臺灣博碩士論文加值系統

由於近期歐美股市掀起了關於「Smart Beta」的討論熱潮，並愈來愈受全球投資者對β值的關注。因此，本研究結合大數據分析與財金的領域，為使投資人降低風險，運用預測股票市場的風險係數β值作為股市風險衡量的依據。
本研究以五個技術指標乖離率(BIAS)、相對強弱指標(RSI)、威廉指數(Williams %R)、動向指數(DMI)、心理線(PSY)等作為投資標的特徵值，利用滾動式財務預測，並結合KNN演算法，從過去的巨量歷史資料中找出短(21天)、中(65天)、長(250天)期之五個特徵值最相似的β值樣本，作為未來β值的預測基礎，透過這些技術指標背後所隱含的物理力量，嘗試找出更貼近實務面的β值，再與實際β值加以比對，並以台灣股票市場中重要的權值股台積電及TW50為例。
研究結果顯示與大盤連動性高的TW50以中、短天期為基礎之β預測值較為準確，而台積電則以短天期的β預測值較為準確。希望透過本研究能夠讓投資者在進入股市之前，能更精準地掌握個股的風險，做出最佳的投資策略，以便投資人能夠降低風險並獲得最大的利潤。

Due to the recent lively discussion on "Smart Beta" in Europe and the US stock market, it has begun growing interest in β value by global investors. Therefore, this research paper combined two fields of knowledge, big data analysis and finance. In order to reduce the risk for investors, we applied β coefficient of stock market to measure the risk of a stock.
Five technical indicators such as Bias Ratio(BIAS), Relative Strength Index(RSI), Williams Overbought / Oversold Index (Williams %R), Directional Movement Index(DMI), and Psychological Line(PSY) are used as characteristic values of an investment stock target. Through physical forces behind these technical indicators, we apply KNN algorithm from past historical data to identify each of the different days of the five most similar characteristic values to predict the most similar β samples to find out more practical β value, and then compare with the actual β value. Studies have shown that the short-day period and medium-day period basis is more accurate predictive value of β for TW50, and short-day period basis is more accurate predictive value of β for TSMC.
In this study, we expect that before the investors entering the stock market can properly grasp the risk of an individual stock, and make the best profits from stock-picking strategy.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章　緒論 1
1.1　研究背景與動機 1
1.2　研究目的 3
1.3　研究流程 5
第二章　文獻回顧 6
2.1　β 係數 6
2.2　技術指標 8
2.2.1乖離率(BIAS) 8
2.2.2威廉指數(Williams %R) 9
2.2.3相對強弱指標(RSI) 10
2.2.4心理線(PSY) 11
2.2.5動向指數(DMI) 12
2.3　KNN演算法 14
2.4　滾動式預測 15
2.5　股市系統風險之相關研究 16
2.6　Smart Beta 19
第三章　研究方法 24
3.1　研究架構 24
3.2　研究資料 25
3.3　研究模型資料歸納 26
3.4　資料前置處理 26
3.5　滾動式預測整合KNN演算法預測β值 27
3.6　實驗資料取樣的分組設計 29
第四章　實驗結果與分析 31
4.1　實驗組績效評估-預測準確度 31
4.2實證結果 32
4.3　實驗組之總合評判 41
第五章　結論與未來研究方向 44
5.1　結論 44
5.2　未來研究方向 45
參考文獻 47
附錄 50

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