臺灣博碩士論文加值系統

面臨大數據時代，影響股票市場的各種因素使股票預測有些複雜和困難，準確的股票指數預測能幫助決策者採取正確的行動來發展更好的經濟。大多數傳統的時間序列模型在預測中只使用一個變數，多數使用收盤價格單一變數預測隔日收盤價格，而本研究所採用5個變數作為模型輸入，預測模型應該使用更多變數來提高預測的準確性，本研究提出一個以模糊類神經系統(Neuro-Fuzzy System, NFS)為架構，其結合Takagi-Sugeno模糊系統形成本研究模型，使其與傳統的類神經模型進行比較。在參數學習，以粒子群演算法(Particle Swarm Optimization, PSO)結合遞迴最小平方演算法(Recursive Least Squares Estimator, RLSE)，成為PSO-RLSE複合型演算法，進行參數的優化，發揮效用。本研究以三個實驗使用多種真實世界驗證模型的效能與研究理論，實驗一為台股指數預測與利潤計算，實驗二為恆生指數預測與利潤計算，實驗三為日經指數預測與利潤計算，實驗結果說明本研究模型在時間序列預測上有良好效能。

Faced with the era of big data, various factors affecting the stock market make stock forecasting which are complicated and difficult. In order to obtain accurate stock index forecasts, we hope to help decision makers take the right actions to develop a better economy. Most traditional time series models use only one variable in the forecast. Most use the single variable of the closing price to predict the closing price of the next day. In this study, three variables are used as input to the model. The forecasting model should use more variables to improve the forecast. This study proposes a Neuro-Fuzzy System (NFS) architecture that combines the Takagi-Sugeno fuzzy system to form the model structure of this study, which is compared with the traditional neural network model. In the parameter learning, Particle Swarm Optimization (PSO) combined with Recursive Least Squares Estimator (RLSE) is used as a PSO-RLSE composite algorithm to optimize parameters. This study used a variety of real-world data sets to validate the model's efficacy and research theory in three experiments. The results of individual experiments are compared with the previous literature. The research results show that the model has good performance in time series prediction.

章節
頁次
中文摘要 iv
英文摘要 ii
致謝 vi
目錄 vii
圖目錄 x
表目錄 xii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 研究方法概述 3
1.4 論文架構 4
第二章 文獻探討 6
2.1 股價分析理論 6
2.1.1 效率市場理論 6
2.1.2 股票價格分析法 7
2.2 股票市場上不同的預測模型 9
2.3 類神經模型 10
2.4 倒傳遞演算法 11
2.5 模糊模型 13
2.6 粒子群演算法 14
第三章 研究方法 16
3.1 模糊類神經模型 16
3.1.1 第一層 輸入層 18
3.1.2 第二層 模糊集合層 19
3.1.3 第三層 啟動強度層 19
3.1.4 第四層 正規化層 19
3.1.5 第五層 後鑑部層 19
3.1.6 第六層 輸出層 20
3.2 粒子群演算法 20
3.3 遞迴式最小平方演算法 21
3.4 應用型態 23
3.5 PSO-RLSE複合演算法 24
3.6 投資策略 27
第四章 實驗與研究結果 30
4.1 實驗一：台股加權指數時間序列預測 30
4.1.1 RMSE 比較 35
4.1.2 不同模型於文獻規則之利潤比較 36
4.1.3 不同模型於改良規則之利潤比較 39
4.2 實驗二：香港恆生指數時間序列預測 40
4.2.1 RMSE比較 45
4.2.2 不同模型於文獻規則之利潤比較 46
4.2.3 不同模型於改良規則之利潤比較 48
4.3 實驗三：日本日經指數時間序列預測 50
4.3.1 RMSE 比較 55
4.3.2 不同模型於文獻規則之利潤比較 56
4.3.3 不同模型於改良規則之利潤比較 59
第五章 討論 61
第六章 結論與未來研究方向 65
6.1 結論 65
6.2 未來研究方向 66
參考文獻 68

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