Trend following with momentum versus moving averages: a tale of differences

Despite the ever-growing interest in trend following and a series of publications in academic journals, there is a dearth of theoretical results on the properties of trend-following rules. Our paper fills this gap by comparing and contrasting the two most popular trend-following rules, the momentum (MOM) and moving average (MA) rules, from a theoretical perspective. We provide theoretical results on the similarity between different trend-following rules and the forecast accuracy of trading rules. Our results show that the similarity between the MOM and MA rules is high and increases with the strength of the trend. However, compared to the MOM rule, the MA rules exhibit more robust forecast accuracy for the future direction of price trends. In this paper, we also develop a hypothesis about uncertain market dynamics. We show that this hypothesis, coupled with our analytical results, has far-reaching practical implications and can explain a number of empirical observations. Among other things, our hypothesis explains why the empirical performance of the MA rules is better than that of the MOM rule. We broaden the appeal and practical importance of our theoretical results by offering various illustrations and real-world examples.     

Re-examining differences between momentum and time series momentum among individual stocks

In this paper, we provide a comprehensive comparison of momentum (CSMOM) and time series momentum (TSMOM) among individual stocks in the US stock market from January 1964 to December 2015 and find that the two are distinct among individual stocks. With zero-cost strategies on both of them, we find that the explanations in Goyal and Jegadeesh (2018) cannot fully explain the differences between the performances of CSMOM strategies and TSMOM strategies.     

Stock returns after major price shocks: The impact of information

This paper focuses on stocks that experience major price changes. Using analyst reports as a proxy, I find that price events accompanied by information are followed by drift, while no-information ones result in reversals. One interpretation of these results is that investors underreact to news about fundamentals and overreact to other shocks that move stock prices. Consistent with this hypothesis, information-based price changes are more strongly correlated with future earnings surprises than no-information ones. Furthermore, drift exists only when the direction of the price move and of the change in analyst recommendations have the same sign. Finally, the ratio of no-information to information-based price shocks is strongly correlated with aggregate implied volatility and also forecasts momentum returns.     

区域科技金融生态系统共生与进化实证研究

基于生态学理论和金融共生理论,分析区域科技金融生态系统内生态种群共生关系及共生效应,以2010—2018年中国（内地）31个省域为研究对象,运用共生度测度模型和融合速度特征的共生进化动量模型,测度区域科技金融生态系统共生水平与共生进化动量。实证结果表明,东部地区在考察期内科技金融生态系统的共生度较高,但其共生进化呈疲软甚至恶化态势。相较而言,虽然中西部地区在考察期内科技金融生态系统共生度较低,但其共生进化呈良性上升态势。据此,提出强化区域科技金融生态系统共生与共生进化动量的对策建议。     

惯性投资策略对股票收益惯性影响的实证研究——基于证券投资基金经理投资行为

选取沪深两市2003年6月至2007年6月共55只封闭式基金与18只开放式基金及其重仓持有的126支股票为研究对象,通过构建MT指标考察中国证券投资基金经理惯性投资策略对股价惯性的影响.研究表明,中国开放式基金经理大多倾向于采取追涨杀跌的惯性投资策略,并且惯性收益随MT值的上升而上升,说明证券投资基金经理的惯性投资策略影响了重仓股股票的惯性收益.     

Profitability of technical stock trading: Has it moved from daily to intraday data?

This paper investigates how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market. When based on daily data, the profitability of 2580 technical models has steadily declined since 1960, and has been unprofitable since the early 1990s. However, when based on 30-minutes-data the same models produce an average gross return of 7.2% per year between 1983 and 2007. These results do not change substantially when trading is tested over eight subperiods. In particular, there is no clear trend of a declining profitability of technical stock trading based on 30-minutes-data. Those 25 models which performed best over the most recent subperiod produce a significantly higher gross return over the subsequent subperiod than all models. Between 2001 and 2007 the 2580 models perform worse than over the 1980s and 1990s. This result could be due to stock markets becoming recently more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.     

中国股市的惯性与反转效应研究

由于中国股市具有政策市的特征,本文在Hong和Stein(1999)模型的基础上,研究政策市背景下的中国股市的惯性与反转效应。我们的主要结果有:中国股市的反转效应相对于惯性效应要更明显一些,并且除了中期惯性与长期反转外,还存在一个超短期的惯性与短期的反转;同时,小公司股票相对大公司股票,惯性运动的趋势较弱,更容易发生反转,成交量大的股票相对于成交量小的股票,惯性运动的趋势较弱,更容易发生反转。最后通过实证分析,在中国股票市场对上述结果进行了检验,实证结果支持了我们的结论。     

改革开放以来中国经济增长动力转换的时空特征

基于中国1978~2012年的省级面板数据,采用检验后的超越对数生产函数的随机前沿模型,利用两套资本存量核算方法,对中国经济增长动力的来源及其时空特征进行分析,并对2008年金融危机的4万亿元投资政策进行了效率评价。研究结果表明,中国属于典型的投资主导型经济,资本投入是中国经济增长持续稳定的最主要来源,TFP贡献率呈现逐年下降的趋势;中国经济增长动力由改革开放初期的资本、劳动力和TFP三驾马车平衡拉动,形成了现阶段的资本投入与TFP反向角力态势;区域经济差距主要源于资本投入与TFP双重差异,但TFP差异是最重要因素;4万亿元经济刺激政策下中国经济复苏属于典型的“投资主导型复苏”,是以牺牲中国生产率为代价的,TFP在2008年后呈现断崖式下降,平均拉低中国TFP达0.23~0.32个百分点。     

基于牛市和熊市不同周期的股票市场动量效应研究

在参考国外研究方法的基础上,以周作为检验周期,将1997年6月至2001年6月的股市作为牛市,2001年6月至2005年6月的股市作为熊市,然后分别检验股市在这两个不同时期的动量效应.研究发现,赢家组合在牛市中存在着正的动量效应,输家组合在熊市中存在着负的动量效应.而牛市中的输家组合和熊市中的赢家组合都存在着价格的反转.