New Forthcoming Publication in the ESWA

"Investment factor timing: Harvesting the low-risk anomaly using artificial neural networks''. (Philipp A. Dirkx and Thomas L.A.Heil)


We perform investment factor timing based on risk forecasts exploiting the low-risk anomaly. Among various

risk measures, we find downside deviation most suited for this task. We apply Long Short Term Memory

Artificial Neural Networks (LSTM ANNs) to model the relationship between macro-economic as well as financial

market data and the downside deviation of factors. The LSTM ANNs allow for complex, non-linear long-term

dependencies. We use LSTM-based forecasts to select high- and low-risk factors in setting up an investment

strategy. The strategy succeeds in differentiating positive from negative yielding factor investments, and an

accordingly constructed investment strategy outperforms every factor individually as well as LASSO and

Multilayer Perceptron neural network benchmark models.

The paper can be downloaded the next 30 days for free via the share link: https://authors.elsevier.com/a/1e2Of3PiGTI0K3

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