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

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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

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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Evaluating macro trading signals in three simple steps

May 18, 2024

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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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Inventory scores and metal futures returns

May 4, 2024

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Inventory scores are quantamental (point-in-time) indicators of the inventory states and dynamics of economies or commodity sectors. Inventory scores plausibly predict base metal futures returns due to two effects. First, they influence the convenience yield of a metal and the discount at which futures are trading relative to physical stock. Second, they predict demand changes for restocking by producers and industrial consumers. Inventory scores are available for finished manufacturing goods and base metals themselves. An empirical analysis for 2000-2024 shows the strong predictive power of finished goods inventory scores and some modest additional predictive power of commodity-specific inventory scores.

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FX trading signals with regression-based learning

April 23, 2024

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Regression-based statistical learning helps build trading signals from multiple candidate constituents. The method optimizes models and hyperparameters sequentially and produces point-in-time signals for backtesting and live trading. This post applies regression-based learning to macro trading factors for developed market FX trading, using a novel cross-validation method for expanding panel data. Sequentially optimized models consider nine theoretically valid macro trend indicators to predict FX forward returns. The learning process has delivered significant predictors of returns and consistent positive PnL generation for over 20 years. The most important macro-FX signals, in the long run, have been relative labor market trends, manufacturing business sentiment changes, relative inflation expectations, and terms of trade dynamics.

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Macroeconomic data and systematic trading strategies

April 23, 2024

While economic information undeniably wields a significant and widespread influence on financial markets, the systematic incorporation of macroeconomic data into trading strategies has thus far been limited. This reflects skepticism towards economic theory and serious data problems, such as revisions, distortions, calendar effects, and, generally, the lack of point-in-time formats. However, the emergence of industry-wide quantamental indicators and the rise of statistical learning methods in financial markets make macroeconomic information more practical and powerful. Successful demonstrations of statistical learning and macro-quantamental indicators have been achieved, with various machine learning techniques poised to further improve the utilization of economic information.

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Macro trends and equity allocation: a brief introduction

April 4, 2024

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Macroeconomic trends affect stocks differently, depending on their lines of business and their home markets. Hence, point-in-time macro trend indicators can support two types of investment decisions: allocation across sectors within the same country and allocation across countries within the same sector. Panel analysis for 11 sectors and 12 countries over the last 25 years reveals examples for both. Across sectors, export growth, services business sentiment, and consumer confidence have predicted the outperformance of energy stocks, services stocks, and real estate stocks, respectively. Across countries, relative export growth, manufacturing sentiment changes, and financial conditions have predicted the outperformance of local stocks versus foreign ones for the overall market and within sectors.

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Understanding dollar shortages and related market dynamics

March 9, 2024

A dollar shortage is a state of FX and rates markets where covered interest rate parity between the U.S. and another currency area would result in excess dollar demand. Covered interest rate parity is the equality for short-term interest rate differentials and FX forward implied carry. Since the great financial crisis, arbitrage between onshore and offshore dollar credit markets through FX swaps has been impaired. In contrast, the dollar’s dominance in international transactions has remained intact. The consequence of market segmentation and dollar dominance has been sporadic dollar shortfalls in times of market turmoil or tightening financial conditions: a rush for liquidity turns into a net “dash for dollars,” and dollar rates in the offshore market rise above those in the onshore markets. Since higher dollar rates in the offshore market drive both offshore borrowers and lenders to buy dollars in the FX spot market directly, the dollar appreciates, at least temporarily.

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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
ABOUT US

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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