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Value and momentum recently: analysis of quantitative investment strategy
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| 3.84 MB | Adobe PDF |
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Both momentum and value strategies earn consistent and
significant premia and are negatively correlated, with their equal weight combination improving the risk-return trade-off. This paper
shows that allocation based on market volatility further improves
the risk-return trade-off, particularly by limiting the large
drawdowns momentum experiences in market crashes, where value
tends to perform better. Both long-short strategy legs achieve
comparably low Sharpe ratios in the past 20 years. There is no clear
picture of high momentum stocks performing better than their low
momentum counterparts, similar for value, which seems to off-set
the long-short returns, while the long legs perform comparably
well.
The group report tests the combination of five different sub strategies, resembling the performance of a multi-strategy hedge
fund benchmarked against the popular buy-and-hold S&P 500
investing approach. The sub-strategies are: residual momentum,
value including intangibles, value and momentum, volatility
forecasting, and a long short-term memory strategy, the latter two
being machine-learning-based, and all investing in the U.S.
universe. The combined strategy’s performance is analyzed by
three weighting schemes: equal-weight, momentum, and mean variance, resulting in a gamut of robustness and performance. The
combined strategies reap diversification benefits, thereby giving
investors a superior risk-reward trade-off compared to the buy-and hold S&P 500 approach
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Systematic trading strategy Momentum Value Volatility United States Python Quantitative trading strategy