Macro-quantamental scorecards
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.
Global foreign exchange markets are subject to a wide range of macroeconomic influences. The sheer breadth of related information and required analyses often prevent their systematic use in trading. However, modern macro-quantamental scorecards can condense ample point-in-time macroeconomic data into thematic scores for easy systematic visualization and empirical evaluation.
This post demonstrates how to create structured macro-quantamental scorecards for FX forward trading in Python. It uses indicators related to economic growth differentials, monetary policy divergences, external balances, valuation metrics, and price competitiveness. Resulting scorecards provide point-in-time snapshots of macroeconomic conditions across all liquid currencies. They also summarize historical and thematic perspectives. Empirical analysis highlights the predictive power and trading value of macro-quantamental scores.
Macro risk premium scores are differences between market-implied risk and point-in-time quantified macroeconomic risk. Two principal types of scores can be calculated for credit markets: spread-based risk premium scores and rating-based risk premium scores. This post proposes a small set of these scores for EM foreign-currency sovereign debt, targeting 24 country sub-indices of the EMBI Global. The macroeconomic component captures four risk dimensions: general government finance, external balances, international investment flows, and foreign debt sustainability.
Macro risk premium scores are constructed on a point-in-time basis, making them suitable for backtesting. Both individual and aggregated scores have shown strong and statistically significant predictive power for subsequent returns of country indices. Portfolios of EM sovereign bonds weighted by risk premium scores have consistently outperformed those based on equal weights or risk parity. Risk premium scores have also generated material cross-country relative value. Most importantly, macro risk premia offer a responsible and profitable approach to adjusting weights of emerging market bond indices.
Macro-quantamental scorecards are systematic enhancements of discretionary portfolio management. They offer (a) information efficiency by structuring and condensing key macroeconomic data series, and (b) empirical validation of predictive power and trading value using historic point-in-time information. Scorecards can be readily built in Python, with pandas and existing classes and methods.
Macro-quantamental scores support capital allocation across country equity markets, which is critical for long-term wealth generation by professional investment managers and private investors alike. This post demonstrates how to construct a simple tactical scorecard based on real equity carry, real exchange rate valuation, terms-of-trade dynamics, external balance strength, international investment position changes, and economic confidence trends. There is strong evidence that such a systematic approach delivers predictive power and sustained value generation.