How “beta learning” improves macro trading strategies

Jupyter Notebook

Macro beta is the sensitivity of a financial contract’s return to a broad economic or market factor. Macro betas broaden the traditional concept of equity market betas and can often be estimated using financial contract baskets. Macro sensitivities are endemic in trading strategies, diluting alpha, undermining portfolio diversification, and distorting backtests. However, it is possible to immunize strategies through “beta learning,” a statistical learning method that supports identifying appropriate models and hyperparameters and allows backtesting of hedged strategies without look-ahead bias. The process can be easily implemented with existing Python classes and methods. This post illustrates the powerful beneficial impact of macro beta estimation and its application on an emerging market FX carry strategy.

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Generic derivative returns and carry (for strategy testing)

Backtesting of macro trading strategies requires good approximate profit-and-loss data for standard derivatives positions, particularly in equity, foreign exchange, and rates markets. Practical calculation methods of generic proxy returns not only deliver valid strategy targets but are also the basis of volatility adjustments of trading factors and for calculating nominal and real “carry” of macro derivatives. A methodological summary for equity index futures, FX forwards, and interest rate swaps shows that generic return and carry formulas need not be complicated. However, decisions on how to simplify and set conventions require good judgment and adjustment to institutional needs.

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FX trend following and macro headwinds

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Trend following can benefit from consideration of macro trends. One reason is that macroeconomic data indicate headwinds (or tailwinds) for the continuation of market price trends. This is particularly obvious in the foreign-exchange space. For example, the positive return trend of a currency is less likely to be sustained if concurrent economic data signal a deterioration in the competitiveness of the local economy. Macro indicators of such setback risk can slip through the net of statistical detection of return predictors because their effects compete with dominant trends and are often non-linear and concentrated. As a simple example, empirical evidence shows that standard global FX trend following would have benefited significantly merely from adjusting for changes in external balances.

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Macroeconomic cycles and asset class returns

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Indicators of growth and inflation cycles are plausible and successful predictors of asset class returns. For proof of concept, we propose a single balanced “cyclical strength score” based on point-in-time quantamental indicators of excess GDP growth, labor market tightening, and excess inflation. It has clear theoretical implications for all major asset markets, as rising operating rates and consumer price pressure raise real discount factors. Empirically, the cyclical strength score has displayed significant predictive power for equity, FX, and fixed income returns, as well as relative asset class positions. The direction of relationships has been in accordance with standard economic theory. Predictive power can be explained by rational inattention. Naïve PnLs based on cyclical strength scores have each produced long-term Sharpe ratios between 0.4 and 1 with little correlation with risk benchmarks. This suggests that a single indicator of cyclical economic strength can be the basis of a diversified portfolio.

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Excess inflation and asset class returns

Jupyter Notebook

Excess inflation means consumer price trends over and above the inflation target. In a credible inflation targeting regime, positive excess inflation skews the balance of risks of monetary policy towards tightening. An inflation shortfall tips the risk balance towards easing. Assuming that these shifting balances are not always fully priced by the market, excess inflation in a local currency area should negatively predict local rates market and equity market returns, and positively local-currency FX returns. Indeed, these hypotheses are strongly supported by empirical evidence for 10 developed markets since 2000. For fixed income and FX excess inflation has not just been a directional but also a relative cross-country trading signal. The deployment of excess inflation as a trading signal across asset classes has added notable economic value.

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FX trades after volatility shocks

Currency areas with negative external balances are – all other things equal – more vulnerable to financing shocks. Jumps in market price volatility often indicate such shocks. Realistically it takes a few days for the market to fully price the consequences of shocks consistently across currencies. Hence, the products of external balances-based “resilience scores” and volatility shocks are plausible indicators of “post-shock currency hazards”. This means that they should serve as signals for differences in currency returns after market volatility has surged or dropped. An empirical analysis based on 28 currencies since 2000 shows that a most simple “post-shock currency hazard” measure has significantly helped predict subsequent short-term returns and would have added positive PnL to FX trading strategies, particularly in times of turbulence.

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Jobs growth as trading signal

Employment growth is an important and underestimated macro factor of financial market trends. Since the expansion of jobs relative to the workforce is indicative of changes in slack or tightness in an economy it serves as a predictor of monetary policy and cost pressure. High employment growth is therefore a natural headwind for equity markets. Similarly, the expansion of jobs in one country relative to another is indicative of relative monetary tightening and economic performance. High relative employment growth is therefore a tailwind for the local currency. These propositions are strongly supported by empirical evidence. Employment growth-based trading signals would have added significant value to directional equity and FX trading strategies since 2000.

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How to use FX carry in trading strategies

FX forward-implied carry is a valid basis for trading strategies because it is related to divergences in monetary and financial conditions. However, nominal carry is a cheap and rough indicator: related PnLs are highly seasonal, sensitive to global equity markets, and prone to large drawdowns. Simple alternative concepts such as real carry, interest rate differentials, and volatility-adjusted carry metrics have specific benefits but broadly fail to mitigate these shortcomings. However, the consideration of a market beta premium, adjustment for inflation expectations, and the consideration of other macro-quantamental factors make huge positive differences. Not only do these modifications greatly enhance the theoretical plausibility of value generation, but they also would have almost doubled the PnL generation over the past 20 years, removed most of its equity market dependence, and greatly reduced seasonality.

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Prospect theory value as investment factor

Prospect theory value as investment factor

Prospect theory value is a valid investment factor, particularly in episodes of apparent market inefficiency. Prospect theory is a popular model of irrational decision making. It emphasizes a realistic mental representation of expected gains and losses and an individual’s evaluation of such representations. Prospect theory explains asymmetric loss aversion (view post here) and gambling preferences (view post here). Since mental representations of expected returns and volatility are often driven by price charts, prospect theory value can be estimated based on historic asset return distributions. Assets with a high prospect theory value should have low subsequent returns and vice versa. This proposition holds even if part of the market is fully rational as long as there are balance sheet and risk limits. Empirical academic papers have confirmed the prospect theory value in international equity, corporate bond and foreign exchange markets.

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Understanding international capital flows and shocks

Macro trading factors for FX must foremostly consider (gross) external investment positions. That is because modern international capital flows are mainly about financing, i.e. exchanges of money and financial assets, rather than saving, real investments and consumption (which are goods market concepts). Trades in financial assets are much larger than physical resource trades. Also, financing flows simultaneously create aggregate purchasing power, bank assets and liabilities. The vulnerability of currencies depends on gross rather than net external debt. Current account balances, which indicate current net payment flows, can be misleading. The nature and gravity of financial inflow shocks, physical saving shocks, credit shocks and – most importantly – ‘sudden stops’ all depend critically on international financing.

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