<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&amp;P500 index by 43% (115%).</p>
</abstract>

For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However, what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries. The strategy is driven by a state-of-the-art recurrent neural network (RNN) that predicts next-day market volatility. An analysis conducted over a twelve year out-of-sample period found that with DTVEBA an investor may reduce treasury allocation by two (three) times to get the same Sharpe (Calmar) ratio and overper-forms the S&P500 index by 43% (115%).

How to fly to safety without overpaying for the ticket

Department of Investment and Financial Markets, Institute of Finance

Poznań University of Economics and Business

Economics and Business Review

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"How to fly to safety without overpaying for the ticket"}

For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly...

How to fly to safety without overpaying for the ticket

Publié en ligne: 26 juil. 2023

Pages: 160 - 183

Reçu: 15 mars 2023

Accepté: 16 juin 2023

DOI: https://doi.org/10.18559/ebr.2023.2.738

Mots clés
asset allocation strategy, target volatility, flight-to-safety, recurrent neural networks, machine learning

© 2023 Tomasz Kaczmarek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

How to fly to safety without overpaying for the ticket

Publié en ligne: 26 juil. 2023

Pages: 160 - 183

Reçu: 15 mars 2023

Accepté: 16 juin 2023

DOI: https://doi.org/10.18559/ebr.2023.2.738

Mots clésasset allocation strategy, target volatility, flight-to-safety, recurrent neural networks, machine learning

© 2023 Tomasz Kaczmarek et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
asset allocation strategy, target volatility, flight-to-safety, recurrent neural networks, machine learning