Maximum drawdown is also used in the definitions of the Calmar ratio and the Sterling ratio to assess the performance and risk of mutual funds and hedge funds. One of the most popular risk measures in practice is maximum drawdown defined as the worst cumulative loss in a given period. These results are also consistent with the low-volatility anomaly ( Baker et al. 2021) that penalizes riskier assets and rewards less riskier assets in portfolio construction also delivers more advanced performance and risk management under less complicated portfolio construction methods. Similarly, diversified reward-risk parity ( Choi et al. Regardless of asset class and market, it was found that the alternative strategies ranked by the CTS reward-risk measures exhibit improved profitability and reward-risk profiles. ( 2007), reward-risk momentum strategies using classical tempered stable (CTS) distributions were implemented in various asset classes ( Choi et al. As an extension of the work by Rachev et al. It is also noteworthy that their alternative portfolios were less risky with thinner downside tails. It was reported that the tail behavior and risk can predict future price directions of equities in the S&P 500 component universe. ( 2007) paid attention to reward-risk measures such as Sharpe ratio, STAR ratio, and Rachev ratio. Testing the 52-week low price as a selection rule is not the only approach for incorporating downside risks into the momentum-style portfolio construction process. 2000), symmetry breaking of arbitrage ( Choi 2012), and transaction cost ( Lesmond et al. 2012 Okunev and White 2003 Rouwenhorst 1998, 1999), it is still not fully explicable with numerous alternative explanations on the anomaly such as autocorrelation and cross-sectional correlation ( Lewellen 2002 Lo and MacKinlay 1990), sector momentum ( Moskowitz and Grinblatt 1999), investors’ behavioral aspects to news dissemination ( Barberis et al. 2013 Erb and Harvey 2006 Moskowitz et al. Although the price momentum has been found in many asset classes and markets ( Asness et al. Among various market inefficiencies, price momentum ( Jegadeesh and Titman 1993) is one of the most well-known market anomalies that are not explained by the Fama-French three-factor model ( Fama and French 1993, 1996). Empirically existent systematic arbitrages are not only experimental counter-examples to the efficient market hypothesis proposed by Fama ( 1965) and Samuelson ( 1965), but also the sources of lucrative trading strategies to the practitioners. Seeking statistical arbitrages in financial markets is the most important task to both academics and practitioners in finance. 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. Moreover, turnover rates of these momentum/contrarian portfolios are also reduced with respect to the benchmark portfolios. 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 weekly time scales, recovery-related stock selection rules are the best ranking criteria for detecting mean-reversion. 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 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. We empirically test predictability on asset price using stock selection rules based on maximum drawdown and its consecutive recovery.
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