Nowcasting for financial markets

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits,...

Joint predictability of FX and bond returns

When macroeconomic conditions change rational inattention and cognitive frictions plausibly prevent markets from adjusting expectations for futures interest rates immediately and fully. This is...

Lagged correlation between asset prices

Efficient market theory assumes that all market prices incorporate all information at the same time. Realistically, different market segments focus on different news flows,...

Tracking investor expectations with ETF data

Retail investors' return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate...

The q-factor model for equity returns

Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital...

The predictive superiority of ensemble methods for CDS spreads

Through 'R' and 'Python' one can apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as...

Basic factor investment for bonds

Popular factors for government bond investment are “carry”, “momentum”, “value” and “defensive”. “Carry” depends on the steepness of the yield curve, which to some...

A method for de-trending asset prices

Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also...

Tradable economics

Tradable economics is a technology for building systematic trading strategies based on economic data. Economic data are statistics that - unlike market prices -...

Reinforcement learning and its potential for trading systems

In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being...

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R tidyverse for macro trading research

The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because it...

Nowcasting with MIDAS regressions

Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and...

Market-implied macro shocks

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’...

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