for all of the world’s 25 most liquid local equity markets. This predictability can be used to enhance the precision of real-time earnings yield data and avoid misleading trading signals.

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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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Predicting equity volatility with return dispersion

September 15, 2018

Equity return dispersion is measured as the standard deviation of returns across different stocks or portfolios. Unlike volatility it can be measured even for a single relevant period and, thus, can record changing market conditions fast. Academic literature has shown a clear positive relation between return dispersion, volatility and economic conditions. New empirical research suggests that return dispersion can predict both future equity return volatility and equity premia. The predictive relation has been non-linear, suggesting that it is the large changes in dispersion that matter.

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U.S. dollar exchange rate before FOMC decisions

August 15, 2018

Since the mid-1990s the dollar exchange rate has mostly anticipated the outcome of FOMC meetings: it appreciated in the days before a rate hike and depreciated in the days before a rate cut. This suggests that since fixed income markets usually predict policy rate moves early and correctly their information content can be used to trade the exchange rate. A recent paper proposes a systematic trading rule for trading USD before FOMC meetings based upon what is priced into the each Fed meeting from Fed fund futures and claims that such a strategy would have delivered a respectable Sharpe ratio.

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Seasonal effects in commodity futures curves

July 7, 2018

Seasonal fluctuations are evident for many commodity prices. However, their exact size can be quite uncertain. Hence, seasons affect commodity futures curves in two ways. First, they bias the expected futures price of a specific expiry month relative that of other months. Second, their uncertainty is an independent source of risk that affects the overall risk premia priced into the curve. Integrating seasonal factor uncertainty into an affine (linear) term structure model of commodity futures allows more realistic and granular estimates of various risk premia or ‘cost-of-carry factors’. This can serve as basis for investors to decide whether to receive or pay the risk premia implied in the future curve.

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Term premia and macro factors

June 30, 2018

The fixed income term premium is the difference between the yield of a longer-maturity bond and the average expected risk-free short-term rate for that maturity. Abstractly, it is a price for commitment. The term premium is not directly observable but needs to be estimated based on the assumptions of a term structure model that separates expected short-term rates and risk premia. Model assumptions become a lot more realistic if one includes macroeconomic variables. In particular, long-term inflation expectations plausibly shape the long-term trend in yield levels. Also cyclical fluctuations in inflation and unemployment explain slope and curvature to some extent. A recent IMF paper proposes a methodology for integrating macroeconomic variables in a conventional affine term structure model.

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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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