Inflation and equity markets

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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

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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

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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

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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

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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

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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

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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

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

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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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Evaluating macro trading signals in three simple steps

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Meaningful evaluation of macro trading signals must consider their seasonality and diversity across countries. This post proposes a three-step process to this end. The first step runs significance tests of proposed predictive relations using a panel of markets. The second step reviews the reliability of predictive relations based on accuracy and different correlation metrics across time and markets. The third step estimates the economic value of the signal based on performance metrics of a standardized naïve PnL. All these steps can be implemented with special Python classes of the Macrosynergy package. Conscientious evaluation of macro signals not only benefits their selection for live trading. It also paints a realistic picture of the PnL profile, which is critical for setting risk limits and for broader portfolio integration.

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