Transaction costs and portfolio strategies

Transaction costs are a key consideration for the development of trading strategies; and not just in final profitability checks. Indeed, disregard for trading costs...

Copulas and trading strategies

Reliance on linear correlation coefficients and joint normal distribution of returns in multi-asset trading strategies can be badly misleading. Such conventions often overestimate diversification...

Predicting volatility with neural networks

Predicting realized volatility is critical for trading signals and position calibration. Econometric models, such as GARCH and HAR, forecast future volatility based on past...

Statistical learning and macro trading: the basics

The rise of data science and statistical programming has made statistical learning a key force in macro trading. Beyond standard price-based trading algorithms, statistical...

How to estimate factor exposure, risk premia, and discount factors

The basic idea behind factor models is that a large range of assets’ returns can be explained by exposure to a small range of...

Classifying market regimes

Market regimes are clusters of persistent market conditions. They affect the relevance of investment factors and the success of trading strategies. The practical challenge...

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...

Ten things investors should know about nowcasting

Nowcasting in financial markets is mainly about forecasting forthcoming data reports, particularly GDP releases. However, nowcasting models are more versatile and can be used...

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...

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Identifying the drivers of the commodity market

Commodity futures returns are correlated across many different raw materials and products. Research has identified various types of factors behind this commonality: macroeconomic...

Macro factors of the risk-parity trade

Risk-parity positioning in equity and (fixed income) duration has been a popular and successful investment strategy in past decades. However, part of that success...

Identifying market regimes via asset class correlations

A recent paper suggests identifying financial market regimes through the correlations of asset class returns. The basic idea is to calculate correlation matrixes for...

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