| dc.contributor.advisor | 蕭又新 | zh_TW |
| dc.contributor.advisor | Shiau, Yuo Hsien | en_US |
| dc.contributor.author (Authors) | 賴昱君 | zh_TW |
| dc.contributor.author (Authors) | Lai, Yu Chun | en_US |
| dc.creator (作者) | 賴昱君 | zh_TW |
| dc.creator (作者) | Lai, Yu Chun | en_US |
| dc.date (日期) | 2013 | en_US |
| dc.date.accessioned | 6-Aug-2014 11:47:34 (UTC+8) | - |
| dc.date.available | 6-Aug-2014 11:47:34 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-Aug-2014 11:47:34 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G1017550031 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/68269 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 應用物理研究所 | zh_TW |
| dc.description (描述) | 101755003 | zh_TW |
| dc.description (描述) | 102 | zh_TW |
| dc.description.abstract (摘要) | 對投資者而言，投資股市的目的就是賺錢，但影響股價因素眾多，我們要如何判斷明天是漲是跌？因此如何建立一個準確的預測模型，一直是財務市場研究的課題之一，然而財務市場一直被認為是一個複雜.充滿不確定性及非線性的動態系統，這也是在建構模型上一個很大的阻礙，本篇研究中使用的EEMD方法則適合解決如金融市場或氣候等此類的非線性問題及有趨勢性的資料上。 在本研究中，我們將EEMD結合ANN建構出兩種不同形式的模型去進行台股個股的預測，也試圖改善ARMA模型使其預測效果較好；此外為了能夠達到分散風險的效果，採用了投資組合的方式，在權重的決定上，我們結合動態與靜態的方式來計算權重；至於在交易策略上，本研究也加入了移動平均線，希望能找到最適合的預測模型，本研究所使用的標的物為曾在該期間被列為注意股票的10檔股票。 另外，我們也分析了影響台股個股價格波動的因素，透過EEMD拆解，我們能夠從中得到具有不同意義的本徵模態函數(IMF)，藉由統計值分析重要的IMF其所代表的意義。例如：影響高頻波動的重要因素為新聞媒體或突發事件，影響中頻的重要因素為法人買賣及季報，而影響低頻的重要因素則為季節循環。 結果顯示，EEMD-ANN Model 1是一個穩健的模型，能夠創造出將近20%的年報酬率，其次為EEMD-ANN Model 2，在搭配移動平均線的策略後，表現與Model 1差不多，但在沒有配合移動平均線策略時，雖報酬率仍為正，但較不穩定，因此從研究結果也可以看到，EEMD-ANN的模型皆表現比ARMA的預測模型好。 | zh_TW |
| dc.description.abstract (摘要) | The main purpose of investing is to earn profits for an investor, but there are many factors that can influence stock price. Investments want to know the price will rise or fall tomorrow. Therefore, how to establish an accurate forecasting model is one of the important issue that researched by researchers of financial market. However, the financial market is considered of a complex, uncertainty, and non-linear dynamic systems. These characteristics are obstacles on constructing model. The measure, EEMD, used in this study is suitable to solve questions that are non-linear but have trends such as financial market, climate and so on. In this thesis, we used three models including ARMA model and two types of EEMD-ANN composite models to forecast the stock price. In addition, we tried to improve ARMA model, so a new model was proposed. Through EEMD, the fluctuation of stock price can be decomposed into several IMFs with different economical meanings. Moreover, we adopted portfolio approach to spread risks. We integrate the static weight and the dynamic weight to decide the optimal weights. Also, we added the moving average indicator to our trading strategy. The subject matters in this study are 10 attention stocks. Our results showed that EEMD-ANN Model 1 is a robust model. It is not only the best model but also can produce near 20% of 1-year return ratio. We also find that our EEMD-ANN model have better outcome than those of the traditional ARMA model. Owing to that, the increases of trading performance would be expected via the selected EEMD-ANN model. | en_US |
| dc.description.tableofcontents | CHAPTER 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 LITERATURE REVIEW 3 CHAPTER 2 METHODOLOGY 7 2.1 EMPIRICAL MODE DECOMPOSITION (EMD) 7 2.2 ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD) 10 2.3 ARTIFICIAL NEURAL NETWORKS (ANNS) 13 2.4 EEMD-BASED NEURAL NETWORK LEARNING PARADIGM 18 2.5 ARMA MODEL 20 CHAPTER 3 FORECASTING EXPERIMENTS 21 3.1 DATA DESCRIPTION 21 3.1.1 Attention-grabbing stocks 21 3.1.2 Stocks price daily data 22 3.2 OPERATION OF THE MARKET 24 3.3 STATISTICAL MEASURES 25 3.4 SIGNIFICANT IMFS 29 3.4.1 High frequency term 32 3.4.2 Mid frequency term 33 3.4.3 Low frequency term 34 3.5 EXPERIMENT DESIGN 35 3.5.1 Models 35 3.5.2 Times of experiments 40 CHAPTER 4 ALGORITHMIC TRADING 41 4.1 TRADING STRATEGY 41 4.1.1 Weights 41 4.1.2 Moving average indicator 41 4.2 PERFORMANCE 43 CHAPTER 5 CONCLUSION 47 REFERENCE 48 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G1017550031 | en_US |
| dc.subject (關鍵詞) | 類神經網路 | zh_TW |
| dc.subject (關鍵詞) | 交易策略 | zh_TW |
| dc.subject (關鍵詞) | 本徵模態函數 | zh_TW |
| dc.subject (關鍵詞) | 自回歸移動平均模型 | zh_TW |
| dc.subject (關鍵詞) | 預測模型 | zh_TW |
| dc.subject (關鍵詞) | ARMA | en_US |
| dc.subject (關鍵詞) | Ensemble Empirical Mode Decomposition | en_US |
| dc.subject (關鍵詞) | forecasting model | en_US |
| dc.subject (關鍵詞) | Artificial Neural Network | en_US |
| dc.subject (關鍵詞) | trading strategy | en_US |
| dc.title (題名) | 基於EEMD與類神經網路預測方法進行台股投資組合交易策略 | zh_TW |
| dc.title (題名) | Portfolio of stocks trading by using EEMD-based neural network learning paradigms | en_US |
| dc.type (資料類型) | thesis | en |
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