蘇永成臺灣大學：財務金融學研究所邱堅彰Chiu, Chien-ChangChien-ChangChiu2007-11-282018-07-092007-11-282018-07-092007http://ntur.lib.ntu.edu.tw//handle/246246/60597In 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.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 34327049 bytesapplication/pdfen-US價量關係買賣單不對稱資訊不對稱price-volume relationorder imbalanceinformation asymmetrythesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/60597/1/ntu-96-R94723090-1.pdf