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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/79210


    Title: 堅韌與超額動能策略:績效與成因;Robust and Excess Momentum Strategies: Performance and Rationale
    Authors: 周賓凰
    Contributors: 國立中央大學財務金融學系
    Keywords: 無母數統計;投資策略;投資人情緒;資產定價;;nonparametric statistic;rank;trading strategies;investor sentiment;asset-pricing model.
    Date: 2019-02-21
    Issue Date: 2019-02-21 15:03:20 (UTC+8)
    Publisher: 科技部
    Abstract: 不論是個別資產或投資組合的層次,資產報酬偏離常態分配已是眾所皆知的現象。由於傳統定價理論多奠基於報酬的前二動差(亦即平均數與變異數),因此很多所謂無法為傳統定價理論所解釋的異常現象的出現也就不令人意外。眾多異常現象中, Jegadeesh and Titman (1993)的中短期動能現象是最「堅韌」的異常現象之一,尤其是動能之後又伴隨著長期反轉現象。我們認為短期動能與長期反轉共存係源於「真正」報酬的衡量問題。因為投資人會過度反應與反應不足,因此觀察到的報酬會由兩部分構成:真正報酬與偏離報酬。「偏離」報酬反映投資人的非理性反應或者是對先前非理性反應的調整或更正。本計畫提出一拆解方法,將觀察到的報酬拆成兩部分:「堅韌」與「殘差」報酬。假定每期橫斷面真實報酬呈常態,因此我們可利用各股票報酬的相對百分位數,根據每期的橫斷面平均與標準差,以常態分配反函數倒推一報酬率,稱之為「堅韌」報酬。此堅韌報酬比較不受極端報酬的影響,因此如果我們以此報酬來做為動能策略的排序依據, 所選出的贏家與輸家應比較不會受到投資人過度反應的影響,因此其績效應較傳統動能持續。我們稱此為「堅韌動能策略」。相對地,定義真實報酬與堅韌報酬之差為「殘差」報酬(或超額報酬),其係反映投資人的非理性反應。可想見,極大的正(負)殘差報酬可能反映投資人對正(負)面訊息的過度反應。因此如果我們以此報酬來界定贏家與輸家,據此所形成的動能應會有較強、較快速的反轉現象;我們稱此為「超額」或「殘差」動能策略。 本計畫將探討我們所提的方法的效度與成因,初期以美國股市樣本為對象,並將擴展至全球市場。 ;One of the most robust anomalies is the price momentum effect identified by Jegadeesh and Titman (1993) , which is followed by a long-term reversal pattern, initially documented by De Bondt and Thaler (1985, 1987). In recent years, tremendous empirical evidence indicates that investors may overreact to news with salient features, while at the same time underreact to non-salient news; consequently stock returns are overstated in the former case and understated in the latter case. In this project, I propose a method that decomposes observed stock returns into two components: robust returns and residual (or excess) returns, where the latter reflects investors misreaction or correction to previous misreaction. I assume that the cross-section of true stock returns for each month has a normal distribution. A stock's true return is recovered by plugging the percentile of the observed return back to the inverse normal distribution, with mean and variance being their sample counterparts, resulting in a number which is referred to as the "robust return." I then use the robust returns to form robust momentum strategies as in Jegadeesh and Titman (1993). As the robust returns are presumably less affected by the presence of extreme observations, we expect the performance persistence of robust momentum to be stronger. Similarly, the difference between the observed and robust returns is defined as the residual return. We expect the residual momentum constructed on the basis of residual returns to experience quicker reverse with respect to either traditional momentum or the robust momentum. Indeed, the preliminary results support my conjecture. The robust momentum persists, and experiences no return reversals. By contrast, the residual momentum experiences reversals immediately following formation. I believe the project shall provide important insight into behavioral finance literature. This 3-year project shall explore the profitability of proposed strategies from both rational and behavioral perspectives.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[財務金融學系] 研究計畫

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