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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 | Chien-Chang Chiu | en |
dc.contributor.author | 邱堅彰 | zh_TW |
dc.date.accessioned | 2021-06-08T06:13:19Z | - |
dc.date.copyright | 2007-07-03 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-06-20 | |
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2.Barclay, M. and J. Warner, 1993, “Stealth Trading and Volatility,” Journal of Financial Economics, 34, 281-305. 3.Barclay, M. J., T. Hendershott and D. T. Mccormick, 2003, “Competition Among Trading Venues: Information and Trading on Electronic 4.Bollerslev, T., 1986, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 31, 307-327. 5.Booth, G. G., J. C. Lin, T. Martikainen, and Y. Tse, 2002, “Trading and Pricing in Upstairs and Downstairs Stock Markets,” Review of Financial Studies, 15, 1111-1135. 6.Campbell, J. Y., S. J. Grossman, and J. Wang, 1993, “Trading Volume and Serial Correlation in Stock Returns,” Quarterly Journal of Economics, 108, 905-939. 7.Chakravarty, S., 2001, “Stealth-trading: Which Traders’ Trades Move Stock Prices?” Journal of Financial Economics, 61, 289-307 8.Chordia, T. and A. Subrahmanyam, 1998, “Order Imbalance and Individual Stock Returns,” the eScholarship Repository, University of California. 9.Chordia, T., R. Roll, and A. Subrahmanyam, 2002, “Order Imbalance, Liquidity, and Market Returns,” Journal of Financial Economics, 65, 111-130. 10.Chordia, T., R. Roll, and A. Subrahmanyam, 2004, “Order Imbalance, Liquidity, and Market Returns,” Journal of Financial Economics, 72, 486-518. 11.Chou, M. J., 2006, “Intraday Return – Order Imbalance Relation in NASDAQ Hedging Top Gainers,” Graduate Institute of Finance of National Taiwan University. 12.Copeland, T. E., 1976, “A model of Asset Trading under the Assumption of Sequential Information Arrival,” Journal of Finance, 31, 1149-1168. 13.Wang, F. A., 1998, “Strategic Trading, Asymmetric Information and Heterogeneous Prior Beliefs,” Journal of Financial Markets, 1,321-352. 14.Foster, D. F. and S. Viswanathan, 1994, “Strategic Trading with Asymmetric Informed Traders and Long-Lived Information,” Journal of Financial and Quantitative Analysis, 29, 499-518. 15.Foster, D. F. and S. Viswanathan, 1996, “Strategic Trading When Agents Forecast the Forecasts of Others,” Journal of Finance, 51, 1437-1478. 16.Gallant, R., P. Rossi, and G. Tauchen, 1992, “Stock Prices and Volume,” Review of Financial Studies, 5, 199-242. 17.He, H., and J. Wang, 1995, “Differential Information and Dynamic Behavior of Trading Volume,” Review of Financial Studies, 8, 919-972. 18.Hong, H., and J. Wang, 2000, “Trading and Returns under Periodic Market Closures,” Journal of Finance, 55, 297-354. 19.Jones, C., G. Kaul, and M. Lipson, 1994, “Transactions, Volume and Volatility,” Review of Financial Studies, 7, 631-652. 20.Kahneman, D. and A. Tversky, 1979, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47, 263-291. 21.Karpoff, J., 1987, “The Relation between Price Changes and Trading Volume: A Survey,” Journal of Financial and Quantitative Analysis, 22, 109-126. 22.Kyle, A., 1985, “Continuous Auctions and Insider Trading,” Econometrica, 53, 1315-1335. 23.Lamoureux, C., and W. Lastrapes, 1990, “Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects,” Journal of Finance, 45, 221-229. 24.Lee, M. C., and Ready, M. J., 1991, “Inferring Trade Direction from Intraday Data,” Journal of Finance, 46, 733-746. 25.Lee, F. Y., 2005, “Intraday Return – Order Imbalance Relation in NASDAQ Speculative New Lows ” Graduate Institute of Finance of National Taiwan University. 26.Lee, Y. T., Y.J. Liu, R. Roll and A. Subrahmanyam, 2003, “Order Imbalances and Market Efficiency: Evidence from the Taiwan Stock Exchange,” Journal of Financial and Quantitative Analysis, 2001 27.Lin, J.C., 2004, “Price-Volume Relation: A Time Varying Model with Censored and Camouflage Effects,” Graduate Institute of Finance of National Taiwan University. 28.Lin, J. C., G. C. Sanger, and G. G. Booth, 1995, “Trade Size and Components of the Bid-Ask Spread,” Review of Financial Studies, 8, 1153-1183. 29.Llorente, G., R. Michaely, G. Saar, and J. Wang, 2002, “Dynamic Volume-Return Relation of Individual Stocks,” Review of Financial Studies, 15, 1005-1047. 30.Lo, A. and J. Wang, 2000, “Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory,” Review of Financial Studies, 13, 257-300. 31.Lo, A. W. and A. C. MacKinlay, 1990, “An Econometric Analysis of Nonsynchronous Trading,” Journal of Econometrics, 45, 181-211. 32.Lo, A. W., and A. C. MacKinlay, 1988, “Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test,” Review of Financial Studies, 1, 41-66. 33.Shen, C. H., 2005, “Intraday Return – Order Imbalance Relation in NASDAQ Speculative Top Gainers” Graduate Institute of Finance of National Taiwan University. 34.Wang, J., 1993, “A Model of Intertemporal Asset Prices Under Asymmetric Information,” Review of Economic Studies, 60, 249-282. 35.Wang, J., 1994, “A Model of Competitive Stock Trading Volume,” Journal of Political Economy, 102, 127-168. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25435 | - |
dc.description.abstract | In general, investors trade for two reasons: to hedge and share risk and to speculate on the private information. Previous research suggests that the dynamic relation between volume and returns lies in the underlying motivations. For aggressive investors, their hedging actions (i.e. rotation) tend to result in abrupt price soaring and subsequent reversal in a short period of time. In this paper, by introducing specific selection criteria, we try to screen the potential targets and develop the trading strategy.
The results indicate that for samples with maximum loss below 5%, the results reveal a paradox of high upside and low downside. Top three sectors account for more than half of the samples, which implies that hedge initiators seem to prefer specific sectors when screening potential rotation targets. In addition, the practice of “clearing the floats” plays a key role in analyzing the waiting period. It is found that most price jumps are likely to accompany volume augmentation, and most samples show price reversal on the jump day. Lastly, with GARCH(1,1) model, we verify the fitness of GARCH model in capturing the time variant property of returns and the relationship between order imbalance and returns. The results reveal that order imbalance indeed presents positively significant influence on returns of most samples. However, the relationship between order imbalance coefficients and market cap fails to present significance, which implies that size effect may not exist. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T06:13:19Z (GMT). No. of bitstreams: 1 ntu-96-R94723090-1.pdf: 327049 bytes, checksum: 17525bfcf568d2a4afec96ce0cf3687f (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1 Motivations 1 1.2 Framework of the Study 4 Chapter 2 Literature Review 5 2.1 Trading Behavior under Information Asymmetry 5 2.2 Price-Volume Relations in Previous Studies 8 2.1.1 Price-Volume Relations 8 2.1.2 Relationship between Order Imbalance and Returns 10 Chapter 3 Data 13 3.1 Data Sample and Sources 13 3.1.1 Reasons to Sample from NASDAQ 13 3.1.2 Data Sources and Sample Period 13 3.1.3 Inclusion Requirements 13 3.1.4 Data Computing Rules 14 3.2 Selection Criteria and Trading Strategies 14 3.2.1 Criterion 1: Stationary Price 14 3.2.2 Criterion 2: Declining Volume 15 3.2.3 Criterion 3: Price Range 15 3.2.4 Trading Strategies 15 Chapter 4 Methodology 16 4.1 Data Processing Methodology 16 4.2 GARCH Model and Variables 16 4.3 Contemporaneous and Lagged Effect Test 18 4.4 Size Effect Test 19 Chapter 5 Empirical Results 20 5.1 Trading Strategy Results 20 5.1.1 All Samples 20 5.1.2 Samples with Maximum Return above 10% 21 5.1.3 The First Trading Day with Maximum Return above 10% 22 5.2 GARCH Application 25 5.3 Contemporaneous and Lagged Effect 28 5.4 Bid-Ask Spread on and prior to the Jump Day 28 5.5 Size Effects 29 Chapter 6 Conclusion 31 6.1 Review of Research Findings 31 6.2 Recommendations for Future Research 32 References 34 | |
dc.language.iso | zh-TW | |
dc.title | NASDAQ避險型個股買賣單不對稱關係及交易策略研究 | zh_TW |
dc.title | Order Imbalance Relation and Trading Strategies in NASDAQ Hedging Stocks | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 胡星陽,王耀輝 | |
dc.subject.keyword | 價量關係,買賣單不對稱,資訊不對稱, | zh_TW |
dc.subject.keyword | price-volume relation,order imbalance,information asymmetry, | en |
dc.relation.page | 36 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2007-06-22 | |
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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