Macro scorecards for local-currency EM bonds

November 8, 2025

Jupyter Notebook

Macro-quantamental scorecards enable portfolio managers to systematically exploit point-in-time economic information for the rebalancing of their positions. This post illustrates how to construct quantamental factors and scorecards for dollar-based investors in emerging local bond markets, a segment that demands close attention to macroeconomic developments.
A plausible set of relevant macro factors includes changes in external balances, relative economic growth, terms-of-trade dynamics, international investment positions, government balances, and duration term premia. The dynamics and current states of these factors, along with their composite indicators, can be visualized through snapshot scorecards, historical heatmaps, and country-specific scorecards. Empirical evidence demonstrates that the resulting scores possess significant predictive power, high accuracy, and material PnL value-generation potential.

(more…)

Diversified trend following in emerging FX markets

October 25, 2025

Jupyter Notebook

Trend-following strategies exploit sustained past price movements, capitalizing on inefficiencies such as the gradual dissemination of information, inertia in portfolio reallocation, and disposition effects. However, the exclusive reliance on price data makes them highly sensitive to prevailing market regimes, neglects accompanying economic dynamics, and often leads to crowded positioning. These limitations can be mitigated by incorporating point-in-time information on concurrent macroeconomic trends.
We demonstrate the advantages of diversifying trend signals in the emerging-market FX space. While purely price-based strategies performed exceptionally during the EM boom and the global financial crisis of the 2000s, they struggled in the largely trendless markets of the 2010s and 2020s. By integrating macro support and headwind factors, one can construct macro-enhanced trends, i.e., signals that draw on a broader and more stable information set. Such approaches have delivered significantly higher risk-adjusted returns and sustained positive PnL even during periods of weak market trends.

(more…)

Terms of trade as trading signals

October 11, 2025

Jupyter Notebook

Terms of trade measure an economy’s ratio of export prices to import prices. Changes in these ratios drive divergences in economic performance across currency areas and influence foreign exchange and other asset market returns. Consequently, timely estimates of terms-of-trade dynamics, often derived from commodity prices, have long been valued as leading indicators by investors. Today, point-in-time versions of these measures provide a robust foundation for statistical analysis and backtesting of systematic strategies.
This post illustrates the trading value of real-time terms-of-trade changes through five simple strategies. Individually, such single-signal strategies yield only modest standalone performance ratios. However, their profit-and-loss streams are largely uncorrelated with major market benchmarks and only moderately correlated with each other. This suggests that, when applied systematically and across a broad set of markets, terms-of-trade dynamics can serve as a significant and independent source of true alpha in systematic macro trading.

(more…)

A scorecard for global equity allocation

September 27, 2025

Jupyter Notebook

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.

(more…)

Economic surprises and commodity future returns

June 28, 2025

Jupyter Notebook

Surprises in industry and construction activity are plausible predictors of short-term future returns for commodities that are heavily used in these sectors. This hypothesis can be evaluated using quantamental economic surprises, which are point-in-time differences between estimated market expectations and actual reported values of economic indicators, measured at a daily frequency.
We specifically assess the predictive power and economic value of global surprises in survey and production indicators across 32 economies. Country-level surprises are first standardised and then aggregated into global composites, weighted by each economy’s share in global industry. Since 2000, the resulting global surprise indicator has been a statistically significant predictor of returns for an industrial commodity futures basket. A simple daily trading strategy based solely on this signal would have generated material risk-adjusted returns with little correlation to global equity markets. Further hedging against non-growth-related factors, such as monetary risk proxies, can improve performance.

(more…)

Cross-country relative duration strategies with macro factors

June 7, 2025

Jupyter Notebook

Cross-country relative duration strategies take vol-adjusted fixed receiver positions in interest rate swaps of one currency area versus others. Similar to directional IRS strategies, cross-country trading factors can be based on point-in-time indicators of economic developments. The main difference is that cross-country factors are typically related to relative economic performance.
This post constructs 12 conceptual macro factors that theoretically should help predict cross-country vol-targeted duration returns. Empirically, all of them have displayed at least some modest predictive power, while their cross-correlations have been mostly modest and very diverse. Composite signals can be generated by conceptual parity or sequential machine learning methods. All of these have produced material, consistent, and uncorrelated risk-adjusted returns over the past two decades with few major drawdowns.

(more…)

Macro-aware risk parity

May 24, 2025

Jupyter Notebook

Risk parity is an investment strategy that allocates risk exposure equally across asset types through volatility-based calibration and leverage. A most profitable risk parity strategy in the past decades has been the equity-duration “long-long”, which harvests combined equity and long fixed-income risk premia, while containing return volatility through diversification. Alas, this position is vulnerable to “autonomous” monetary tightening shocks, i.e., surges in real interest rates without commensurate economic improvements.
The probability of such downside shocks depends on the macroeconomic environment. Hence, by adapting volatility-targeted risk-parity positions to economic overheating measures, strategies become macro-aware and more information-efficient. We show that simple point-in-time macro scores of excess inflation, growth, and overconfidence have predicted equity-duration returns across eight developed markets and for over three decades. Macro-aware signals have materially outperformed simple volatility-targeted positions, both in terms of absolute and risk-adjusted returns. The long-term effects of macro awareness on cumulative net asset value have been huge.

(more…)

Equity trend-following with market and macro data

May 10, 2025

Jupyter Notebook

The popularity of trend-following bears the risk of market excesses. Medium-term market price trends often fuel economic trends that eventually oppose them (”macro headwinds”). Fortunately, relevant point-in-time economic indicators can provide critical information on the sustainability of medium-term market movements and are a natural complement to standard trend signals.
This post illustrates the benefit of combining market and macro trends in equity markets. Since the 1990s, “robust” market price trend signals alone have created value for equity index future strategies in developed markets. However, risk-adjusted returns would have been enhanced materially if one adjusted market signals for natural macro headwinds, such as the state of the business cycle, inflation, and equity valuations.

(more…)

Boosting macro trading signals

April 26, 2025

Jupyter Notebook

Boosting is a machine learning ensemble method that combines the predictions of a chain of basic models, whereby each model seeks to address the shortcomings of the previous one. This post applies adaptive boosting (Adaboost) to trading signal optimisation. Signals are constructed with macro factors to guide positioning in a broad range of global FX forwards.
Boosting is beneficial for learning from a wide and heterogeneous set of markets over time, because it is well-suited for exploiting the diversity of experiences across countries and global economic states. Empirically, we generate machine learning-based signals that use regularized regression and random forest regression, and compare processes with and without adaptive boosting methods. For both regression types, machine learning prefers boosting as datasets get larger and, by doing so, creates more profitable signals.

(more…)

Quantamental economic surprise indicators: a primer

April 12, 2025

Jupyter Notebook

Quantamental economic surprises are point-in-time measures of deviations of economic indicators from expected values. There are two types of surprises: first-print events and pure revisions. First-print events feature new observation periods, and the surprise element depends on market expectations of the indicator. Market surveys can approximate such expectations, but only for a limited number of indicators. Quantamental surprises use econometric prediction models and can be calculated for all indicators and transformations, principally using the whole information state.
This post introduces economic surprises in global industry and construction and shows how they can be transformed into short-term macro trading signals for commodities. There is clear empirical evidence for the predictive power of such surprises for a basket of industrial commodity futures at a daily and weekly frequency. Related simulated PnL generation produces risk-adjusted alpha, albeit mainly in seasons of large swings in manufacturing and construction.

(more…)

Topics - Browse by tag

Research
Quantamental Academy
About Us
Macro-quantamental investment handbook