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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇永成 | |
dc.contributor.author | Yen-Tzu Chen | en |
dc.contributor.author | 陳晏慈 | zh_TW |
dc.date.accessioned | 2021-06-13T15:58:16Z | - |
dc.date.available | 2011-06-25 | |
dc.date.copyright | 2008-06-25 | |
dc.date.issued | 2008 | |
dc.date.submitted | 2008-05-30 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38041 | - |
dc.description.abstract | 本研究採用鉅額跌幅投機型個股的日內資料取代過去研究者所使用的日資料來進行研究。在延續前人的研究探討日內買賣單不平衡對於個股報酬率的影響外,另加入了波動性的因子以探討日內買賣單不平衡對於波動性的影響。此外也嘗試建立以買賣單不平衡為基礎的交易策略以期獲得超額報酬。
首先我們以GARCH(1,1)模型及複迴歸模型來研究日內買賣單不平衡與個股報酬率間的關係,發現兩者呈現正向的顯著關係,與前人的研究結論相同。而在不考慮當期下,前一期的買賣單不對稱與股價報酬率間則呈現負向的顯著關係。接著我們以修正過的GARCH(1,1)模型進行日內買賣單不平衡與波動性間關係的探討,發現結果呈現正向的顯著關係,較大量買賣單不平衡會使得報酬率波動較激烈。接著我們以簡單迴歸模型來驗證買賣單不平衡和公司規模間是否存在著小型股效果。實證結果顯示兩者之間僅有相當微弱的負顯著關係。 最後,本研究嘗試以買賣單不平衡為基礎發展交易策略並檢視其獲利性。由於本研究是以鉅額跌幅投機型個股為樣本,故以放空後回補做為我們的策略,結果發現,在進行交易量篩選之後,此交易策略能夠替投資者賺取超額報酬。 | zh_TW |
dc.description.abstract | This study adopts intraday return instead daily return used by previous researches to examine the effect of order imbalance not only on the individual stock return but also volatility among extreme losers. After that, we build up order imbalance-based trading strategies to gain profit.
First, the contemporaneous order imbalance-return relation is examined by GARCH (1,1) model and time-series regression model. The data presents significantly positive relation in both models as previous studies. Second, we focus on the lagged effect of the return and find that such relation is negatively significant while contemporaneous imbalance has positive significant. Third, we examine the volatility-order imbalance relationship by revised GARCH (1,1) model. The positive relationship is consistent with our expectation that larger imbalance would make return more volatile. Then, our empirical test of the small firm effect shows the weakly negative relation between order imbalance and market capital. At last, we design two order imbalance-based trading strategies based on different price matched to the imbalance: the trading price and bid-ask price, separately and test the profitability. Due to the characteristics of our extreme losers, we adopt short selling strategy. Our results show the huge profitability of the two strategies when we pick up only the extreme volume. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T15:58:16Z (GMT). No. of bitstreams: 1 ntu-97-R94723018-1.pdf: 513288 bytes, checksum: 65158148b5da85a2c538f316452cb095 (MD5) Previous issue date: 2008 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Motives and purpose 1 1.2 Framework of the thesis 4 Chapter 2 Literature Review 5 2.1 Trader’s Behavior Under Information Asymmetry 5 2.2 Relation Between Return and Trading Volume 9 2.3 Volatility 13 Chapter 3 Data 15 3.1 Data sample and sources 15 3.2 Descriptive Statistics 17 Chapter 4 Methodology 19 4.1 GARCH (1,1) Model 19 4.2 Intraday Time-Series Regression model 21 4.3 Dynamic Volatility-Order imbalance Relationship 23 4.4 Size effect 23 4.5 Order Imbalance-based Trading Strategies 24 Chapter 5 Empirical Results 27 5.1 Dynamic Return-Order Imbalance Relationship 27 5.2 Intraday Time-Series Regression Model 28 5.3 Dynamic Volatility -Order Imbalance Relationship 33 5.4 Size effect 36 5.5 Order Imbalance-based Trading Strategies 37 Chapter 6 Conclusion 41 References 75 Figure 1 Distribution of Market Capital of Our 53 Samples 44 Figure 2 Distribution of α1, The Contemporaneous Coefficient of GARCH (1,1) Model 45 Figure 3 Distribution of γi0, The Contemporaneous Coefficient of Time-Series Regression Model 46 Figure 4 Distribution of δi1, The Lag-One Period Coefficient of Time-Series Regression Model 47 Figure 5 Distribution of C1, The Contemporaneous Coefficient of GARCH (1,1) Model In Testing Volatility 48 Table 1 The Summary Statistics of The Data of 53 Samples 49 Table 2 The Results and Fitness of GARCH (1,1) Model 50 Table 3 Significance of The Coefficients Estimated By Contemporaneous Time-Series Regression Model 51 Table 4 Significance of The Coefficients Estimated By Lag-One Period Time-Series Regression Model 52 Table 5 GARCH (1,1) Model In Testing Volatility 53 Table 6 Size Effect 54 Table 7 The Intraday Return of The Order Imbalance-based Trading Strategies 55 Table 8 The Profitability of The Order Imbalance-based Trading Strategies 56 | |
dc.language.iso | en | |
dc.title | 鉅額跌幅投機型個股報酬率、波動性與買賣單不平衡之動態關係研究 | zh_TW |
dc.title | Dynamic relations between order imbalance, return and volatility of extreme losers | en |
dc.type | Thesis | |
dc.date.schoolyear | 96-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 胡星陽,王耀輝 | |
dc.subject.keyword | 買賣單不平衡,資訊不對稱,波動性, | zh_TW |
dc.subject.keyword | order imbalance,information asymmetry,volatility, | en |
dc.relation.page | 80 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2008-06-03 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
顯示於系所單位: | 財務金融學系 |
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