Systematic Value Archives

Economic surprises and commodity future returns

June 28, 2025

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…)

EM sovereign bond allocation with macro risk premium scores

March 29, 2025

Jupyter Notebook

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.

(more…)

Macro trading signal optimization: basic statistical learning methods

March 15, 2025

Jupyter Notebook

A key task of macro strategy development is condensing candidate factors into a single positioning signal. Statistical learning offers methods for selecting factors, combining them to a return prediction, and classifying the market state. These methods efficiently incorporate diverse information sets and allow running realistic backtests.
This post applies sequential statistical learning to optimal signal generation for interest rate swap positions. Sequential methods update, estimate, and select models over time, adapting to growing development data sets, and apply signals based on the latest optimal model each month. These methods require intelligent choices on model versions, hyperparameters, cross-validation splitters, and model quality criteria. Sequential statistical learning has generally done a good job in discarding irrelevant information and has produced greater accuracy and higher risk-adjusted returns than simple factor averages.

(more…)

Global FX management with systematic macro scores

March 1, 2025

Jupyter Notebook

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.

(more…)

Classifying credit markets with macro factors

February 15, 2025

Jupyter Notebook

Macro credit trades can be implemented through CDS indices. Due to obligors’ default option, long credit positions typically feature a positive mean and negative skew of returns. At the macro level, downside skew is reinforced by fragile liquidity and the potential for escalating credit crises. To enhance performance and create a chance to contain drawdowns, credit markets can be classified based on point-in-time macro factors, such as bank lending surveys, private credit dynamics, real estate price growth, business confidence dynamics, real interest rates, and credit spread dynamics. These factors support statistical learning processes that sequentially select and apply versions of four popular classification methods: naive Bayes, logistic regression, nearest neighbours, and random forest.
With only two decades and four liquid markets of CDS index trading, empirical results are still tentative. Yet they suggest that machine learning classification can detect the medium-term bias of returns and produce good monthly accuracy and balanced accuracy ratios. The random forest method stands out regarding predictive power and economic value generation.

(more…)

Economic surprises and commodity future returns

Cross-country relative duration strategies with macro factors

Macro-aware risk parity

Equity trend-following with market and macro data

Boosting macro trading signals

Quantamental economic surprise indicators: a primer

EM sovereign bond allocation with macro risk premium scores

Macro trading signal optimization: basic statistical learning methods

Global FX management with systematic macro scores

Classifying credit markets with macro factors

Tags

ABOUT US

FOLLOW US

LEGAL