Debt-weighted exchange rates

Trade-weighted exchange rates help assessing the impact of past currency depreciation on economic growth through the external trade channel. Debt-weighted exchange rates help assessing the impact of past currency depreciation on economic growth through the financial channel. Since these effects are usually opposite looking at both simultaneously is crucial for using exchange rate changes as a predictor of economic and local market performance. For example, as a consequence of the financial channel many EM economies fail to benefit from currency depreciation in the way that small developed economies do.

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Using SVAR for macro trading strategies

Structural vector autoregression may be the most practical model class for empirical macroeconomics. Yet, it can also be employed for macro trading strategies, because it helps identifying specific market and macro shocks. For example, SVAR can identify short-term policy, growth or inflation expectation shocks. Once a shock is identified it can be used for trading in two ways. First, one can compare the type of shock implied by markets with the actual news flow and detect fundamental inconsistencies. Second, different types of shocks may entail different types of subsequent asset price dynamics and may, hence, be a basis for systematic strategies.

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Improving asset return forecasts with wavelets

Time series that are used for forecasting asset returns can carry information on trends of different persistence. Therefore, frequency decomposition of standard signals based on wavelets can improve and expand potential predictors. Similarly, asset returns can be decomposed into parts of different persistence. These can be forecast separately and summed up eventually. This “sum-of-parts” method seems to improve forecast accuracy because its aligns predictors and return trends and helps separating signal from noise.

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The drivers of commodity cycles

Demand shocks have been the dominant force behind non-oil commodity price cycles, according to a 145-year empirical analysis. They have been linked to global recessions or recoveries and displayed persistent effects of 10 years or more. The second most import driver has been so-called “inventory shocks”, which have been less long-lived. Supply shocks have not played an important role in driving long-term price deviations of most commodities. They were mostly commodity-specific and transient.

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Inflation differentials and equity returns

Inflation differentials and equity returns

There is evidence that equity markets fail to adjust to persistent cross-country shifts in inflation in a timely and efficient manner. While equity investors focus on tracking firm-specific price effects and cash flows, they seem to pay less attention to aggregate local inflation and appear sluggish in adjusting long-term discount factors across countries. Since equity is a long duration asset even small calibration errors in discount factors have a large impact. Empirically, real equity returns in lower-inflation markets tend to outperform those in higher-inflation markets. No such effect can be found in fixed income markets.

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Policy rates and equity returns: the “slope factor”

A long-term empirical analysis suggests that faster expected monetary policy tightening in future months leads equity market underperformance. The predictive factor can be modelled as a change in the slope in future implied future policy rates. It has had a meaningful and consistent effect on weekly U.S. equity returns for more than 25 years. Faster future policy tightening can mean either that the central bank has become more hawkish or that it has acted dovishly but thereby fallen behind the curve.

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Term premia in the times of “lift-off”

Equilibrium models suggest that as long as the policy rate is firmly near zero, the term premium on longer-dated yields is compressed by a reduced sensitivity of rates to economic change. However, when policy rates are on the move again this sensitivity recovers, while proximity of the zero lower bound implies high economic risks and a surcharge on the term premia. Hence, term premium uncertainty would be highest at the time of “lift-off”, when policy rates are expected to move upward from near zero.

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The irrational neglect of optimal betting strategies

A recent betting experiment among finance students and professionals based on biased coin flipping revealed a wide gap between rational and actual behavior. The optimal strategy, which would have been constant and moderate risk taking (“Kelly criterion”), was not widely applied, notwithstanding education and training in finance. Instead, the experiment revealed a range of common behavioral biases. It challenges the general assumption of rational decision-making of finance professionals under uncertainty.

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The importance of statistical programming for investment managers

Almost every portfolio manager uses some form of quantitative analysis. Most still rely on Excel spreadsheets, but this popular tool constrains the creativity of analysis and struggles to cope with large data sets. Statistical programming in R and Python both facilitates and widens the scope of analysis. In particular, it allows using high-frequency data, alternative data sets, textual information and machine learning. And it greatly enhances the display and presentation of analytical findings.

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The impact of U.S. economic data surprises

A new paper estimated the short-term effects of U.S. economic data surprises on treasury notes and USD exchange rates over the past 20 years. All of 21 commonly followed data releases produced highly significant surprise effects at least for parts of the sample. However, only non-farm payrolls produced a consistently highly significant impact. After short-term interest rates reached the zero lower bound, the importance of surprises to CPI inflation, housing indicators and weekly jobless claims increased noticeably, possibly related to the Fed’s struggle with its dual mandate.

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