Optimal Timing Trading Strategy

本文基于数据驱动的统计套利策略，以收益指标累计回报和风险效率指标年化Sharpe比率作为策略优劣的度量，分别考虑了均线策略、月初买入月末卖出策略、涨跌买卖策略以及一直持有四种交易策略的优劣。研究发现：均线策略中，“短期移动均线上穿长期移动均线时买入，长期移动均线上穿短期移动均线时卖出”方法对于不同交易频率、有无交易成本、个股或者大盘股这类波动较大的资产均能获得正向收益且风险效率较高；四种策略中，均线策略最优，月初买入月末卖出策略次之，然后是一直持有策略，涨跌买卖策略最差。本文还发现，经济基本面与股市收盘价表现显著正相关。通过对大盘股和个股进行不同策略的建模及量化分析，本文论证了均线策略最优，且它对于波动较大的资产有一定通用性和可靠性，试图为辅助交易和自动化做市提供操作依据。Based on the data-driven statistical arbitrage strategy, this paper considers the advantages and disadvantages of several trading strategies, namely the mean strategy, buy at the beginning of the month and sell at the end of the month strategy, buy and sell strategy, and hold all the time strategy, using the cumulative return, an income metric, and the annualized Sharpe ratio, a risk efficiency metric as measures of strategy’s advantages and disadvantages. We find that among the strategy of moving average (SMA), “buy when the short-term moving average crosses the long-term and sell when the long-term moving average crosses the short-term” can achieve positive returns and high-risk efficiency for assets with different trading frequencies, with or without transaction costs, and for volatile assets such as individual stocks or large-cap stocks. Meanwhile, among several strategies, the moving average strategy is the best, followed by buying at the beginning of the month and selling at the end of the month, then holding all the time, and buying and selling at the end of the month is the worst. Finally, this paper also finds that economic fundamentals are significantly positively correlated with stock market closing price performance. Therefore, by modeling and quantitatively analyzing different strategies for large-cap and individual stocks, this paper argues that the SMA strategy is optimal and that it is versatile and reliable for volatile assets, in an attempt to provide an operational basis for assisted trading and automated market making.