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

Expert Systems with Applications, Volume 189, 1 March 2022, 116093

Posted: 27 Apr 2020 Last revised: 11 Nov 2021

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 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.

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). Expert Systems with Applications, Volume 189, 1 March 2022, 116093, 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

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
1,140
PlumX Metrics