Macro trends for trading models

Unlike market price trends, macroeconomic trends are hard to track in real-time. Conventional econometric models are immutable and not backtestable for algorithmic trading. That...

Machine learning for portfolio diversification

Dimension reduction methods of machine learning are suited for detecting latent factors of a broad set of asset prices. These factors can then be...

Statistical arbitrage risk premium

Any asset can use a portfolio of similar assets to hedge against its factor exposure. The factor residual risk of the hedged position is...

Classifying market states

Typically, we cannot predict a meaningful portion of daily or higher-frequency market returns. A more realistic approach is classifying the state of the market...

Real-time growth estimation with reinforcement learning

Survey data and asset prices can be combined to estimate high-frequency growth expectations. This is a specific form of nowcasting that implicitly captures all...

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’...

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,...

Predicting volatility with heterogeneous autoregressive models

Heterogeneous autoregressive models of realized volatility have become a popular standard in financial market research. They use high-frequency volatility measures and the assumption that...

The predictive power score

The predictive power score is a summary metric for predictive relations between data series. Like correlation, it is suitable for quick data exploration. Unlike...

RECENT ARTICLES

Measuring the value-added of algorithmic trading strategies

Standard performance statistics are insufficient and potentially misleading for evaluating algorithmic trading strategies. Metrics based on prediction errors mistakenly assume that all errors matter...

The emotion beta of stocks

Stock markets cater to both the financial and emotional needs of investors. In particular, integral emotions, which are caused by decisions themselves, are useful...

Risk premia in energy futures markets

Energy futures markets allow transferring risk from producers or consumers to financial investors. According to the hedging pressure hypothesis, net shorts of industrial producers...

POPULAR ARTICLES