Macro information changes as systematic trading signals

September 28, 2024

Jupyter Notebook

Macro information state changes are point-in-time updates of recorded economic developments. They can refer to a specific indicator or a broad development, such as growth or inflation. The broader the economic concept, the higher the frequency of changes. Information state changes are valuable trading indicators. They provide daily or weekly signals and naturally thrive in periods of underestimated escalatory economic change, adding a layer of tail risk protection.
This post illustrates the application of information state changes to interest rate swap trading across developed and emerging markets, focusing on six broad macro developments: economic growth, sentiment, labour markets, inflation, and financing conditions. For trading, we introduce the concept of normalized information state changes that are comparable across economic groups and countries and, hence, can be aggregated to local and global signals. The predictive power of aggregate information state changes has been strong, with material and consistent PnL generation over the past 25 years.

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Inflation and equity markets

September 14, 2024

Jupyter Notebook

Rising inflation is a natural headwind for equity markets in economies with inflation-targeting central banks. As consumer prices accelerate, expected monetary policy rates and discount factors tend to increase more than dividend growth. Over the past three and a half decades, there has been a strong negative correlation between changes in reported inflation and simultaneous global equity futures returns. A similar negative relationship is evident between seasonally adjusted CPI trends and concurrent returns.
Furthermore, inflation dynamics have shown predictive power. Short-term changes and trends in consumer price growth have proven to be valuable early warning signals for serious market downturns and leading indicators of recoveries. Also, inflation-sensitive strategies have performed on par with long-only portfolios during stable periods. Overall, they enhanced risk-adjusted returns by improving the timing of equity risk exposure.

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Statistical learning for sectoral equity allocation

September 4, 2024

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There is sound reason and evidence for the predictive power of macro indicators for relative sectoral equity returns. However, the relations between economic information and equity sector performance can be complex. Considering the broad range of available point-in-time macro-categories that are now available, statistical learning has become a compelling method for discovering macro predictors and supporting prudent and realistic backtests of related strategies. This post shows a simple five-step method to use statistical learning to select and combine macro predictors from a broad set of categories for the 11 major equity sectors in 12 developed countries. The learning process produces signals based on changing models and factors per the statistical evidence. These signals have been positive predictors for relative returns of all sectors versus a broad basket. Combined into a single strategy, these signals create material and uncorrelated investor value through sectoral allocation alone.

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Cross-country equity futures strategies

August 17, 2024

Jupyter Notebook

Developing macro strategies for cross-country equity futures trading is challenging due to the diverse and dynamic nature of equity indices and the global integration of corporations. This complexity makes it difficult to align futures prices with country-specific economic factors. Therefore, success in cross-country macro trading often relies on differentiating indicators related to monetary policy and corporate earnings growth in local currency. Additionally, cross-country strategies benefit from a broad and diverse set of countries to generate value consistently.
We tested five simple, thematic, and potentially differentiating macro scores across a panel of 16 developed and emerging markets. Our findings suggest that a straightforward, non-optimized composite score could have added significant value beyond a risk-parity exposure to global equity index futures. Furthermore, a purely relative value equity index futures strategy would have produced respectable long-term returns, complementing passive equity exposure.

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Macro-quantamental scorecards: A Python kit for fixed-income markets

August 3, 2024

 Jupyter Notebook

Macro-quantamental scorecards are condensed visualizations of point-in-time economic information for a specific financial market. Their defining characteristic is the combination of efficient presentation and evidence of empirical power. This post and the accompanying Python code show how to build scorecards for duration exposure based on six thematic scores: excess inflation, excess economic growth, overconfidence, labour market tightening, financial conditions, and government finance. All thematic scores have displayed predictive power for interest rate swap returns in the U.S. and the euro area over the past 25 years. Since economic change is often gradual and requires attention to a broad range of indicators, monitoring can be tedious and costly. The influence of such change can, therefore, build surreptitiously. Macro-quantamental scorecards cut information costs and attention time and, hence, improve the information efficiency of the investment process.

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How to adjust regression-based trading signals for reliability

July 20, 2024

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Regression-based statistical learning is convenient for combining candidate trading factors into single signals (view post here). Models and signals are updated sequentially using expanding time windows of empirical evidence and offering a realistic basis for backtesting. However, simple regression-based predictions disregard statistical reliability, which tends to increase as time passes or decrease after structural breaks. This short methodological post proposes signals based on regression coefficients adjusted for statistical precision. The adjustment correctly aligns intertemporal risk-taking with the predictive power of signals. PnLs become less seasonal and outperform as sample size and statistical quality grow.

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Reported economic changes and the Treasury market: impact and payback

July 13, 2024

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Financial markets pay great attention to reported changes in key economic statistics, particularly when they are unexpected. For quantitative analysis, we introduce the concept of information state changes and the methods of aggregating them across time and indicators. We apply these to a few popular U.S. indicators and investigate how information state changes have affected the bond market. In line with theory, monthly changes in economic growth, inflation, and employment growth have all been negatively correlated with concurrent Treasury returns over the past 25 years. However, there has been subsequent payback: the correlation reverses for subsequent monthly Treasury returns. This supports the hypothesis that high publicity volatile indicators are easily “overtraded.” Cognitive biases may systematically exaggerate positioning toward the latest “surprises” or publicized changes.

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Macro factors and sectoral equity allocation

June 29, 2024

Jupyter Notebook of factor calculation Jupyter Notebook of factor analysis

Returns of major equity sector indices relative to the overall market plausibly depend on macroeconomic trends. Certain economic developments, such as the state of the business cycle, relative price trends, or financial conditions, drive divergences in business conditions. We test the predictive power of plausible point-in-time macro factors for the relative performance of the 11 major equity sectors in 12 developed countries over an almost 25-year period since 2000.
While not all plausible simple macro hypotheses are supported by the evidence, “conceptual parity scores” that simply average all (normalized) factors have displayed significant predictive power for relative returns of most sectors. The joint risk-adjusted returns generated by relative allocation across all 11 sectors are sizable, with a Sharpe ratio of over 1. This suggests that macro factor-based allocation may more than double the risk-adjusted returns of standard equity portfolios.

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Macroeconomic trends and financial markets: theory and evidence

June 15, 2024

Trend detection is one type of macroeconomics-based trading strategy (other types are fundamental value estimates, implicit subsidies, and endogenous market risks). Macroeconomic trends predict asset returns for two principal reasons: They affect investors’ attitudes toward risk and influence the expected risk-neutral payoff of a financial contract. The market impact of macroeconomic trends is typically more pronounced over longer horizons (such as months) than over shorter horizons (such as days). The relevance and predictive power of point-in-time macro trends have been demonstrated in applied research for all major asset classes: fixed income, foreign exchange, equities, commodities, credit derivatives, and cross-asset return correlation. The alignment of macroeconomic trend information and trading positions is often simple and straightforward. However, the logical transformation of the information states and hedging of target positions are sometimes essential.

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How “beta learning” improves macro trading strategies

June 1, 2024

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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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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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