臺灣博碩士論文加值系統

預測股市走勢一直是投資人所努力的方向，不論是基本分析或者是技術分析，所想要達到的目的無非就是了解股市的變化規則，以作為進出股市的依據。本研究以網際網路搜尋引擎的搜尋量Google Trends為分析對象，透過Google Trends的搜尋量與台灣加權股價指數研究分析之間的相關性。利用Google Trends所提供的關鍵字搜尋量，進行相關性檢定與單根檢定，再將所得到的關鍵字分別進行實驗一-機器學習與實驗二-搜尋趨勢中做分析，經過實證分析後，發現在實驗一中的類神經網路表現優於支援向量機與決策樹，進而挑選類神經網路作為與實驗二的搜尋趨勢方法做比較，透過報酬值計算的比較分析，發現搜尋趨勢的報酬值優於類神經網路的報酬值。因此，本論文發現以台灣50指數公司名稱作為搜尋關鍵字與台灣加權股價指數的漲跌存在關聯性。

Investors have always tried to predict the stock market trend. Whether it is fundamental analysis or technical analysis, what they want is nothing more than to understand the rule of changes in the stock market and use it as a basis to trade in the stock market. This study used the amount of Internet search on Google Trend and analyzed the correlation between the search volume on Google Trend and Taiwan Weighted Stock Index. The keyword search volume provided by Google Trend was used in the correlation test and the unit root test. Then the keywords obtained were analyzed in two experiments – first, machine learning, and second, search trend. After empirical analysis, it was found that neural network in experiment one performed better than support vector machine and decision trees. Therefore, neural network was selected to compare with the search trend in the second experiment. Through comparative analysis of calculation of return values, it was found that the return value in search trend is higher than that of the neural network. Therefore, this paper revealed that there was a correlation between using company names of Taiwan 50 Index as search keywords and the rise and fall of TAIEX index.

中文摘要 I
英文摘要 II
誌謝 III
表目錄 VI
圖目錄 VIII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 論文架構 4
第二章 文獻回顧 6
第一節 Google Trends 6
第二節 恆定性檢定 9
第三節 類神經網路 11
第三章 研究方法 16
第一節 研究架構 16
第二節 Pearson相關檢定 18
第三節 ADF、PP檢定 20
第四節 倒傳遞類神經網路 22
第五節 支援向量機 24
第六節 決策樹 25
第七節 搜尋趨勢 26
第四章 實證分析 28
第一節 研究樣本與研究期間 28
第二節 Google Trends關鍵字與台股加權指數之Pearson相關檢定 33
第三節 Google Trends關鍵字與台股加權指數之恆定性檢定 35
第四節 機器學習之分析結果 42
第五節 搜尋趨勢之分析結果 44
第六節 實驗結果討論 45
第五章 結論與未來展望 46
第一節 結論 46
第二節 研究限制與未來發展 46
參考文獻 48
附錄1 53
附錄2 55

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