How to fly to safety without overpaying for the ticket

Kaczmarek Tomasz () and Grobelny Przemysław ()
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Kaczmarek Tomasz: Department of Investment and Financial Markets, Institute of Finance, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland
Grobelny Przemysław: Department of Investment and Financial Markets, Institute of Finance, Poznań University of Economics and Business, al. Niepodległości 10, 61-875 Poznań, Poland

Economics and Business Review, 2023, vol. 9, issue 2, 160-183

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%).

Keywords: asset allocation strategy; target volatility; flight-to-safety; recurrent neural networks; machine learning (search for similar items in EconPapers)
JEL-codes: C32 C45 C58 G11 G15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecobur:v:9:y:2023:i:2:p:160-183:n:2

DOI: 10.18559/ebr.2023.2.738

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