臺灣博碩士論文加值系統

論文摘要
這次的金融海嘯再度引發對於市場效率假說的批評，有些學者甚至認為這次的金融危機部分歸因於太相信市場是有效率的，而使資產泡沫的危機長期的被低估。我們在這邊想要探討的便是針對商業銀行來研究在金融危機下市場是否仍具效率性。
首先我們以多元線性回歸模型檢驗同期或前期之日內買賣單不平衡對股票報酬率的影響，並分成三個情況討論：全部樣本、個股買賣單不平衡對本身報酬率、個股買賣單不平衡對其他個股報酬率。實證結果顯示，同期之買賣單不平衡對報酬率在三種情況下皆有顯著之正向影響；前一期的買賣單不平衡對報酬在沒有考慮當期時，在全部樣本以及個股買賣單不平衡對其他個股報酬率的情況下其影響方向都為正，但不如Chordia and Subrahmanya (2004)的結果來的顯著，而在考慮當期後，前期買賣單不平衡對報酬的影響在三種情況下皆變成負向關係。
再來我們以GARCH(1,1)模型觀察同期買賣單不平衡對報酬率的影響，結果顯示有正顯著之關係，並且可觀察到市場效率之收斂過程。另外，我們也以GARCH(1,1)模型觀察股價波動性與買賣單不平衡之間的關係，研究顯示其關係並不顯著，代表造市者對股價波動性的控制良好。
最後，我們以買賣單不平衡為指標來建立日內的交易策略，並測試是否能夠獲得超額報酬。實證結果發現無法擊敗個股之原始報酬率。

Abstract

In recent years, there has been a dramatic proliferation of research concerned with market efficiency while recent global financial crisis has led to renewed criticism of the hypothesis. The main purpose of our study is to investigate commercial bank market efficiency in financial crisis.
First, we use a multi-regression model to examine the relation between returns and contemporaneous as well as lagged order imbalances. We divide our sample into overall effect, autocorrelated effect and cross-correlated effect situation to discuss separately. The empirical result shows that the contemporaneous order imbalances have a significantly positive impact on returns in all three situations, and the lagged-one imbalances also have a positive impact on returns disregarding the contemporaneous imbalances in overall effect condition and cross-correlated effect condition, but the positive relation is not as significant as the empirical result of Chordia and Subrahmanyam (2004). Once we condition on the contemporaneous imbalances, most of the coefficients of lagged-one imbalances turn to be significantly negative, which is consistent with Chordia and Subrahmanyam (2004).
Besides, we adopt GARCH(1,1) model and observe positive relation between contemporaneous order imbalances and returns. We also find there exists the convergence process toward efficiency. In addition, we examine the relation between volatility and order imbalances by using a GARCH(1,1) model. We observe low connection between order imbalances and price volatility, and it could be explained that market makers have good control on commercial banks’ price volatility.
Finally, we form an intraday trading strategy based on the sign of large order imbalances to test if the trading strategy can beat the market or not. We find our trading strategy cannot beat the market.

Contents

CHAPTER 1 INTRODUCTION 1
1.1 MOTIVES AND PURPOSES 1
1.2 FRAME WORK OF THE THESIS 12
CHAPTER 2 DATA AND METHODOLOGY 13
2.1 THE DATA 13
2.1.1 Data Sources 13
2.1.2 Data Processing Methods 13
2.1.3 Descriptive Statistics 14
2.2 METHODOLOGY 15
2.2.1 Unconditional Lagged Return-Order Imbalances OLS Model 15
2.2.2 Conditional Contemporaneous Return-Order Imbalances OLS Model 16
2.2.3 Dynamic Return-Order Imbalance GARCH(1, 1) Model 17
2.2.4 Dynamic Volatility-Order Imbalance GARCH(1, 1) Model 18
CHAPTER 3 EMPIRICAL RESULTS 20
3.1 UNCONDITIONAL LAGGED RETURN-ORDER IMBALANCES RELATION 20
3.2 CONDITIONAL CONTEMPORANEOUS RETURN-ORDER IMBALANCES RELATION 25
3.3 DYNAMIC RETURN -ORDER IMBALANCE GARCH(1, 1) RELATION 26
3.3 DYNAMIC VOLATILITY -ORDER IMBALANCE GARCH(1, 1) RELATION 28
3.5 TRADING STRATEGY 30
CHAPTER 4 CONCLUSION 33
REFERENCES 37

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