Terms of trade as trading signals

October 11, 2025

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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.

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A scorecard for global equity allocation

September 27, 2025

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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.

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The (hidden) trading value of central bank liquidity information

August 30, 2025

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Central banks regularly adjust the economy’s monetary base through foreign exchange interventions and open market operations. Point-in-time information on such intervention-based liquidity expansion has predictive power for asset returns. That is because such operations often come in longer-term trends, and there are lagged effects, for example, through private sector portfolio rebalancing. Alas, the discovery of the economic value of intervention-liquidity signals is often obscured by “ugly backtests”: as a single type of information applied to a single asset type, simulations typically show patchy relevance and uneven value profiles. However, across strategy types and asset classes, intervention-driven liquidity growth has consistently contributed value. A simulation combining two directional and two relative-value strategies demonstrates steady and meaningful PnL generation with low correlation to major market benchmarks.
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Systematic equity allocation across countries for dollar-based investors

August 16, 2025

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This post demonstrates that country allocation with macroeconomic factors can materially enhance the returns on international equity portfolios in dollar terms. We identify a range of economic developments that, according to standard theory and in conjunction with market inattention, should predict the outperformance of countries either through exchange rate appreciation or higher local-currency equity returns. These developments are captured in backtestable economic factor scores, built from point-in-time macro-quantamental indicators. To select and combine these factors into trading signals without hindsight bias, we employ sequential machine learning. Empirical evidence based on 19 international markets shows highly significant predictive power and consistent material value arising from cross-country allocation alone. A simulation of U.S. dollar-based cross-country allocation of real money investment also reveals material long-term outperformance relative to an equally-weighted international portfolio or a U.S. portfolio.

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Equity trend-following with market and macro data

May 10, 2025

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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.

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Cross-country equity risk allocation with statistical learning

February 1, 2025

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Macroeconomic factors plausibly cause divergences in equity market returns across countries. Factors related to monetary policy, financial conditions, and competitiveness should all systematically help detect such divergences. In the presence of rational inattention, point-in-time indicators should also have predictive power.
We apply statistical learning to investigate the signalling value of nine candidate macro factors for cross-country excess returns within major equity sectors for a set of 12 countries. Most factors turn out to be relevant predictors. Cross-country trading signals based on point-in-time macro factors and models would have added material uncorrelated PnL value to an equity portfolio.

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Conditional short-term trend signals

January 18, 2025

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There are plausible relations between past and future short-term trends across and within financial markets. This is because market returns affect expected physical payoffs, risk premia, and the monetary policy outlook. However, the relations between past and future returns are unstable and often depend on the economic environment. As an example, this post shows that the impact of short-term commodity future trends on subsequent S&P500 future returns depends on the inflationary pressure in the U.S. economy. Empirical analysis suggests that macro-conditional trend signals outperform unconditional short-term trend signals regarding predictive power, accuracy and naïve PnL generations.

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Crowded trades and consequences

January 4, 2025

A crowded trade is a position with a high ratio of active institutional investor involvement relative to its liquidity. Crowding is a form of endogenous market risk as it arises not from contracts’ fundamentals but from the market itself. The risk of crowding has increased in past decades due to the growing share of institutional investors in the market, particularly the activity of hedge funds. Liquidations of crowded positions can trigger price distortions and, in cases of self-reinforcing deleveraging, even systemic pressure.
Crowdedness can be measured by the total value of active institutional positioning in an asset relative to its trading volume. It indicates how long it would take institutions to exit their trades under normal market conditions. For U.S. stocks, these ratios can be calculated based on reported data. Crowding typically skews risk to the downside. This point has been proven empirically for the U.S. equity market. However, crowdedness should also command excess premia. Historically, crowded stocks have outperformed non-crowded stocks materially and with high statistical significance.

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Statistical learning for sectoral equity allocation

September 4, 2024

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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

August 17, 2024

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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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