Portfolio rebalancing based on time series momentum and downside risk

Date
2021-11-13
Authors
Guo, Xiaoshi
Ryan, Sarah
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Oxford University Press
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Industrial and Manufacturing Systems Engineering
Abstract
To examine the familiar tradeoff between risk and return in financial investments, we use a rolling two-stage stochastic program to compare mean-risk optimization models with time series momentum strategies. In a backtest of allocating investment between a market index and a risk-free asset, we generate scenarios of future return according to a momentum-based stochastic process model. A new hybrid approach, time series momentum strategy controlling downside risk (TSMDR), frequently dominates traditional approaches by generating trading signals according to a modified momentum measure while setting the risky asset position to control the conditional value-at-risk (CVaR) of return. For insight into the outperformance of TSMDR, we decompose each strategy into two aspects, the trading signal and the asset allocation model that determines the risky asset position. We find that 1) weighted moving average can better capture the trend of the stock market than time series momentum computed as past 12-month excess return, 2) mean-risk strategies generally provide better returns whereas risk parity strategies have less investment risk and 3) controlling CVaR limits the investment risk better than controlling variance does.
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This article has been published in IMA Journal of Management Mathematics. DOI: 10.1093/imaman/dpab037. Copyright 2021 The Author(s). Attribution 4.0 International (CC BY 4.0). Published by Oxford University Press. Posted with permission.
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time series momentum, mean-risk, risk parity, conditional value-at-risk, stochastic programming, scenario generation
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