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講座預(yù)告 | 李有偉：動(dòng)量效應(yīng)

天津大學(xué)管理與經(jīng)濟(jì)學(xué)部

2021-06-17 16:42 瀏覽量: 2963

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動(dòng)量效應(yīng) 講座時(shí)間：6月21日（周一）17：00 參與方式：騰訊會(huì)議：108230092 主講人：李有偉 01主講人介紹李有偉教授目前就職于赫爾大學(xué)商學(xué)院會(huì)計(jì)與金融系，目前擔(dān)任 02講座...

動(dòng)量效應(yīng)

講座時(shí)間：6月21日（周一）17：00

參與方式：騰訊會(huì)議：108230092

主講人：李有偉

01主講人介紹

李有偉教授目前就職于赫爾大學(xué)商學(xué)院會(huì)計(jì)與金融系，目前擔(dān)任

02講座內(nèi)容

本報(bào)告從時(shí)間序列和橫截面兩個(gè)維度、從理論和實(shí)證兩個(gè)角度討論動(dòng)量效應(yīng)。本研究對(duì)橫截面動(dòng)量異象的大量競(jìng)爭(zhēng)性解釋給出了綜合評(píng)價(jià)和排序，也討論了“動(dòng)量崩潰”（動(dòng)量策略在某一時(shí)期會(huì)表現(xiàn)出顯著的下行風(fēng)險(xiǎn)）。研究發(fā)現(xiàn)，動(dòng)量策略的高度不確定性來(lái)源于個(gè)股的橫截面波動(dòng)性。在形成期內(nèi)已實(shí)現(xiàn)波動(dòng)率高的股票往往不具有動(dòng)量效應(yīng)，而已實(shí)現(xiàn)波動(dòng)率低的股票則表現(xiàn)出強(qiáng)勁的動(dòng)量效應(yīng)。本研究提出了一種新的方法：廣義風(fēng)險(xiǎn)調(diào)整動(dòng)量（GRJMOM），以緩解高動(dòng)量風(fēng)險(xiǎn)的負(fù)面影響。在包括英國(guó)股票、大宗商品、全球股票指數(shù)和固定收益證券在內(nèi)的多種資產(chǎn)類(lèi)別中，GRJMOM被證明比現(xiàn)有的動(dòng)量排序方法盈利更高，風(fēng)險(xiǎn)更低。最后，對(duì)因子動(dòng)量效應(yīng)進(jìn)行了檢驗(yàn)?；?0個(gè)因子的數(shù)據(jù)集，發(fā)現(xiàn)因子動(dòng)量效應(yīng)總體較弱。6個(gè)因素表現(xiàn)出強(qiáng)勁的收益延續(xù)性，并主導(dǎo)了因子動(dòng)量投資組合，而其余14個(gè)因素則沒(méi)有。因子的選擇影響因子動(dòng)量解釋個(gè)股動(dòng)量的能力。本研究對(duì)包括中國(guó)股票市場(chǎng)和期貨市場(chǎng)、美國(guó)股票市場(chǎng)和國(guó)際大宗商品市場(chǎng)等大量的市場(chǎng)進(jìn)行了實(shí)證分析。

This talk discusses momentum from both a time-series and cross section dimension, and from both a theoretical and empirical perspective. It comprehensively evaluates and ranks a large number of competing explanations for the cross-section momentum anomaly. It also discusses "momentum crashes," momentum strategies exhibit significant downside risks over certain periods. It is found that the high uncertainty of momentum strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realized volatility over the formation period tend to lose momentum effect, while stocks with low realized volatility show strong momentum. A new approach, generalized risk-adjusted momentum (GRJMOM), is introduced to mitigate the negative impact of high momentum risks. GRJMOM is proven to be more profitable and less risky than the existing momentum ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets. Finally, it examines factor momentum. Based on a dataset covering 20 factors, it is found that the factor momentum effect is weak in general. Six factors exhibit strong return continuation and dominate the factor momentum portfolio, while the remaining 14 factors do not. The choice of factors affects the ability of factor momentum to explain individual stock momentum. The underlying empirical analysis of the talk covers a wide range of markets including Chinese stock markets and futures markets, US stock market and international commodity markets among others.