模型選擇與台灣股市轉折點即時判定

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姓名

鍾良(Chung Liang) 查詢紙本館藏

畢業系所

經濟學系

論文名稱

模型選擇與台灣股市轉折點即時判定

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至系統瀏覽論文 (2024-6-30以後開放)

摘要(中)

本文以即時預測股市狀態為研究主題，透過區分全樣本認定與遞迴認定兩種認定流程來探討在不同前提假設下對於模型的影響，本研究發現在使用非參數認定法進行狀態認定時，若採用遞迴認定，則PS法則會發生認定翻轉問題與認定延遲問題，實證結果則顯示PS法則在改善認定翻轉問題前難以應用於即時預測領域，而由於LT法則只有認定延遲問題，因此可以藉由改善遞迴預測流程來達到初步解決認定延遲問題。除此之外，為解決高維資料下的過度適配與多重共線性問題，本文在變數篩選方法採用階層式分群法(Hierarchical Clustering)與LASSO迴歸來篩選重要變數，研究結果顯示此方法的變數篩選能力十分優秀。同時本文也測試不同模型組合下的預測能力表現，包含動態結構、相依性結構與股市狀態結構，研究結果發現在三種結構同時存在的模型有最佳的預測能力。

摘要(英)

This paper takes real-time prediction of stock market status as the research topic, discussing the impact on the model under different assumptions by distinguishing between the two identification processes of full sample identification and recursive identification. We find that under the recursive identification using the PS rule, the problem of identification inversion and identification delay will occur, while under the LT rule, there is only the problem of identification delay under the recursive identification. The empirical results show that the PS rule is difficult to apply to the real-time prediction field before solving the identification inversion problem, and the LT rule can solve the identification delay problem by improving the recursive prediction process. In order to solve the problem of overfitting and multicollinearity under High-Dimensional data, we use Hierarchical Clustering and LASSO regression to select key variables. The results also show that this method performs very well. In addition, we also test the performance of forecasting ability under different model settings, including dynamic structure, dependency structure and stock market state structure. The result shows that the model with all structures has the best forecasting ability.

關鍵字(中)

★ 股市狀態預測
★ 羅吉斯迴歸
★ 階層式分群法
★ LASSO

關鍵字(英)

★ Bear stock markets
★ Logistic Regression
★ Hierarchical Clustering
★ LASSO

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1 研究動機與目的 1
1-2 研究流程與架構 4
二、文獻探討 5
2-1 熊市與牛市定義 5
2-2 計量模型 6
2-3 特徵選擇 7
2-4 特徵篩選 8
三、研究方法 10
3-1 股市狀態認定 10
3-1-1 PS法則 12
3-1-2 LT法則 13
3-2 模型設定 14
3-2-1 進階設定 15
3-2-2 樣本範圍 16
3-3 變數篩選 17
3-3-1 Augmented Dickey-Fuller GLS (ADF-GLS)檢定 18
3-3-2 HC+LASSO兩階段篩選法 19
3-4 遞迴預測流程替代方案 21
3-5 績效衡量方法 26
3-5-1 模型適配度衡量 26
3-5-2 重要性衡量 28
3-6 資料與基本統計 28
3-6-1 資料來源與處理 28
3-6-2 研究流程圖 36
四、實證分析 37
4-1 PS與LT法則認定結果 37
4-1-1 牛市與熊市認定 37
4-1-2 認定延遲與認定翻轉問題 40
4-2 不同模型結構設定下預測能力優異 44
4-2-1 模型結構分析 44
4-2-2 重要變數 46
4-3 採用遞迴認定對模型的負面影響與解決方法 48
五、結論 53
5-1 研究結果 53
5-2 研究建議 54
六、附錄 55
七、參考文獻 68

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指導教授

徐之強王銘正(Chih-Chiang Hsu Ming-Cheng Wang)

審核日期

2022-7-6

推文