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Register a new userMaximum drawdown, recovery, and momentum
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Authors
Jaehyung Choi
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We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directions of asset prices but also in capturing cross-sectional return differentials. In monthly periods, the alternative portfolios ranked by maximum drawdown measures exhibit outperformance over other alternative momentum portfolios including traditional cumulative return-based momentum portfolios. In weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. For the alternative portfolios and their ranking baskets, improved risk profiles in various reward-risk measures also imply more consistent prediction on the direction of assets in future. In the Carhart four-factor analysis, higher factor-neutral intercepts for the alternative strategies are another evidence for the robust prediction by the alternative stock selection rules.
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We test the price momentum effect in the Korean stock markets under the momentum universe shrinkage to subuniverses of the KOSPI 200. Performance of the momentum strategy is not homogeneous with respect to change of the momentum universe. It is found that some submarkets generate the higher momentum returns than other universes do but large-size companies such as the KOSPI 50 components hinder the performance of the momentum strategy. The observation is also cross-checked with size portfolios and liquidity portfolios. Transactions by investor groups, in particular, the trading patterns by foreign investors can be a source of the momentum universe shrinkage effect in the momentum returns.
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The total duration of drawdowns is shown to provide a moment-free, unbiased, efficient and robust estimator of Sharpe ratios both for Gaussian and heavy-tailed price returns. We then use this quantity to infer an analytic expression of the bias of moment-based Sharpe ratio estimators as a function of the return distribution tail exponent. The heterogeneity of tail exponents at any given time among assets implies that our new method yields significantly different asset rankings than those of moment-based methods, especially in periods large volatility. This is fully confirmed by using 20 years of historical data on 3449 liquid US equities.
This paper presents probability distributions for price and returns random processes for averaging time interval {Delta}. These probabilities determine properties of price and returns volatility. We define statistical moments for price and returns random processes as functions of the costs and the volumes of market trades aggregated during interval {Delta}. These sets of statistical moments determine characteristic functionals for price and returns probability distributions. Volatilities are described by first two statistical moments. Second statistical moments are described by functions of second degree of the cost and the volumes of market trades aggregated during interval {Delta}. We present price and returns volatilities as functions of number of trades and second degree costs and volumes of market trades aggregated during interval {Delta}. These expressions support numerous results on correlations between returns volatility, number of trades and the volume of market transactions. Forecasting the price and returns volatilities depend on modeling the second degree of the costs and the volumes of market trades aggregated during interval {Delta}. Second degree market trades impact second degree of macro variables and expectations. Description of the second degree market trades, macro variables and expectations doubles the complexity of the current macroeconomic and financial theory.
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