Investment Factor Timing: Harvesting The Low-Risk Anomaly Using Artificial Neural Networks

35 Pages Posted: 27 Apr 2020 Last revised: 5 May 2020

See all articles by Philipp Dirkx

Philipp Dirkx

Zeppelin University; ODDO BHF Group

Thomas Heil

Zeppelin Universität

Date Written: April 18, 2020

Abstract

We perform investment factor timing based on risk forecasts in reference to 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 to set up an investment strategy. The strategy succeeds in differentiating positive from negative yielding factors and an accordingly constructed investment strategy outperforms every factor individually as well as a GARCH benchmark model.

Keywords: Factor Investing, Neural Networks, LSTM, Low-Risk Anomaly

JEL Classification: C58, C63, E37, G11, G17

Suggested Citation

Dirkx, Philipp and Heil, Thomas, Investment Factor Timing: Harvesting The Low-Risk Anomaly Using Artificial Neural Networks (April 18, 2020). Available at SSRN: https://ssrn.com/abstract=3579333 or http://dx.doi.org/10.2139/ssrn.3579333

Philipp Dirkx (Contact Author)

Zeppelin University ( email )

Am Seemooser Horn 20
Friedrichshafen, Lake Constance 88045
Germany

ODDO BHF Group ( email )

Boulevard de la Madeleine 12
Paris, 75440
France

Thomas Heil

Zeppelin Universität ( email )

Am Seemooser Horn 20
DE-88045 Friedrichshafen
Germany

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