本研究利用黃鄂博士於1998年提出的Ensemble Empirical Mode Decomposition (EEMD)，將拆解完的本質模態函數(IMF)以及剩餘的趨勢項帶入倒傳遞類神經網路中，訓練類神經網路並對樣本外資料進行預測。經驗解構法是希爾伯特-黃轉換之中的技術，可以將時間序列資料拆解成數個本質模態函數和一個殘餘項，此方法被廣泛應用在各個領域的訊號分析當中。本研究使用此模型的重點在於，僅僅使用歷史價格這個變數即可進行股市崩盤的預測，不需要受限於各種經濟變數取得的問題，對研究人員來說，提供相當大的便利性。
本文對含括台、美的七個國際指數進行實證，實證結果顯示此模型對out-of-sample的崩盤具有一定程度的預測能力。當我們看全樣本的預測準確性，最低的是道瓊工業指數的70.8%，準確性最高的是澳洲股票指數的99.1%；若我們觀察的是崩盤期間的預測，準確性最低的是香港的33%，最高的是新加坡、英國跟澳洲的股價指數，這三者的正確性都是100%。可以發現此模型對崩盤期間的預測準確度波動較大，存在完全正確(100%)的情況但也存在非常低(33%)。除此之外，我們與Zouaoui, Nouyrigat, and Beer (2011)提出的羅吉斯回歸比較預測準確性，此模型除了總體變數外也加入投資人情緒變數在當中，結果顯示本文模型的預測能力再兩種情況都領先羅吉斯回歸，特別是對崩盤期間的預測，本研究的正確性58.3%是羅吉斯回歸25%的2倍。
最後，本文嘗試分析本質模態函數背後代表的經濟意義，因為EEMD的拆解基礎是物理特性，因此我們根據各個IMF的圖形以及在類神經網路裡佔的權重分類，將其分為高、中、低三組數列，結果顯示低頻資料和總體經濟變數間的關係非常顯著而且判定係數超過90%，中頻資料則是跟投資人情緒變數有顯著的正相關。

In this paper, we forecast stock market crashes by innovative EEMD-based neural network approach with stock market indices. The concept of EMD is to decompose nonlinear and nonstationary time series signal for obtaining instantaneous frequency data. In order to predict stock crashes for early-warning proposes, this study uses Ensemble Empirical Mode Decomposition to decompose historical prices into several frequency component and a residual trend, and then take these components as inputs in neural network.
The result of our research shows that the prediction model is significant by better forecasting performances. In seven sample countries, if we take a look on the forecasting result of the out of sample that including crisis periods and non-crisis periods, the lowest accuracy is 70.8% for Dow Jones Average Industrial and the highest is 99.1% of Australia Index. If we only take a look of the forecasting result of the crisis period alone, the lowest accuracy is 33% for Hang Seng Index, and the highest is 100% for Singapore STI Index, UK FTSE All-Share Index, and Australia ASX Index. Compare the forecasting performance with a logit model, the forecasting accuracy of the logit model in crisis period is much lower than the EEMD-based neural network approach. Finally, we explain the factors explaining the various price components, and found a significant relationship between low frequency price component and microeconomic variables, while mid-frequency components of IMFs can be explained by investor sentiment.

摘要 I
ABSTRACT II
I. INTRODUCTION 1
1. LITERATURE REVIEW 7
2.1 Econometrics 8
2.2 Operation research 9
2.3 Log-Periodic Power Law, LPPL 11
3. DATA AND METHODOLOGY 14
3.1 Data description 16
3.2 Identification of stock market crash 18
3.3 Ensemble Empirical Mode Decomposition 20
3.4 Artificial Neural Network 24
3.5 EEMD-based multiscale neural network 30
3.6 The logit model 32
4. EMPIRICAL RESULT 33
4.1 Stock market crisis 35
4.2 The decompositions of each stock market indices 39
4.3 Forecasting results 41
4.4 Comparison with other method 46
4.5 Analysis of IMFs 51
5. CONCLUSION 55
REFERENCE 58

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