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A statistical learning workflow for macro trading strategies

Statistical learning for macro trading involves model training, model validation and learning method testing. A simple workflow determines form and parameters of trading...

The basics of low-risk strategies

Low-risk investment strategies prefer leveraged low-risk assets over high-risk assets. The measure of risk can be based on price statistics, such as volatility and...

How loss aversion increases market volatility and predicts returns

Loss aversion means that people are more sensitive to losses than to gains. This asymmetry is backed by ample experimental evidence. Loss aversion is...

Reward-risk timing

Reward-risk timing refers to methods for allocating between a risky market index and a risk-free asset. It is a combination of reward timing, based...

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,...

Detecting market price distortions with neural networks

Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at...

Systemic risk under non-conventional monetary policy

Central bank operations in the form of quantitative easing, qualitative easing, forward guidance and collateral policies wield great influence over market prices of risk....

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...

Machine learning and macro trading strategies

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or...

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...

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Estimating the positioning of trend followers

There is a simple method of approximating trend follower positioning in real-time and without lag. It is based on normalized returns in liquid futures...

Forecasting energy markets with macro data

Recent academic papers illustrate how macroeconomic data support predictions of energy market flows and prices. Valid macro indicators include shipping costs, industrial production measures,...

Fundamental trend following

Fundamental trend following uses moving averages of past fundamental data, such as valuation metrics or economic indicators, to predict future fundamentals, analogously to the...

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