Measures of economic uncertainty help investors to track popular fear or complacency for the purpose of trading strategies. Academic papers propose various methods: keyword frequencies in news, equity market volatility, earnings forecast dispersion and economic forecast disagreements. Composite measures suggest that uncertainty typically rises abruptly but declines just gradually.
Two recent papers provide useful techniques for “nowcasting” EM economies. The first uses “dynamic factor models” with high frequency indicators to estimate GDP growth in countries with scant and noisy data. The second uses seasonal adjustment with modifications for time-varying holidays that can track underlying trends in China and other countries with lunar year and Islamic holidays.
A two-decade empirical study shows that bond and equity market prices are more likely to “jump” on days with U.S. economic data releases. In particular, surprises in news announcements tend to lead to higher volatility and larger price moves. The impact of key data surprises on bond markets seems clearer and simpler. The impact on equity markets depends on the state of the business cycle
A common fallacy is that current account deficits measure dependence on external financing. In reality, external balances and cross border financing are only vaguely related. Vulnerability to “stops” in financial flows does not depend on trade and capital flows (“net concept”) but only on the volume and origin of financing (“gross concept”). Currency crises are not about current accounts that need to adjust, but about funding gaps that need to be closed.
A Banca d’ Italia paper reminds us that the market impact of economic data surprises depends on the state of the economy and forecast diversity. In particular, the surprise impact tends to be greater, when predictions are tightly clustered around a ‘consensus’. Conversely, uncertainty seems to help preparing markets for shocks.
A new IJCB article shows that historically [i] inflation expectations had a strong impact on long-term yields and [ii] economic data surprises had a strong impact on inflation expectations. However, the influence of compensation for inflation and inflation risk on U.S. bond yields has faded in the era of non-conventional monetary policy.
A new paper estimates that U.S. economic data explain more than a third of bond price fluctuations on a quarterly basis. The economic data impact on daily fluctuations is much weaker. It grows with the time horizon because economic factors are more persistent than non-fundamental factors. The simple powerful message is that economic news flow is crucial (and probably underestimated) for identifying market trends.
U.S. weekly jobless claims are a key early indicator for the U.S. economy and global financial markets. A new Kansas City Fed paper suggests that to use these data efficiently one should first estimate a time varying benchmark for the “neutral level” of claims. Claims above (below) the benchmark would indicate deterioration (improvement) of the U.S. labor market.
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.
