臺灣博碩士論文加值系統

本研究應用數值方法－對數週期冪次定律(Log-Periodic Power Law, LPPL) 模型於台灣股票市場以預測2008年金融危機的反轉時間點，並嘗試結合投資人從眾行為因子與數值模型來解釋股價崩盤前之對數週期震盪現象，進而賦予此類數值方法更為直觀的預測因子。
相較於原模型，本研究認為崩盤前因投資人意見不一致所產生的股價週期震盪現象比臨界時間之估計區間更有預測力，並以此作為崩盤發生前的先兆。同時，為探討投資人從眾行為對崩盤之影響，採用與市場共識偏離程度之從眾變數―S統計量來區分投資人從眾與反從眾行為。最後建立門檻向量自我回歸模型來觀察不同類別投資人的從眾及反從眾行為與股價崩盤間之連動變化關係。
本文實證發現，法人反從眾行為會加強股價對數週期震盪現象，進而推升股價引發崩盤可能，而散戶反從眾行為則恰好相反；法人的從眾行為會顯著減弱股價對數週期震盪現象。雖然此結果與一般直覺相反，但亦指出投資人的從眾與反從眾行為確實能真實反映金融市場的複雜交易系統，並可作為預測股市金融危機的參考指標。

This paper applies Log-Periodic Power Law (LPPL) model to Taiwan stock market to predict the regime-switching time of the 2008 bubble and crash. Moreover, this paper is dedicated to explaining the log-periodic price pattern with investor herding behaviors and granting the model a more intuitive financial interpretation.
In contrast to the original methodology proposed by Johansen, Ledoit, and Sornette (2000), rather than the estimated range of critical time, we focused on the log-periodic price pattern (specifically, the log-periodic oscillation parameter), a crucial phenomenon before the crash as a prophetic sign for the crisis. Furthermore, to decipher the impact of herding on crash, we use a conditional probabilistic herding measure for the concept of deviation from market consensus, S-statistic, to distinguish anti-herding from herding by investor types. Finally, we construct a two-regime Threshold VAR model to examine the dynamic relations among herding, anti-herding and log-periodic price pattern.
To our surprise, the study finds that anti-herding behaviors of institutional investors strengthen the log-periodic oscillations while the effect is opposite for individual investors anti-herding behaviors. Institutional herding weakens the log-periodic pattern. This result may be counterintuitive, however, this result indicates that herding and anti-herding can truly reflect the complex mechanism of financial market before the crash and thus these behavioral factors are qualified as predictive indicators for financial crisis.

論文審定書 i
摘要 ii
ABSTRACT iii
1. Introduction 1
2. Literature Review 4
2.1 Log-Periodic Power Law Model 4
2.2 Investor Herding 11
3. Data and Methodology 17
3.1 Data Description 17
3.2 Bubble and Crash Definition 19
3.3 Log-Periodic Power Law Model 21
3.4 Herding and Anti-herding 23
3.5 Threshold Vector Autoregression Model 26
4. Empirical Results 28
4.1 Descriptive Statistics 28
4.2 Threshold VAR 36
5. Conclusion 40
REPERENCES 42

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