Search

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.

Stochastic programming models for portfolio optimization rely on scenario paths for returns derived from stochastic process models. This paper investigates a variant of the geometric Brownian motion process for stock index returns that incorporates index momentum. Based on this model, three different processes for generating scenarios on a rolling basis are devised, which differ according to how frequently the momentum parameter is updated and whether it is estimated according to a simple moving average or an exponentially weighted moving average of returns. The reliability of scenario sets generated for multiple instances is assessed by applying a recently developed statistical tool. Backtesting is conducted in case studies of the Standard & Poor's 500 Index and the Financial Times Stock Exchange 100 Index for two different historical periods. The numerical results show that the frequency with which the expected return is updated does not significantly influence the performance of the scenario generation procedure, whereas how the expected return is calculated affects the autocorrelation and dispersion of generated scenarios drastically. All scenario generation schemes are highly sensitive to the estimated volatility of the index returns. Among the three processes tested, the algorithms that incorporate momentum estimates according to an exponentially weighted moving average can generate reliable scenarios when the volatility estimation error is small.

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 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.

Background: Intrauterine adhesion seriously affects reproductive health in women. Hysteroscopic adhesiolysis using cold scissors or electrosurgery is the main treatment, although there is no consensus on the preferable method. This review aimed to compare the efficacy and safety of these methods for treating moderate to severe intrauterine adhesion. Methods: PubMed, EMBASE, MEDLINE, Cochrane Database of Systematic Reviews, Web of Science, Chinese Biomedical Literature Database, and China National Knowledge Infrastructure were searched on April 30, 2020. Randomized controlled trials and observational studies that were published in all languages (must contain English abstracts) and compared hysteroscopic cold scissors with electrosurgery for the treatment of intrauterine adhesion were included. Mean differences, odds ratios, and 95% confidence intervals (CIs) were reported. Bias was evaluated using the Cochrane Risk of Bias assessment tool for randomized controlled trials and the Newcastle-Ottawa Scale for observational studies. Data were analyzed using RevMan software (Review Manager version 5.3, The Cochrane Collaboration, 2014). Two researchers independently extracted data and assessed the quality of the included studies. If a consensus was not reached, a third researcher was consulted. Results: Nine studies (n = 761; 6 randomized controlled trials and 3 retrospective studies) were included. The intrauterine adhesion recurrence rate with second look hysteroscopy was significantly lower (odds ratio = 0.30, 95% CI = 0.16–0.56; P = .0002) with hysteroscopic cold scissors than with electrosurgery. The total operation time was significantly shorter (mean difference = –7.78, 95% confidence interval = –8.50 to –7.07; P